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Global Forest Watch Blog

<p><strong>Stay atop the latest forest research and news! The Global Forest Watch blog uses data to illuminate the state of forests worldwide and tells the stories of people dedicated to protecting them. Read about rainforests, deforestation, fires, sustainable agriculture, forest management and other topics critical to the future of forests.</strong></p>

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<p>Total tropical primary forest loss in 2023 totaled 3.7 million hectares, the equivalent of losing almost 10 football (soccer) fields of forest per minute. <a href="https://gfw.global/3TPufvy" rel="noopener" target="_blank">View on the Global Forest Review.</a></p>


2023 Tree Cover Loss Data Analysis

Data

Data | Global Forest Watch Blog

mapping

mapping | Global Forest Watch Blog

Sarah Carter

Liz Goldman

Jan Hooijmans

<p>Global Forest Watch (GFW) and the Global Forest Resources Assessment (FRA) from the Food and Agricultural Organization of the United Nations (FAO) both provide global data sets with information on the world’s forests. The latest FRA was released in 2020, and when compared to 2020 data on forest area from the University of Maryland (UMD), available on GFW, we see similarities and differences. So how similar are the numbers and what could explain the differences? This blog compares the forest extent from these two data sets, discusses why there are differences and explains what this means for users. To learn more about general differences between data on GFW and FRA data, <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">check out this blog</a>.</p>



<h2 class="wp-block-heading">What was the area of trees and forests in 2020 according to UMD and FRA data?</h2>



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<aside id="textbox"><em><b>Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance </b><p></p>
Some of the primary distinctions between GFW and FRA are in their data sources and definitions (<a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">read more here</a>).<br><br>
For 2020, GFW uses the UMD <a href="https://gfw.global/2Mei4Z0" target="_blank" rel="noopener noreferrer">2020 tree cover height data</a> to report on the extent of tree cover — a purely biophysical definition related to height and extent of trees. This satellite data picks up all trees with a minimum canopy height, which users can set when visualizing the data. For this analysis, we use a minimum height threshold of 5 meters. GFW also has a tree cover extent data set for 2000 and 2010 from UMD based on the percent of canopy density, which is used in many functions on GFW, but this was largely not used in this blog. <br><br>
The FRA relies on country-reported data based on a definition of forest that includes both a biophysical and land use component. In the FRA data, forests have a tree canopy height of at least 5 meters and canopy cover of at least 10% or are capable of reaching this threshold. Additionally, the land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or timber production.<br><br>
This means that in the FRA data, land that is temporarily devoid of trees — for example, recently logged or burned areas — would still be classified as forest if the official land use stipulates that trees will be allowed to regenerate in the future. Conversely, logged and burned areas are not captured as tree cover by the UMD data. Land that contains trees but is designated for agricultural or urban uses — for example, an oil palm plantation, shade grown cocoa (agroforestry) or a city park — would not be considered a forest by the FRA, but would be included in UMD tree cover data. 

</em> </aside>



<h4 class="wp-block-heading">Extent of trees and forests globally and by region in 2020 (in million hectares — Mha)</h4>



<table>
	<tbody>
		<tr>
			<td style="width:180px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Africa</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>Asia including Russian Federation</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>Europe, excluding Russian Federation</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>North and Central America</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>South America</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Oceania</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Global </strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span></p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>UMD tree cover extent 2020</strong></span></p>
			</td>
			<td style="width:69px;height:10px;">
			<p><span style="font-size:10px;">674 Mha</span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:10px;">
			<p><span style="font-size:10px;">1,483 Mha</span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:10px;">
			<p><span style="font-size:10px;">204 Mha</span></p>
			</td>
			<td style="width:76px;height:10px;">
			<p><span style="font-size:10px;">675 Mha</span></p>
			</td>
			<td style="width:68px;height:10px;">
			<p><span style="font-size:10px;">834 Mha</span></p>
			</td>
			<td style="width:71px;height:10px;">
			<p><span style="font-size:10px;">147 Mha</span></p>
			</td>
			<td style="width:112px;height:10px;">
			<p><span style="font-size:10px;">4,018 Mha</span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>FRA forest area 2020</strong></span></p>
			</td>
			<td style="width:69px;height:20px;">
			<p><span style="font-size:10px;">637 Mha</span></p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;">1,438 Mha</span></p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;">202 Mha</span></p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;">753 Mha</span></p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;">844 Mha</span></p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;">184 Mha</span></p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;">4,059 Mha</span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>Difference of UMD extent as compared to FRA</strong></span></p>
			</td>
			<td style="width:69px;height:20px;">
			<p><span style="font-size:10px;"><strong>+5.6%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;"><strong>+3.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;"><strong>+1.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;"><strong>-11.5%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.2%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;"><strong>-24.8%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.02%</strong></span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
	</tbody>
</table>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%"><small><em>*The regional figures compare the same list of countries, with some adjustments made to accommodate different country boundaries in the Database of Global Administrative Areas (GADM) and FRA. </em></small></div>



<p>Globally and regionally, tree/forest cover as estimated by UMD data and the FRA are similar. Global estimates of forest area in 2020 only vary by about 1% (4,059 million hectares — Mha — for the FRA and 4,018 Mha for the UMD data).</p>



<p>At the regional level there are many similarities, with very small differences for Asia, Europe and South America. Oceania shows the largest difference at the regional scale, which is almost entirely attributable to the FRA’s greater estimate of forest area in Australia — Australia’s FRA forest area is 52% greater than UMD’s tree cover extent. This may be because Australia’s definition of “forest” specifies a minimum vegetation height of 2 meters, which is used in the FRA estimate. The majority of countries in the FRA follow the FAO definition of a minimum vegetation height of 5 meters, which is also the height threshold used in this blog for analysis of the UMD data.</p>



<h4 class="wp-block-heading">Forest area/tree cover 2020 according to UMD data on GFW and the FRA</h4>



<div class="flourish-embed flourish-map" data-src="visualisation/15325037?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>In central Africa and Southeast Asia, UMD’s tree cover estimates are typically greater than the FRA’s forest area estimations. In Malaysia, for example, UMD’s estimate is 33% higher than the FRA’s (28.6 Mha for UMD vs. 19.1 Mha for FRA). This may be explained by the difference in definitions, for example Southeast Asia typically contains large areas of oil palm plantations, which would be considered tree cover according to UMD but not forest according to the FRA.</p>



<p>For a better comparison, it would be useful subtract the area of tree plantations recorded in the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which incorporates a number of different tree crops and planted trees, from the UMD tree cover estimate to better match the UMD data with the FAO forest definition. In the instance of Malaysia for example, excluding oil palm plantations from UMD tree cover results in an area considerably closer to the forest area reported in the FRA.</p>



<p>For some countries, the UMD tree cover extent matches the FRA forest extent very closely. For example, FRA’s forest area estimate for Chile is only 0.1% less than UMD’s. Another example is Nigeria, where the FRA forest area is just 5% less than UMD’s tree cover (21.6 Mha for FRA and 22.7 Mha for UMD).</p>



<p>The forest area according to the FRA and UMD data have not always matched so well though — the FRA estimate for forest extent in 2000 (reported in the FRA 2015) was 46% less than UMD’s tree cover area (for the year 2000). Nigeria significantly changed their forest area figures for 2000 as reported in the 2020 FRA, based on the availability of more and better data, resulting in the FRA and UMD figures becoming much more similar (3% difference).</p>



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<aside id="textbox"><em><b>Improvements in Monitoring Mean More Similar Numbers</b><p></p>
Due to the improvements in data quality from both the FRA and UMD, the data is becoming more and more similar. 
A <a href="https://www.sciencedirect.com/science/article/pii/S0048969722048872" target="_blank" rel="noopener noreferrer">scientific study</a> found that in many countries, the quality of data sources used for forest area estimates as reported to the FRA have increased over time. Specifically, the percentage of forest area monitored by satellites with a quality indication of “good” or “very good” in the FRA has increased. To incorporate these data advances, older data points are recalculated if methodologies/data have been revised — for example, the forest extent in 2000 presented in the FRA 2020 is not the same as was presented in the FRA 2000.<br><br>
At the same time, the data on GFW has also improved. The original UMD tree cover data on GFW, which is still used as a baseline for the annual tree cover loss data, is based on canopy density (<a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noopener noreferrer">Hansen et al. 2013</a>) for 2000 and 2010 (thresholded in this case to 30% canopy cover). New data, also from UMD, is based on tree height (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" target="_blank" rel="noopener noreferrer">Potapov et al. 2021</a>) for 2000 and 2020. The new data incorporates updated methods, and new data such as GEDI which better captures the structure of trees.<br><br>
Thanks to these improvements, the difference estimates of the FRA forest area and UMD height-based tree cover extent has shrunk to less than 1% (when comparing UMD’s height-based data with the most recent FRA estimate).

<br><br><Strong>GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*</strong>

<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:157px;height:20px;">
			<p><strong>FRA 2000</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>FRA 2020</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>UMD canopy density data (2000)</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>UMD tree height data (2020)</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:157px;height:20px;">
			<p>3,869 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>4,158 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>3,993 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>4,122 Mha</p>
			</td>
		</tr>
	</tbody>
</table>
*These figures are the globally reported figures

</em> </aside>



<p>Some countries showed higher estimates for FRA forest areas than UMD tree cover extent, for instance, many of the Nordic European countries and Canada. Again, the difference between the definitions of tree cover used in UMD and forest area used by FRA likely explains the differences. According to the FRA 2020, a substantial proportion of forested areas in these countries are production forests, meaning that vast areas of land temporarily unstocked due to timber harvesting are included as forest area for the FRA. UMD does not include these regions, nor areas that have been regenerated but have not yet reached the UMD height threshold, as tree cover.</p>



<h2 class="wp-block-heading">What does this mean for those using UMD and FRA data?</h2>



<p>Estimates by UMD and the FRA of tree cover and forest can vary, in some cases substantially within a country. The same patterns are not seen for all countries; some countries have higher estimates according to UMD, and for others, FRA estimates are higher. In many cases, these can be explained when taking into account the different forest definitions used. </p>



<p>Because of their differences, both data sets can be useful for different stakeholders. Read our blog <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/"><em>Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</em></a><em> </em>to learn more about different ways the UMD and FRA data can be used. In the end, it’s important for users to know the differences between the data sets to help them decide which data will best meet their needs.</p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> We acknowledge and thank the FAO for their helpful comments on this blog.

 

</div> 

 

<hr>



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<p></p>



<p></p>

Comparing Forest Extent in 2020 from Global Forest Watch and the Forest Resources Assessment

<p>Global Forest Watch (GFW) and the Global Forest Resources Assessment (FRA) from the Food and Agricultural Organization of the United Nations (FAO) both provide global data sets with information on the world's forests. The latest FRA was released in 2020, and when compared to 2020 data on forest area from the University of Maryland (UMD), available on GFW, we see similarities and differences. So how similar are the numbers and what could explain the differences? This blog compares the forest extent from these two data sets, discusses why there are differences and explains what this means for users. To learn more about general differences between data on GFW and FRA data, <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">check out this blog</a>.</p>



<h2 class="wp-block-heading">What was the area of trees and forests in 2020 according to UMD and FRA data?</h2>



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<aside id="textbox"><em><b>Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance </b><p></p>
Some of the primary distinctions between GFW and FRA are in their data sources and definitions (<a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">read more here</a>).<br><br>
For 2020, GFW uses the UMD <a href="https://gfw.global/2Mei4Z0" target="_blank" rel="noopener noreferrer">2020 tree cover height data</a> to report on the extent of tree cover — a purely biophysical definition related to height and extent of trees. This satellite data picks up all trees with a minimum canopy height, which users can set when visualizing the data. For this analysis, we use a minimum height threshold of 5 meters. GFW also has a tree cover extent data set for 2000 and 2010 from UMD based on the percent of canopy density, which is used in many functions on GFW, but this was largely not used in this blog. <br><br>
The FRA relies on country-reported data based on a definition of forest that includes both a biophysical and land use component. In the FRA data, forests have a tree canopy height of at least 5 meters and canopy cover of at least 10% or are capable of reaching this threshold. Additionally, the land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or timber production.<br><br>
This means that in the FRA data, land that is temporarily devoid of trees — for example, recently logged or burned areas — would still be classified as forest if the official land use stipulates that trees will be allowed to regenerate in the future. Conversely, logged and burned areas are not captured as tree cover by the UMD data. Land that contains trees but is designated for agricultural or urban uses — for example, an oil palm plantation, shade grown cocoa (agroforestry) or a city park — would not be considered a forest by the FRA, but would be included in UMD tree cover data. 

</em> </aside>



<h4 class="wp-block-heading">Extent of trees and forests globally and by region in 2020 (in million hectares — Mha)</h4>



<table>
	<tbody>
		<tr>
			<td style="width:180px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Africa</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>Asia including Russian Federation</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>Europe, excluding Russian Federation</strong></span></p>
			</td>
			<td style="width:95px;height:20px;">
			<p><span style="font-size:10px;"><strong>North and Central America</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>South America</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Oceania</strong></span></p>
			</td>
			<td style="width:80px;height:20px;">
			<p><span style="font-size:10px;"><strong>Global </strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span></p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>UMD tree cover extent 2020</strong></span></p>
			</td>
			<td style="width:69px;height:10px;">
			<p><span style="font-size:10px;">674 Mha</span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:10px;">
			<p><span style="font-size:10px;">1,483 Mha</span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:10px;">
			<p><span style="font-size:10px;">204 Mha</span></p>
			</td>
			<td style="width:76px;height:10px;">
			<p><span style="font-size:10px;">675 Mha</span></p>
			</td>
			<td style="width:68px;height:10px;">
			<p><span style="font-size:10px;">834 Mha</span></p>
			</td>
			<td style="width:71px;height:10px;">
			<p><span style="font-size:10px;">147 Mha</span></p>
			</td>
			<td style="width:112px;height:10px;">
			<p><span style="font-size:10px;">4,018 Mha</span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>FRA forest area 2020</strong></span></p>
			</td>
			<td style="width:69px;height:20px;">
			<p><span style="font-size:10px;">637 Mha</span></p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;">1,438 Mha</span></p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;">202 Mha</span></p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;">753 Mha</span></p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;">844 Mha</span></p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;">184 Mha</span></p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;">4,059 Mha</span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			<p><span style="font-size:10px;"><strong>Difference of UMD extent as compared to FRA</strong></span></p>
			</td>
			<td style="width:69px;height:20px;">
			<p><span style="font-size:10px;"><strong>+5.6%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;"><strong>+3.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;"><strong>+1.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;"><strong>-11.5%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.2%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;"><strong>-24.8%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.02%</strong></span></p>

			<p>&nbsp;</p>
			</td>
		</tr>
	</tbody>
</table>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%"><small><em>*The regional figures compare the same list of countries, with some adjustments made to accommodate different country boundaries in the Database of Global Administrative Areas (GADM) and FRA. </em></small></div>



<p>Globally and regionally, tree/forest cover as estimated by UMD data and the FRA are similar. Global estimates of forest area in 2020 only vary by about 1% (4,059 million hectares — Mha — for the FRA and 4,018 Mha for the UMD data).</p>



<p>At the regional level there are many similarities, with very small differences for Asia, Europe and South America. Oceania shows the largest difference at the regional scale, which is almost entirely attributable to the FRA’s greater estimate of forest area in Australia — Australia’s FRA forest area is 52% greater than UMD’s tree cover extent. This may be because Australia’s definition of “forest” specifies a minimum vegetation height of 2 meters, which is used in the FRA estimate. The majority of countries in the FRA follow the FAO definition of a minimum vegetation height of 5 meters, which is also the height threshold used in this blog for analysis of the UMD data.</p>



<h4 class="wp-block-heading">Forest area/tree cover 2020 according to UMD data on GFW and the FRA</h4>



<div class="flourish-embed flourish-map" data-src="visualisation/15325037?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>In central Africa and Southeast Asia, UMD’s tree cover estimates are typically greater than the FRA’s forest area estimations. In Malaysia, for example, UMD's estimate is 33% higher than the FRA's (28.6 Mha for UMD vs. 19.1 Mha for FRA). This may be explained by the difference in definitions, for example Southeast Asia typically contains large areas of oil palm plantations, which would be considered tree cover according to UMD but not forest according to the FRA.</p>



<p>For a better comparison, it would be useful subtract the area of tree plantations recorded in the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which incorporates a number of different tree crops and planted trees, from the UMD tree cover estimate to better match the UMD data with the FAO forest definition. In the instance of Malaysia for example, excluding oil palm plantations from UMD tree cover results in an area considerably closer to the forest area reported in the FRA.</p>



<p>For some countries, the UMD tree cover extent matches the FRA forest extent very closely. For example, FRA’s forest area estimate for Chile is only 0.1% less than UMD’s. Another example is Nigeria, where the FRA forest area is just 5% less than UMD's tree cover (21.6 Mha for FRA and 22.7 Mha for UMD).</p>



<p>The forest area according to the FRA and UMD data have not always matched so well though — the FRA estimate for forest extent in 2000 (reported in the FRA 2015) was 46% less than UMD’s tree cover area (for the year 2000). Nigeria significantly changed their forest area figures for 2000 as reported in the 2020 FRA, based on the availability of more and better data, resulting in the FRA and UMD figures becoming much more similar (3% difference).</p>



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<aside id="textbox"><em><b>Improvements in Monitoring Mean More Similar Numbers</b><p></p>
Due to the improvements in data quality from both the FRA and UMD, the data is becoming more and more similar. 
A <a href="https://www.sciencedirect.com/science/article/pii/S0048969722048872" target="_blank" rel="noopener noreferrer">scientific study</a> found that in many countries, the quality of data sources used for forest area estimates as reported to the FRA have increased over time. Specifically, the percentage of forest area monitored by satellites with a quality indication of “good” or “very good” in the FRA has increased. To incorporate these data advances, older data points are recalculated if methodologies/data have been revised — for example, the forest extent in 2000 presented in the FRA 2020 is not the same as was presented in the FRA 2000.<br><br>
At the same time, the data on GFW has also improved. The original UMD tree cover data on GFW, which is still used as a baseline for the annual tree cover loss data, is based on canopy density (<a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noopener noreferrer">Hansen et al. 2013</a>) for 2000 and 2010 (thresholded in this case to 30% canopy cover). New data, also from UMD, is based on tree height (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" target="_blank" rel="noopener noreferrer">Potapov et al. 2021</a>) for 2000 and 2020. The new data incorporates updated methods, and new data such as GEDI which better captures the structure of trees.<br><br>
Thanks to these improvements, the difference estimates of the FRA forest area and UMD height-based tree cover extent has shrunk to less than 1% (when comparing UMD’s height-based data with the most recent FRA estimate).

<br><br><Strong>GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*</strong>

<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:157px;height:20px;">
			<p><strong>FRA 2000</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>FRA 2020</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>UMD canopy density data (2000)</strong></p>
			</td>
			<td style="width:157px;height:20px;">
			<p><strong>UMD tree height data (2020)</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:157px;height:20px;">
			<p>3,869 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>4,158 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>3,993 Mha</p>
			</td>
			<td style="width:157px;height:20px;">
			<p>4,122 Mha</p>
			</td>
		</tr>
	</tbody>
</table>
*These figures are the globally reported figures

</em> </aside>



<p>Some countries showed higher estimates for FRA forest areas than UMD tree cover extent, for instance, many of the Nordic European countries and Canada. Again, the difference between the definitions of tree cover used in UMD and forest area used by FRA likely explains the differences. According to the FRA 2020, a substantial proportion of forested areas in these countries are production forests, meaning that vast areas of land temporarily unstocked due to timber harvesting are included as forest area for the FRA. UMD does not include these regions, nor areas that have been regenerated but have not yet reached the UMD height threshold, as tree cover.</p>



<h2 class="wp-block-heading">What does this mean for those using UMD and FRA data?</h2>



<p>Estimates by UMD and the FRA of tree cover and forest can vary, in some cases substantially within a country. The same patterns are not seen for all countries; some countries have higher estimates according to UMD, and for others, FRA estimates are higher. In many cases, these can be explained when taking into account the different forest definitions used. </p>



<p>Because of their differences, both data sets can be useful for different stakeholders. Read our blog <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/"><em>Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</em></a><em> </em>to learn more about different ways the UMD and FRA data can be used. In the end, it’s important for users to know the differences between the data sets to help them decide which data will best meet their needs.</p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> We acknowledge and thank the FAO for their helpful comments on this blog.

 

</div> 

 

<hr>



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<p></p>



<p></p>

This blog compares the forest extent in 2020 for data from UMD on GFW and the FAO Forest Resources Assessment and explains the differences.

Forest Extent from GFW and the Forest Resources Assessment

Comment

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Paige Johnson

Side by side comparison of UMD tree cover loss and JRC Tropical Moist Forest data

deforestation

deforestation | Global Forest Watch Blog

forest change

forest change | Global Forest Watch Blog

forest quality

forest quality | Global Forest Watch Blog

Michelle Sims

Peter Potapov

Christelle Vancutsem

Clément Bourgoin

Fréderic Achard

<p>The wide availability of forest and land cover maps based on satellite data provides more information on the world’s forests than ever before. However, this plethora of information can also be challenging for people trying to understand differences in data sources and decide which data to use for a specific purpose.</p>



<p>In the past, we’ve compared the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a> from the University of Maryland (UMD) and available on Global Forest Watch (GFW) with national data from <a href="https://www.globalforestwatch.org/blog/data-and-research/2022-tree-cover-loss-data-explained/">Brazil and Indonesia</a>, as well as with data from the United Nations Food and Agriculture Organization’s (FAO) <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Global Forest Resources Assessment</a>, to shed light on some of these differences.</p>



<p>Another data set that tracks forest change in the tropics is the <a href="https://forobs.jrc.ec.europa.eu/TMF" target="_blank" rel="noreferrer noopener">Tropical Moist Forest (TMF) data set</a> from the European Commission Joint Research Centre (JRC). Like the UMD tree cover loss data, the JRC TMF data is based on Landsat satellite imagery and is updated annually. Yet while both data sets map forest disturbances, the maps and numbers look a little different.</p>



<p>In this blog, we explain some key differences in the definitions and methods used for each data set that impact the resulting maps and discuss how the disturbances mapped by both data sets compare (while the data sets also map forest gain or regrowth, this blog focuses on comparing their mapping of forest disturbances). While understanding these differences can help determine the contexts for which each data set might be useful, note that this analysis does not evaluate the quality or accuracy of either data set.</p>



<h2 class="wp-block-heading">What areas do the JRC and UMD data sets cover, and how do they define forests?</h2>



<p>The UMD data set maps forests globally based on <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree cover extent</a> in 2000. <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">Tree cover</a> is defined as woody vegetation with a minimum height of at least five meters at 30-meter resolution, which includes all types of tree cover — including natural or managed forest and plantations — and is not limited to a specific forest type. The GFW map shows tree cover with a 30% canopy density, which we use in our analyses and in this comparison, although users can adjust the threshold based on their needs.</p>



<p>The JRC data set, on the other hand, <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">maps forests</a> beginning in 1990. The data includes all closed primary and secondary forests in the humid tropics and does not apply a minimum height or canopy density threshold to delineate forest extent.</p>



<p>As a result of these definitional differences, some areas included in the 2000 UMD tree cover extent in the tropics like dry forests, young secondary forests and plantations are not classified as forests in the JRC data set in 2000 — a total of 655 million hectares (Mha), or 34%, of the UMD tree cover extent area that overlaps with the JRC data. Likewise, the JRC forest extent in 2000 includes some areas that do not meet the height and 30% canopy density threshold in the UMD data or are otherwise not mapped as tree cover by the UMD data — 17.2 Mha, or 1% of the JRC forest extent.</p>



<p>Because of the differences in geographic coverage and scope between the two data sets, this blog only compares forest disturbances from 2001-2022 for areas that, in the year 2000, are mapped both as tree cover by UMD and as forest by JRC.</p>



<h2 class="wp-block-heading">What types of forest disturbances do the UMD and JRC data map?</h2>



<h3 class="wp-block-heading"><em>The JRC Tropical Moist Forest data set maps degradation and deforestation in the humid tropics</em></h3>



<p>The JRC TMF data set maps disturbances to tropical moist forests — including degradation and deforestation — from 1990 to 2022. The JRC team maps these changes using an algorithm that detects forest disruptions, meaning an absence of tree foliage cover within a 30-meter pixel for each available Landsat image, and then classifies the status of forest based on how long these disruptions are visible.</p>



<p>The TMF data set <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">defines degradation</a> as temporary disturbances occurring for up to 2.5 years, while it defines deforestation as longer-term disturbances where disruptions to tree cover are visible for more than 2.5 years and no tree regrowth is detected over the last three years. In any given year, a forest is considered disturbed if there is a disruption detected in at least one Landsat image during that year.</p>



<p>Thus, pixels classified as degraded are still considered forest, with existing or regrowing trees, while pixels classified as deforested are undergoing a longer-term loss of forest. Because disruptions are recorded and analyzed for the entire time series, the data set classifies dynamics over time; for example, a forest pixel may be classified as degraded in one year after a short-term disturbance, then after a period of recovery may later be classified as deforested after ongoing disturbances with no tree regrowth. The JRC deforestation class therefore includes direct deforestation (deforestation of undisturbed forest) or deforestation after degradation (deforestation of forest that was previously classified as degraded).</p>



<p>The data set also analyzes the frequency, duration of disruptions and time of forest recovery in between disruption events to identify deforestation following degradation versus multiple degradation events.</p>



<h3 class="wp-block-heading"><em>The UMD tree cover loss data set on GFW maps all types of tree cover loss globally</em></h3>



<p>The UMD tree cover loss data set maps annual tree cover loss from 2001 to 2022. The UMD team uses an algorithm that detects loss using change detection metrics based on comparison of Landsat images between the present and past years. Tree cover loss, <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">defined as</a> a clearing of tree cover of at least a half of a 30-meter Landsat pixel, includes both temporary and permanent losses without defining or differentiating which loss is considered degradation or deforestation. Once classified as loss, pixels are masked from further updates, meaning that any following forest change dynamics — such as regrowth or subsequent disturbances — are not recorded.</p>



<p>The UMD tree cover loss data can be combined with contextual data layers, such as <a href="https://gfw.global/3kqcoqa">UMD’s map of humid tropical primary forest</a>, to see loss within specific forest types.</p>



<h4 class="wp-block-heading">Key differences between the JRC TMF and UMD tree cover loss data</h4>



<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:165px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>JRC TMF</strong></p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>UMD tree cover loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Temporal coverage</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>1990-2022</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>&nbsp;2000-2022</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Geographic coverage and extent</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Tropical moist forest:</strong> closed primary and secondary forests in the humid tropics in 1990.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Global tree cover: </strong>woody vegetation with a minimum height of at least five meters at 30-meter resolution in 2000.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Definition of disturbance</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>An absence of tree foliage cover within a 30-meter pixel.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Clearing of at least half of tree cover within a 30-meter pixel.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Disturbance classes</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Disturbances are classified as <strong>degradation </strong>if they are visible for less than 2.5 years, and <strong>deforestation </strong>if they are visible for more than 2.5 years with no vegetative regrowth over the last three years.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>All disturbances classified as <strong>tree cover loss. </strong>The length of disturbance is not considered.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Methods</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies valid single-date Landsat images and derives disturbance classes by analyzing the single-date classifications. Dynamics of disturbance events over time are recorded.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies change using change detection metrics derived from all valid Landsat images over the present and past years. Only the first loss event that is detected is recorded.</p>
			</td>
		</tr>
	</tbody>
</table>



