Forest change data, which shows how tree cover changes in the world’s forests, is the backbone of our work at Global Forest Watch, and we’re constantly striving to provide better, more up-to-date data for our users. But, as we add more data, it can become harder to understand how data sets differ, and know which ones to use for your purposes. Below, we answer some common user questions about the forest change data currently on GFW, and new data that’s coming soon.
Where does all this data come from?
All of the “forest change data” on Global Forest Watch comes from satellite images (other types of data, like “forest use” and “concessions” might be derived from other sources like government surveys, field measurements, and models). Satellite images are a good way to detect forest change because they cover large areas, are consistent across time and space, and can serve as a source of information about the past. Our data mainly comes from two NASA satellite-based sensors: Landsat and MODIS, because all of the images from these two systems are free. Scientists use a variety of techniques to detect forest changes from these images, including manually identifying the changes, statistical modelling, and “machine learning” (computers are shown samples images of forest change and develop pattern recognition). The techniques used in each of the data sets are too complicated to explore in depth here, but you can read about the methods of creating each data set in its information window on GFW (accessed by clicking the icon).
What is resolution?
Satellite images, like all pictures, are made up of pixels. The resolution of an image is how large of an area each pixel captures—for example, a 30-meter resolution image has pixels that each capture a 30 by 30-meter land area or roughly the size of a baseball diamond. Higher resolution data allows us to see changes at a much finer scale. Most of the data on GFW is at either 30-meter or 250-meter resolution, because those are the maximum resolutions of Landsat and MODIS images, respectively. Generally speaking, satellites with higher resolution images cover a smaller area of land each day. Landsat sensors currently take 8 days to cover the Earth, while MODIS sensors now pass every spot on Earth twice a day. Because of the differences in resolution and frequency, there is a tradeoff in our data sets. Many of our lower resolution data sets can be updated more often, but don’t have as much detail as some of the higher-resolution data sets.
Why are there so many data sets?
Global Forest Watch is committed to providing the best information available on forest change. That gets tricky, however, because different data sets have advantages for different areas or purposes. Some of the data sets are best used for measuring trends and areas of deforestation (e.g. GLAD tree cover loss, PRODES), while others are better suited for monitoring forest changes that are happening now (e.g. FORMA, Terra-i, GLAD alerts). Some of the data sets cover the entire world, or most of it, while others are calibrated for a particular area and may be more accurate. Some of the data on GFW is “official” data from countries (e.g. PRODES, MINAM) and thus analysis with that data will be more respected by those governments. The table below explains the best purpose for each data set.
What’s the difference between alert and non-alert data?
Many of the “alert” products like Terra-i and FORMA, are intended to help identify areas where recent clearing is likely to have occurred—calculated based on changes in the vegetation as seen from satellites. They are often coarse, but updated as frequently as daily. Their pixels of loss are more warnings than confirmation of wholesale clearing like the annual GLAD tree cover loss. Some of the upcoming data products, such as GLAD alerts (distinct from the annual global data) and FORMA 250 (currently, FORMA is at 500-m resolution), offer more reliable estimates of loss, but are still intended to flag new places of potential loss rather than the exact location of loss. Ideally, these alerts will help users prioritize areas for further investigation using verification methods, such as field visits, scrutinizing up-to-date satellite imagery, or flying drones to confirm recent changes.
Which data are right?
There may be cases where different data sets show different locations of forest change. This is inevitable—all data sets use different methods (some of them slight differences, some of them major), may measure different things, and all have some amount of inaccuracy. It is impossible to say which data is “right” and which data is “wrong” in every case. A conservative approach is to focus on areas where multiple alert products agree—these areas are very likely to have real forest changes. You can also always reach out to the GFW discussion forum to see if other users have suggestions for how to address your needs.
Here’s a quick guide to the various forest change data sets currently on Global Forest Watch, and those coming soon. If you have questions on these data sets, please reach out to our community through the GFW discussion forum.
|Name||Coverage||Resolution||Start Date||Updates||Source||Best Purpose|
|GLAD tree cover loss||Global||30 m||2001||Annually||Hansen/ UMD/ Google/ USGS/ NASA||Estimate the area of tree cover loss globally, regionally, nationally, and sub-nationally; explore trends over the last 14 years|
|FORMA||Humid Tropics||500 m||Jan 2006||Monthly||WRI/ CGD||Detect potential new places of large scale clearing in the humid tropics|
|Terra-i||Latin America||250 m||Jan 2004||Monthly||CIAT||Detect potential new places of tree cover loss in Latin America|
|SAD||Brazilian Amazon||250 m||Jan 2008||Monthly||Imazon||Detect potential new places of deforestation and degradation in the Brazilian Amazon|
|QUICC||All land below 37°N||5 km||Oct 2011||Quarterly||NASA||Detect potential new places of recent vegetation cover loss, including outside of the humid tropics|
|Name||Coverage||Resolution||Start Date||Updates||Source||Best Purpose|
|GLAD tree cover loss alerts||Peru, Republic of Congo, Kalimantan (Indonesia), soon humid tropics||30 m||Jan 2015||Weekly||Hansen/ UMD||Rapidly detect potential new places of tree cover loss at a high spatial resolution|
|FORMA 250||Humid Tropics||250 m||TBD||Every 16 days||WRI/ CGD||Rapidly detect potential new places of tree cover loss in the humid tropics|
|SAD+||Brazilian Amazon||30 m||TBD||Monthly||Imazon||Detect potential new places of deforestation and degradation in the Brazilian Amazon at a high spatial resolution|
|Terra-i||Humid Tropics||250 m||TBD||Monthly||CIAT||Detect potential new places of tree cover loss in the humid tropics|
|PRODES||Brazilian Amazon||30 m||2000||Annually||INPE (Brazilian government)||Estimate “official” deforestation area and rates in the Brazilian Amazon|
|Guyra||Gran Chaco (Paraguay, Bolivia, and Argentina)||30 m||2011||Monthly||Guyra Paraguay||Estimating deforestation area and rates, and detect new places of deforestation in the dry forests of the Gran Chaco at a high spatial resolution|
|MINAM||Peru||30 m||2001||Annually||MINAM (Peruvian government)||Estimate “official” deforestation area and rates in the humid tropical forests of Peru|