Coming Soon: FORMA 250 to Improve Detail, Frequency and Accuracy

Apr 06, 2016||3 minutes
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  • FORMA
  • Platform

By Brookie Guzder-Williams, Mikaela Weisse and Rachael Petersen Global Forest Watch seeks to provide the timeliest and most accurate data on forest change. With this in mind, one of the first data sets we published was FORMA (FORest Monitoring for Action), a monthly, 500-meter resolution alert product that flags areas of forest in the humid tropics where loss likely occurred. The goal was to provide users with information about potential clearings while they still had time to act. FORMA was a good first step, but not perfect. GFW users indicated that FORMA was often too coarse to provide meaningful information in the field. For example, park rangers in the thick of the Amazon can’t easily patrol a 500-meter by 500-meter area to find clearings. It also often reported loss where there was none, due to cloud cover and flooding. In order to address these issues and better serve our user community, we are retiring the original FORMA system and will soon be rolling out a new, improved version we’re calling “FORMA 250.”

FORMA 250 Offers Greater Detail, Frequency and Accuracy

The most obvious improvement FORMA 250 will have over the original FORMA is the change in resolution from 500 meters to 250 meters, which will detect tree cover loss in areas a quarter of the original size. Another improvement is the increased frequency of data updates. FORMA was restricted to monthly updates due to the availability of certain inputs like rainfall data, while the new system relies only on imagery from the MODIS satellite and daily fire data, and therefore can be updated every 16 days when new images become available. We’ve also made FORMA 250 more accurate by changing the algorithm to better detect loss in cloudy and flooded areas and using more up-to-date tree cover loss data to calibrate the algorithm. While FORMA 500 used data from 2000-2005, FORMA 250 will use the GLAD tree cover loss data which covers 2001-2014 and is updated annually. This means that as deforestation patterns change across different regions, the system will update in a timelier manner to recognize those changes and update accordingly. Finally, using GLAD annual tree cover loss data will expand the coverage of FORMA 250 to include humid and dry tropical forests. The old training data only covered the former.

FORMAFORMA 250
500 × 500 meter pixels250 × 250 meter pixels
Updates every monthUpdates every 16-days, with activity alerts available daily
Last update August 2015Coming summer 2016

When is FORMA 250 coming?

We’re currently testing FORMA 250 on several smaller areas across the world, and will be scaling up to the humid tropical forest biome in the next few months. We hope to launch FORMA 250 during summer 2016. In order to do this as quickly as possible, we are discontinuing FORMA in order to focus our resources.

What to Use in the Meantime

If you have been using FORMA in your work, there are several alternatives that can help fill the gap until FORMA 250 comes online:

  • Terra-i alerts are similar to FORMA, updated monthly at 250-meter resolution. Terra-i currently only covers Latin America, but will be expanding to the rest of the humid tropics in spring 2016.
  • GLAD alerts flag new loss at 30-meter resolution every time cloud-free Landsat images are available—as often as every 8 days. They currently cover Peru, the Republic of Congo, and Kalimantan (Indonesia).
  • Annual GLAD tree cover loss data are not updated as frequently as the monthly alert systems, but can still offer valuable insight into tree cover loss. The data shows tree cover loss from 2001 to 2014 at 30-meter resolution.

We are very excited about this upgrade and hope it will improve the state of near-real time forest monitoring. If you have any questions, you can reach out to us at gfw@wri.org or on our Discussion Forum.


BANNER PHOTO: Slash and burn agriculture in the Amazon Source: Matt Zimmerman (Flickr).

Category
  • Uncategorized
Topics
  • Alerts
  • FORMA
  • Platform

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