<h2 class="wp-block-heading">What do the UMD and JRC data show about forest disturbances in the tropics?</h2>



<p>Overall, the JRC data detects a larger area of disturbances across the tropics from 2001-2022 compared with the UMD data. The JRC data detects 258 Mha of disturbances, including 113 Mha of degradation and 145 Mha of deforestation, while the UMD data detects 143 Mha of tree cover loss. However, trends in total deforestation mapped by JRC and tree cover loss mapped by UMD are generally aligned: They are largely similar for the first decade; then from 2013-2015, JRC detects a larger area as deforestation than UMD detects as loss, and after 2016, UMD detects a larger area.</p>



<div class="flourish-embed" data-src="story/2112582?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>We further separated out the disturbance types mapped by the JRC data and overlaid the data sets to evaluate how the two data sets compare at each pixel.</p>



<p>Among all the JRC data’s disturbance classes, the JRC direct deforestation class aligned most closely with UMD tree cover loss. Across the tropics from 2001-2022, 46.8 Mha, or 68%, of the area classified as direct deforestation by the JRC data was also classified as tree cover loss in the UMD data. This varied by region and was highest in Asia/Oceania and the Americas (66% and 75% respectively) and lowest in Africa (50%). The UMD data less often classified areas that JRC identified as deforestation after degradation as tree cover loss (38.1 Mha, or 50% across the tropics).</p>



<p>In some areas, disturbances classified as degradation by the JRC TMF data set were classified as tree cover loss by the UMD data. Across the tropics from 2001-2022, 32.6 Mha, or 29%, of the area the JRC data classified as degradation was also classified as tree cover loss in the UMD data. The JRC degradation class includes three types of degradation: short-duration (less than one year), long-duration (1-2.5 years) and short-duration over 2-3 periods (short-duration degradation that occurs two to three separate times throughout the time period). The JRC data also measures “intensity” of degradation based on the total number of Landsat images where disruptions in tree cover were visible. We found that, in general, the UMD data:</p>



<ul>
<li>More often detected longer duration degradation as tree cover loss. Short-duration degradation (including events occurring just once or over 2-3 periods), which represents 85% of the forest area mapped as degraded by JRC since 2001, is usually not shown as tree cover loss by the UMD data.</li>



<li>Classified degradation events as tree cover loss that had an intensity of greater than six images on average, while those UMD did not classify as loss had approximately three images or less on average.</li>



<li>Detected a larger proportion of the area JRC classified as degradation from 2015 onwards, which may be <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">due to the algorithm changes</a> in the UMD data that increase forest loss detection sensitivity in case of fires, selective logging and other disturbance types that JRC classified as degradation.</li>
</ul>



<p>In other areas across the tropics, the UMD data detected tree cover loss for 6.4 Mha, or 7%, of areas where there the JRC data detected no disturbance events.</p>



<figure class="wp-block-image size-full is-resized"><img decoding="async" loading="lazy" width="1222" height="1163" src="https://content.globalforestwatch.org/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png" alt="Comparison of JRC TMF and UMD tree cover loss disturbances across the tropics" class="wp-image-16572" style="aspect-ratio:1.0507308684436802;width:771px;height:auto" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png 1222w, https://content.globalforestwatch.org/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table-1024x975.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table-768x731.png 768w" sizes="(max-width: 1222px) 100vw, 1222px" /></figure>



<h4 class="wp-block-heading">Forest disturbances related to shifting cultivation in Mongala, DRC</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16055613?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h4 class="wp-block-heading">Large scale deforestation along BR-230 in Amazonas, Brazil</h4>



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<h4 class="wp-block-heading">Selective logging in Pará, Brazil</h4>



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<h2 class="wp-block-heading">Why does the UMD and JRC data align in some ways and differ in others?</h2>



<p>The two data sets align more closely for more abrupt disturbances that are likely to meet the definitions used in both data sets, such as intense fires (which the JRC data may classify as long-duration degradation) or clearance of tree cover for agricultural expansion (usually classified as deforestation in the JRC data). The two data sets are less likely to detect the same disturbance when it is followed by a rapid recovery (such as short-duration degradation in the JRC data, including disturbances like drought, selective logging or low-intensity fires, which may only be detected in as few as one Landsat image) or disturbances resulting in gradual reduction of tree cover over a longer time period (such as degradation followed by deforestation in the JRC data). In these cases, the disturbance may not meet the UMD definition of tree cover loss and therefore is less likely to be detected in the UMD data.</p>



<p>Both data sets have different strengths. The UMD data is global in scope, allowing for comparative analysis across regions due to global consistency in definitions and methods. Within the tropics, the UMD data includes broader coverage of forest types beyond tropical moist forests, including tropical dry forests.<a href="https://gfw.global/3GEmSQh"></a> The UMD data also provides flexibility for users to adjust the canopy density threshold according to their preference or needs, which can be used to align with a specific forest definition or forest characteristics of a specific biome.</p>



<p>Meanwhile, the JRC data detects degradation and less intense disturbances, which can be helpful for users who want to understand where forests have experienced various types of stresses that have not resulted in complete loss. The JRC data also monitors forests after initial disturbances to track forest recovery and subsequent disturbances, which is helpful for users who would like to understand yearly change dynamics and identify where there are repeated disturbances.</p>



<p>However, it is important to note that we are not evaluating the accuracy of either data set. Assessing which data set is more accurate for a specific use case or geography would require a formal validation exercise.</p>



<p>Ultimately, these two data sets present complementary information that can help us better understand the various types of changes occurring in tropical forests.</p>



<hr> 

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Note: This blog investigates the JRC TMF map, which is a separate product from the JRC global map of forest cover for year 2020, released on December 7, 2023.

 

</div> 

 

<hr>



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Differences Between Global Forest Watch’s Tree Cover Loss Data and JRC’s Tropical Moist Forest Data Explained

<p>The wide availability of forest and land cover maps based on satellite data provides more information on the world’s forests than ever before. However, this plethora of information can also be challenging for people trying to understand differences in data sources and decide which data to use for a specific purpose.</p>



<p>In the past, we’ve compared the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a> from the University of Maryland (UMD) and available on Global Forest Watch (GFW) with national data from <a href="https://www.globalforestwatch.org/blog/data-and-research/2022-tree-cover-loss-data-explained/">Brazil and Indonesia</a>, as well as with data from the United Nations Food and Agriculture Organization’s (FAO) <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Global Forest Resources Assessment</a>, to shed light on some of these differences.</p>



<p>Another data set that tracks forest change in the tropics is the <a href="https://forobs.jrc.ec.europa.eu/TMF" target="_blank" rel="noreferrer noopener">Tropical Moist Forest (TMF) data set</a> from the European Commission Joint Research Centre (JRC). Like the UMD tree cover loss data, the JRC TMF data is based on Landsat satellite imagery and is updated annually. Yet while both data sets map forest disturbances, the maps and numbers look a little different.</p>



<p>In this blog, we explain some key differences in the definitions and methods used for each data set that impact the resulting maps and discuss how the disturbances mapped by both data sets compare (while the data sets also map forest gain or regrowth, this blog focuses on comparing their mapping of forest disturbances). While understanding these differences can help determine the contexts for which each data set might be useful, note that this analysis does not evaluate the quality or accuracy of either data set.</p>



<h2 class="wp-block-heading">What areas do the JRC and UMD data sets cover, and how do they define forests?</h2>



<p>The UMD data set maps forests globally based on <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree cover extent</a> in 2000. <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">Tree cover</a> is defined as woody vegetation with a minimum height of at least five meters at 30-meter resolution, which includes all types of tree cover — including natural or managed forest and plantations — and is not limited to a specific forest type. The GFW map shows tree cover with a 30% canopy density, which we use in our analyses and in this comparison, although users can adjust the threshold based on their needs.</p>



<p>The JRC data set, on the other hand, <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">maps forests</a> beginning in 1990. The data includes all closed primary and secondary forests in the humid tropics and does not apply a minimum height or canopy density threshold to delineate forest extent.</p>



<p>As a result of these definitional differences, some areas included in the 2000 UMD tree cover extent in the tropics like dry forests, young secondary forests and plantations are not classified as forests in the JRC data set in 2000 — a total of 655 million hectares (Mha), or 34%, of the UMD tree cover extent area that overlaps with the JRC data. Likewise, the JRC forest extent in 2000 includes some areas that do not meet the height and 30% canopy density threshold in the UMD data or are otherwise not mapped as tree cover by the UMD data — 17.2 Mha, or 1% of the JRC forest extent.</p>



<p>Because of the differences in geographic coverage and scope between the two data sets, this blog only compares forest disturbances from 2001-2022 for areas that, in the year 2000, are mapped both as tree cover by UMD and as forest by JRC.</p>



<h2 class="wp-block-heading">What types of forest disturbances do the UMD and JRC data map?</h2>



<h3 class="wp-block-heading"><em>The JRC Tropical Moist Forest data set maps degradation and deforestation in the humid tropics</em></h3>



<p>The JRC TMF data set maps disturbances to tropical moist forests — including degradation and deforestation — from 1990 to 2022. The JRC team maps these changes using an algorithm that detects forest disruptions, meaning an absence of tree foliage cover within a 30-meter pixel for each available Landsat image, and then classifies the status of forest based on how long these disruptions are visible.</p>



<p>The TMF data set <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">defines degradation</a> as temporary disturbances occurring for up to 2.5 years, while it defines deforestation as longer-term disturbances where disruptions to tree cover are visible for more than 2.5 years and no tree regrowth is detected over the last three years. In any given year, a forest is considered disturbed if there is a disruption detected in at least one Landsat image during that year.</p>



<p>Thus, pixels classified as degraded are still considered forest, with existing or regrowing trees, while pixels classified as deforested are undergoing a longer-term loss of forest. Because disruptions are recorded and analyzed for the entire time series, the data set classifies dynamics over time; for example, a forest pixel may be classified as degraded in one year after a short-term disturbance, then after a period of recovery may later be classified as deforested after ongoing disturbances with no tree regrowth. The JRC deforestation class therefore includes direct deforestation (deforestation of undisturbed forest) or deforestation after degradation (deforestation of forest that was previously classified as degraded).</p>



<p>The data set also analyzes the frequency, duration of disruptions and time of forest recovery in between disruption events to identify deforestation following degradation versus multiple degradation events.</p>



<h3 class="wp-block-heading"><em>The UMD tree cover loss data set on GFW maps all types of tree cover loss globally</em></h3>



<p>The UMD tree cover loss data set maps annual tree cover loss from 2001 to 2022. The UMD team uses an algorithm that detects loss using change detection metrics based on comparison of Landsat images between the present and past years. Tree cover loss, <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">defined as</a> a clearing of tree cover of at least a half of a 30-meter Landsat pixel, includes both temporary and permanent losses without defining or differentiating which loss is considered degradation or deforestation. Once classified as loss, pixels are masked from further updates, meaning that any following forest change dynamics — such as regrowth or subsequent disturbances — are not recorded.</p>



<p>The UMD tree cover loss data can be combined with contextual data layers, such as <a href="https://gfw.global/3kqcoqa">UMD’s map of humid tropical primary forest</a>, to see loss within specific forest types.</p>



<h4 class="wp-block-heading">Key differences between the JRC TMF and UMD tree cover loss data</h4>



<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:165px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>JRC TMF</strong></p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>UMD tree cover loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Temporal coverage</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>1990-2022</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>&nbsp;2000-2022</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Geographic coverage and extent</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Tropical moist forest:</strong> closed primary and secondary forests in the humid tropics in 1990.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Global tree cover: </strong>woody vegetation with a minimum height of at least five meters at 30-meter resolution in 2000.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Definition of disturbance</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>An absence of tree foliage cover within a 30-meter pixel.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Clearing of at least half of tree cover within a 30-meter pixel.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Disturbance classes</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Disturbances are classified as <strong>degradation </strong>if they are visible for less than 2.5 years, and <strong>deforestation </strong>if they are visible for more than 2.5 years with no vegetative regrowth over the last three years.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>All disturbances classified as <strong>tree cover loss. </strong>The length of disturbance is not considered.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Methods</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies valid single-date Landsat images and derives disturbance classes by analyzing the single-date classifications. Dynamics of disturbance events over time are recorded.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies change using change detection metrics derived from all valid Landsat images over the present and past years. Only the first loss event that is detected is recorded.</p>
			</td>
		</tr>
	</tbody>
</table>



<h2 class="wp-block-heading">What do the UMD and JRC data show about forest disturbances in the tropics?</h2>



<p>Overall, the JRC data detects a larger area of disturbances across the tropics from 2001-2022 compared with the UMD data. The JRC data detects 258 Mha of disturbances, including 113 Mha of degradation and 145 Mha of deforestation, while the UMD data detects 143 Mha of tree cover loss. However, trends in total deforestation mapped by JRC and tree cover loss mapped by UMD are generally aligned: They are largely similar for the first decade; then from 2013-2015, JRC detects a larger area as deforestation than UMD detects as loss, and after 2016, UMD detects a larger area.</p>



<div class="flourish-embed" data-src="story/2112582?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>We further separated out the disturbance types mapped by the JRC data and overlaid the data sets to evaluate how the two data sets compare at each pixel.</p>



<p>Among all the JRC data’s disturbance classes, the JRC direct deforestation class aligned most closely with UMD tree cover loss. Across the tropics from 2001-2022, 46.8 Mha, or 68%, of the area classified as direct deforestation by the JRC data was also classified as tree cover loss in the UMD data. This varied by region and was highest in Asia/Oceania and the Americas (66% and 75% respectively) and lowest in Africa (50%). The UMD data less often classified areas that JRC identified as deforestation after degradation as tree cover loss (38.1 Mha, or 50% across the tropics).</p>



<p>In some areas, disturbances classified as degradation by the JRC TMF data set were classified as tree cover loss by the UMD data. Across the tropics from 2001-2022, 32.6 Mha, or 29%, of the area the JRC data classified as degradation was also classified as tree cover loss in the UMD data. The JRC degradation class includes three types of degradation: short-duration (less than one year), long-duration (1-2.5 years) and short-duration over 2-3 periods (short-duration degradation that occurs two to three separate times throughout the time period). The JRC data also measures “intensity” of degradation based on the total number of Landsat images where disruptions in tree cover were visible. We found that, in general, the UMD data:</p>



<ul>
<li>More often detected longer duration degradation as tree cover loss. Short-duration degradation (including events occurring just once or over 2-3 periods), which represents 85% of the forest area mapped as degraded by JRC since 2001, is usually not shown as tree cover loss by the UMD data.</li>



<li>Classified degradation events as tree cover loss that had an intensity of greater than six images on average, while those UMD did not classify as loss had approximately three images or less on average.</li>



<li>Detected a larger proportion of the area JRC classified as degradation from 2015 onwards, which may be <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">due to the algorithm changes</a> in the UMD data that increase forest loss detection sensitivity in case of fires, selective logging and other disturbance types that JRC classified as degradation.</li>
</ul>



<p>In other areas across the tropics, the UMD data detected tree cover loss for 6.4 Mha, or 7%, of areas where there the JRC data detected no disturbance events.</p>



<figure class="wp-block-image size-full is-resized"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png" alt="Comparison of JRC TMF and UMD tree cover loss disturbances across the tropics" class="wp-image-16572" style="aspect-ratio:1.0507308684436802;width:771px;height:auto"/></figure>



<h4 class="wp-block-heading">Forest disturbances related to shifting cultivation in Mongala, DRC</h4>



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<h4 class="wp-block-heading">Large scale deforestation along BR-230 in Amazonas, Brazil</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16055698?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h4 class="wp-block-heading">Selective logging in Pará, Brazil</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16365261?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h2 class="wp-block-heading">Why does the UMD and JRC data align in some ways and differ in others?</h2>



<p>The two data sets align more closely for more abrupt disturbances that are likely to meet the definitions used in both data sets, such as intense fires (which the JRC data may classify as long-duration degradation) or clearance of tree cover for agricultural expansion (usually classified as deforestation in the JRC data). The two data sets are less likely to detect the same disturbance when it is followed by a rapid recovery (such as short-duration degradation in the JRC data, including disturbances like drought, selective logging or low-intensity fires, which may only be detected in as few as one Landsat image) or disturbances resulting in gradual reduction of tree cover over a longer time period (such as degradation followed by deforestation in the JRC data). In these cases, the disturbance may not meet the UMD definition of tree cover loss and therefore is less likely to be detected in the UMD data.</p>



<p>Both data sets have different strengths. The UMD data is global in scope, allowing for comparative analysis across regions due to global consistency in definitions and methods. Within the tropics, the UMD data includes broader coverage of forest types beyond tropical moist forests, including tropical dry forests.<a href="https://gfw.global/3GEmSQh"></a> The UMD data also provides flexibility for users to adjust the canopy density threshold according to their preference or needs, which can be used to align with a specific forest definition or forest characteristics of a specific biome.</p>



<p>Meanwhile, the JRC data detects degradation and less intense disturbances, which can be helpful for users who want to understand where forests have experienced various types of stresses that have not resulted in complete loss. The JRC data also monitors forests after initial disturbances to track forest recovery and subsequent disturbances, which is helpful for users who would like to understand yearly change dynamics and identify where there are repeated disturbances.</p>



<p>However, it is important to note that we are not evaluating the accuracy of either data set. Assessing which data set is more accurate for a specific use case or geography would require a formal validation exercise.</p>



<p>Ultimately, these two data sets present complementary information that can help us better understand the various types of changes occurring in tropical forests.</p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”="">
 

Note: This blog investigates the JRC TMF map, which is a separate product from the JRC global map of forest cover for year 2020, released on December 7, 2023.

 

</div> 

 

<hr>

We explain key differences between two data sets that track forest change in the tropics: UMD tree cover loss and JRC Tropical Moist Forest.

Tree Cover Loss and Tropical Moist Forest Differences, Explained

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<p><em>“We are part of nature and nature lives in coexistence. Everything is interconnected and everything depends on each other … If we don’t work collaboratively and in a connected way, we sure won’t be able to move forward.” </em>– Tatiana Espinosa, President of ARBIO Perú ACOMAD – Madre De Dios, and a member of the Amazonía Que Late Network</p>



<iframe loading="lazy" width="450" height="400" src="https://www.youtube.com/embed/konVVZhR5_M?si=G9R976DqJWz0eRVz" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>



<p></p>



<p>Deep in the Amazon, volunteers from the Voluntary and Communal Conservation Network of San Martin receive a deforestation alert in <a href="https://www.globalforestwatch.org/help/forest-watcher/">Forest Watcher</a>: the forest monitoring app shows an <a href="https://www.globalforestwatch.org/blog/data-and-research/integrated-deforestation-alerts/">alert</a> detecting a disturbance in a monitored area. The volunteers arrive by various means — from small boat, motorcycle, or horseback to endless walking — to an area that has been cleared, illegally. Within the Forest Watcher app, they document a report and capture evidence of the impact. Network volunteers alert the local authorities who launch an official investigation.</p>



<p>Guardians of the forest in San Martín, Peru, use Global Forest Watch’s (GFW) data and technologies such as Forest Watcher daily to monitor forests, prevent deforestation and defend biodiversity. <a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener">Amazónicos por la Amazonía</a> (AMPA) — a nonprofit organization committed to conserving Peru’s natural and cultural heritage and improving the sustainable development of the people of the Andean Amazon, and a GFW Small Grants Fund recipient — works with allies such as conservation concession holders from the San Martin Network to monitor and patrol forest areas (conservation and ecotourism concessions are public forest areas where legal management rights are granted to groups such as NGOs, like AMPA, that are organized by the civil society of the communities that voluntarily conserve the forests).</p>



<p>AMPA works with local communities and authorities to carry out joint actions to verify deforestation and raise awareness of environmental crimes. Since 2017, they actively monitored forest loss in the regions of San Martín, Loreto and Ucayali — an area that totals over 900,000 hectares. Through this work, they provided the San Martin network with a space to connect and collaborate on the conservation of the forest lands that were assigned to them for management and protection.</p>



<h2 class="wp-block-heading">Leveraging GFW tools to build forest monitoring capacity</h2>



<p>AMPA is a four-time recipient of the GFW <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Small Grants Fund</a> (SGF), which provides financial and technical assistance to organizations to monitor and respond to deforestation. AMPA’s projects aim to pair local knowledge with a more robust use of GFW data and tools to improve the response capacity and awareness of environmental crimes in Peru’s San Martin region. During their projects, AMPA has trained volunteers on how to use tools like GFW data and the Forest Watcher app.</p>



<p>GFW’s partnership with AMPA started in 2017 during their first year as a SGF grantee. AMPA used GFW data and tools to implement a control and surveillance system within the conservation initiatives in the San Martin Network.</p>



<p>“Associations that protect forests use [GFW] as part of their surveillance and control systems, allowing them to organize patrol watches, tours and identify areas according to the degree of threat and vulnerability,” explained Jorge Fachín, Director of the Forest Program, Conservation and Monitoring of AMPA</p>



<p>GFW tools and monitoring equipment help AMPA improve forest monitoring activities of the volunteer network by enabling them to lead patrols more easily and rapidly. The use of drones and mobile apps like Forest Watcher help the volunteers collect sufficient information to carry out field activities with authorities, collect necessary information and evidence, and generate patrol reports.</p>



<p>Over the course of several SGF projects, AMPA worked with conservation networks in the Peruvian Amazon, including San Martin to use GFW solutions and develop&nbsp;their monitoring capacity, evolving each project based on lessons learned from their previous work. This involved the use and management of deforestation alerts and the development of skills to handle GPS, drones and cameras, and a “train the trainers” approach.</p>



<p>This equipped the San Martin Network to train their members on how to use GFW tools and identify deforestation alerts, and provided young network members with personalized training for data management and tools.</p>



<h2 class="wp-block-heading">GIS and monitoring unit ready to respond to environmental crimes</h2>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="768" height="432" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/AMPA_photo-2_resized.jpg" alt="Volunteers using a drone" class="wp-image-16483"/><figcaption class="wp-element-caption">Volunteers using a drone. Credit: AMPA.</figcaption></figure>



<p>AMPA also expanded its focus to continue to build capacity in forest custodian accreditation workshops, which include members of the Peruvian Amazon networks AMPA works with. This includes the formation of a Geographic Information System (GIS) and monitoring unit to monitor areas with GFW and remote sensing technology. The GIS and monitoring unit helped improve the verification of deforestation alerts and provide detailed information on the forest cover changes in the conservation areas.</p>



<p>Collaborating with technically capable monitoring brigades and forest custodians from the Peruvian Amazon networks strengthened AMPA’s ability to work directly with authorities to verify and respond to environmental crimes. The GIS and monitoring unit provided technical support in verification procedures to the authorities and prepared deforestation and field verification reports that were shared with Specialized Prosecution in Environmental Affairs (FEMA) and the Regional Environmental Authority (ARA). These reports support legal and administrative complaints against those responsible for environmental crimes, such as illegal deforestation.</p>



<p>Ultimately, authorities from FEMA and ARA officially recognized AMPA’ reports, along with GLAD alerts and Planet and drone imagery, as official evidence of environmental crimes.</p>



<p>Joint actions between AMPA’s GIS and monitoring unit, local actors and authorities helped strengthen and improve attention to environmental crimes with the active participation and involvement of local partners playing a key role. Due to the quality of reports submitted to FEMA on behalf of the San Martin Network, AMPA now has a formal inter-institutional agreement to assist the FEMA office in San Martin with collecting information on possible illegal deforestation using drones and Planet imagery.</p>



<h2 class="wp-block-heading">Alliances with local authorities</h2>



<p>AMPA organized workshops with representatives of the institutions directly involved in addressing environmental crimes in the region, which included designing a unified process for representatives and partners to collect key information for investigations through technical procedures that integrated GFW data and tools. Additionally, AMPA provided trainings for FEMA and ARA members on how to best use the data and tools.</p>



<p>AMPA helped prosecutors and technicians obtain accreditation as operators of remotely piloted aircraft systems (RPAS, or drones) which strengthened their ability to conduct verifications. Furthermore, AMPA also supported monitoring and conservation partners to get accredited as RPAS operators, improving their ability to verify deforestation alerts in the areas they investigated, which in turn strengthened their reporting of forest-related crimes to authorities.</p>



<p>To share insights from their strategic approach for verifying and reporting crimes, AMPA developed a roadmap on integrating technology use and collaboration with local actors and authorities, including information on strategic alliances with different stakeholders (communities and local, regional and national governments).</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1024" height="724" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/infographic_road-map-for-responding-environmental-crimes-using-glad-alerts-ampa-1024x724-1.png" alt="Infographic: Road map for responding to environmental crime" class="wp-image-16484" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/11/infographic_road-map-for-responding-environmental-crimes-using-glad-alerts-ampa-1024x724-1.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/11/infographic_road-map-for-responding-environmental-crimes-using-glad-alerts-ampa-1024x724-1-768x543.png 768w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<p>This manual was created as part of the San Martin Network’s efforts to raise awareness and help address environmental crimes. They also shared this manual with partners on the platform of “<a href="https://www.amazoniaquelate.com/" target="_blank" rel="noreferrer noopener"><em>Escuela</em> <em>Amazonía que Late</em></a>,” an initiative led by AMPA that brings together local communities, Indigenous Peoples, organized civil society and local governments, and other partners across various conservation initiatives in Peru to learn about strategies to protect its natural and cultural heritage.</p>



<h2 class="wp-block-heading">Raising awareness of community forest monitoring efforts</h2>



<p>The surveillance and control system for forest monitoring developed by AMPA resulted in community advocacy efforts to raise awareness of environmental crimes within local, regional and national law enforcement authorities, including the prosecutor’s office, to reduce illegal deforestation. AMPA’s reports based on the deforestation alerts <a href="https://diariovoces.com.pe/197474/fiscalia-ambiental-de-alto-amazonas-reforzara-acciones-de-control-y-vigilancia?fbclid=IwAR3QvgpG4hFv62o7gpVVntlM7RzPtfA_3KrwUAVp-BxbsIxq50qF-4Tdfuo" target="_blank" rel="noreferrer noopener">received media coverage</a> and not only caught the attention of authorities to identify offenders, but their complaints were raised to the prosecutor’s office. To date, two reports based on AMPA’s approach that were referred to the authorities have resulted in sentencing of those responsible for environmental crimes.</p>



<p>As part of their communication strategy, AMPA prepared press releases and audiovisual materials to feature the main activities of their project that were disseminated across local, regional and national media and partner websites. These communication efforts helped emphasize and highlight the role of AMPA’s participatory monitoring work and raise awareness among stakeholders of environmental crimes.</p>



<p>Additionally, AMPA has hosted meetings and workshops with public institutions responsible for forest management in Peru to explain their control and surveillance system. National and subnational government agencies have recognized AMPA as an important stakeholder in the conservation and management of forests in the San Martin region and in the Peruvian Amazon, and several additional national and regional government agencies have requested training and technical support from them.</p>



<p>“We’re proud of the renewal of the inter-institutional agreement with the Specialized Prosecution in Environmental Affairs (FEMA) and the interest of other donors to continue promoting and executing projects with forest monitoring components based on our experience in other regions,” said Jorge Fachín.</p>



<h2 class="wp-block-heading">From deforestation alert to community action and beyond</h2>



<p>A key element of AMPA’s success is their approach to building partnerships with local communities and law enforcement authorities to use tools like GFW to verify deforestation and address environmental crimes, together. Today, AMPA continues their work within the network of community conservation concessions and local law enforcement to combat deforestation in San Martin. They are building off the momentum of their first successful collaboration with law enforcement and are working with forest monitors to continue participating in field visits with FEMA. Additionally, they are expanding their geographic reach and response to environmental crimes and are working with prosecutors from four provinces, including Loreto, Amazonas and two in San Martin.</p>



<p>AMPA‘s work continues to be recognized by regional and national authorities. Notably, their inter-institutional agreement with FEMA has been renewed, and they continue to work with strategic partners and allies to promote and execute forest monitoring projects in the region. They plan to build on capacity-strengthening efforts of local forest conservation associations through participatory implementation of surveillance systems and processes for their conservation initiatives. Furthermore, they will continue to provide technical support to FEMA and coordinate with authorities and local actors that are helping deliver justice for those affected by environmental crimes, and ultimately improve the lives of the communities that depend on forests.</p>



<p><em>To learn more about AMPA, </em><a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener"><em>visit their website.</em></a></p>



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Forest Guardians in Action: Amazónicos por la Amazonía Tackles Environmental Crimes with GFW

<p><em>“We are part of nature and nature lives in coexistence. Everything is interconnected and everything depends on each other ... If we don’t work collaboratively and in a connected way, we sure won’t be able to move forward.” </em>- Tatiana Espinosa, President of ARBIO Perú ACOMAD – Madre De Dios, and a member of the Amazonía Que Late Network</p>



<iframe width="450" height="400" src="https://www.youtube.com/embed/konVVZhR5_M?si=G9R976DqJWz0eRVz" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>



<p></p>



<p>Deep in the Amazon, volunteers from the Voluntary and Communal Conservation Network of San Martin receive a deforestation alert in <a href="https://www.globalforestwatch.org/help/forest-watcher/">Forest Watcher</a>: the forest monitoring app shows an <a href="https://www.globalforestwatch.org/blog/data-and-research/integrated-deforestation-alerts/">alert</a> detecting a disturbance in a monitored area. The volunteers arrive by various means — from small boat, motorcycle, or horseback to endless walking — to an area that has been cleared, illegally. Within the Forest Watcher app, they document a report and capture evidence of the impact. Network volunteers alert the local authorities who launch an official investigation.</p>



<p>Guardians of the forest in San Martín, Peru, use Global Forest Watch’s (GFW) data and technologies such as Forest Watcher daily to monitor forests, prevent deforestation and defend biodiversity. <a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener">Amazónicos por la Amazonía</a> (AMPA) — a nonprofit organization committed to conserving Peru’s natural and cultural heritage and improving the sustainable development of the people of the Andean Amazon, and a GFW Small Grants Fund recipient — works with allies such as conservation concession holders from the San Martin Network to monitor and patrol forest areas (conservation and ecotourism concessions are public forest areas where legal management rights are granted to groups such as NGOs, like AMPA, that are organized by the civil society of the communities that voluntarily conserve the forests).</p>



<p>AMPA works with local communities and authorities to carry out joint actions to verify deforestation and raise awareness of environmental crimes. Since 2017, they actively monitored forest loss in the regions of San Martín, Loreto and Ucayali — an area that totals over 900,000 hectares. Through this work, they provided the San Martin network with a space to connect and collaborate on the conservation of the forest lands that were assigned to them for management and protection.</p>



<h2 class="wp-block-heading">Leveraging GFW tools to build forest monitoring capacity</h2>



<p>AMPA is a four-time recipient of the GFW <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Small Grants Fund</a> (SGF), which provides financial and technical assistance to organizations to monitor and respond to deforestation. AMPA’s projects aim to pair local knowledge with a more robust use of GFW data and tools to improve the response capacity and awareness of environmental crimes in Peru’s San Martin region. During their projects, AMPA has trained volunteers on how to use tools like GFW data and the Forest Watcher app.</p>



<p>GFW’s partnership with AMPA started in 2017 during their first year as a SGF grantee. AMPA used GFW data and tools to implement a control and surveillance system within the conservation initiatives in the San Martin Network.</p>



<p>“Associations that protect forests use [GFW] as part of their surveillance and control systems, allowing them to organize patrol watches, tours and identify areas according to the degree of threat and vulnerability,” explained Jorge Fachín, Director of the Forest Program, Conservation and Monitoring of AMPA</p>



<p>GFW tools and monitoring equipment help AMPA improve forest monitoring activities of the volunteer network by enabling them to lead patrols more easily and rapidly. The use of drones and mobile apps like Forest Watcher help the volunteers collect sufficient information to carry out field activities with authorities, collect necessary information and evidence, and generate patrol reports.</p>



<p>Over the course of several SGF projects, AMPA worked with conservation networks in the Peruvian Amazon, including San Martin to use GFW solutions and develop&nbsp;their monitoring capacity, evolving each project based on lessons learned from their previous work. This involved the use and management of deforestation alerts and the development of skills to handle GPS, drones and cameras, and a “train the trainers” approach.</p>



<p>This equipped the San Martin Network to train their members on how to use GFW tools and identify deforestation alerts, and provided young network members with personalized training for data management and tools.</p>



<h2 class="wp-block-heading">GIS and monitoring unit ready to respond to environmental crimes</h2>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/AMPA_photo-2_resized.jpg" alt="Volunteers using a drone" class="wp-image-16483"/><figcaption class="wp-element-caption">Volunteers using a drone. Credit: AMPA.</figcaption></figure>



<p>AMPA also expanded its focus to continue to build capacity in forest custodian accreditation workshops, which include members of the Peruvian Amazon networks AMPA works with. This includes the formation of a Geographic Information System (GIS) and monitoring unit to monitor areas with GFW and remote sensing technology. The GIS and monitoring unit helped improve the verification of deforestation alerts and provide detailed information on the forest cover changes in the conservation areas.</p>



<p>Collaborating with technically capable monitoring brigades and forest custodians from the Peruvian Amazon networks strengthened AMPA’s ability to work directly with authorities to verify and respond to environmental crimes. The GIS and monitoring unit provided technical support in verification procedures to the authorities and prepared deforestation and field verification reports that were shared with Specialized Prosecution in Environmental Affairs (FEMA) and the Regional Environmental Authority (ARA). These reports support legal and administrative complaints against those responsible for environmental crimes, such as illegal deforestation.</p>



<p>Ultimately, authorities from FEMA and ARA officially recognized AMPA’ reports, along with GLAD alerts and Planet and drone imagery, as official evidence of environmental crimes.</p>



<p>Joint actions between AMPA’s GIS and monitoring unit, local actors and authorities helped strengthen and improve attention to environmental crimes with the active participation and involvement of local partners playing a key role. Due to the quality of reports submitted to FEMA on behalf of the San Martin Network, AMPA now has a formal inter-institutional agreement to assist the FEMA office in San Martin with collecting information on possible illegal deforestation using drones and Planet imagery.</p>



<h2 class="wp-block-heading">Alliances with local authorities</h2>



<p>AMPA organized workshops with representatives of the institutions directly involved in addressing environmental crimes in the region, which included designing a unified process for representatives and partners to collect key information for investigations through technical procedures that integrated GFW data and tools. Additionally, AMPA provided trainings for FEMA and ARA members on how to best use the data and tools.</p>



<p>AMPA helped prosecutors and technicians obtain accreditation as operators of remotely piloted aircraft systems (RPAS, or drones) which strengthened their ability to conduct verifications. Furthermore, AMPA also supported monitoring and conservation partners to get accredited as RPAS operators, improving their ability to verify deforestation alerts in the areas they investigated, which in turn strengthened their reporting of forest-related crimes to authorities.</p>



<p>To share insights from their strategic approach for verifying and reporting crimes, AMPA developed a roadmap on integrating technology use and collaboration with local actors and authorities, including information on strategic alliances with different stakeholders (communities and local, regional and national governments).</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/infographic_road-map-for-responding-environmental-crimes-using-glad-alerts-ampa-1024x724-1.png" alt="Infographic: Road map for responding to environmental crime" class="wp-image-16484"/></figure>



<p>This manual was created as part of the San Martin Network’s efforts to raise awareness and help address environmental crimes. They also shared this manual with partners on the platform of “<a href="https://www.amazoniaquelate.com/" target="_blank" rel="noreferrer noopener"><em>Escuela</em> <em>Amazonía que Late</em></a>,” an initiative led by AMPA that brings together local communities, Indigenous Peoples, organized civil society and local governments, and other partners across various conservation initiatives in Peru to learn about strategies to protect its natural and cultural heritage.</p>



<h2 class="wp-block-heading">Raising awareness of community forest monitoring efforts</h2>



<p>The surveillance and control system for forest monitoring developed by AMPA resulted in community advocacy efforts to raise awareness of environmental crimes within local, regional and national law enforcement authorities, including the prosecutor's office, to reduce illegal deforestation. AMPA’s reports based on the deforestation alerts <a href="https://diariovoces.com.pe/197474/fiscalia-ambiental-de-alto-amazonas-reforzara-acciones-de-control-y-vigilancia?fbclid=IwAR3QvgpG4hFv62o7gpVVntlM7RzPtfA_3KrwUAVp-BxbsIxq50qF-4Tdfuo" target="_blank" rel="noreferrer noopener">received media coverage</a> and not only caught the attention of authorities to identify offenders, but their complaints were raised to the prosecutor’s office. To date, two reports based on AMPA’s approach that were referred to the authorities have resulted in sentencing of those responsible for environmental crimes.</p>



<p>As part of their communication strategy, AMPA prepared press releases and audiovisual materials to feature the main activities of their project that were disseminated across local, regional and national media and partner websites. These communication efforts helped emphasize and highlight the role of AMPA’s participatory monitoring work and raise awareness among stakeholders of environmental crimes.</p>



<p>Additionally, AMPA has hosted meetings and workshops with public institutions responsible for forest management in Peru to explain their control and surveillance system. National and subnational government agencies have recognized AMPA as an important stakeholder in the conservation and management of forests in the San Martin region and in the Peruvian Amazon, and several additional national and regional government agencies have requested training and technical support from them.</p>



<p>“We’re proud of the renewal of the inter-institutional agreement with the Specialized Prosecution in Environmental Affairs (FEMA) and the interest of other donors to continue promoting and executing projects with forest monitoring components based on our experience in other regions,” said Jorge Fachín.</p>



<h2 class="wp-block-heading">From deforestation alert to community action and beyond</h2>



<p>A key element of AMPA’s success is their approach to building partnerships with local communities and law enforcement authorities to use tools like GFW to verify deforestation and address environmental crimes, together. Today, AMPA continues their work within the network of community conservation concessions and local law enforcement to combat deforestation in San Martin. They are building off the momentum of their first successful collaboration with law enforcement and are working with forest monitors to continue participating in field visits with FEMA. Additionally, they are expanding their geographic reach and response to environmental crimes and are working with prosecutors from four provinces, including Loreto, Amazonas and two in San Martin.</p>



<p>AMPA‘s work continues to be recognized by regional and national authorities. Notably, their inter-institutional agreement with FEMA has been renewed, and they continue to work with strategic partners and allies to promote and execute forest monitoring projects in the region. They plan to build on capacity-strengthening efforts of local forest conservation associations through participatory implementation of surveillance systems and processes for their conservation initiatives. Furthermore, they will continue to provide technical support to FEMA and coordinate with authorities and local actors that are helping deliver justice for those affected by environmental crimes, and ultimately improve the lives of the communities that depend on forests.</p>



<p><em>To learn more about AMPA, </em><a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener"><em>visit their website.</em></a></p>



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<p><em>“Somos parte de la naturaleza y la naturaleza vive en conjunto. Todo está interconectado y todo depende una cosa de otra,.. Si no trabajamos en una manera colaborativa y de manera vinculada, no vamos a poder seguir adelante.”</em> - Tatiana Espinosa, Presidenta ARBIO Perú ACOMAD – Madre De Dios, Integrante de la Red Amazonía Que Late</p>



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<p>En lo profundo del Amazonía Andina Peruana, los integrantes de la Red Conservación Voluntaria y Comunal de San Martín reciben una alerta de deforestación en <a href="https://www.globalforestwatch.org/help/forest-watcher/">Forest Watcher</a>: el aplicativo de monitoreo forestal muestra una <a href="https://www.globalforestwatch.org/blog/data-and-research/integrated-deforestation-alerts/">alerta</a> que detecta una perturbación en un área monitoreada. Los voluntarios de la red se dirigen por diferentes medios (navegando en su peque – peque o moto, o interminables caminatas o a lomo de acémila) hasta las coordenadas y llegan a una zona que ha sido deforestada ilegalmente. Dentro de la aplicación Forest Watcher, documentan un informe y capturan evidencias del impacto. Los voluntarios de la red alertan a las autoridades locales que inician una investigación oficial.&nbsp;</p>



<p>Los guardianes del bosque en San Martín, Perú, utilizan diariamente los datos y tecnologías de Global Forest Watch (GFW), así como Forest Watcher, para monitorear los bosques, prevenir la deforestación y defender la biodiversidad. <a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener">Amazónicos por la Amazonía</a> (AMPA), una organización sin fines de lucro comprometida con la conservación del patrimonio natural y cultural del Perú y la mejora del desarrollo sostenible de los pueblos de la Amazonía andina, y receptora del Fondo de Pequeñas Subvenciones de GFW, trabaja con aliados como los titulares de concesiones de conservación del Red San Martín para monitorear y patrullar áreas forestales (las concesiones para conservación/ecoturismo son áreas de bosque público en las que se otorgan derechos legales de manejo a grupos como ONG como AMPA y sociedad civil organizada de las comunidades que voluntariamente vienen conservando los bosques).&nbsp;</p>



<p>AMPA trabaja con comunidades y autoridades locales para realizar acciones conjuntas para verificar la deforestación y generar conciencia sobre los delitos ambientales. Desde el 2017 monitorean activamente la pérdida de bosques en las regiones de San Martín, Loreto y Ucayali, un área que suma más de 900,00hectáreas. A través de este trabajo, brindaron a la red San Martín un espacio para conectarse y colaborar en la conservación de las tierras forestales que les fueron asignadas para su manejo y protección.&nbsp;</p>



<h2 class="wp-block-heading">Aprovechar las herramientas de GFW para desarrollar la capacidad de monitoreo forestal</h2>



<p>AMPA ha recibido por cuatro oportunidades el <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Fondo de Pequeñas Subvenciones</a> (SGF) de GFW, que brinda asistencia financiera y técnica a organizaciones para monitorear y responder a la deforestación. Sus proyectos tenían como objetivo combinar el conocimiento local con un uso más sólido de los datos y herramientas de GFW para mejorar la capacidad de respuesta y hacer incidencia sobre los delitos ambientales en la región de San Martín, en la Amazonia de Perú. Durante su proyecto, AMPA ha capacitado a los integrantes de la red de conservación de San Martin sobre cómo utilizar herramientas como los datos de GFW y la aplicación Forest Watcher.&nbsp;</p>



<p>La alianza de GFW con AMPA comenzó en 2017, durante su primer año como beneficiario del SGF. AMPA utilizó datos y herramientas de GFW para implementar un sistema de control y vigilancia dentro de las iniciativas de conservación en la Red San Martín.&nbsp;</p>



<p>“Las asociaciones locales que protegen los bosques utilizan [GFW] como parte de sus sistemas de vigilancia y control, lo que les permite organizar patrullajes, recorridos e identificar áreas según el grado de amenaza y vulnerabilidad”, explicó Jorge Fachín, Director del Programa de Conservación y Monitoreo de AMPA.&nbsp;</p>



<p>Las herramientas y el equipo de monitoreo de GFW ayudan a AMPA a mejorar las actividades de monitoreo forestal de la red de conservación de San Martin al permitirles liderar patrullas de manera más fácil y rápida. El uso de los drones y las aplicaciones móviles como Forest Watcher los ayudan a recopilar información importante para llevar a cabo actividades de campo con las autoridades, recopilar la información y evidencia necesarias y generar informes de patrullaje.&nbsp;</p>



<p>En el transcurso de los diferentes proyectos del SGF, AMPA trabajó con redes de conservación de la Amazonia Peruana, que incluye a San Martín en, para utilizar las soluciones de GFW y desarrollar sus capacidades de monitoreo, evolucionando cada proyecto en función de las lecciones aprendidas de su trabajo anterior. Esto implicó el uso y gestión de alertas de deforestación y el desarrollo de habilidades para manejar GPS, drones y cámaras fotográficas, y un enfoque de “formación de formadores”. Esto equipó a la Red San Martín para capacitar a sus miembros sobre cómo utilizar las herramientas de GFW e identificar alertas de deforestación, y brindó a los miembros jóvenes de la red capacitación personalizada para la gestión de datos y herramientas.&nbsp;</p>



<h2 class="wp-block-heading">Unidad de sistemas de información geográfica (SIG) y monitoreo listos para la atención sobre delitos ambientales</h2>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/AMPA_photo-2_resized.jpg" alt="Volunteers using a drone" class="wp-image-16483"/><figcaption class="wp-element-caption">Crédito: AMPA.</figcaption></figure>



<p>AMPA también amplió su enfoque para continuar desarrollando capacidades en los talleres de acreditación de custodios forestales, que incluyen a los integrantes de las redes de la Amazonia peruana con las que trabaja AMPA, incluida la conformación de una unidad de Sistemas de Información Geográfica (SIG) y monitoreo, que brinda soporte continuo para monitorear áreas con GFW, sensores remotos y tecnología.&nbsp;&nbsp;</p>



<p>La unidad de Sistemas de Información Geográfica (SIG) y monitoreo ayudaron a mejorar la verificación de las alertas de deforestación y proporcionar información detallada sobre los cambios en la cubierta forestal en las áreas de conservación.&nbsp;</p>



<p>La colaboración con brigadas de monitoreo con capacidad técnica y custodios forestales de las redes de la Amazonía peruana fortaleció la capacidad de AMPA para trabajar directamente con las autoridades para verificar y responder a los delitos ambientales. La unidad de Sistemas de Información Geográfica (SIG) y monitoreo brindaron soporte técnico en los procedimientos de verificación a las autoridades, elaboraron informes de deforestación y verificación de campo que fueron compartidos con la Fiscalía Especializada en Asuntos Ambientales (FEMA) y la Autoridad Ambiental Regional (ARA). Estos informes sustentan denuncias judiciales y administrativas contra los responsables de delitos ambientales, como la deforestación ilegal.&nbsp;&nbsp;</p>



<p>Finalmente, las autoridades de FEMA y ARA reconocieron oficialmente los informes de AMPA, junto con las alertas de GLAD, las imágenes&nbsp; Planet y las imágenes de drones como evidencia oficial de delitos ambientales.&nbsp;</p>



<p>Las acciones conjuntas entre La unidad de Sistemas de Información Geográfica (SIG) y monitoreo, los actores y autoridades locales ayudaron a fortalecer y mejorar la atención a los delitos ambientales, con la participación activa y el involucramiento de los socios locales jugando un papel clave. Debido a la calidad de los informes presentados a FEMA en nombre de la Red San Martín, AMPA ahora tiene un acuerdo de cooperación interinstitucional formal para coadyuvar a las fiscalías de la FEMA en San Martín a recopilar información sobre posible deforestación ilegal utilizando drones e imágenes de Planet.&nbsp;</p>



<h2 class="wp-block-heading">Alianzas con autoridades locales</h2>



<p>AMPA organizó talleres y mesas de trabajo con representantes de las instituciones directamente involucradas en abordar los delitos ambientales en la región, que incluyeron el diseño de un formato unificado para que representantes y socios recopilen información clave para las investigaciones a través de procedimientos técnicos que integraron datos y herramientas de GFW. Además, AMPA brindó capacitación a los miembros de Fiscalia Especializada en Materia Ambienta (FEMA) y la Autoridad Regional Ambiental (ARA sobre cómo utilizar mejor los datos y las herramientas.&nbsp;</p>



<p>AMPA ayudó a fiscales y técnicos a obtener la acreditación como operadores de sistemas de aeronaves pilotadas a distancia (RPAS o drones), lo que fortaleció su capacidad para realizar verificaciones. Además, AMPA también apoyó a los socios de monitoreo y conservación para obtener la acreditación como operadores de RPAS, mejorando su capacidad para verificar las alertas de deforestación en las áreas que investigaron, lo que a su vez fortaleció su presentación de delitos relacionados con los bosques ante las autoridades.&nbsp;</p>



<p>Para compartir conocimientos de su enfoque estratégico para verificar y denunciar delitos, AMPA desarrolló una hoja de ruta sobre la integración del uso de la tecnología y la colaboración con actores y autoridades locales, incluida información sobre alianzas estratégicas con diferentes partes interesadas (comunidades y gobiernos locales, regionales y nacionales).&nbsp;</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/infografia_ruta-de-atencion-a-delitos-ambientales-usando-alertas-glad-ampa-1024x724-1.jpeg" alt="Infografia: Ruta de atencion a delitos amientales" class="wp-image-16495"/></figure>



<p>Este manual fue creado como parte de los esfuerzos de la Red San Martín para crear conciencia y ayudar a abordar los delitos ambientales. También compartieron este manual en la plataforma de la “<a href="https://www.amazoniaquelate.com/" target="_blank" rel="noreferrer noopener">Escuela Amazonía que Late</a>”, una iniciativa liderada por AMPA, y que fue diseñada para las comunidades locales, pueblos indígenas, sociedad civil organizada y gobiernos locales, y otros socios de varias iniciativas de conservación en Perú para aprender sobre estrategias para proteger su patrimonio natural y cultural.&nbsp;</p>



<h2 class="wp-block-heading">Crear conciencia sobre los esfuerzos de monitoreo forestal comunitario</h2>



<p>El sistema de vigilancia y control para el monitoreo forestal desarrollado por AMPA dio como resultado esfuerzos de promoción comunitaria para crear conciencia sobre los delitos ambientales dentro de las autoridades policiales locales, regionales y nacionales, incluida la fiscalía, para reducir la deforestación ilegal. Los informes del AMPA basados en las alertas de deforestación <a href="https://diariovoces.com.pe/197474/fiscalia-ambiental-de-alto-amazonas-reforzara-acciones-de-control-y-vigilancia?fbclid=IwAR3QvgpG4hFv62o7gpVVntlM7RzPtfA_3KrwUAVp-BxbsIxq50qF-4Tdfuo" target="_blank" rel="noreferrer noopener">recibieron cobertura mediática</a> y no sólo llamaron la atención de las autoridades para identificar a los infractores, sino que sus denuncias fueron elevadas a la fiscalía. Hasta el momento, dos informes que se derivaron a las autoridades competentes y que contiene el enfoque de AMPA han resultado en sentencias contra los responsables de los delitos ambientales.&nbsp;</p>



<p>Como parte de su estrategia de comunicación, AMPA preparó comunicados de prensa y materiales audiovisuales para presentar las principales actividades de su proyecto que se difundieron en los medios locales, regionales y nacionales y en los sitios web de los socios. Estos esfuerzos de comunicación ayudaron a enfatizar y resaltar el papel del trabajo de monitoreo participativo de AMPA y crear conciencia entre las partes interesadas sobre los delitos ambientales.&nbsp;</p>



<p>Además, AMPA ha organizado reuniones y talleres con instituciones públicas responsables del manejo forestal en Perú para explicar su sistema de control y vigilancia. Las agencias gubernamentales nacionales y subnacionales han reconocido al AMPA como un actor importante en la conservación y el manejo de los bosques en la región de San Martín y en la Amazonía peruana, y varias agencias gubernamentales nacionales y regionales adicionales les han solicitado capacitación y apoyo técnico.&nbsp;</p>



<p>“Estamos orgullosos de la renovación del convenio interinstitucional con la Fiscalía Especializada en Asuntos Ambientales (FEMA) y del interés de otros donantes de seguir impulsando y ejecutando proyectos con componentes de monitoreo forestal basados en nuestra experiencia en otras regiones”, dijo Jorge Fachín.</p>



<h2 class="wp-block-heading">De la alerta de deforestación a la acción comunitaria y más allá</h2>



<p>Un elemento clave del éxito de AMPA es su enfoque para generar alianzas con comunidades locales y autoridades encargadas de hacer cumplir la ley para utilizar herramientas como GFW para verificar la deforestación y abordar los delitos ambientales, juntos. Hoy, AMPA continúa su trabajo dentro de la red de concesiones de conservación comunitaria y aplicación de la ley local para combatir la deforestación en San Martín. Están aprovechando el impulso de su primera colaboración exitosa con las autoridades y están trabajando con monitores forestales para continuar participando en visitas de campo con FEMA. Además, están ampliando su alcance geográfico y su respuesta para la atención a los delitos ambientales y están trabajando con fiscalías de cuatro regiones, incluidas Loreto, Amazonas y dos de San Martín.&nbsp;</p>



<p>El trabajo de AMPA sigue siendo reconocido por las autoridades regionales y nacionales. Cabe destacar que han renovado su acuerdo interinstitucional con FEMA y continúan trabajando con socios y aliados estratégicos para promover y ejecutar proyectos de monitoreo forestal en la región. Planean aprovechar los esfuerzos de fortalecimiento de capacidades de las asociaciones locales que conservan los bosques mediante la implementación participativa de sistemas y procesos de vigilancia para sus iniciativas de conservación. Además, continuarán brindando apoyo técnico a FEMA y coordinando con autoridades y actores locales que están ayudando a impartir justicia a los afectados por delitos ambientales contribuyendo de esta manera a la mejorar las vidas de las comunidades que dependen de los bosques.&nbsp;</p>



<p><em>Para obtener más información sobre AMPA, </em><a href="https://ampaperu.info/" target="_blank" rel="noreferrer noopener"><em>visite su página web</em></a><em>.</em></p>



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Guardianes del bosque en acción: Amazónicos por la Amazonía abordan los delitos ambientales con GFW

GFW Small Grants Fund recipient AMPA is working with communities and authorities to to verify and raise awareness of environmental crimes.

AMPA Tackles Environmental Crimes with GFW | GFW Blog

Insights

Insights | Global Forest Watch Blog

logging

logging | Global Forest Watch Blog

policy

policy | Global Forest Watch Blog

Fred Stolle

Mikaela Weisse

Stientje van Veldhoven

<p>A recent analysis found that tall, mature forests — which are critical for storing carbon and safeguarding biodiversity — are declining significantly in some parts of Europe. This is primarily due to a rise in timber harvesting as well as natural disturbances, such as wildfires and pests, which are often exacerbated by climate change.</p>



<p>There was some good news as well.&nbsp;<a href="https://www.sciencedirect.com/science/article/pii/S0034425723003486" target="_blank" rel="noreferrer noopener">The study</a>, from the University of Maryland and WRI in partnership with European researchers, shows that Europe’s total tree cover increased slightly over the last two decades. But that’s not the full story: Some regions within Europe saw significant declines in forest area. And the total amount of&nbsp;<em>tall forests&nbsp;</em>(forests with trees taller than 15 meters) declined by 2.25 million hectares — an area half the size of Denmark. The decline was highest in Nordic countries, which saw a 20% reduction in tall forests, followed by areas in Southeastern Europe. While tall forests have often been replaced with new trees, these can take decades to mature to the point where they provide equivalent climate and ecosystem benefits, meaning overall forest health in these areas is being reduced.</p>



<p>These findings underscore the need for ambitious action on the part of EU countries to protect and maintain existing forests and the vital services they provide. Progress is underway, with a number of new laws aimed at curbing deforestation and forest degradation. But to be successful, these must be informed by timely, robust data on forest health and disturbances. The proposed <a href="https://ec.europa.eu/commission/presscorner/detail/en/ip_23_5909" target="_blank" rel="noreferrer noopener">EU Forest Monitoring Framework</a>, issued on November 22, 2023, should support these efforts by ensuring harmonized and improved forest monitoring in Europe — if it mandates comparable and consistent reporting on key forest health indicators throughout the EU.</p>



<h2 class="wp-block-heading">Europe Lost 2.25 Million Hectares of Tall Forests From 2001-2021</h2>



<p>Between 2001 and 2021, Europe experienced a small net gain in tree cover of around 1%, or 1.5 million hectares. With tree cover declining&nbsp;<a href="https://research.wri.org/gfr/forest-extent-indicators/forest-extent#is-global-tree-cover-increasing-or-decreasing" target="_blank" rel="noreferrer noopener">in almost all other areas</a>&nbsp;around the world, this is an important achievement. But despite this overall gain, the findings also show that Europe’s tall forests&nbsp;<em>declined&nbsp;</em>by 3%, or 2.25 million hectares, over the same period. Tall forests were disproportionately affected by an acceleration of forest disturbance in recent years, including both harvesting activities and natural disturbances.</p>



<p>The analysis shows strong variability between regions, with some experiencing significant losses in forest area while others saw more gains. The Nordic region (Norway, Sweden, Denmark and Finland) saw the biggest declines, losing 3.5% of its total forests and 20% of its tall forests over the last two decades.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1950" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-2048x1950.png" alt="Map showing net change in tall tree cover (height greater than or equal to 15 meters) in Europe 2001-2021" class="wp-image-16502" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-2048x1950.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-1024x975.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-768x731.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-1536x1463.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p>The vast majority (around 87%) of tree cover loss was due to intensive timber harvesting — which can take the form of direct felling or salvage logging after a disturbance — as well as natural disturbances like wildfires and insect outbreaks, which are increasing in extent and severity due to climate change.</p>



<p>Most tree cover loss will be temporary as new forest growth in Europe is largely compensating for forest loss in terms of area alone. But tall forests are being cleared at a faster rate than new forests can regrow, leading to a gradual decline in forest height and a reduction of forest services such as carbon storage over time. So while Europe isn’t losing tree cover overall, the health of its forests is declining.</p>



<p>This decline in Europe’s tall forests is a sign that timber harvesting may already exceed rates that can be sustained into the future. With global demand for timber&nbsp;<a href="https://www.fao.org/documents/card/en/c/cc2265en" target="_blank" rel="noreferrer noopener">expected to grow</a>&nbsp;in the coming decades, Europe should heed this warning and take action to avoid running down its precious forest assets.</p>



<h2 class="wp-block-heading">Why Are Tall Forests So Important?</h2>



<p>While most forests in Europe have been intensively managed by humans for centuries, these forests still provide immense value for people and nature.</p>



<p>For one, the carbon storage capacity of trees increases with height and age, meaning the loss of tall trees and a resulting shift toward shorter, younger trees reduces the carbon storage capacity of Europe’s forests. While the full carbon picture is complicated (for example, young forests absorb carbon from the atmosphere at a faster rate than older standing forests),&nbsp;<a href="https://www.wri.org/insights/forests-absorb-twice-much-carbon-they-emit-each-year" target="_blank" rel="noreferrer noopener">protecting mature forests</a>&nbsp;is critical for curbing climate change.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="760" height="477" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/20_01_21_Carbon_Flux_v2_Graphic_1.png" alt="Illustration: Forests act as both a source and a sink for carbon" class="wp-image-16503"/></figure>



<p>These tall forests also include many of Europe’s old growth forests, which take centuries to grow and cannot be replaced by young, regrowing forests. Old growth forests&nbsp;<a href="https://www.nature.com/articles/s41597-021-00988-7" target="_blank" rel="noreferrer noopener">play a key role</a>&nbsp;in climate change mitigation by storing large quantities of carbon both in their biomass and in their soil. They provide ecosystem services like protecting soil health and regulating water drainage, and act as a critical refuge for wildlife and biodiversity. They are also a valuable part of cultural heritage for European communities and nations.</p>



<p>Further, even though the last two decades saw a slight increase in tree cover in Europe overall, much of this increase is due to expansion of tree plantations — specifically plantations of non-native tree species such as eucalyptus, black locust and Sitka spruce. This likely has&nbsp;<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13440" target="_blank" rel="noreferrer noopener">negative consequences</a>&nbsp;for ecosystems, such as reduced diversity and abundance of plants and animals, particularly where more diverse natural forests are replaced by monoculture plantations.</p>



<h2 class="wp-block-heading">New EU Regulations Could Curb Forest Degradation, but Improved Monitoring Is Needed</h2>



<p>Europe is working to improve forest management and address its contribution to forest loss worldwide through a number of new and developing laws and strategies. These include:</p>



<ul>
<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/strategy/forest-strategy_en" target="_blank" rel="noreferrer noopener">EU Forest Strategy for 2030</a>:</strong>&nbsp;A strategy to improve the quantity and quality of EU forests and strengthen their protection, restoration and resilience.</li>



<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/strategy/biodiversity-strategy-2030_en" target="_blank" rel="noreferrer noopener">EU Biodiversity Strategy for 2030</a>:</strong>&nbsp;A comprehensive long-term plan to protect nature and reverse ecosystem degradation in the EU.</li>



<li><strong>The proposed&nbsp;<a href="https://ec.europa.eu/commission/presscorner/detail/en/IP_23_5662" target="_blank" rel="noreferrer noopener">Nature Restoration Law</a>:</strong>&nbsp;A binding regulation to restore nature, including putting restoration measures in place for at least 20% of the EU’s land areas and 20% of its seas by 2030.</li>



<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/topics/forests/deforestation/regulation-deforestation-free-products_en" target="_blank" rel="noreferrer noopener">EU Deforestation Regulation</a>:</strong>&nbsp;A binding regulation to ensure that certain commodities exported to or placed on the EU market are <a href="https://www.globalforestwatch.org/blog/commodities/satellite-data-eu-regulation-deforestation-free-supply-chains/">deforestation free</a>.</li>
</ul>



<p>These initiatives show progress is moving in the right direction, but they must be supported by improved monitoring and transparency to be most effective. This is the aim of the proposed <a href="https://ec.europa.eu/commission/presscorner/detail/en/ip_23_5909" target="_blank" rel="noreferrer noopener">Forest Monitoring Framework</a> — a regulatory initiative to ensure that European member states provide detailed, robust and timely information on the condition and management of EU forests, including indicators on their health, quality and trends over time.</p>



<p>With better monitoring, Europe will be able to assess whether its forests are managed sustainably, evaluate the ability of forests to contribute to climate change mitigation and identify priority forests to protect. Monitoring efforts under the framework must be harmonized to allow comparison and understanding across the EU, meaning countries should be responsible for reporting on the same set of indicators. They should also be transparent to encourage public accountability and timely to enable data-based decision making on appropriate time scales.</p>



<p>Forests are crucial to fighting climate change and to protecting ecosystems and biodiversity. To maximize their potential, Europe should look deeper into its forests. Satellite monitoring and data products, like the ones used in this study or through the open-source&nbsp;<a href="https://www.globalforestwatch.org/">Global Forest Watch</a>, combined with ground-based measurements can support implementation of the Forest Monitoring Framework Regulation and help ensure all of Europe’s forests are on the right track.</p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”="">
 

Originally appeared on <a href="https://www.wri.org/insights/european-tall-forest-decline" target="_blank" rel="noopener noreferrer">WRI Insights</a>. 
 

</div> 

 

<hr>



<style type="text/css"></style>

Timber Harvesting and Climate Change Are Depleting Europe’s Mature Forests

<p>A recent analysis found that tall, mature forests — which are critical for storing carbon and safeguarding biodiversity — are declining significantly in some parts of Europe. This is primarily due to a rise in timber harvesting as well as natural disturbances, such as wildfires and pests, which are often exacerbated by climate change.</p>



<p>There was some good news as well.&nbsp;<a href="https://www.sciencedirect.com/science/article/pii/S0034425723003486" target="_blank" rel="noreferrer noopener">The study</a>, from the University of Maryland and WRI in partnership with European researchers, shows that Europe’s total tree cover increased slightly over the last two decades. But that’s not the full story: Some regions within Europe saw significant declines in forest area. And the total amount of&nbsp;<em>tall forests&nbsp;</em>(forests with trees taller than 15 meters) declined by 2.25 million hectares — an area half the size of Denmark. The decline was highest in Nordic countries, which saw a 20% reduction in tall forests, followed by areas in Southeastern Europe. While tall forests have often been replaced with new trees, these can take decades to mature to the point where they provide equivalent climate and ecosystem benefits, meaning overall forest health in these areas is being reduced.</p>



<p>These findings underscore the need for ambitious action on the part of EU countries to protect and maintain existing forests and the vital services they provide. Progress is underway, with a number of new laws aimed at curbing deforestation and forest degradation. But to be successful, these must be informed by timely, robust data on forest health and disturbances. The proposed <a href="https://ec.europa.eu/commission/presscorner/detail/en/ip_23_5909" target="_blank" rel="noreferrer noopener">EU Forest Monitoring Framework</a>, issued on November 22, 2023, should support these efforts by ensuring harmonized and improved forest monitoring in Europe — if it mandates comparable and consistent reporting on key forest health indicators throughout the EU.</p>



<h2 class="wp-block-heading">Europe Lost 2.25 Million Hectares of Tall Forests From 2001-2021</h2>



<p>Between 2001 and 2021, Europe experienced a small net gain in tree cover of around 1%, or 1.5 million hectares. With tree cover declining&nbsp;<a href="https://research.wri.org/gfr/forest-extent-indicators/forest-extent#is-global-tree-cover-increasing-or-decreasing" target="_blank" rel="noreferrer noopener">in almost all other areas</a>&nbsp;around the world, this is an important achievement. But despite this overall gain, the findings also show that Europe’s tall forests&nbsp;<em>declined&nbsp;</em>by 3%, or 2.25 million hectares, over the same period. Tall forests were disproportionately affected by an acceleration of forest disturbance in recent years, including both harvesting activities and natural disturbances.</p>



<p>The analysis shows strong variability between regions, with some experiencing significant losses in forest area while others saw more gains. The Nordic region (Norway, Sweden, Denmark and Finland) saw the biggest declines, losing 3.5% of its total forests and 20% of its tall forests over the last two decades.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/23-11-14-European-Forest-Decline_Insights-no-logo-1-2048x1950.png" alt="Map showing net change in tall tree cover (height greater than or equal to 15 meters) in Europe 2001-2021" class="wp-image-16502"/></figure>



<p>The vast majority (around 87%) of tree cover loss was due to intensive timber harvesting — which can take the form of direct felling or salvage logging after a disturbance — as well as natural disturbances like wildfires and insect outbreaks, which are increasing in extent and severity due to climate change.</p>



<p>Most tree cover loss will be temporary as new forest growth in Europe is largely compensating for forest loss in terms of area alone. But tall forests are being cleared at a faster rate than new forests can regrow, leading to a gradual decline in forest height and a reduction of forest services such as carbon storage over time. So while Europe isn’t losing tree cover overall, the health of its forests is declining.</p>



<p>This decline in Europe’s tall forests is a sign that timber harvesting may already exceed rates that can be sustained into the future. With global demand for timber&nbsp;<a href="https://www.fao.org/documents/card/en/c/cc2265en" target="_blank" rel="noreferrer noopener">expected to grow</a>&nbsp;in the coming decades, Europe should heed this warning and take action to avoid running down its precious forest assets.</p>



<h2 class="wp-block-heading">Why Are Tall Forests So Important?</h2>



<p>While most forests in Europe have been intensively managed by humans for centuries, these forests still provide immense value for people and nature.</p>



<p>For one, the carbon storage capacity of trees increases with height and age, meaning the loss of tall trees and a resulting shift toward shorter, younger trees reduces the carbon storage capacity of Europe’s forests. While the full carbon picture is complicated (for example, young forests absorb carbon from the atmosphere at a faster rate than older standing forests),&nbsp;<a href="https://www.wri.org/insights/forests-absorb-twice-much-carbon-they-emit-each-year" target="_blank" rel="noreferrer noopener">protecting mature forests</a>&nbsp;is critical for curbing climate change.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/20_01_21_Carbon_Flux_v2_Graphic_1.png" alt="Illustration: Forests act as both a source and a sink for carbon" class="wp-image-16503"/></figure>



<p>These tall forests also include many of Europe’s old growth forests, which take centuries to grow and cannot be replaced by young, regrowing forests. Old growth forests&nbsp;<a href="https://www.nature.com/articles/s41597-021-00988-7" target="_blank" rel="noreferrer noopener">play a key role</a>&nbsp;in climate change mitigation by storing large quantities of carbon both in their biomass and in their soil. They provide ecosystem services like protecting soil health and regulating water drainage, and act as a critical refuge for wildlife and biodiversity. They are also a valuable part of cultural heritage for European communities and nations.</p>



<p>Further, even though the last two decades saw a slight increase in tree cover in Europe overall, much of this increase is due to expansion of tree plantations — specifically plantations of non-native tree species such as eucalyptus, black locust and Sitka spruce. This likely has&nbsp;<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13440" target="_blank" rel="noreferrer noopener">negative consequences</a>&nbsp;for ecosystems, such as reduced diversity and abundance of plants and animals, particularly where more diverse natural forests are replaced by monoculture plantations.</p>



<h2 class="wp-block-heading">New EU Regulations Could Curb Forest Degradation, but Improved Monitoring Is Needed</h2>



<p>Europe is working to improve forest management and address its contribution to forest loss worldwide through a number of new and developing laws and strategies. These include:</p>



<ul>
<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/strategy/forest-strategy_en" target="_blank" rel="noreferrer noopener">EU Forest Strategy for 2030</a>:</strong>&nbsp;A strategy to improve the quantity and quality of EU forests and strengthen their protection, restoration and resilience.</li>



<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/strategy/biodiversity-strategy-2030_en" target="_blank" rel="noreferrer noopener">EU Biodiversity Strategy for 2030</a>:</strong>&nbsp;A comprehensive long-term plan to protect nature and reverse ecosystem degradation in the EU.</li>



<li><strong>The proposed&nbsp;<a href="https://ec.europa.eu/commission/presscorner/detail/en/IP_23_5662" target="_blank" rel="noreferrer noopener">Nature Restoration Law</a>:</strong>&nbsp;A binding regulation to restore nature, including putting restoration measures in place for at least 20% of the EU's land areas and 20% of its seas by 2030.</li>



<li><strong>The&nbsp;<a href="https://environment.ec.europa.eu/topics/forests/deforestation/regulation-deforestation-free-products_en" target="_blank" rel="noreferrer noopener">EU Deforestation Regulation</a>:</strong>&nbsp;A binding regulation to ensure that certain commodities exported to or placed on the EU market are <a href="https://www.globalforestwatch.org/blog/commodities/satellite-data-eu-regulation-deforestation-free-supply-chains/">deforestation free</a>.</li>
</ul>



<p>These initiatives show progress is moving in the right direction, but they must be supported by improved monitoring and transparency to be most effective. This is the aim of the proposed <a href="https://ec.europa.eu/commission/presscorner/detail/en/ip_23_5909" target="_blank" rel="noreferrer noopener">Forest Monitoring Framework</a> — a regulatory initiative to ensure that European member states provide detailed, robust and timely information on the condition and management of EU forests, including indicators on their health, quality and trends over time.</p>



<p>With better monitoring, Europe will be able to assess whether its forests are managed sustainably, evaluate the ability of forests to contribute to climate change mitigation and identify priority forests to protect. Monitoring efforts under the framework must be harmonized to allow comparison and understanding across the EU, meaning countries should be responsible for reporting on the same set of indicators. They should also be transparent to encourage public accountability and timely to enable data-based decision making on appropriate time scales.</p>



<p>Forests are crucial to fighting climate change and to protecting ecosystems and biodiversity. To maximize their potential, Europe should look deeper into its forests. Satellite monitoring and data products, like the ones used in this study or through the open-source&nbsp;<a href="https://www.globalforestwatch.org/">Global Forest Watch</a>, combined with ground-based measurements can support implementation of the Forest Monitoring Framework Regulation and help ensure all of Europe’s forests are on the right track.</p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”="">
 

Originally appeared on <a href="https://www.wri.org/insights/european-tall-forest-decline" target="_blank" rel="noopener noreferrer">WRI Insights</a>. 
 

</div> 

 

<hr>

Timber Harvesting and Climate Change Are Depleting Europe's Mature Forests

A new study found that important tall, mature forests are declining in some parts of Europe due to timber harvesting and climate change.

Timber Harvesting and Climate Change Are Depleting Europe&#8217;s Mature Forests

indigenous peoples

indigenous peoples | Global Forest Watch Blog

Indonesia

Indonesia | Global Forest Watch Blog

Katie Reytar

Jessica Webb

Peter Veit

<p>The Saamaka, an Afro-descendent tribal community, reside deep in the heart of Suriname’s Amazonian rainforest. They’ve maintained their plots of land and small farms for generations, relying on the bounty of fruits, nuts and medicines the surrounding tropical forest provides. &nbsp;</p>



<p>But lately, their forested territory has seen unwelcome visitors.&nbsp;</p>



<p>A logging company began bulldozing a road right through the Saamaka’s forest in early 2023. While the Suriname government sanctioned the logging concession, it came without the Saamaka’s consent, despite a 2007 ruling from the Inter-American Court of Human Rights requiring their approval for such projects. The road is just the latest threat facing the Saamaka’s land, on top of flooding from hydropower dams and water pollution from nearby mining operations. &nbsp;</p>



<p>&nbsp;“We are losing our very way of life — our food, our water, our land,” said Hugo Jabini, a community leader and spokesperson for the Saamaka. “We can no longer afford to be invisible.”</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" loading="lazy" width="455" height="342" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/HugoPPT3.jpg" alt="Saamaka leader Hugo Jabini leads a kuutu, a meeting of Saamakan communities." class="wp-image-16456"/><figcaption class="wp-element-caption">Hugo Jabini leads a kuutu, a meeting of Saamakan communities.&nbsp;</figcaption></figure></div>


<p>The Saamaka’s story is a common one. Indigenous Peoples and local communities hold some of the most pristine and resource-rich lands in the world — areas highly coveted by mining and logging companies and other profiteers.&nbsp; Land grabs and other threats are especially severe in places where the government does not recognize communities’ land rights, or where anti-deforestation and other laws are weak or poorly enforced. It’s the reason many Indigenous Peoples and local communities often take land monitoring into their own hands — and some are now using digital tools to do it.&nbsp;</p>



<p>Freely available satellite imagery and data from sites like&nbsp;<a href="https://www.globalforestwatch.org/map/">Global Forest Watch</a>&nbsp;and&nbsp;<a href="https://www.landmarkmap.org/data/" target="_blank" rel="noreferrer noopener">LandMark</a>&nbsp;provide near-real-time information that tracks deforestation and land degradation. Indigenous and local communities are increasingly using tools like this to gather evidence that deforestation and degradation are happening on their lands, build their case against illegal activities and take legal action to prevent it from continuing.&nbsp;&nbsp;</p>



<p>Three examples from Suriname, Indonesia and Peru illustrate a growing trend in fighting land rights violations with data.&nbsp;&nbsp;</p>



<h2 class="wp-block-heading">In Suriname, Deforestation Data Reveals Threats to the Saamaka’s Land Rights &nbsp;</h2>



<p>In 2007, the Inter-American Court of Human Rights compelled the government of Suriname to recognize the traditional land rights of the Saamaka and other Indigenous and community groups in Suriname. &nbsp;Yet satellite data from Global Forest Watch shows that since the judgment in 2007, tree cover loss actually increased in the Saamaka’s lands, largely from logging and mining activities. Tree cover loss inside government-granted logging concessions in and surrounding the Saamaka territory totaled 44,700 hectares, a 5.5% loss in tree cover from 2001-2022.&nbsp;&nbsp;</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/15824378?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>Monthly satellite images from March 2023 to October 2023 show the gradual construction of a road being carved through the heart of the Saamaka territory and leading to a government-granted logging concession. The Saamaka say the road was constructed without their knowledge or consent.&nbsp;</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1226" height="531" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/Suriname-logging-road.gif" alt="Gif of road construction progress through the Saamaka’s forested territory. March-October 2023" class="wp-image-16455"/><figcaption class="wp-element-caption">Road construction progresses through the Saamaka’s forested territory. Source: Planet imagery on Global Forest Watch.</figcaption></figure>



<p>Armed with this data and imagery, Jabini and seven other representatives from Suriname’s Indigenous and local communities traveled more than 2,500 miles to Washington, D.C., presenting their evidence to the Inter-American Commission on Human Rights in November 2023. Jabini hopes the commission will reopen its court case against the Suriname government to strengthen traditional land rights.&nbsp;&nbsp;</p>



<p>“We are not here to fight the government; we are just fighting for our rights,” Jabini said.</p>



<h2 class="wp-block-heading">In Indonesia, Indigenous Peoples Use Satellite Data to Track and Report Illegal Timber &nbsp;</h2>



<p>In the eastern-most edges of Indonesia lies an archipelago of 832 islands containing some of the country’s most pristine remaining rainforests. The Aru Islands are home to more than 60,000 Indigenous Aruese, who speak 14 different tribal languages. Their lives are deeply intertwined with the forests they call home, hunting deer, harvesting tree gum, and maintaining a strong spiritual connection with the forests believed to harbor the souls of their ancestors.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="965" height="651" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/aru-islands-2.jpg" alt="Indonesia’s Aru Islands" class="wp-image-16458" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/11/aru-islands-2.jpg 965w, https://content.globalforestwatch.org/wp-content/uploads/2023/11/aru-islands-2-768x518.jpg 768w" sizes="(max-width: 965px) 100vw, 965px" /><figcaption class="wp-element-caption">Indonesia’s Aru Islands hold some of the country’s most pristine rainforests and are home to more than 60,000 Indigenous Aruese. Photo by Forest Watch Indonesia.</figcaption></figure>



<p>As forests in more easily accessible areas of Indonesia such as Java and Borneo have been cleared, increasing pressure for valuable timber species and land for growing crops like sugar has reached the shores of the Aru Islands. Aru Indigenous Peoples and&nbsp;<a href="https://fwi.or.id/en/" rel="noreferrer noopener" target="_blank">Forest Watch Indonesia (FWI),</a>&nbsp;a civil society organization, have been working together to monitor, document and halt illegal logging using satellite data.&nbsp;&nbsp;</p>



<p>Using&nbsp;<a href="https://www.globalforestwatch.org/blog/data-and-research/early-warning-systems-for-deforestation-an-explainer/">deforestation alerts</a>&nbsp;from Global Forest Watch, FWI conducts desk analyses to identify priority locations for forest monitoring. They train Aru Forest Monitors on how to navigate to areas of forest where satellites detected recent clearings, using the&nbsp;<a href="https://forestwatcher.globalforestwatch.org/">Forest Watcher mobile app</a>. They also document evidence of deforestation with the app by taking photos and recording written or audio observations of the causes of the clearing.&nbsp;</p>



<p>Forest Monitors then continually track the extraction of timber from these deforestation sites, oftentimes following the wood to local ports, photographing identifiable information of ships containing illegally harvested timber, and transmitting this information to FWI. The organization then synthesizes the information and passes it to Indonesian authorities, who can then intercept the ships when they dock in Java before reaching international waters. In February 2019, the evidence that Indigenous Forest Monitors documented prompted the Indonesian Ministry of Environment and Forestry to seize 38 containers of illegally harvested timber. High-profile examples like this can serve as a deterrent for future illegal logging.</p>



<h2 class="wp-block-heading">Indigenous Communities in Peru Deploy Technology to Reduce Illegal Deforestation and Strengthen Land Rights</h2>



<p>A&nbsp;<a href="https://www.globalforestwatch.org/blog/people/indigenous-forest-monitors-reduce-deforestation/">two-year study</a>&nbsp;in the Northeastern Amazon region of Peru found that communities who used satellite-based forest monitoring technologies and data significantly reduced deforestation on their lands.</p>



<p>Equipped with deforestation alerts accessed from Forest Watcher and Global Forest Watch, forest monitors representing 36 local communities patrolled their ancestral lands. They documented evidence of encroachment from illicit coca plantations and illegal logging, using satellite imagery, photos, written documentation and drone footage. The monitors presented this information in community assemblies to collectively determine what action to take. Depending on the situation, they then either asked the invaders to leave by showing evidence that they had crossed over into community lands, or submitted documentation to Peruvian law enforcement, who then followed up with official field investigations.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" loading="lazy" width="455" height="607" src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/p_paisa_20150105_113737-scaled-1.jpg" alt="" class="wp-image-16459"/><figcaption class="wp-element-caption">A community monitor in Remanso del Amazonas identifies early warning signs of deforestation. Photo by Beder Olortegui.</figcaption></figure></div>


<p>Ultimately, the communities that used forest monitoring technologies reduced deforestation in their territories by 52% in the first year and an additional 21% in the second year, as compared to a similar number of communities in a control group. The communities using monitoring technologies also reported higher satisfaction with forest governance.</p>



<p>Since the study concluded, communities with experience using monitoring technologies are now training communities in the control group to do the same. The communities who succeeded in reducing deforestation have also received direct payments from the&nbsp;<a href="https://rainforestfoundation.org/our-work/approaches/rainforest-alert/" rel="noreferrer noopener" target="_blank">Rainforest Foundation US’s Rainforest Alert program</a>, a payment system for keeping forests conserved.</p>



<p>Most recently, some communities allocated that funding towards strengthening and expanding legal recognition of their ancestral lands. Community land titling in Peru is a slow and bureaucratic process. With the funds, communities were able to georeference the limits of their lands; contract soil analysts, lawyers and Geographic Information System (GIS) specialists; and petition the government for land titles. They succeeded in obtaining titles to their territories in just 10 months – a process that normally takes years or even decades.</p>



<h2 class="wp-block-heading">Open Data Supports Communities, but More Secure Land Rights Are Essential&nbsp;</h2>



<p>Satellite data and imagery are critical tools because they provide transparent, independent evidence of suspected violations. They allow communities to rapidly monitor and document forest disturbances over large areas and long periods of time at low cost, thus reducing expensive field patrols. Moreover, much of the monitoring and documentation can be done remotely from the safety of a laptop, without risking face-to-face confrontation with encroachers. This is especially important because environmental defenders face growing threats. In 2022, at least&nbsp;<a href="https://www.globalwitness.org/en/campaigns/environmental-activists/standing-firm/" target="_blank" rel="noreferrer noopener">177 environmental defenders were killed</a>, 36% of whom were Indigenous.&nbsp;</p>



<p>Given the power of satellite data to help communities protect their land rights, data creators and managers need to ensure that communities continue to have access to open data and technology. Communities also need support in data use, including tailored&nbsp;<a href="https://www.globalforestwatch.org/grants-and-fellowships/about/">training resources</a>,&nbsp;<a href="https://www.globalforestwatch.org/grants-and-fellowships/about/" target="_blank" rel="noreferrer noopener">financial support</a>&nbsp;and facilitating the&nbsp;<a href="https://www.globalforestwatch.org/help/map/webinars/webinar-strengthening-the-role-of-indigenous-youth-in-forest-protection-perspectives-from-latin-america/">sharing of experiences</a>.</p>



<p>However, while satellite data helps communities document and take action on illegal deforestation and related infractions, governments ultimately need to recognize and enforce Indigenous Peoples’ and communities’ land rights. Stronger and more secure land rights — with higher penalties for infractions — are more likely to deter illegal activity from occurring in the first place, or at least ensure a swift and decisive end to violations.</p>



<p>Indigenous Peoples and local communities have stewarded their lands and natural resources for centuries. Though more secure land rights are ultimately needed, satellite data can help them manage threats to their lands now and conserve their homes for generations to come.</p>



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Indigenous Peoples and Local Communities Are Using Satellite Data to Fight Deforestation

<p>The Saamaka, an Afro-descendent tribal community, reside deep in the heart of Suriname’s Amazonian rainforest. They’ve maintained their plots of land and small farms for generations, relying on the bounty of fruits, nuts and medicines the surrounding tropical forest provides. &nbsp;</p>



<p>But lately, their forested territory has seen unwelcome visitors.&nbsp;</p>



<p>A logging company began bulldozing a road right through the Saamaka’s forest in early 2023. While the Suriname government sanctioned the logging concession, it came without the Saamaka’s consent, despite a 2007 ruling from the Inter-American Court of Human Rights requiring their approval for such projects. The road is just the latest threat facing the Saamaka’s land, on top of flooding from hydropower dams and water pollution from nearby mining operations. &nbsp;</p>



<p>&nbsp;“We are losing our very way of life — our food, our water, our land,” said Hugo Jabini, a community leader and spokesperson for the Saamaka. “We can no longer afford to be invisible.”</p>



<figure class="wp-block-image aligncenter size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/HugoPPT3.jpg" alt="Saamaka leader Hugo Jabini leads a kuutu, a meeting of Saamakan communities." class="wp-image-16456"/><figcaption class="wp-element-caption">Hugo Jabini leads a kuutu, a meeting of Saamakan communities.&nbsp;</figcaption></figure>



<p>The Saamaka’s story is a common one. Indigenous Peoples and local communities hold some of the most pristine and resource-rich lands in the world — areas highly coveted by mining and logging companies and other profiteers.&nbsp; Land grabs and other threats are especially severe in places where the government does not recognize communities’ land rights, or where anti-deforestation and other laws are weak or poorly enforced. It’s the reason many Indigenous Peoples and local communities often take land monitoring into their own hands — and some are now using digital tools to do it.&nbsp;</p>



<p>Freely available satellite imagery and data from sites like&nbsp;<a href="https://www.globalforestwatch.org/map/">Global Forest Watch</a>&nbsp;and&nbsp;<a href="https://www.landmarkmap.org/data/" target="_blank" rel="noreferrer noopener">LandMark</a>&nbsp;provide near-real-time information that tracks deforestation and land degradation. Indigenous and local communities are increasingly using tools like this to gather evidence that deforestation and degradation are happening on their lands, build their case against illegal activities and take legal action to prevent it from continuing.&nbsp;&nbsp;</p>



<p>Three examples from Suriname, Indonesia and Peru illustrate a growing trend in fighting land rights violations with data.&nbsp;&nbsp;</p>



<h2 class="wp-block-heading">In Suriname, Deforestation Data Reveals Threats to the Saamaka’s Land Rights &nbsp;</h2>



<p>In 2007, the Inter-American Court of Human Rights compelled the government of Suriname to recognize the traditional land rights of the Saamaka and other Indigenous and community groups in Suriname. &nbsp;Yet satellite data from Global Forest Watch shows that since the judgment in 2007, tree cover loss actually increased in the Saamaka’s lands, largely from logging and mining activities. Tree cover loss inside government-granted logging concessions in and surrounding the Saamaka territory totaled 44,700 hectares, a 5.5% loss in tree cover from 2001-2022.&nbsp;&nbsp;</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/15824378?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>Monthly satellite images from March 2023 to October 2023 show the gradual construction of a road being carved through the heart of the Saamaka territory and leading to a government-granted logging concession. The Saamaka say the road was constructed without their knowledge or consent.&nbsp;</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/Suriname-logging-road.gif" alt="Gif of road construction progress through the Saamaka’s forested territory. March-October 2023" class="wp-image-16455"/><figcaption class="wp-element-caption">Road construction progresses through the Saamaka’s forested territory. Source: Planet imagery on Global Forest Watch.</figcaption></figure>



<p>Armed with this data and imagery, Jabini and seven other representatives from Suriname’s Indigenous and local communities traveled more than 2,500 miles to Washington, D.C., presenting their evidence to the Inter-American Commission on Human Rights in November 2023. Jabini hopes the commission will reopen its court case against the Suriname government to strengthen traditional land rights.&nbsp;&nbsp;</p>



<p>“We are not here to fight the government; we are just fighting for our rights,” Jabini said.</p>



<h2 class="wp-block-heading">In Indonesia, Indigenous Peoples Use Satellite Data to Track and Report Illegal Timber &nbsp;</h2>



<p>In the eastern-most edges of Indonesia lies an archipelago of 832 islands containing some of the country’s most pristine remaining rainforests. The Aru Islands are home to more than 60,000 Indigenous Aruese, who speak 14 different tribal languages. Their lives are deeply intertwined with the forests they call home, hunting deer, harvesting tree gum, and maintaining a strong spiritual connection with the forests believed to harbor the souls of their ancestors.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/aru-islands-2.jpg" alt="Indonesia’s Aru Islands" class="wp-image-16458"/><figcaption class="wp-element-caption">Indonesia’s Aru Islands hold some of the country’s most pristine rainforests and are home to more than 60,000 Indigenous Aruese. Photo by Forest Watch Indonesia.</figcaption></figure>



<p>As forests in more easily accessible areas of Indonesia such as Java and Borneo have been cleared, increasing pressure for valuable timber species and land for growing crops like sugar has reached the shores of the Aru Islands. Aru Indigenous Peoples and&nbsp;<a href="https://fwi.or.id/en/" rel="noreferrer noopener" target="_blank">Forest Watch Indonesia (FWI),</a>&nbsp;a civil society organization, have been working together to monitor, document and halt illegal logging using satellite data.&nbsp;&nbsp;</p>



<p>Using&nbsp;<a href="https://www.globalforestwatch.org/blog/data-and-research/early-warning-systems-for-deforestation-an-explainer/">deforestation alerts</a>&nbsp;from Global Forest Watch, FWI conducts desk analyses to identify priority locations for forest monitoring. They train Aru Forest Monitors on how to navigate to areas of forest where satellites detected recent clearings, using the&nbsp;<a href="https://forestwatcher.globalforestwatch.org/">Forest Watcher mobile app</a>. They also document evidence of deforestation with the app by taking photos and recording written or audio observations of the causes of the clearing.&nbsp;</p>



<p>Forest Monitors then continually track the extraction of timber from these deforestation sites, oftentimes following the wood to local ports, photographing identifiable information of ships containing illegally harvested timber, and transmitting this information to FWI. The organization then synthesizes the information and passes it to Indonesian authorities, who can then intercept the ships when they dock in Java before reaching international waters. In February 2019, the evidence that Indigenous Forest Monitors documented prompted the Indonesian Ministry of Environment and Forestry to seize 38 containers of illegally harvested timber. High-profile examples like this can serve as a deterrent for future illegal logging.</p>



<h2 class="wp-block-heading">Indigenous Communities in Peru Deploy Technology to Reduce Illegal Deforestation and Strengthen Land Rights</h2>



<p>A&nbsp;<a href="https://www.globalforestwatch.org/blog/people/indigenous-forest-monitors-reduce-deforestation/">two-year study</a>&nbsp;in the Northeastern Amazon region of Peru found that communities who used satellite-based forest monitoring technologies and data significantly reduced deforestation on their lands.</p>



<p>Equipped with deforestation alerts accessed from Forest Watcher and Global Forest Watch, forest monitors representing 36 local communities patrolled their ancestral lands. They documented evidence of encroachment from illicit coca plantations and illegal logging, using satellite imagery, photos, written documentation and drone footage. The monitors presented this information in community assemblies to collectively determine what action to take. Depending on the situation, they then either asked the invaders to leave by showing evidence that they had crossed over into community lands, or submitted documentation to Peruvian law enforcement, who then followed up with official field investigations.&nbsp;</p>



<figure class="wp-block-image aligncenter size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/11/p_paisa_20150105_113737-scaled-1.jpg" alt="" class="wp-image-16459"/><figcaption class="wp-element-caption">A community monitor in Remanso del Amazonas identifies early warning signs of deforestation. Photo by Beder Olortegui.</figcaption></figure>



<p>Ultimately, the communities that used forest monitoring technologies reduced deforestation in their territories by 52% in the first year and an additional 21% in the second year, as compared to a similar number of communities in a control group. The communities using monitoring technologies also reported higher satisfaction with forest governance.</p>



<p>Since the study concluded, communities with experience using monitoring technologies are now training communities in the control group to do the same. The communities who succeeded in reducing deforestation have also received direct payments from the&nbsp;<a href="https://rainforestfoundation.org/our-work/approaches/rainforest-alert/" rel="noreferrer noopener" target="_blank">Rainforest Foundation US’s Rainforest Alert program</a>, a payment system for keeping forests conserved.</p>



<p>Most recently, some communities allocated that funding towards strengthening and expanding legal recognition of their ancestral lands. Community land titling in Peru is a slow and bureaucratic process. With the funds, communities were able to georeference the limits of their lands; contract soil analysts, lawyers and Geographic Information System (GIS) specialists; and petition the government for land titles. They succeeded in obtaining titles to their territories in just 10 months – a process that normally takes years or even decades.</p>



<h2 class="wp-block-heading">Open Data Supports Communities, but More Secure Land Rights Are Essential&nbsp;</h2>



<p>Satellite data and imagery are critical tools because they provide transparent, independent evidence of suspected violations. They allow communities to rapidly monitor and document forest disturbances over large areas and long periods of time at low cost, thus reducing expensive field patrols. Moreover, much of the monitoring and documentation can be done remotely from the safety of a laptop, without risking face-to-face confrontation with encroachers. This is especially important because environmental defenders face growing threats. In 2022, at least&nbsp;<a href="https://www.globalwitness.org/en/campaigns/environmental-activists/standing-firm/" target="_blank" rel="noreferrer noopener">177 environmental defenders were killed</a>, 36% of whom were Indigenous.&nbsp;</p>



<p>Given the power of satellite data to help communities protect their land rights, data creators and managers need to ensure that communities continue to have access to open data and technology. Communities also need support in data use, including tailored&nbsp;<a href="https://www.globalforestwatch.org/grants-and-fellowships/about/">training resources</a>,&nbsp;<a href="https://www.globalforestwatch.org/grants-and-fellowships/about/" target="_blank" rel="noreferrer noopener">financial support</a>&nbsp;and facilitating the&nbsp;<a href="https://www.globalforestwatch.org/help/map/webinars/webinar-strengthening-the-role-of-indigenous-youth-in-forest-protection-perspectives-from-latin-america/">sharing of experiences</a>.</p>



<p>However, while satellite data helps communities document and take action on illegal deforestation and related infractions, governments ultimately need to recognize and enforce Indigenous Peoples’ and communities’ land rights. Stronger and more secure land rights — with higher penalties for infractions — are more likely to deter illegal activity from occurring in the first place, or at least ensure a swift and decisive end to violations.</p>



<p>Indigenous Peoples and local communities have stewarded their lands and natural resources for centuries. Though more secure land rights are ultimately needed, satellite data can help them manage threats to their lands now and conserve their homes for generations to come.</p>



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Originally appeared on <a href="https://www.wri.org/insights/indigenous-peoples-local-communities-use-satellite-data-deforestation" target="_blank" rel="noopener noreferrer">WRI Insights</a>. 
 

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Communities are increasingly using tools like GFW to gather evidence of deforestation and degradation on their lands and take legal action.  

Communities Are Using Satellite Data to Fight Deforestation

commodities

commodities | Global Forest Watch Blog

Tina Schneider

Ruth Nogueron

Monitoring Forest Degradation for the EU Deforestation Regulation Using GFW

Under the EUDR, wood products entering the EU market must be degradation-free. Tools like GFW can help companies monitor for degradation.

Monitoring Forest Degradation for the EUDR | GFW Blog

African savannah elephants in the Maasai Mara

Biodiversity

Biodiversity | Global Forest Watch Blog

Gabrielle Nussbaum

<p>Kenya is known for its incredible ecosystem biodiversity, including lowland and montane forests, savannah and scrubland. Among them is the Greater Mara Ecosystem, world famous for its wildlife, with populations of lions, wildebeest, gazelles, cheetahs and <a href="https://www.iucnredlist.org/species/181008073/223031019" target="_blank" rel="noreferrer noopener">African savannah elephants</a>, the largest land mammal on Earth. The Maasai Mara National Reserve, which covers 1,510 square kilometers in southern Kenya, and its surrounding area is home to an estimated 2,600 elephants, which use Kenya’s forest habitats for shelter, food and water.&nbsp;</p>



<p>Three forests play an important role in the Greater Mara: Mau, Loita and Nyakweri. Often referred to as “<a href="https://watertowers.go.ke/" target="_blank" rel="noreferrer noopener">water towers</a>” because of their deep importance to water security, these forests are a <a href="https://www2.cifor.org/water-towers/" target="_blank" rel="noreferrer noopener">main water source</a> for 12 rivers including the Mara River, a vital source for life in the Maasai Mara National Reserve. For communities in this area, the forests provide water, food and traditional medicine. For the wildlife, the forests offer a refuge during the dry season.</p>



<p>Mara Elephant Project, a local organization, is helping alongside partners the Wildlife Research and Training Institute, Kenya Forest Service and Kenya Wildlife Service to protect these critical forests aided by data from Global Forest Watch.</p>



<h2 class="wp-block-heading">Using GFW data to help protect critical forest habitats</h2>



<p>Since 2001, the natural forest cover of one of the critical forests has shrunk by 60% due to illegal logging and encroachment.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="1242" height="384" src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Forest-cover-change-in-Nyakweri-Forest.png" alt="Forest cover change in Nyakweri Forest" class="wp-image-16310" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Forest-cover-change-in-Nyakweri-Forest.png 1242w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Forest-cover-change-in-Nyakweri-Forest-1024x317.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Forest-cover-change-in-Nyakweri-Forest-768x237.png 768w" sizes="(max-width: 1242px) 100vw, 1242px" /><figcaption class="wp-element-caption">Forest cover change in Nyakweri Forest. 60% of the forest has been cut down since 2001. <em>Data (left): Hansen Global Forest Cover Change. Credit (right): Chags Photography.</em></figcaption></figure>



<p><a href="https://maraelephantproject.org/" target="_blank" rel="noreferrer noopener">Mara Elephant Project</a> (MEP) is a local organization protecting wildlife, communities and habitats in the Maasai Mara. MEP works to protect elephants and their habitats in the Mau (258,000 ha), Loita (41,000 ha) and Nyakweri (8,000 ha) forests, which together support a population of nearly 1,000 elephants.</p>



<p>Due to the vast size and impenetrable thickness of these forests, monitoring for deforestation and loss can often be a challenge. Satellite data can help monitor large areas that would otherwise be impossible to patrol on foot.</p>



<p>To facilitate this, MEP uses <a href="https://www.earthranger.com/" target="_blank" rel="noreferrer noopener">EarthRanger</a>, a software that collects, integrates and displays remote sensing data along with tracking and field reports into one unified platform. Global Forest Watch (GFW) has partnered with EarthRanger to provide satellite data, including <a href="https://gfw.global/409wpa9">GLAD deforestation alerts</a> and <a href="https://gfw.global/3FMgnLq">VIIRs fire alerts</a>, which are crucial for MEP and partner organizations to monitor these forests.</p>



<p>“The use of satellite imagery through GLAD alerts has enhanced the way vast areas are monitored,” said EarthRanger’s Director Jes Lefcourt. “This empowers conservationists to be aware of deforestation in remote places and to deal with previously impossible challenges of scale.”</p>



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<p>“Satellite-based alerts really help us understand what’s happening in places that are remote and difficult for our rangers to reach,” said Dr. Jake Wall, MEP’s Director of Research and Conservation.</p>



<p>The MEP team of Maasai rangers and researchers use GLAD and fire alerts, along with aerial monitoring, to detect illegal logging and <a href="https://maraelephantproject.org/our-approach/protect/" target="_blank" rel="noreferrer noopener">deploy rangers on patrol</a>&nbsp;to investigate areas of the Greater Mara that are vulnerable to illegal habitat destruction.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="1900" height="1264" src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography.jpg" alt="Mau Rangers help protect critical forests" class="wp-image-16311" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography.jpg 1900w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography-1024x681.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography-768x511.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography-1536x1022.jpg 1536w" sizes="(max-width: 1900px) 100vw, 1900px" /><figcaption class="wp-element-caption">Mau Rangers. <em>Credit: Chags Photography</em>.</figcaption></figure>



<p>MEP supports regular patrols of these forested areas alongside government partners to abate and halt destructive activities.</p>



<p>“We’ve seen an increase annually in arrests and seizures related to habitat destruction,” said MEP’s CEO Marc Goss, who leads the deployment of patrol units.</p>



<h2 class="wp-block-heading">Partnering to share data for decision making</h2>



<p>By using satellite data available from GFW, MEP has been able to react more quickly than ever and is working on developing more efficient ways to bring this data to government agencies who can use it to influence their policy and decision making. MEP is now in the process of creating an automated system to share data with local government, and along with partners across the Mara, MEP is helping to develop a monitoring framework that would include statistics on forest cover and fires derived from satellite data as key indicators.</p>



<p>“The automated system MEP has developed is called Ecoscope,” explained Dr. Wall. “We can cut the time out of generating key conservation outputs through automation using this software and visualize those outputs in an efficient way. This saves us both time and resources and makes the data far more accessible.”</p>



<h2 class="wp-block-heading">Working with local communities to prevent forest loss</h2>



<p><a href="https://journals.law.harvard.edu/ilj/2023/01/after-decades-of-illegal-evictions-indigenous-ogiek-win-reparations-ruling-against-the-republic-of-kenya/" target="_blank" rel="noreferrer noopener">Land tenure in these areas</a>&nbsp;has often been unclear and communities’ role in protecting these forests is critical. MEP employs rangers recruited from local communities as a pragmatic strategy to conserve these areas. Six ranger units comprised of men and women recruited from the bordering community and trained by MEP are deployed to protect the forest from loggers, charcoal makers and poachers.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="1900" height="1264" src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography.jpg" alt="Nyakweri Rangers  help protect critical forests" class="wp-image-16312" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography.jpg 1900w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography-1024x681.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography-768x511.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography-1536x1022.jpg 1536w" sizes="(max-width: 1900px) 100vw, 1900px" /><figcaption class="wp-element-caption">Nyakweri Rangers. <em>Credit: Chags Photography</em>.</figcaption></figure>



<p>Whenever it can, MEP relies on the knowledge and traditions of local communities to protect forests. The Loita Forest, for example, is managed by the local Maasai community who live there and value its traditional importance. As a result, only a 3% forest loss has been recorded in this forest within the last 20 years.</p>



<p>MEP’s Project Conservation Manager, Wilson Sairowua, works closely with local communities like Loita and emphasized the important role they play in forest protection, explaining “[t]he Loita people are a special lot since they have been protecting the forest using the Laibon [Maasai healer] and their traditional spiritual leaders. They consider the forest to be an important source of food and medicine, therefore it is forbidden by culture to cut trees.”&nbsp;</p>



<p>While protecting forests can be challenging, as access to data improves, so does response time and impact. Using near-real-time satellite data, MEP is able to work with community and government partners to monitor and understand changes in forests, and with partners, respond to them more efficiently. As long as threats exist to these forest ecosystems that communities and wildlife critically depend on, MEP will use data, build partnerships and work with communities to protect them.</p>



<p>&nbsp;“The better we can monitor forests,” said Dr. Wall, “the better we can help protect them.”</p>



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Protecting Critical Forest Habitats in the Maasai Mara with Mara Elephant Project

<p>Kenya is known for its incredible ecosystem biodiversity, including lowland and montane forests, savannah and scrubland. Among them is the Greater Mara Ecosystem, world famous for its wildlife, with populations of lions, wildebeest, gazelles, cheetahs and <a href="https://www.iucnredlist.org/species/181008073/223031019" target="_blank" rel="noreferrer noopener">African savannah elephants</a>, the largest land mammal on Earth. The Maasai Mara National Reserve, which covers 1,510 square kilometers in southern Kenya, and its surrounding area is home to an estimated 2,600 elephants, which use Kenya’s forest habitats for shelter, food and water.&nbsp;</p>



<p>Three forests play an important role in the Greater Mara: Mau, Loita and Nyakweri. Often referred to as “<a href="https://watertowers.go.ke/" target="_blank" rel="noreferrer noopener">water towers</a>” because of their deep importance to water security, these forests are a <a href="https://www2.cifor.org/water-towers/" target="_blank" rel="noreferrer noopener">main water source</a> for 12 rivers including the Mara River, a vital source for life in the Maasai Mara National Reserve. For communities in this area, the forests provide water, food and traditional medicine. For the wildlife, the forests offer a refuge during the dry season.</p>



<p>Mara Elephant Project, a local organization, is helping alongside partners the Wildlife Research and Training Institute, Kenya Forest Service and Kenya Wildlife Service to protect these critical forests aided by data from Global Forest Watch.</p>



<h2 class="wp-block-heading">Using GFW data to help protect critical forest habitats</h2>



<p>Since 2001, the natural forest cover of one of the critical forests has shrunk by 60% due to illegal logging and encroachment.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Forest-cover-change-in-Nyakweri-Forest.png" alt="Forest cover change in Nyakweri Forest" class="wp-image-16310"/><figcaption class="wp-element-caption">Forest cover change in Nyakweri Forest. 60% of the forest has been cut down since 2001. <em>Data (left): Hansen Global Forest Cover Change. Credit (right): Chags Photography.</em></figcaption></figure>



<p><a href="https://maraelephantproject.org/" target="_blank" rel="noreferrer noopener">Mara Elephant Project</a> (MEP) is a local organization protecting wildlife, communities and habitats in the Maasai Mara. MEP works to protect elephants and their habitats in the Mau (258,000 ha), Loita (41,000 ha) and Nyakweri (8,000 ha) forests, which together support a population of nearly 1,000 elephants.</p>



<p>Due to the vast size and impenetrable thickness of these forests, monitoring for deforestation and loss can often be a challenge. Satellite data can help monitor large areas that would otherwise be impossible to patrol on foot.</p>



<p>To facilitate this, MEP uses <a href="https://www.earthranger.com/" target="_blank" rel="noreferrer noopener">EarthRanger</a>, a software that collects, integrates and displays remote sensing data along with tracking and field reports into one unified platform. Global Forest Watch (GFW) has partnered with EarthRanger to provide satellite data, including <a href="https://gfw.global/409wpa9">GLAD deforestation alerts</a> and <a href="https://gfw.global/3FMgnLq">VIIRs fire alerts</a>, which are crucial for MEP and partner organizations to monitor these forests.</p>



<p>“The use of satellite imagery through GLAD alerts has enhanced the way vast areas are monitored,” said EarthRanger’s Director Jes Lefcourt. "This empowers conservationists to be aware of deforestation in remote places and to deal with previously impossible challenges of scale."</p>



<iframe width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=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"></iframe>



<p>“Satellite-based alerts really help us understand what’s happening in places that are remote and difficult for our rangers to reach,” said Dr. Jake Wall, MEP’s Director of Research and Conservation.</p>



<p>The MEP team of Maasai rangers and researchers use GLAD and fire alerts, along with aerial monitoring, to detect illegal logging and <a href="https://maraelephantproject.org/our-approach/protect/" target="_blank" rel="noreferrer noopener">deploy rangers on patrol</a>&nbsp;to investigate areas of the Greater Mara that are vulnerable to illegal habitat destruction.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Mau-Rangers-1-Chags-Photography.jpg" alt="Mau Rangers help protect critical forests" class="wp-image-16311"/><figcaption class="wp-element-caption">Mau Rangers. <em>Credit: Chags Photography</em>.</figcaption></figure>



<p>MEP supports regular patrols of these forested areas alongside government partners to abate and halt destructive activities.</p>



<p>“We’ve seen an increase annually in arrests and seizures related to habitat destruction,” said MEP’s CEO Marc Goss, who leads the deployment of patrol units.</p>



<h2 class="wp-block-heading">Partnering to share data for decision making</h2>



<p>By using satellite data available from GFW, MEP has been able to react more quickly than ever and is working on developing more efficient ways to bring this data to government agencies who can use it to influence their policy and decision making. MEP is now in the process of creating an automated system to share data with local government, and along with partners across the Mara, MEP is helping to develop a monitoring framework that would include statistics on forest cover and fires derived from satellite data as key indicators.</p>



<p>“The automated system MEP has developed is called Ecoscope,” explained Dr. Wall. “We can cut the time out of generating key conservation outputs through automation using this software and visualize those outputs in an efficient way. This saves us both time and resources and makes the data far more accessible.”</p>



<h2 class="wp-block-heading">Working with local communities to prevent forest loss</h2>



<p><a href="https://journals.law.harvard.edu/ilj/2023/01/after-decades-of-illegal-evictions-indigenous-ogiek-win-reparations-ruling-against-the-republic-of-kenya/" target="_blank" rel="noreferrer noopener">Land tenure in these areas</a>&nbsp;has often been unclear and communities’ role in protecting these forests is critical. MEP employs rangers recruited from local communities as a pragmatic strategy to conserve these areas. Six ranger units comprised of men and women recruited from the bordering community and trained by MEP are deployed to protect the forest from loggers, charcoal makers and poachers.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/09/Nyakweri-Rangers-Chags-Photography.jpg" alt="Nyakweri Rangers  help protect critical forests" class="wp-image-16312"/><figcaption class="wp-element-caption">Nyakweri Rangers. <em>Credit: Chags Photography</em>.</figcaption></figure>



<p>Whenever it can, MEP relies on the knowledge and traditions of local communities to protect forests. The Loita Forest, for example, is managed by the local Maasai community who live there and value its traditional importance. As a result, only a 3% forest loss has been recorded in this forest within the last 20 years.</p>



<p>MEP’s Project Conservation Manager, Wilson Sairowua, works closely with local communities like Loita and emphasized the important role they play in forest protection, explaining "[t]he Loita people are a special lot since they have been protecting the forest using the Laibon [Maasai healer] and their traditional spiritual leaders. They consider the forest to be an important source of food and medicine, therefore it is forbidden by culture to cut trees."&nbsp;</p>



<p>While protecting forests can be challenging, as access to data improves, so does response time and impact. Using near-real-time satellite data, MEP is able to work with community and government partners to monitor and understand changes in forests, and with partners, respond to them more efficiently. As long as threats exist to these forest ecosystems that communities and wildlife critically depend on, MEP will use data, build partnerships and work with communities to protect them.</p>



<p>&nbsp;“The better we can monitor forests,” said Dr. Wall, “the better we can help protect them.”</p>



<style type="text/css"></style>

Maasai Mara forests in Kenya are vital for communities and wildlife. Mara Elephant Project helps protect these forests with data from GFW.

Protecting Forest Habitats in the Maasai Mara with Mara Elephant Project

fires

fires | Global Forest Watch Blog

James MacCarthy

Jessica Richter

Sasha Tyukavina

Nancy Harris

The Latest Data Confirms: Forest Fires Are Getting Worse

The latest data on forest fires confirms: Fires are becoming more widespread, burning nearly twice as much tree cover today as 20 years ago.

The Latest Data Confirms: Forest Fires Are Getting Worse | GFW Blog

An aerial shot shows the contrast between forest and agricultural landscapes near Rio Branco, Acre, Brazil.

Congo Basin

Congo Basin | Global Forest Watch Blog

Tropical Primary Forest Loss Worsened in 2022, Despite International Commitments to End Deforestation

La perte de forêts primaires tropicales s’est aggravée en 2022, malgré les engagements internationaux visant à mettre fin à la déforestation

Perda de florestas tropicais primárias piora em 2022 apesar dos compromissos internacionais para acabar com o desmatamento

Empeoró La Pérdida De Bosques Tropicales Primarios en 2022, A Pesar De Los Compromisos Internacionales Contra La Deforestación

Tingkat Kehilangan Hutan Primer Tropis pada tahun 2022 Memburuk, Terlepas dari Komitmen Global untuk Mengakhiri Deforestasi

Tropical primary forest loss was 10% higher in 2022 than in 2021 despite international commitments, according to new data on GFW.

Tropical Primary Forest Loss Worsened in 2022 | GFW Blog

overview of a river in Pantanal, Brazil

Brazil

Brazil | Global Forest Watch Blog

Tropical Forest Loss Drops Steeply in Brazil and Colombia, but High Rates Persist Overall

Despite progress in some countries, the rate of tropical primary forest loss in 2023 remained persistent, according to new data from UMD on GFW.

Aerial footage of palm oil and the forest in Sentabai Village, West Kalimantan, 2017.

<p>New tree cover loss data from the University of Maryland’s GLAD lab (UMD) and available on Global Forest Watch (GFW) shows declines in primary forest loss in some countries but a persistent rate overall in 2023. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.</p>



<h2 class="wp-block-heading">What tree cover loss measures and how this differs from deforestation</h2>



<p>The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2023. The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops (though notably, the primary forest data we use to filter the loss in the tropics excludes plantations, tree crops and regrowing forests). Loss of this tree cover can be due to human or natural causes and can be permanent or temporary.</p>



<p>Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">read more about the differences here</a>. Loss in tree cover, whether deforestation or not, can be legal or illegal.</p>



<p>In some cases, such as in the <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Deforestation and Restoration Targets Tracker</a> on the Global Forest Review, we use a proxy for deforestation. That proxy includes losses that are from small-scale agriculture in humid tropical primary forests and all commodity-driven deforestation and urbanization (inside and outside of humid tropical primary forests). Temporary losses, such as from fires or forestry activities, are not included. We don’t use this deforestation proxy in all of our reporting because it is based on coarse resolution data that is designed to limit the inclusion of temporary losses or losses in planted forests at the global scale only. Deforestation continues to be difficult to track with globally consistent data sets and there are inherent delays in confirming whether loss detected in satellite imagery is temporary or permanent.</p>



<h2 class="wp-block-heading">Data improvements over time result in some inconsistencies in the data timeseries</h2>



<p>Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>.</p>



<p>This year, the loss detection algorithm used by the University of Maryland to create the tree cover loss data set was supplemented by information from the <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">DIST-ALERT land disturbance data set</a>. Late season changes are sometimes missed in the tree cover loss data due to insufficient satellite data and cloud cover and are attributed to the following year. DIST-ALERT showed patches of tree cover loss that were originally missed by the tree cover loss data, and these were manually reviewed and included for 2023.</p>



<p>Employing DIST-ALERT helped especially to detect loss from late season forest fires in boreal forests and late season clearing in the tropics that would have otherwise appeared in the 2024 data. The DIST-ALERT data also helped detect some tree cover loss due to flooding and mining. The use of alerts resulted in a 3.7% increase in global tree cover loss detected. Most of the changes affected the boreal regions, while for the rest of the globe, the increase was only 1.7%. It is important to note that since most of the added areas would have otherwise been detected in 2024, we do not believe this change in the mapping approach for 2023 will have any impact on long-term trends in the data.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>



<p>Additional changes to the time series include adjustments to the algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013. These changes make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture. Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.</p>



<p>To address these inconsistencies, we:</p>



<ul>
<li>Focus our analysis on trends post-2015.</li>



<li>Assess the three-year moving average to interpret longer-term trends.</li>



<li>Disregard the increase in loss post-2012 in places dominated by small-scale agriculture, like Central Africa.</li>
</ul>



<h2 class="wp-block-heading">What does tree cover loss from fire capture?</h2>



<p>The tree cover loss from fire data from UMD allows us to distinguish fire-driven loss from other tree cover loss by assigning likelihood of loss due to fire to each 30-meter tree cover loss pixel, breaking the data into loss due to fire and loss due to other drivers, such as agriculture or logging. The tree cover loss due to fire data includes natural or human-ignited fires that result in direct loss of tree canopy cover. The data captures wildfires, fires used to clear land for another use and intentionally set fires that result in tree cover loss (including escaped fires started by humans for purposes related to agriculture, hunting, recreation or arson). Instances where trees are felled and later burned are not included, however, since the initial driver of loss is mechanical removal.</p>



<p>This data allows us to better understand how fire impacted tree cover loss in 2023. For example, fire played especially large roles in Bolivia and Canada — <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">learn more about our findings from the 2023 data here</a>.</p>



<p>Even though fires often do not result in a permanent change in land use, they are still an important source of carbon emissions and can <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">lead to feedback loops</a> of increasing emissions, hotter temperatures, drier forests and more fires.</p>



<h2 class="wp-block-heading">Why we focus on the tropics</h2>



<p>In our <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&utm_medium=bitly&utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world’s deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.</p>



<h2 class="wp-block-heading">How does tree cover loss compare with official country data in Brazil and Indonesia?</h2>



<p><em>Brazil</em></p>



<p>PRODES — the official forest monitoring system for the Amazon from Brazil’s National Institute for Space Research (INPE) — and the UMD data have methodological and definitional differences that are important to understand when comparing these two data sources. PRODES measures clear cut deforestation and anthropogenic forest losses by fire larger than 6.25 hectares, whereas UMD captures loss larger than 0.09 hectares of all trees taller than five meters. These are both important measures that help us understand how forests are changing since deforestation, forest fires and small disturbances to forest canopy can all lead to impacts on climate, biodiversity and ecosystem services.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1348" height="1042" src="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png" alt="UMD and PRODES differences explained" class="wp-image-15866" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png 1348w, https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN-1024x792.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN-768x594.png 768w" sizes="(max-width: 1348px) 100vw, 1348px" /></figure>



<p>Both systems show a downward trend in loss for 2023 — 22% decrease for PRODES and 39% decrease for UMD (42% decrease for non-fire loss) in the Amazon biome, confirming the positive news for the Amazon.</p>



<h4 class="wp-block-heading">PRODES VS. UMD DATA IN THE BRAZILIAN AMAZON</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17268908?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><em>Indonesia</em></p>



<p>Overlaying the UMD/GFW primary forest loss data for 2023 with the official Indonesia land cover map from the Ministry of Environment and Forestry (MoEF) shows that 70% occurred within Indonesia’s official forest cover classes, with the remaining 30% taking place in mixed dry land agriculture, shrubland, swamp shrub and other land cover types. Approximately 144 thousand hectares of loss were found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 144 thousand hectares, about 15 thousand hectares were within areas legally classified as primary forest in Indonesia.</p>



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			<p><strong>MoEF class</strong></p>
			</td>
			<td style="width:550px;height:39px;">
			<p><strong>Percent of UMD/GFW 2023 Primary Forest Loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Primary forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>9%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Secondary forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>60%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Plantation forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>1%</p>
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			<p>Non-forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>30%</p>
			</td>
		</tr>
	</tbody>
</table>



<p></p>



<style type="text/css"></style>



<p></p>



<p></p>

Global Forest Watch’s 2023 Tree Cover Loss Data Explained

<p>New tree cover loss data from the University of Maryland’s GLAD lab (UMD) and available on Global Forest Watch (GFW) shows declines in primary forest loss in some countries but a persistent rate overall in 2023. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.</p>



<h2 class="wp-block-heading">What tree cover loss measures and how this differs from deforestation</h2>



<p>The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2023. The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops (though notably, the primary forest data we use to filter the loss in the tropics excludes plantations, tree crops and regrowing forests). Loss of this tree cover can be due to human or natural causes and can be permanent or temporary.</p>



<p>Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">read more about the differences here</a>. Loss in tree cover, whether deforestation or not, can be legal or illegal.</p>



<p>In some cases, such as in the <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Deforestation and Restoration Targets Tracker</a> on the Global Forest Review, we use a proxy for deforestation. That proxy includes losses that are from small-scale agriculture in humid tropical primary forests and all commodity-driven deforestation and urbanization (inside and outside of humid tropical primary forests). Temporary losses, such as from fires or forestry activities, are not included. We don’t use this deforestation proxy in all of our reporting because it is based on coarse resolution data that is designed to limit the inclusion of temporary losses or losses in planted forests at the global scale only. Deforestation continues to be difficult to track with globally consistent data sets and there are inherent delays in confirming whether loss detected in satellite imagery is temporary or permanent.</p>



<h2 class="wp-block-heading">Data improvements over time result in some inconsistencies in the data timeseries</h2>



<p>Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>.</p>



<p>This year, the loss detection algorithm used by the University of Maryland to create the tree cover loss data set was supplemented by information from the <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">DIST-ALERT land disturbance data set</a>. Late season changes are sometimes missed in the tree cover loss data due to insufficient satellite data and cloud cover and are attributed to the following year. DIST-ALERT showed patches of tree cover loss that were originally missed by the tree cover loss data, and these were manually reviewed and included for 2023.</p>



<p>Employing DIST-ALERT helped especially to detect loss from late season forest fires in boreal forests and late season clearing in the tropics that would have otherwise appeared in the 2024 data. The DIST-ALERT data also helped detect some tree cover loss due to flooding and mining. The use of alerts resulted in a 3.7% increase in global tree cover loss detected. Most of the changes affected the boreal regions, while for the rest of the globe, the increase was only 1.7%. It is important to note that since most of the added areas would have otherwise been detected in 2024, we do not believe this change in the mapping approach for 2023 will have any impact on long-term trends in the data.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>



<p>Additional changes to the time series include adjustments to the algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013. These changes make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture. Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.</p>



<p>To address these inconsistencies, we:</p>



<ul>
<li>Focus our analysis on trends post-2015.</li>



<li>Assess the three-year moving average to interpret longer-term trends.</li>



<li>Disregard the increase in loss post-2012 in places dominated by small-scale agriculture, like Central Africa.</li>
</ul>



<h2 class="wp-block-heading">What does tree cover loss from fire capture?</h2>



<p>The tree cover loss from fire data from UMD allows us to distinguish fire-driven loss from other tree cover loss by assigning likelihood of loss due to fire to each 30-meter tree cover loss pixel, breaking the data into loss due to fire and loss due to other drivers, such as agriculture or logging. The tree cover loss due to fire data includes natural or human-ignited fires that result in direct loss of tree canopy cover. The data captures wildfires, fires used to clear land for another use and intentionally set fires that result in tree cover loss (including escaped fires started by humans for purposes related to agriculture, hunting, recreation or arson). Instances where trees are felled and later burned are not included, however, since the initial driver of loss is mechanical removal.</p>



<p>This data allows us to better understand how fire impacted tree cover loss in 2023. For example, fire played especially large roles in Bolivia and Canada — <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">learn more about our findings from the 2023 data here</a>.</p>



<p>Even though fires often do not result in a permanent change in land use, they are still an important source of carbon emissions and can <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">lead to feedback loops</a> of increasing emissions, hotter temperatures, drier forests and more fires.</p>



<h2 class="wp-block-heading">Why we focus on the tropics</h2>



<p>In our <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world’s deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.</p>



<h2 class="wp-block-heading">How does tree cover loss compare with official country data in Brazil and Indonesia?</h2>



<p><em>Brazil</em></p>



<p>PRODES — the official forest monitoring system for the Amazon from Brazil’s National Institute for Space Research (INPE) — and the UMD data have methodological and definitional differences that are important to understand when comparing these two data sources. PRODES measures clear cut deforestation and anthropogenic forest losses by fire larger than 6.25 hectares, whereas UMD captures loss larger than 0.09 hectares of all trees taller than five meters. These are both important measures that help us understand how forests are changing since deforestation, forest fires and small disturbances to forest canopy can all lead to impacts on climate, biodiversity and ecosystem services.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png" alt="UMD and PRODES differences explained" class="wp-image-15866"/></figure>



<p>Both systems show a downward trend in loss for 2023 — 22% decrease for PRODES and 39% decrease for UMD (42% decrease for non-fire loss) in the Amazon biome, confirming the positive news for the Amazon.</p>



<h4 class="wp-block-heading">PRODES VS. UMD DATA IN THE BRAZILIAN AMAZON</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17268908?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><em>Indonesia</em></p>



<p>Overlaying the UMD/GFW primary forest loss data for 2023 with the official Indonesia land cover map from the Ministry of Environment and Forestry (MoEF) shows that 70% occurred within Indonesia’s official forest cover classes, with the remaining 30% taking place in mixed dry land agriculture, shrubland, swamp shrub and other land cover types. Approximately 144 thousand hectares of loss were found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 144 thousand hectares, about 15 thousand hectares were within areas legally classified as primary forest in Indonesia.</p>



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			<p><strong>MoEF class</strong></p>
			</td>
			<td style="width:550px;height:39px;">
			<p><strong>Percent of UMD/GFW 2023 Primary Forest Loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Primary forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>9%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Secondary forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>60%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Plantation forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>1%</p>
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			<p>Non-forest</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>30%</p>
			</td>
		</tr>
	</tbody>
</table>



<p></p>



<style type="text/css"></style>



<p></p>



<p></p>

<p>Novos dados de perda de cobertura arbórea do GLAD Lab da University of Maryland (UMD) e disponíveis na plataforma Global Forest Watch (GFW) mostram uma redução na perda de floresta primária em alguns países, mas uma taxa geral persistente em 2023. O que os dados medem, o que há de diferente este ano e como eles se comparam a outras estimativas oficiais de desmatamento? Aqui está o que você precisa saber sobre os novos dados.</p>



<h2 class="wp-block-heading">O que a perda de cobertura arbórea mede e como ela difere do desmatamento </h2>



<p>Os dados de perda de cobertura arbórea da UMD capturam a perda anual de todas as árvores com mais de cinco metros de altura entre os anos de 2001 e 2023. Os dados sobre perda de cobertura arbórea incluem a perda de árvores em florestas naturais, bem como em plantações e cultivo arbóreo (embora seja importante notar que os dados sobre florestas que usamos para filtrar a perda nos trópicos excluem plantações, cultivos de árvores e florestas em regeneração). A perda dessa cobertura pode ser devido a causas humanas ou naturais, permanente ou temporária.</p>



<p>O desmatamento difere por se referir apenas a uma mudança permanente e causada pelo homem, partindo do estado de floresta para outros usos da terra. Algumas formas de perda de cobertura arbórea, como a conversão de uma floresta natural em terras agrícolas, são consideradas desmatamento, enquanto outras formas, como a extração de madeira em florestas plantadas ou distúrbios naturais, não são. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Leia mais sobre as diferenças aqui</a>. A perda de cobertura arbórea, seja desmatamento ou não, pode ser legal ou ilegal.</p>



<p>Em alguns casos, como no <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Targets Tracker de Desmatamento e Restauração</a> no Global Forest Review, usamos um indicador para desmatamento. Esse indicador inclui as perdas que são de agricultura de pequena escala em florestas tropicais primárias úmidas e todo desmatamento e urbanização impulsionados por <em>commodities </em>(dentro e fora de florestas tropicais primárias úmidas). Perdas temporárias, como as decorrentes de incêndios ou atividades florestais, não foram incluídas. Não usamos esse indicador de desmatamento em todos os nossos relatórios porque é baseado em dados de baixa resolução que são concebidos para limitar a inclusão de perdas temporárias ou perdas em florestas plantadas apenas em escala global. O desmatamento continua a ser difícil de rastrear com conjuntos de dados consistentes globalmente e há atrasos inerentes para confirmar se as perdas detectadas em imagens de satélite são temporárias ou permanentes.</p>



<h2 class="wp-block-heading">Melhorias de dados ao longo do tempo resultam em algumas inconsistências na série temporal de dados</h2>



<p>Ajustes de algoritmo e melhores dados de satélite <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">melhoraram o conjunto de dados de perda de cobertura arbórea ao longo do tempo</a>.</p>



<p>Este ano, o algoritmo de detecção de perda usado pela University of Maryland para criar o conjunto de dados de perda de cobertura arbórea foi suplementado pelas informações do <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">conjunto de dados de alteração do solo do DIST-ALERT</a>. Mudanças da última estação às vezes são perdidas nos dados de perda de cobertura arbórea devido a dados de satélite insuficientes e cobertura de nuvens e são atribuídos ao ano seguinte. O DIST-ALERT mostrou fragmentos de perda de cobertura arbórea que originalmente foram omitidos pelos dados de perda de cobertura arbórea e incluídos para 2023 e esses foram revisados manualmente e incluídos em 2023.</p>



<p>Utilizar o DIST-ALERT ajudou especialmente a detectar a perda por queimadas da última estação em florestas boreais e desmatamento da última estação nos trópicos que de outro modo apareceriam nos dados de 2024. Os dados do DIST-ALERT também ajudaram a detectar parte da perda de cobertura arbórea devido à inundação e mineração. O uso de alertas resultou em um aumento de 3,7% em perda de cobertura arbórea detectada. A maioria das mudanças afetou as regiões boreais, embora para o restante do globo, o aumento foi de apenas 1,7%. É importante observar que, como a maioria das áreas adicionadas teria sido detectada em 2024 de outra forma, não acreditamos que essa mudança na abordagem de mapeamento em 2023 terá algum impacto nas tendências de longo prazo nos dados.</p>



<p>Mudanças adicionais para a série temporal inclui ajustes no algoritmo para os anos de 2011 a 2014 e a partir de 2015, bem como a incorporação de dados do Landsat 8 com início em 2013. Essas alterações facilitam a detecção de mudanças em escalas maiores, como a perda devido a incêndios, exploração madeireira seletiva e agricultura de rotação. Variações na disponibilidade de imagens por satélite também significam que há inconsistências na qualidade e no número de imagens disponíveis para capturar dados a cada ano.</p>



<p>Para resolver essas inconsistências, nós:</p>



<ul>
<li>concentramos nossa análise em tendências após 2015;</li>



<li>avaliamos a média móvel de três anos para interpretar as tendências de longo prazo; e</li>



<li>desconsideramos o aumento da perda após 2012 em locais dominados pela agricultura de pequena escala, como a África Central.</li>
</ul>



<h2 class="wp-block-heading">O que a perda de cobertura arbórea devido a incêndio capta?</h2>



<p>Os dados de perda de cobertura arbórea devido a incêndio da UMD nos permitem distinguir de outras mudanças na cobertura florestal ao atribuir a probabilidade de perda devido a incêndio para cada pixel de perda de cobertura arbórea de 30 metros, dividindo os dados em perda devido a incêndio e perda devido a outros fatores, como agricultura ou extração de madeira. Esses dados incluem incêndios naturais ou provocados pelo homem que resultam em perda direta de cobertura de copa das árvores. Os dados capturam incêndios florestais, incêndios usados para limpar a terra para outro uso e incêndios intencionais que resultam em perda de cobertura arbórea (incluindo incêndios escapados iniciados por humanos para fins relacionados à agricultura, caça, recreação ou incêndio criminoso). No entanto, os casos em que as árvores são derrubadas e posteriormente queimadas não são incluídos, uma vez que o fator inicial de perda é a remoção mecânica.</p>



<p>Esses dados nos permitem entender melhor como incêndios afetaram a perda de cobertura arbórea em 2023. Por exemplo, incêndios desempenharam papéis especialmente grandes na Bolívia e Canadá. <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">Saiba mais sobre nossas descobertas a partir dos dados de 2023 aqui</a>.</p>



<p>Embora os incêndios muitas vezes não resultem em uma mudança permanente no uso da terra, eles ainda são uma importante fonte de emissões de carbono e podem <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">levar a ciclos de retroalimentação</a> de emissões crescentes, temperaturas mais quentes, florestas mais secas e mais incêndios.</p>



<h2 class="wp-block-heading">Por que nos concentramos nos trópicos? </h2>



<p>Na <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">análise dos dados da UMD</a>, nós nos concentramos principalmente na perda de cobertura arbórea em florestas primárias tropicais, pois as florestas tropicais sofrem <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">a grande maioria</a> (mais de 96%) do desmatamento mundial, e a perda nessas áreas tem impactos enormes sobre a biodiversidade e o armazenamento de carbono. Mesmo que essas perdas acabem sendo revertidas, <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">levará décadas</a> para que esses habitats e estoques de carbono se recuperem, e poderá ocorrer uma perda permanente de biodiversidade.&nbsp;</p>



<h2 class="wp-block-heading">Como a perda de cobertura arbórea se compara aos dados nacionais oficiais no Brasil e na Indonésia?  </h2>



<p><em>Brasil</em>&nbsp;</p>



<p>O PRODES – o sistema oficial de monitoramento florestal para a Amazônia do Instituto Nacional de Pesquisas Espaciais (INPE) – e os dados da UMD têm diferenças metodológicas e de definição que são importantes de serem compreendidas ao comparar essas duas fontes de dados. O PRODES mede o desmatamento nítido e as perdas florestais antropogênicas por incêndio maiores que 6,25 hectares, enquanto a UMD captura perdas maiores que 0,09 hectare de todas as árvores com altura superior a cinco metros. Ambas são medidas importantes que nos ajudam a entender como as florestas estão mudando, uma vez que o desmatamento, as queimadas e pequenos distúrbios no dossel florestal podem levar a impactos no clima, na biodiversidade e nos serviços ecossistêmicos.&nbsp;</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/06/PT-23-05-04-gfr-technical-blog_UMDvsProdes-PT.png" alt="" class="wp-image-16134" style="aspect-ratio:3/2;object-fit:cover"/></figure>



<p>Os sistemas mostram uma tendência de queda na perda para 2023. Uma diminuição de 22% para o PRODES e 39% para a UMD (42% de redução para a perda não relacionada a incêndios) no bioma da Amazônia, confirmando as notícias positivas para a Amazônia.</p>



<h4 class="wp-block-heading">Dados do PRODES vs. UMD sobre a Amazônia brasileira</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17601358?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><em>Indonésia</em></p>



<p>A sobreposição dos dados de perda de floresta primária da UMD/GFW de 2023 com o mapa oficial de cobertura terrestre da Indonésia do Ministério do Meio Ambiente e Florestas (Ministry of Environment and Forestry, MoEF) mostra que 70% ocorreu em classes oficiais de cobertura florestal da Indonésia, com os restantes 30% ocorrendo em áreas de agricultura em terras secas mistas, matagais, arbustos de pântanos e outros tipos de cobertura do solo. Cerca de 144 mil hectares de perda revelaram ser tanto classes oficiais de cobertura florestal da Indonésia quanto ter um tamanho de fragmento maior do que dois hectares. Dos 144 mil hectares, cerca de 15 mil hectares estavam em áreas legalmente classificadas como floresta primária na Indonésia.</p>



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			<p><strong>Classe do MoEF</strong></p>
			</td>
			<td style="width:550px;height:39px;">
			<p><strong>Porcentagem de perda florestal primária de 2023 da UMD/GFW</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Floresta primária</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>9%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Floresta secundária</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>60%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Floresta plantada</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>1%</p>
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			<p>Não florestal</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>30%</p>
			</td>
		</tr>
	</tbody>
</table>



<p></p>



<style type="text/css"></style>



<p></p>



<p></p>

Explicação dos dados do Global Forest Watch sobre a perda de cobertura arbórea de 2023

<p>Los nuevos datos de pérdida de cobertura arbórea del laboratorio GLAD de la Universidad de Maryland (UMD) y disponibles en Global Forest Watch (GFW) muestran disminuciones en la pérdida de bosques primarios en algunos países, pero una tasa persistente en general en 2023. ¿Qué miden los datos, qué ha cambiado este año y cómo se comparan los datos a otras estimaciones oficiales de deforestación? Esto es lo que usted debe saber sobre los nuevos datos.&nbsp;</p>



<h2 class="wp-block-heading">Qué mide la pérdida de cobertura arbórea y cuál es su diferencia con la deforestación &nbsp;</h2>



<p>Los datos de pérdida de cobertura arbórea de UMD capturan la pérdida anual de todos los árboles de más de cinco metros de altura entre el 2001 y el 2023. Los datos de pérdida de cobertura arbórea incluyen la pérdida de árboles en bosques naturales, así como en plantaciones y cultivos arbóreos (aunque es importante recalcar que los datos de bosques primarios que utilizamos para filtrar la pérdida en los trópicos excluyen las plantaciones, los cultivos arbóreos y los bosques en regeneración). La pérdida de esta cobertura arbórea puede ser de origen humano o natural y puede ser permanente o temporal.&nbsp;&nbsp;</p>



<p>La deforestación se diferencia en que solo se refiere a un cambio de cobertura arbórea permanente de un bosque a otro uso de la tierra provocado por seres humanos (y no por causas naturales). Algunas formas de pérdida de cobertura arbórea, como la conversión de un bosque natural a tierras agrícolas, se consideran deforestación, mientras que otras formas de pérdida de cobertura arbórea, como la tala de madera en plantaciones forestales o las alteraciones naturales, no; <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">lea más sobre las diferencias aquí</a>. La pérdida de cobertura arbórea, ya sea deforestación o no, puede ser legal o ilegal.&nbsp;&nbsp;</p>



<p>En algunos casos, como en el <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Seguimiento de Objetivos de Deforestación y Restauración</a> de Global Forest Review, utilizamos un indicador que representa la deforestación. Ese indicador incluye las pérdidas derivadas de la agricultura a pequeña escala en los bosques primarios tropicales húmedos y toda la deforestación y urbanización impulsada por los productos básicos de consumo (dentro y fuera de los bosques primarios tropicales húmedos). Las pérdidas temporales, como aquellas provocadas por incendios o actividades forestales, no están incluidas. No utilizamos este indicador de deforestación en todos nuestros informes porque está basado en datos de resolución aproximada que están diseñados para limitar la inclusión de pérdidas temporales o pérdidas en bosques plantados solo a escala global. La deforestación sigue siendo difícil de rastrear con conjuntos de datos consistentes a nivel mundial y existen retrasos inherentes para confirmar si la pérdida detectada en las imágenes satelitales es temporal o permanente.&nbsp;</p>



<p><strong>Las mejoras en los datos a lo largo del tiempo generan algunas incoherencias en las series temporales de datos</strong>&nbsp;</p>



<p>Los ajustes del algoritmo y la mejora de los datos satelitales han <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">mejorado el conjunto de datos de pérdida de cobertura arbórea a lo largo del tiempo</a>.&nbsp;&nbsp;</p>



<p>Este año, el algoritmo de detección de pérdidas utilizado por la Universidad de Maryland para crear los datos de pérdida de cobertura arbórea se complementó con información del <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">conjunto de datos de perturbaciones terrestres DIST-ALERT</a>. A veces los cambios de final de temporada de un año se omiten en los datos de pérdida de cobertura arbórea (debido a datos satelitales insuficientes y la cobertura de nubes) y se atribuyen en vez al año siguiente. DIST-ALERT mostró áreas de pérdida de cobertura arbórea que el algoritmo anual no detectó. Estas áreas se revisaron e incluyeron manualmente para el 2023.&nbsp;&nbsp;</p>



<p>El uso de DIST-ALERT ayudó especialmente a detectar pérdidas por incendios forestales de final de temporada en los bosques boreales y la tala de final de temporada en los trópicos que de otro modo habrían aparecido en los datos del 2024. Los datos de DIST-ALERT también ayudaron a detectar pérdida de cobertura arbórea debido a inundaciones y minería. El uso de estas alertas resultó en un aumento del 3,7 % en la pérdida de cobertura arbórea global detectada. La mayoría de los cambios afectaron a las regiones boreales, mientras que para el resto del mundo, el aumento fue de solo el 1,7 %. Es importante tener en cuenta que, dado que la mayoría de las áreas añadidas se habrían detectado de otro modo en 2024, no creemos que este cambio en el enfoque de mapeo para el 2023 tenga ningún impacto en las tendencias a largo plazo de los datos.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>



<p>Los cambios adicionales a la serie temporal incluyen ajustes al algoritmo para los años 2011-2014 y para los años del 2015 en adelante y la incorporación de datos del Landsat 8 a partir del 2013. Estos cambios facilitan la detección de cambios a menor escala, como pérdidas por incendios, tala selectiva y agricultura migratoria. Las variaciones en la disponibilidad de imágenes satelitales también implican inconsistencias en la calidad y el número de imágenes disponibles para capturar la perdida cada año.&nbsp;</p>



<p>Para abordar estas inconsistencias, nosotros:&nbsp;&nbsp;</p>



<ul>
<li>Centramos nuestros análisis en tendencias después del 2015.&nbsp;&nbsp;</li>



<li>Evaluamos el promedio móvil de tres años para interpretar las tendencias a más largo plazo.&nbsp;&nbsp;</li>



<li>Ignoramos el aumento de las pérdidas después de 2012 en lugares donde predomina la agricultura a pequeña escala, como África Central.&nbsp;&nbsp;</li>
</ul>



<p><strong>¿Qué captura la pérdida de cobertura arbórea por incendios?</strong>&nbsp;</p>



<p>Los datos de pérdida de cobertura arbórea a debida a incendios de la UMD nos permiten distinguir la pérdida provocada por incendios de otras pérdidas de cobertura arbórea. Estos datos atribuyen la probabilidad de pérdida debida al fuego de cada píxel de pérdida de cobertura arbórea de 30 metros. Usando estos datos, podemos distinguirla pérdida debida al fuego de la pérdida debida a otros factores, como la agricultura o la explotación forestal. Los datos sobre la pérdida de cobertura arbórea debida a los incendios incluyen los incendios naturales y aquellos provocados por seres humanos, que generan una pérdida directa del dosel de árbol. Los datos capturan los incendios forestales, los incendios utilizados para despejar el terreno para otro uso y los incendios provocados intencionadamente que dan lugar a la pérdida de cobertura arbórea (incluidos los incendios fugitivos iniciados por los seres humanos con fines relacionados con la agricultura, la caza, las actividades recreativas o los incendios provocados). Sin embargo, no se incluyen los casos en los que los árboles se talan y posteriormente se queman, ya que en estos casos el impulsor inicial de la pérdida es la eliminación mecánica.&nbsp;&nbsp;</p>



<p>Estos datos nos permiten comprender mejor el impacto del fuego en la pérdida de cobertura arbórea en el 2023. Por ejemplo, los incendios desempeñaron un papel especialmente importante en Bolivia y Canadá; <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">obtenga más información sobre nuestros hallazgos a partir de los datos de 2023 aquí</a>.&nbsp;</p>



<p>Aunque los incendios no suelen provocar un cambio permanente en el uso del suelo, siguen siendo una fuente importante de emisiones de carbono y pueden <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">dar lugar a bucles de retroalimentación</a> de aumento de emisiones, temperaturas más altas, bosques más secos y más incendios.&nbsp;&nbsp;</p>



<p><strong>¿Por qué nos centramos en los trópicos?</strong> &nbsp;</p>



<p>En nuestro <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">análisis de</a> los datos de la Universidad de Maryland (UMD), nos centramos principalmente en la pérdida de cobertura arbórea en los bosques tropicales primarios, ya que en ellos se registra <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">la mayor parte</a> (más del 96%) de la deforestación mundial, y la pérdida en esas áreas tiene enormes repercusiones sobre la biodiversidad y el almacenamiento de carbono. Aunque estas pérdidas puedan revertirse eventualmente, los hábitats y las reservas de carbono <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">tardarán décadas</a> en recuperarse y podría producirse una pérdida permanente de biodiversidad. &nbsp;</p>



<p><strong>¿Cómo se compara la pérdida de cobertura arbórea con los datos oficiales de Brasil e Indonesia? </strong> &nbsp;</p>



<p><em>Brasil</em> &nbsp;</p>



<p>PRODES (el sistema oficial de monitoreo forestal para la Amazonia del Instituto Nacional De Investigación Espacial de Brasil (INPE)) y los datos de la Universidad de Maryland (UMD) tienen diferencias metodológicas y de definición que son importantes de entender cuando se comparan estas dos fuentes de datos. PRODES mide la deforestación total de más de 6.25 hectáreas y pérdidas de bosques antropogénicos por incendios, mientras que la Universidad de Maryland (UMD) registra la pérdida de más de 0.09 hectáreas de todos los árboles de más de cinco metros de altura. Ambas son medidas importantes que nos ayudan a entender cómo cambian los bosques, ya que la deforestación, los incendios forestales y las pequeñas alteraciones del dosel forestal pueden tener impactos en el clima, la biodiversidad y los servicios de los ecosistemas. &nbsp;</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/04/Picture1.png" alt="" class="wp-image-17345"/></figure>



<p>Ambos sistemas muestran una tendencia a la baja en pérdidas para 2023 en el bioma amazónico: una disminución del 22 % según PRODES y una disminución del 39 % según UMD (disminución del 42 % para pérdidas sin incendios), lo que confirma las noticias positivas para la Amazonia.&nbsp;</p>



<h4 class="wp-block-heading">Datos de PRODES vs. UMD en la Amazonia brasileña</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17690036?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><em>Indonesia</em></p>



<p>La superposición de los datos de pérdida de bosques primarios de UMD/GFW para el 2023 con el mapa oficial de cobertura terrestre de Indonesia del Ministerio de Medioambiente y Silvicultura (Ministry of Environment and Forestry, MoEF) muestra que el 70 % de la pérdida ocurrió dentro de las clases oficiales de cobertura arbórea de Indonesia, y el 30 % restante tuvo lugar en agricultura mixta de tierras secas, matorrales, arbustos pantanosos y otros tipos de cobertura de tierra. Aproximadamente 144 mil hectáreas de pérdida con un tamaño de parche superior a dos hectáreas se encontraron dentro de las clases oficiales de cubierta forestal de Indonesia. De estas 144 mil hectáreas, alrededor de 15 mil hectáreas se encontraron dentro de áreas legalmente clasificadas como bosques primarios en Indonesia.&nbsp;&nbsp;</p>



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			<p><strong>Clase según el MoEF</strong></p>
			</td>
			<td style="width:550px;height:39px;">
			<p><strong>Porcentaje de pérdida de bosques primarios de UMD/GFW en 2023</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Bosques primarios</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>9%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Bosques secundarios</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>60%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Bosques de plantaciones</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>1%</p>
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			<p>No forestal</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>30%</p>
			</td>
		</tr>
	</tbody>
</table>



<p></p>



<style type="text/css"></style>



<p></p>



<p></p>

Explicación de los datos de Global Forest Watch sobre la pérdida de cobertura arbórea en 2023  

<p>Les nouvelles données du laboratoire GLAD de l’Université du Maryland (UMD) sur la perte de la couverture arborée, disponibles sur Global Forest Watch (GFW), indiquent une réduction des pertes de forêts primaires dans certains pays. Le taux global reste cependant constant en 2023. Que mesurent les données, quelles sont les différences cette année et quelle comparaison peut être faite par rapport aux autres estimations officielles de déforestation ? Voici ce qu'il faut savoir sur les nouvelles données.&nbsp;</p>



<h2 class="wp-block-heading">Qu’est-ce que la perte de couverture arborée et en quoi cela diffère-t-elle de la déforestation?   </h2>



<p>Les données de l’UMD sur la perte de la couverture arborée compilent les pertes annuelles d’arbres mesurant plus de 5 mètres au cours des années civiles 2001 à 2023. Ces données comprennent la perte d’arbres dans les forêts naturelles ainsi que dans les plantations et les cultures arbustives (même s’il convient de préciser que les données sur les forêts primaires que nous utilisons pour filtrer les pertes dans les régions tropicales ne prennent pas en compte les plantations, les cultures arbustives et les forêts en cours de reboisement). La perte de cette couverture arborée peut être due à des causes humaines ou naturelles et peut être permanente ou temporaire.&nbsp;&nbsp;</p>



<p>La déforestation diffère en ce sens qu’elle ne fait référence qu’à un changement permanent, causé par l’homme, de la forêt à une autre utilisation des terres. Certaines formes de perte de couverture arborée, comme la conversion d’une forêt naturelle en terres agricoles, sont considérées comme une déforestation, tandis que d’autres formes de perte de couverture arborée, comme la récolte de bois dans les forêts de plantation ou les perturbations naturelles, ne le sont pas — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">pour en savoir plus sur les différences, cliquez ici</a>. La perte de couverture arborée, qu’elle soit due à la déforestation ou non, peut être légale ou illégale.&nbsp;&nbsp;</p>



<p>Dans certains cas, comme avec le <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Targets Tracker, l’outil de suivi de la déforestation et de la restauration</a> utilisé par Global Forest Review, nous utilisons un proxy pour estimer la déforestation. Ce proxy prend en compte les pertes dues à l’agriculture à petite échelle dans les forêts primaires tropicales humides et toute la déforestation liée aux commodités et à l’urbanisation (au sein des forêts primaires tropicales humides et en dehors). Les pertes temporaires, telles que résultant des incendies ou des activités forestières ne sont pas incluses. Nous n’utilisons pas le proxy de la déforestation dans tous nos rapports, car elle repose sur des données de résolution brute conçues pour limiter l’inclusion de pertes temporaires ou pertes dans les forêts plantées à l’échelle mondiale.&nbsp;&nbsp;</p>



<p>Il reste difficile de suivre la déforestation à l'aide d'ensembles de données cohérents à l'échelle mondiale et il y a des retards inhérents à la confirmation du caractère temporaire ou permanent des pertes détectées par l'imagerie satellitaire.&nbsp;</p>



<h2 class="wp-block-heading">L’amélioration des données au fil du temps entraîne des incohérences dans les séries de données chronologiques&nbsp;</h2>



<p>Les ajustements des algorithmes et de meilleures données satellitaires ont <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">amélioré les données de perte de la couverture arborée au cours du temps</a>.&nbsp;&nbsp;</p>



<p>Cette année, à l’algorithme de détection des pertes utilisé par l’Université du Maryland pour créer l’ensemble de données sur la perte de couverture arborée s’est ajouté <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">l’ensemble de données sur les perturbations de terres DIST-ALERT</a>. Les changements observés en fin de saison ne figurent parfois pas dans les données de perte de couverture arborée en raison d’un manque de données satellitaires ou de la couverture nuageuse. Ils sont alors attribués à l’année suivante. Les données DIST-ALERT ont montré des zones de perte de couverture arborée qui, à l’origine, n’avaient pas été relevées dans les données de perte de couverture arborée. Ces zones ont été étudiées et incluses manuellement dans les estimations de 2023.&nbsp;&nbsp;</p>



<p>L’utilisation des données DIST-ALERT a en particulier permis de détecter les pertes survenues en fin de saison dues aux feux de forêts boréales et au déboisement dans les régions tropicales, pertes qui auraient autrement été comptabilisées dans les données 2024. Les données DIST-ALERT ont également contribué à détecter certaines pertes de couverture arborée dues aux inondations et à l’exploitation minière. Grâce aux alertes, il a été possible d’améliorer de 3,7 % la détection des pertes de couverture arborée à l’échelle mondiale. La plupart des changements ont affecté les régions boréales, l’augmentation n’ayant été que de 1,7 % pour le reste de la planète. Il est important de noter que la plupart des zones ajoutées auraient été détectées en 2024, nous ne pensons pas que ce changement dans l'approche cartographique pour 2023 aura un impact sur les tendances à long terme des données.&nbsp;</p>



<p>Les modifications supplémentaires apportées à la série temporelle incluent des ajustements de l’algorithme pour les années 2011 à 2014 et 2015 et suivantes et l’incorporation des données Landsat 8 à partir de 2013. Ces modifications permettent de détecter plus facilement les changements à petite échelle, comme les pertes dues aux incendies, à la coupe sélective et à l’agriculture itinérante. Les variations dans la disponibilité des images satellites signifient également qu'il y a des incohérences dans la qualité et le nombre d'images disponibles pour capturer les données chaque année.&nbsp;&nbsp;</p>



<p>Pour gérer ces incohérences, nous :&nbsp;&nbsp;</p>



<ul>
<li>Concentrons nos analyses sur les tendances après 2015.&nbsp;&nbsp;</li>



<li>Évaluons la moyenne mobile sur trois ans pour interpréter les tendances à long terme.&nbsp;&nbsp;</li>



<li>Ne tenons pas compte de l’augmentation des pertes après 2012 dans les régions dominées par l’agriculture à petite échelle, comme l’Afrique centrale.&nbsp;&nbsp;</li>
</ul>



<h2 class="wp-block-heading">Qu’indique la perte de couverture arborée due aux incendies? </h2>



<p>En attribuant la probabilité de perte due aux incendies à chaque pixel de 30 mètres de perte de couverture arborée, les données de l’UMD sur la perte de couverture arborée nous permettent de distinguer les pertes dues aux incendies des autres pertes, séparant ainsi les données concernant les pertes dues aux incendies et celles dues à d’autres facteurs comme l’agriculture et l’exploitation forestière. Les données de perte de couverture arborée due aux incendies incluent les feux d’origine naturelle ou humaine entraînant une perte directe du couvert arboré. Les données capturent les incendies, les feux utilisés pour défricher la terre à d’autres fins et les incendies intentionnels entraînant une perte de la couverture arborée (ce qui inclut les feux allumés par des humains à des fins agricoles et de chasse, de loisirs ou les incendies criminels qui se sont propagés). Cependant, les cas dans lesquels les arbres sont tombés et ont ensuite été brûlés ne sont pas inclus étant donné que le facteur initial de la perte implique un enlèvement mécanique.&nbsp;&nbsp;</p>



<p>Ces données nous permettent de mieux comprendre l’impact des incendies sur les données de perte de couverture arborée en 2023. Par exemple, les incendies ont joué un rôle particulièrement important en Bolivie et au Canada - <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">pour en savoir plus sur nos conclusions sur les données 2023, cliquez ici</a>.&nbsp;</p>



<p>Même si les incendies entraînent rarement des changements permanents dans l’occupation des sols, ils sont néanmoins une source importante d’émissions de CO2 susceptibles <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">d’entraîner des boucles de rétroaction</a> créant une augmentation des émissions, une hausse des températures, des forêts plus sèches et plus de feux.&nbsp;&nbsp;</p>



<h2 class="wp-block-heading">Pourquoi nous concentrons-nous sur les tropiques?  </h2>



<p>Dans notre <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">analyse des données de l’UMD</a>, nous nous concentrons principalement sur la perte du couvert arboré dans les forêts primaires tropicales. Cette focalisation est justifiée par le fait que les forêts tropicales représentent la <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">grande majorité</a> (plus de 96 %) des zones touchées par la déforestation dans le monde. La perte de ces forêts a un impact considérable sur la biodiversité et le stockage du carbone. Même si ces pertes sont finalement inversées, il faudra <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">des décennies</a> pour que ces habitats et ces stocks de carbone se reconstituent. De plus, une perte permanente de biodiversité est susceptible de se produire. &nbsp;</p>



<h2 class="wp-block-heading">Comment la perte du couvert arboré se compare-t-elle aux données officielles du Brésil et de l’Indonésie?  </h2>



<p><em>Brésil</em> &nbsp;</p>



<p>Il est essentiel de comprendre les différences méthodologiques et définitionnelles entre PRODES, le système officiel de surveillance des forêts amazoniennes de l'Institut national brésilien de recherche spatiale (INPE), et les données de l'UMD lors de la comparaison de ces deux sources d'information. PRODES mesure la déforestation par coupes rases et les pertes forestières anthropiques résultant d'incendies sur des surfaces de plus de 6,25 hectares. En revanche, l'UMD enregistre les pertes du couvert forestier de plus de 0,09 hectare, incluant tous les arbres de plus de cinq mètres de haut. Ces deux mesures sont importantes pour comprendre l'évolution des forêts, car la déforestation, les incendies de forêt et les perturbations mineures du couvert forestier peuvent tous avoir un impact sur le climat, la biodiversité et les services écosystémiques.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/04/FR-Tech-Blog-Graphic_UMDvsProdes-2023.png" alt="" class="wp-image-17409"/></figure>



<p>En 2023, les deux systèmes indiquent une tendance à la baisse pour les pertes : 22 % de réduction pour PRODES et 39 % pour l’UMD (42 % pour les pertes qui ne résultent pas d’incendies) dans le biome amazonien, confirmant les nouvelles positives concernant l’Amazonie.&nbsp;</p>



<h4 class="wp-block-heading">Données PRODES vs UMD dans l’Amazonie brésilienne</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17690123?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><em>Indonésie</em>&nbsp;</p>



<p>En superposant les données de perte de forêts primaires de l’UMD/GFW en 2023 à la carte officielle de la couverture terrestre en Indonésie, publiée par le ministère de l’Environnement et de la Foresterie (MoEF), on observe que 70 % des pertes sont survenues au sein des classes officielles de couverture forestière en Indonésie. Les 30 % restant concernent un mélange d’agriculture dans des zones arides, de zones arbustives, d’arbustes marécageux, et d’autres types de couverture terrestre. Environ 144 000 hectares de perte ont été trouvés à la fois dans les classes officielles de couverture forestière de l'Indonésie et avec une taille de parcelle supérieure à deux hectares. Sur ces 144 000 hectares, environ 15 000 étaient situés dans des zones légalement classées comme forêts primaires en Indonésie.&nbsp;&nbsp;</p>



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			<p><strong>Classe MoEF</strong></p>
			</td>
			<td style="width:550px;height:39px;">
			<p><strong>Pourcentage de perte de forêts primaires selon les données UMD/GFW 2023</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Forêt primaire</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>9%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Forêt secondaire</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>60%</p>
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			<p>Forêt de plantation</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>1%</p>
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			<p>Zones non forestières</p>
			</td>
			<td style="width:550px;height:39px;">
			<p>30%</p>
			</td>
		</tr>
	</tbody>
</table>



<p></p>



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<p></p>



<p></p>

Explication des données du Global Forest Watch sur la perte du couvert arboré en 2023  

New data shows persistent primary forest loss in 2023. What does the data measure and how does it compare to other official estimates of deforestation?

Global Forest Watch’s 2023 Tree Cover Loss Data Explained | GFW Blog

Indigenous Peoples &amp; Local Communities

Indigenous Peoples &amp; Local Communities | Global Forest Watch Blog

restoration

restoration | Global Forest Watch Blog

<p><em>A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our </em><strong><em>Voices of Global Forest Watch</em></strong><em> </em><strong><em>series</em></strong><em> will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.&nbsp;</em>&nbsp;</p>



<iframe loading="lazy" width="560" height="450" src="https://www.youtube.com/embed/qbCzN9S6iCQ?si=7bfjPsSX-rJt3mGf" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>



<p></p>



<p><strong>“We believe at Hen Mpoano that conservation without livelihood is a mere conversation.”</strong> – <em>Justice Camillus Mensah, Project Manager at Hen Mpoano.&nbsp;</em></p>



<p><a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Hen Mpoano</a>, a two-time recipient of GFW’s <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/" target="_blank" rel="noreferrer noopener">Small Grants Fund</a> (SGF), is a nonprofit organization based in Ghana working to protect and restore the Takoradi Forest District in southwestern Ghana, home to two important reserves: Cape Three Points Forest Reserve and Subri River Forest Reserve, which is the largest forest reserve in the country and is designated as a Global Significant Biodiversity Area.&nbsp;</p>



<p>“My passion for forests is from growing up along the coasts and being exposed to mangrove exploitation. But when I got to know the importance of mangroves, I decided to use technology that I love to protect mangroves and any other coastal resource, including the coastal terrestrial forests,” said Justice. “Forest is life. I keep telling the people that indeed when the last tree dies, the last man will also die.”&nbsp;</p>



<p>Justice was able to channel his passion into working with Hen Mpoano in 2013. Over the past decade, he has contributed immensely to ongoing campaigns for coastal ecosystem conservation in Ghana and other African countries.&nbsp;&nbsp;</p>



<p>During Hen Mpoano’s SGF projects, they worked to strengthen effective forest law compliance and enforcement, including pro-conservation action among forest fringing communities — all of which has been effective in reducing deforestation.&nbsp;&nbsp;</p>



<p>According to Justice, forest law enforcement in Ghana is almost impossible without community involvement.&nbsp;&nbsp;</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1365" src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg" alt="Community members training to use GFW tools" class="wp-image-17227" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-768x512.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-1536x1024.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



<p>“We use a community-based approach to forest monitoring and protection. Members from forest fringing communities have been resourced through hands-on training on Global Forest Watch data and tools, in combination with other tools, to collect data on a regular basis,” said Justice.&nbsp;&nbsp;</p>



<p>With smart phones and GPS devices, the community forest monitoring teams collect evidence of forest crimes such as GPS coordinates and geotagged pictures. This data is then used to identify hotspots of forest crimes and used to generate maps using <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> and other software. These maps are shared confidentially with the Forest Services Division to help trigger enforcement action and target limited resources to hotspots.&nbsp;&nbsp;</p>



<p>This work also led to an agreement for the Wildlife Division to provide armed staff support for enforcement in the two reserves. Through this joint effort, more than 70 culprits have been arrested for various infractions, particularly illegal mining.&nbsp;&nbsp;</p>



<p>Hen Mpoano now applies GFW tools and data to monitor mangrove and freshwater swamp in a project funded by the U.S. Forest Service — a landscape also undergoing mangrove restoration through <a href="https://www.africa.terramatch.org/" target="_blank" rel="noreferrer noopener">TerraFund for AFR100</a>. Hen Mpoano’s approach involves not only restoration and planting, but also strengthening conservation efforts in the area by engaging community members to monitor landscape changes using GFW. </p>



<p>Hen Mpoano has faced challenges, particularly with forest patrols where culprits have confronted community monitoring teams, forest officers and even Hen Mpoano staff. However, data and technology such as GFW has helped equip forest monitors with tools to continue their work.&nbsp;</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1365" src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg" alt="Community member using GFW tools" class="wp-image-17226" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-768x512.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-1536x1024.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



<p>“In my opinion, GFW has cemented the collaborative management approach by providing citizens the tools and skill to be a part of forest protection. The community members feel very much empowered,” said Justice. “If Ghana’s status as the leading country in rainforest loss can be reversed, my team has a great role to play in that process. We will not relent until we see positive change.”&nbsp;&nbsp;</p>



<p><a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener"><em>Learn more about Hen Mpoano here.</em></a>&nbsp;</p>



<p><em>If you are interested in supporting Hen Mpoano to continue to expand their work to protect and restore forest landscapes, contact <a href="mailto:info@henmpoano.org" target="_blank" rel="noreferrer noopener">info@henmpoano.org</a>.</em>&nbsp;</p>



<hr> 

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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

</div> 

 

<hr>



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Voices of Global Forest Watch: Justice Camillus Mensah, Hen Mpoano 

<p><em>A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our </em><strong><em>Voices of Global Forest Watch</em></strong><em> </em><strong><em>series</em></strong><em> will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.&nbsp;</em>&nbsp;</p>



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<p><strong>“We believe at Hen Mpoano that conservation without livelihood is a mere conversation.”</strong> - <em>Justice Camillus Mensah, Project Manager at Hen Mpoano.&nbsp;</em></p>



<p><a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Hen Mpoano</a>, a two-time recipient of GFW’s <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/" target="_blank" rel="noreferrer noopener">Small Grants Fund</a> (SGF), is a nonprofit organization based in Ghana working to protect and restore the Takoradi Forest District in southwestern Ghana, home to two important reserves: Cape Three Points Forest Reserve and Subri River Forest Reserve, which is the largest forest reserve in the country and is designated as a Global Significant Biodiversity Area.&nbsp;</p>



<p>“My passion for forests is from growing up along the coasts and being exposed to mangrove exploitation. But when I got to know the importance of mangroves, I decided to use technology that I love to protect mangroves and any other coastal resource, including the coastal terrestrial forests,” said Justice. “Forest is life. I keep telling the people that indeed when the last tree dies, the last man will also die.”&nbsp;</p>



<p>Justice was able to channel his passion into working with Hen Mpoano in 2013. Over the past decade, he has contributed immensely to ongoing campaigns for coastal ecosystem conservation in Ghana and other African countries.&nbsp;&nbsp;</p>



<p>During Hen Mpoano’s SGF projects, they worked to strengthen effective forest law compliance and enforcement, including pro-conservation action among forest fringing communities — all of which has been effective in reducing deforestation.&nbsp;&nbsp;</p>



<p>According to Justice, forest law enforcement in Ghana is almost impossible without community involvement.&nbsp;&nbsp;</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg" alt="Community members training to use GFW tools" class="wp-image-17227"/><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



<p>“We use a community-based approach to forest monitoring and protection. Members from forest fringing communities have been resourced through hands-on training on Global Forest Watch data and tools, in combination with other tools, to collect data on a regular basis,” said Justice.&nbsp;&nbsp;</p>



<p>With smart phones and GPS devices, the community forest monitoring teams collect evidence of forest crimes such as GPS coordinates and geotagged pictures. This data is then used to identify hotspots of forest crimes and used to generate maps using <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> and other software. These maps are shared confidentially with the Forest Services Division to help trigger enforcement action and target limited resources to hotspots.&nbsp;&nbsp;</p>



<p>This work also led to an agreement for the Wildlife Division to provide armed staff support for enforcement in the two reserves. Through this joint effort, more than 70 culprits have been arrested for various infractions, particularly illegal mining.&nbsp;&nbsp;</p>



<p>Hen Mpoano now applies GFW tools and data to monitor mangrove and freshwater swamp in a project funded by the U.S. Forest Service — a landscape also undergoing mangrove restoration through <a href="https://www.africa.terramatch.org/" target="_blank" rel="noreferrer noopener">TerraFund for AFR100</a>. Hen Mpoano’s approach involves not only restoration and planting, but also strengthening conservation efforts in the area by engaging community members to monitor landscape changes using GFW. </p>



<p>Hen Mpoano has faced challenges, particularly with forest patrols where culprits have confronted community monitoring teams, forest officers and even Hen Mpoano staff. However, data and technology such as GFW has helped equip forest monitors with tools to continue their work.&nbsp;</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg" alt="Community member using GFW tools" class="wp-image-17226"/><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



<p>“In my opinion, GFW has cemented the collaborative management approach by providing citizens the tools and skill to be a part of forest protection. The community members feel very much empowered,” said Justice. “If Ghana’s status as the leading country in rainforest loss can be reversed, my team has a great role to play in that process. We will not relent until we see positive change.”&nbsp;&nbsp;</p>



<p><a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener"><em>Learn more about Hen Mpoano here.</em></a>&nbsp;</p>



<p><em>If you are interested in supporting Hen Mpoano to continue to expand their work to protect and restore forest landscapes, contact <a href="mailto:info@henmpoano.org" target="_blank" rel="noreferrer noopener">info@henmpoano.org</a>.</em>&nbsp;</p>



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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

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As part of our Voices of Global Forest Watch series, watch and read about Justice Camillus Mensah's work with Ghana nonprofit Hen Mpoano.

Voices of Global Forest Watch: Justice Camillus Mensah, Hen Mpoano  | People | Global Forest Watch Blog

Ripe Cocoa pods from a cocoa farm in Ghana.

Cocoa

Cocoa | Global Forest Watch Blog

<p>Martina Schneider is a GIS Analyst with Global Forest Watch. She supports the GFW team in preparing data and developing content for the GFW platform and web application.</p>



<p>As a graduate student, Martina specialized in applying GIS in the fields of environmental science and natural resources management. Specifically, she studied wildfire ecology and environmental factors that contribute to risk of ignition. In addition, Martina worked at Ducks Unlimited, the world's leader in wetland and waterfowl conservation, where she was part of a team updating the National Wetlands Inventory in Minnesota.</p>



<p>Martina holds an M.S. in Geographic Information Systems from Eastern Michigan University and a B.S. in Geography from Texas A&amp;M University.</p>



<p>She lives in Baltimore, MD with her husband and dog.</p>

Martina Schneider

Caroline Winchester

Sofia Tenorio Fenton

<p>The global demand for chocolate has had a significant impact on West African forests. Approximately 70% of the global supply of cocoa originates from Ghana and Côte d’Ivoire, where production sites have replaced millions of hectares of forest. Between 2001 and 2015 alone, <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">cocoa was linked to</a> the loss of a third of Ghana’s forest area and a quarter of Côte d’Ivoire’s.</p>



<p>The <a href="https://www.worldcocoafoundation.org/initiative/cocoa-forests-initiative/" target="_blank" rel="noreferrer noopener">Cocoa & Forests Initiative (CFI)</a>, facilitated by World Cocoa Foundation (WCF) and the <a href="https://www.idhsustainabletrade.com/initiative/cocoa-and-forests/" target="_blank" rel="noreferrer noopener">Sustainable Trade Initiative (IDH)</a>, was established in 2017 in an effort to end cocoa-driven deforestation and restore forest areas. Companies that joined the CFI agreed to map their supply chains, align methodologies on deforestation risk assessment and contribute to transparent monitoring and reporting. To support these efforts, World Resources Institute (WRI) partnered with WCF and 19 major cocoa and chocolate companies to create two new resources:*</p>



<ul>
<li>The <strong>West Africa Cocoa dataset (WAC)</strong>, a database of mapped cocoa plot boundaries in the direct supply chains of participating companies that provides an extensive view of mapped cocoa production in Côte d’Ivoire and Ghana. The public version of the dataset is <a href="https://gfw.global/4bEEES2">available on Global Forest Watch (GFW)</a> as a cocoa plot heat map that further anonymizes data to protect the privacy of farmers and companies.</li>



<li>The <strong>Cocoa Deforestation Risk Assessment (Cocoa DRA)</strong>, a map that provides a standardized view of cocoa-driven deforestation risk across the cocoa growing region of these two countries. The <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> is available for download on GFW.</li>
</ul>



<p>These resources are the result of an unprecedented collaboration among companies to share highly sensitive cocoa plot data, and an extensive ethics and legal review to ensure the protection of farmers’ data throughout the process. For the first time, companies and other stakeholders in the cocoa sector have a shared view of priority areas in Ghana and Côte d’Ivoire for coordinated efforts toward a deforestation-free cocoa sector.</p>



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<aside id="textbox"><em><b>From Palm Oil to Cocoa</b><p></p>
The development of the WAC and the Cocoa DRA builds on the success of similar products developed to support the palm oil sector: the <a href="https://www.globalforestwatch.org/blog/commodities/improved-universal-mill-list-expands-partnerships-improves-transparency-in-palm-oil-industry/" target="_blank" rel="noopener noreferrer">Universal Mill List (UML)</a>, a harmonized database of palm oil mill locations, and the <a href="https://www.wri.org/research/palm-risk-assessment-methodology-prioritizing-areas-landscapes-and-mills" target="_blank" rel="noopener noreferrer">Palm Risk Tool</a>, a method of identifying deforestation risks in their vicinity. However, differing supply chain dynamics between palm oil and cocoa were considered, such as:<li>
The locations of palm oil mills in the UML are represented as points while the cocoa plots in the WAC are represented by polygons. Further, the UML maps industrial scale infrastructure, whereas the WAC maps smallholder production. This raised additional privacy concerns, and steps were taken to avoid violating farmer privacy and safeguarding farmer livelihoods.<li>
The Palm Risk Tool analyzes deforestation risk by creating a buffer around each mill point to estimate a potential sourcing area. Oil palm fruit must be delivered to a mill quickly or the quality of the oil will decrease. This places a geographic constraint on how far oil palm fruit can travel to a mill, and stakeholders in the sector rely on a standardized sourcing radius of 50-km for their risk assessments. Cocoa does not have a geographic constraint nor an agreed upon estimation of a sourcing area, meaning that cocoa collected at a central point of purchase could have been grown anywhere in these two countries.<li>
The Palm Risk Tool was developed to assess deforestation risks in the vicinity of a specific type of infrastructure: palm oil mills. The Cocoa DRA was developed to be more flexible, enabling analysis of multiple areas of interest such as the plots in the WAC, cooperatives or other types of collection points and/or warehouses.<ul>

</em> </aside>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1175" height="783" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg" alt="Cocoa pod" class="wp-image-16723" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg 1175w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod-1024x682.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod-768x512.jpg 768w" sizes="(max-width: 1175px) 100vw, 1175px" /><figcaption class="wp-element-caption">Cocoa pod. Credit: Caroline Winchester, WRI.</figcaption></figure>



<h2 class="wp-block-heading"><strong>What is the West Africa</strong> <strong>Cocoa dataset?</strong></h2>



<p>The West Africa Cocoa dataset (WAC) aggregates data from 2021 on cocoa plot locations (a cocoa plot is a subset of a cocoa farm, and a single farm may be represented by multiple plots) in the direct supply chains of 19 cocoa and chocolate companies operating in Ghana and Côte d’Ivoire. Project participants developed and agreed on a data collation protocol and data sharing agreement that outlined submission requirements and the procedure for cleaning and aggregating individual company data sets. To be included in the WAC, each plot had to be current, actively growing cocoa and occupying a single parcel of land. WRI then reviewed, cleaned and aggregated the data.</p>



<p>While the cocoa plot polygon data is considered sensitive and will not be available publicly (see more below), we visualized the data as a heat map, <a href="https://gfw.global/4bEEES2">available on GFW</a>, which avoids disclosing the precise boundaries of the plots in the WAC while giving users access to information on the approximate location of cocoa plots. Additionally, we distributed the plot data to impartial research institutions for use as training data to create derivative products; specifically, public cocoa production extent maps.</p>



<p>These strategies make the data publicly available to support companies and other users in more robust cocoa mapping and monitoring while safeguarding the privacy of individual farmers.</p>



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<aside id="textbox"><em><b>Protecting the Privacy of Cocoa Farmers and Companies</b><p></p>

The raw data used to create the WAC included sensitive supply chain information from both companies and farmers. Cocoa is primarily grown by smallholder farmers who live near the cocoa plots they manage. A public release of this raw data would show the boundaries of specific cocoa plots and could allow individual farmers and their homes to be identified. Further, since there are over 2 million farmers across West Africa, it is difficult to know whether they are all fully aware of how data pertaining to them is being used. <br><br>
To protect farmer and company privacy, the WAC data collection protocol and data sharing agreement were informed by an ethics review to ensure that potentially sensitive information was respected and handled properly, and by a legal review to ensure the data privacy laws of Ghana and Côte d’Ivoire were followed. Based off these reviews, and in order to protect farmer privacy, no personal data was included in the WAC or shared with WRI, and a secure method of transferring data among all parties was established. With farmer privacy in mind, the parties involved also decided that precise locations of cocoa plot boundaries would not be made publicly available.

</em> </aside>



<h2 class="wp-block-heading">What is the Cocoa Deforestation Risk Assessment?</h2>



<p>The Cocoa Deforestation Risk Assessment (Cocoa DRA) is a map that assesses the risk of future deforestation due to new or expanding cocoa production. The Cocoa DRA aligns various methods for assessing risk to provide a standardized view that companies and other stakeholders can use to make decisions around reducing deforestation. It can be used to proactively and efficiently identify deforestation risk across a supply chain that includes tens of thousands of geographically dispersed producers. Once risk has been identified, Cocoa DRA users can implement interventions for promoting sustainable production, protecting remaining forest cover and improving farmer livelihoods.</p>



<p>To create the map, we analyzed spatial variables that influence cocoa-linked deforestation including recent forest loss, proximity to cocoa, accessibility and suitability for cocoa production. The present-day extent of natural forest was approximated using the University of Maryland’s <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree canopy cover</a> data set and removing cocoa plots mapped in the WAC, <a href="https://essd.copernicus.org/articles/13/1211/2021/" target="_blank" rel="noreferrer noopener">other data sets</a> that <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">map planted trees</a> and historic tree cover loss.</p>



<p>Primary and intact forests in West Africa are limited, so to ensure the Cocoa DRA comprehensively assesses where future cocoa-linked deforestation is likely to occur in <em>all</em> natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.</p>



<p>The Cocoa DRA translates risk into five priority rankings at a 1-km resolution (high, mid-high, medium, mid-low and low priority) indicating the likelihood of future deforestation associated with cocoa production. Cocoa DRA users can assess risk at different scales, including groups of farms, supply sheds around cocoa bean collection points and other types of landscapes.</p>



<p>Users can download the <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> on GFW.</p>



<h2 class="wp-block-heading">What have we learned from the WAC and Cocoa DRA?</h2>



<p>Across Ghana and Côte d’Ivoire, the WAC maps 840,000 unique cocoa plots in 2021. Though cocoa plots tend to be small (most plots were between 1 to 2 hectares), the plots included in the WAC cover approximately 1.5 million hectares of planted cocoa area.</p>



<p>Cocoa plot data is most comprehensive in western Côte d’Ivoire as well as near the shared border between Côte d’Ivoire and Ghana. The forests in these areas have been <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">particularly impacted by cocoa production</a>. We found that the Cocoa DRA showed that these same regions had a significant amount of high priority areas (areas with higher likelihood of future cocoa-related deforestation). This is most evident in Côte d’Ivoire near the border of Liberia, while in western Ghana the landscape is more mixed.</p>



<figure class="wp-block-image size-full is-resized"><img decoding="async" loading="lazy" width="1430" height="868" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png" alt="Cocoa deforestation risk assessment priority map for 2023" class="wp-image-16731" style="aspect-ratio:1.6474654377880185;width:840px;height:auto" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png 1430w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-1024x622.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-768x466.png 768w" sizes="(max-width: 1430px) 100vw, 1430px" /></figure>



<p>While creating the Cocoa DRA, we found that proximity to recent loss was an important predictor of future deforestation risk. The influence of this variable is most evident in several high-interest protected areas where, presumably due to effective legal protection, forest loss has been prevented in their boundaries. Because these forests have experienced less loss compared to the surrounding landscape, this resulted in a low priority ranking for the areas.</p>



<p>A good example of this is Taï National Park in western Côte d’Ivoire, which contains the only remaining intact forest within the study area. This is notable because the park is located within a region of Côte d’Ivoire where forests have been significantly impacted by cocoa production. A similar example is Bia National Park in western Ghana, which contains primary forest that has experienced low rates of forest loss compared to the surrounding landscape.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1448" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png" alt="Cocoa deforestation risk assessment priority ranking map (2023) showing protected areas" class="wp-image-16750" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-1024x724.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-768x543.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-1536x1086.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p>That being said, additional data on legal status and conservation value provides important contextual information and we recommend users consider this data when using the Cocoa DRA for prioritizing engagement activities and supply chain interventions. &nbsp;</p>



<h2 class="wp-block-heading">Driving collective action: How companies and others can use the WAC and Cocoa DRA</h2>



<p>Used together, the WAC and the Cocoa DRA can inform decision making and coordinated action to reduce cocoa-driven deforestation. These resources also have utility beyond the scope of the CFI. For example, the public WAC heat map of cocoa plots may be used alongside near-real-time alerts to identify deforestation in cocoa production landscapes, or as a resource to collectively assess the industry’s progress towards ending deforestation. Use of the WAC as training data to develop public cocoa extent maps will also shed light on production across West Africa. Such accessible and holistic extent data is essential for stakeholders striving to tackle cocoa-driven deforestation.</p>



<p>For companies and farmers seeking to comply with the EUDR, the WAC and Cocoa DRA can also be valuable tools. The risk map can enable companies and other stakeholders to identify high-priority landscapes and prevent deforestation before it happens. To achieve this, engagement with farmers in high priority areas will be needed, enabling them to protect remaining forests while safeguarding their livelihoods. The risk map and WAC can also help companies determine where to prioritize further cocoa plot data collection efforts. High priority areas where the WAC shows a lack of mapped cocoa plots is a good indication of where such efforts are needed for enabling compliance with the EUDR’s geolocation requirement. This data will also be key for enabling inclusion of smallholder farmers in the EU market.</p>



<p>As public resources that provide stakeholders with a shared understanding of where it’s most urgent to address deforestation while respecting farmer privacy, the WAC and Cocoa DRA can help realize a more sustainable cocoa sector. Through this collective knowledge, stakeholders can more easily form collaborative approaches to eliminate cocoa-driven deforestation. Furthermore, through ongoing updates based on best available data, both resources can remain a public good and contribute to transparent monitoring of the cocoa sector.</p>



<small><em>* Development of these products was also informed by preliminary research carried out by Climate Focus on other methods of assessing deforestation risk associated cocoa.</small></em><br><br>



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Ending Deforestation from Cocoa in West Africa with New Data-Driven Resources

<p>The global demand for chocolate has had a significant impact on West African forests. Approximately 70% of the global supply of cocoa originates from Ghana and Côte d’Ivoire, where production sites have replaced millions of hectares of forest. Between 2001 and 2015 alone, <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">cocoa was linked to</a> the loss of a third of Ghana’s forest area and a quarter of Côte d’Ivoire’s.</p>



<p>The <a href="https://www.worldcocoafoundation.org/initiative/cocoa-forests-initiative/" target="_blank" rel="noreferrer noopener">Cocoa &amp; Forests Initiative (CFI)</a>, facilitated by World Cocoa Foundation (WCF) and the <a href="https://www.idhsustainabletrade.com/initiative/cocoa-and-forests/" target="_blank" rel="noreferrer noopener">Sustainable Trade Initiative (IDH)</a>, was established in 2017 in an effort to end cocoa-driven deforestation and restore forest areas. Companies that joined the CFI agreed to map their supply chains, align methodologies on deforestation risk assessment and contribute to transparent monitoring and reporting. To support these efforts, World Resources Institute (WRI) partnered with WCF and 19 major cocoa and chocolate companies to create two new resources:*</p>



<ul>
<li>The <strong>West Africa Cocoa dataset (WAC)</strong>, a database of mapped cocoa plot boundaries in the direct supply chains of participating companies that provides an extensive view of mapped cocoa production in Côte d’Ivoire and Ghana. The public version of the dataset is <a href="https://gfw.global/4bEEES2">available on Global Forest Watch (GFW)</a> as a cocoa plot heat map that further anonymizes data to protect the privacy of farmers and companies.</li>



<li>The <strong>Cocoa Deforestation Risk Assessment (Cocoa DRA)</strong>, a map that provides a standardized view of cocoa-driven deforestation risk across the cocoa growing region of these two countries. The <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> is available for download on GFW.</li>
</ul>



<p>These resources are the result of an unprecedented collaboration among companies to share highly sensitive cocoa plot data, and an extensive ethics and legal review to ensure the protection of farmers’ data throughout the process. For the first time, companies and other stakeholders in the cocoa sector have a shared view of priority areas in Ghana and Côte d’Ivoire for coordinated efforts toward a deforestation-free cocoa sector.</p>



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<aside id="textbox"><em><b>From Palm Oil to Cocoa</b><p></p>
The development of the WAC and the Cocoa DRA builds on the success of similar products developed to support the palm oil sector: the <a href="https://www.globalforestwatch.org/blog/commodities/improved-universal-mill-list-expands-partnerships-improves-transparency-in-palm-oil-industry/" target="_blank" rel="noopener noreferrer">Universal Mill List (UML)</a>, a harmonized database of palm oil mill locations, and the <a href="https://www.wri.org/research/palm-risk-assessment-methodology-prioritizing-areas-landscapes-and-mills" target="_blank" rel="noopener noreferrer">Palm Risk Tool</a>, a method of identifying deforestation risks in their vicinity. However, differing supply chain dynamics between palm oil and cocoa were considered, such as:<li>
The locations of palm oil mills in the UML are represented as points while the cocoa plots in the WAC are represented by polygons. Further, the UML maps industrial scale infrastructure, whereas the WAC maps smallholder production. This raised additional privacy concerns, and steps were taken to avoid violating farmer privacy and safeguarding farmer livelihoods.<li>
The Palm Risk Tool analyzes deforestation risk by creating a buffer around each mill point to estimate a potential sourcing area. Oil palm fruit must be delivered to a mill quickly or the quality of the oil will decrease. This places a geographic constraint on how far oil palm fruit can travel to a mill, and stakeholders in the sector rely on a standardized sourcing radius of 50-km for their risk assessments. Cocoa does not have a geographic constraint nor an agreed upon estimation of a sourcing area, meaning that cocoa collected at a central point of purchase could have been grown anywhere in these two countries.<li>
The Palm Risk Tool was developed to assess deforestation risks in the vicinity of a specific type of infrastructure: palm oil mills. The Cocoa DRA was developed to be more flexible, enabling analysis of multiple areas of interest such as the plots in the WAC, cooperatives or other types of collection points and/or warehouses.<ul>

</em> </aside>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg" alt="Cocoa pod" class="wp-image-16723"/><figcaption class="wp-element-caption">Cocoa pod. Credit: Caroline Winchester, WRI.</figcaption></figure>



<h2 class="wp-block-heading"><strong>What is the West Africa</strong> <strong>Cocoa dataset?</strong></h2>



<p>The West Africa Cocoa dataset (WAC) aggregates data from 2021 on cocoa plot locations (a cocoa plot is a subset of a cocoa farm, and a single farm may be represented by multiple plots) in the direct supply chains of 19 cocoa and chocolate companies operating in Ghana and Côte d’Ivoire. Project participants developed and agreed on a data collation protocol and data sharing agreement that outlined submission requirements and the procedure for cleaning and aggregating individual company data sets. To be included in the WAC, each plot had to be current, actively growing cocoa and occupying a single parcel of land. WRI then reviewed, cleaned and aggregated the data.</p>



<p>While the cocoa plot polygon data is considered sensitive and will not be available publicly (see more below), we visualized the data as a heat map, <a href="https://gfw.global/4bEEES2">available on GFW</a>, which avoids disclosing the precise boundaries of the plots in the WAC while giving users access to information on the approximate location of cocoa plots. Additionally, we distributed the plot data to impartial research institutions for use as training data to create derivative products; specifically, public cocoa production extent maps.</p>



<p>These strategies make the data publicly available to support companies and other users in more robust cocoa mapping and monitoring while safeguarding the privacy of individual farmers.</p>



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<aside id="textbox"><em><b>Protecting the Privacy of Cocoa Farmers and Companies</b><p></p>

The raw data used to create the WAC included sensitive supply chain information from both companies and farmers. Cocoa is primarily grown by smallholder farmers who live near the cocoa plots they manage. A public release of this raw data would show the boundaries of specific cocoa plots and could allow individual farmers and their homes to be identified. Further, since there are over 2 million farmers across West Africa, it is difficult to know whether they are all fully aware of how data pertaining to them is being used. <br><br>
To protect farmer and company privacy, the WAC data collection protocol and data sharing agreement were informed by an ethics review to ensure that potentially sensitive information was respected and handled properly, and by a legal review to ensure the data privacy laws of Ghana and Côte d’Ivoire were followed. Based off these reviews, and in order to protect farmer privacy, no personal data was included in the WAC or shared with WRI, and a secure method of transferring data among all parties was established. With farmer privacy in mind, the parties involved also decided that precise locations of cocoa plot boundaries would not be made publicly available.

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<h2 class="wp-block-heading">What is the Cocoa Deforestation Risk Assessment?</h2>



<p>The Cocoa Deforestation Risk Assessment (Cocoa DRA) is a map that assesses the risk of future deforestation due to new or expanding cocoa production. The Cocoa DRA aligns various methods for assessing risk to provide a standardized view that companies and other stakeholders can use to make decisions around reducing deforestation. It can be used to proactively and efficiently identify deforestation risk across a supply chain that includes tens of thousands of geographically dispersed producers. Once risk has been identified, Cocoa DRA users can implement interventions for promoting sustainable production, protecting remaining forest cover and improving farmer livelihoods.</p>



<p>To create the map, we analyzed spatial variables that influence cocoa-linked deforestation including recent forest loss, proximity to cocoa, accessibility and suitability for cocoa production. The present-day extent of natural forest was approximated using the University of Maryland’s <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree canopy cover</a> data set and removing cocoa plots mapped in the WAC, <a href="https://essd.copernicus.org/articles/13/1211/2021/" target="_blank" rel="noreferrer noopener">other data sets</a> that <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">map planted trees</a> and historic tree cover loss.</p>



<p>Primary and intact forests in West Africa are limited, so to ensure the Cocoa DRA comprehensively assesses where future cocoa-linked deforestation is likely to occur in <em>all</em> natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.</p>



<p>The Cocoa DRA translates risk into five priority rankings at a 1-km resolution (high, mid-high, medium, mid-low and low priority) indicating the likelihood of future deforestation associated with cocoa production. Cocoa DRA users can assess risk at different scales, including groups of farms, supply sheds around cocoa bean collection points and other types of landscapes.</p>



<p>Users can download the <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> on GFW.</p>



<h2 class="wp-block-heading">What have we learned from the WAC and Cocoa DRA?</h2>



<p>Across Ghana and Côte d’Ivoire, the WAC maps 840,000 unique cocoa plots in 2021. Though cocoa plots tend to be small (most plots were between 1 to 2 hectares), the plots included in the WAC cover approximately 1.5 million hectares of planted cocoa area.</p>



<p>Cocoa plot data is most comprehensive in western Côte d’Ivoire as well as near the shared border between Côte d’Ivoire and Ghana. The forests in these areas have been <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">particularly impacted by cocoa production</a>. We found that the Cocoa DRA showed that these same regions had a significant amount of high priority areas (areas with higher likelihood of future cocoa-related deforestation). This is most evident in Côte d’Ivoire near the border of Liberia, while in western Ghana the landscape is more mixed.</p>



<figure class="wp-block-image size-full is-resized"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png" alt="Cocoa deforestation risk assessment priority map for 2023" class="wp-image-16731" style="aspect-ratio:1.6474654377880185;width:840px;height:auto"/></figure>



<p>While creating the Cocoa DRA, we found that proximity to recent loss was an important predictor of future deforestation risk. The influence of this variable is most evident in several high-interest protected areas where, presumably due to effective legal protection, forest loss has been prevented in their boundaries. Because these forests have experienced less loss compared to the surrounding landscape, this resulted in a low priority ranking for the areas.</p>



<p>A good example of this is Taï National Park in western Côte d’Ivoire, which contains the only remaining intact forest within the study area. This is notable because the park is located within a region of Côte d’Ivoire where forests have been significantly impacted by cocoa production. A similar example is Bia National Park in western Ghana, which contains primary forest that has experienced low rates of forest loss compared to the surrounding landscape.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png" alt="Cocoa deforestation risk assessment priority ranking map (2023) showing protected areas" class="wp-image-16750"/></figure>



<p>That being said, additional data on legal status and conservation value provides important contextual information and we recommend users consider this data when using the Cocoa DRA for prioritizing engagement activities and supply chain interventions. &nbsp;</p>



<h2 class="wp-block-heading">Driving collective action: How companies and others can use the WAC and Cocoa DRA</h2>



<p>Used together, the WAC and the Cocoa DRA can inform decision making and coordinated action to reduce cocoa-driven deforestation. These resources also have utility beyond the scope of the CFI. For example, the public WAC heat map of cocoa plots may be used alongside near-real-time alerts to identify deforestation in cocoa production landscapes, or as a resource to collectively assess the industry’s progress towards ending deforestation. Use of the WAC as training data to develop public cocoa extent maps will also shed light on production across West Africa. Such accessible and holistic extent data is essential for stakeholders striving to tackle cocoa-driven deforestation.</p>



<p>For companies and farmers seeking to comply with the EUDR, the WAC and Cocoa DRA can also be valuable tools. The risk map can enable companies and other stakeholders to identify high-priority landscapes and prevent deforestation before it happens. To achieve this, engagement with farmers in high priority areas will be needed, enabling them to protect remaining forests while safeguarding their livelihoods. The risk map and WAC can also help companies determine where to prioritize further cocoa plot data collection efforts. High priority areas where the WAC shows a lack of mapped cocoa plots is a good indication of where such efforts are needed for enabling compliance with the EUDR’s geolocation requirement. This data will also be key for enabling inclusion of smallholder farmers in the EU market.</p>



<p>As public resources that provide stakeholders with a shared understanding of where it’s most urgent to address deforestation while respecting farmer privacy, the WAC and Cocoa DRA can help realize a more sustainable cocoa sector. Through this collective knowledge, stakeholders can more easily form collaborative approaches to eliminate cocoa-driven deforestation. Furthermore, through ongoing updates based on best available data, both resources can remain a public good and contribute to transparent monitoring of the cocoa sector.</p>



<small><em>* Development of these products was also informed by preliminary research carried out by Climate Focus on other methods of assessing deforestation risk associated cocoa.</small></em><br><br>



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Two new data-driven resources provide a shared view of priority areas in West Africa and can help realize a a deforestation-free cocoa sector.

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