<p>Curious about what may be causing deforestation alerts? Check out the new <a href="https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6ImRyaXZlcnMtb2YtZGVmb3Jlc3RhdGlvbi1hbGVydCIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWUsImxheWVycyI6WyJkcml2ZXJzLW9mLWRlZm9yZXN0YXRpb24tYWxlcnQiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19">&#8220;Drivers of deforestation alerts&#8221; layer on the map</a>, which offers classification of drivers for three alert types (GLAD-L, GLAD-S2, RADD) at 10m resolution. Read our <a href="https://www.globalforestwatch.org/blog/data-and-tools/drivers-deforestation-alerts/">blog</a> to learn more.</p>


New drivers of deforestation alerts now available!

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

deforestation

deforestation | Global Forest Watch Blog

forest change

forest change | Global Forest Watch Blog

Sarah Carter

Nancy Harris

Crystal Davis

Jan Hooijmans

<p>Today, we have more data about forests than ever before, but we still can’t seem to agree on where, when and why forests are changing around the world. Even two prominent global data sources appear to disagree, at least on the surface. “Deforestation continues to decline” was <a href="https://openknowledge.fao.org/server/api/core/bitstreams/2dee6e93-1988-4659-aa89-30dd20b43b15/content/FRA-2025/key-findings.html" target="_blank" rel="noreferrer noopener">a headline</a> of the <a href="https://www.fao.org/forest-resources-assessment/en/" target="_blank" rel="noreferrer noopener">2025 Global Forest Resources Assessment (FRA)</a> published by the United Nations Food and Agriculture Organization (FAO). At the same time, Global Forest Watch (GFW) <a href="https://gfr.wri.org/latest-analysis-deforestation-trends" target="_blank" rel="noreferrer noopener">reported that</a> “the tropics lost a record-shattering 6.7 million hectares of primary rainforest in 2024 … nearly double that of 2023.”</p>



<p>What gives? Unfortunately, the answer is not simple. Both GFW and FRA provide valuable information on the state of global forests, but they differ substantially in terms of purpose, scope and approach. By answering questions on how and why these two global data sources differ, we can begin to see them as complementary rather than contradictory. Ultimately, our knowledge of global forests remains incomplete, and we need all the information we can get.</p>



<div class="textboxright"><strong>WHAT ARE GLOBAL FOREST WATCH (GFW) AND THE GLOBAL FOREST RESOURCES ASSESSMENT (FRA)?</strong> <br><br>GFW is an independent online forest monitoring platform launched in 2014 by a consortium of partners led by World Resources Institute. GFW seeks to empower forest managers, law enforcement officers and other forest stakeholders with free access to timely and high-resolution data about the current status of forests and recent forest change. GFW uses satellite technology to monitor tree cover loss worldwide with weekly and annual updates, as well as to provide longer term tree cover gain and net change data. Gain, net change and annual loss data are GFW’s core data sets and are provided by the University of Maryland. Other data sets on GFW provide useful contextual information related to forests and forest change.<br><br>

The FRA, which the FAO has published at 5- to 10-year intervals since 1946, seeks to provide a consistent and comprehensive approach to describing the world’s forests and how they are changing. Countries provide reports to FRA based on data from National Forest Inventories, satellite data and extrapolation based on past data. The FAO combines the data submitted by countries into global reports and fills in any gaps where data is not available from the countries. The most recent edition of the FRA in 2025 included national-level statistics on around 50 broad ecological and socio-economic variables reported by 236 countries and territories. </div>



<h2 class="wp-block-heading">What is considered a “forest”?</h2>



<p>GFW and FRA both talk about “forests,” but what are they actually measuring and monitoring? There are nearly&nbsp;<a href="https://www.nrs.fs.usda.gov/pubs/gtr/gtr_wo077/gtr_wo077_011.pdf" target="_blank" rel="noreferrer noopener">800 definitions</a>&nbsp;of “forest” in use, which only adds to the confusion.</p>



<p>Every five years, FRA publishes forest area globally and per country. These area measurements are based on a <a href="https://openknowledge.fao.org/server/api/core/bitstreams/a6e225da-4a31-4e06-818d-ca3aeadfd635/content" target="_blank" rel="noreferrer noopener">standardized&nbsp;definition</a>&nbsp;of “forest” adopted by the FAO in 2000, which takes into account biophysical and land use criteria. The biophysical criterion establishes minimum thresholds for the height, canopy cover and extent of trees. The land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or harvest. 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. At the same time, 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.</p>



<p>GFW provides data on “tree cover,” which is derived from satellite data and therefore cannot discern intended land use. Thus, GFW’s monitoring systems rely entirely on biophysical criteria — related to height, canopy cover and extent of trees. GFW does not adopt a specific definition of forest, but rather monitors all forms of tree cover including natural forests and tree plantations. Using GFW’s interactive online platform, users can filter this data based on their preferred definition of forest. Users can select by canopy cover percent, as well as tree height. Users can also view and derive statistics using other data sets that give additional context on the land use, such as <a href="https://gfw.global/3jyrlta">primary forests</a>, <a href="http://gfw.global/4fQfl03">natural forests</a> and <a href="https://gfw.global/2Qbv7fM">planted trees</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img decoding="async" loading="lazy" width="2048" height="1867" src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-2048x1867.png" alt="What is considered a "Forest" by GFW and FRA?" class="wp-image-16209" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-2048x1867.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-1024x934.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-768x700.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-1536x1401.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure></div>


<h2 class="wp-block-heading">How is “forest change” reported?</h2>



<p>Both FRA and GFW aggregate monitoring data into global statistics about forest change. Like the different ways they measure forests, GFW and FRA apply different lenses to monitor <em>changes to</em> forests, which subsequently influence how both forest expansion and deforestation are reported. GFW reports annual gross loss of tree cover, as well as gross gain for four 5-year periods and net change for a 20-year period. FRA reports on deforestation, net change and forest expansion as separate statistics, though many countries only report forest area, from which net change can be derived.</p>



<h3 class="wp-block-heading">Deforestation and tree cover loss</h3>



<p>Using its land use criterion, the FAO’s definition of deforestation requires permanent land use change to occur. Losses of tree cover considered to be temporary (e.g., forests that have been clear cut but will be left to regrow) are not counted as deforestation.</p>



<p>Conversely, GFW detects and reports all instances of tree cover loss, regardless of whether the loss will be temporary or permanent (e.g., clear cut harvest followed by agriculture). As with forests, GFW does not seek to define deforestation but rather allows users to filter tree cover loss data based on their own preferred definition.</p>



<p>Though GFW data includes all forms of tree cover loss, data on certain drivers of loss is available on GFW, such as <a href="https://gfw.global/3eO7ZjW">tree cover loss due to fires</a> and <a href="https://gfw.global/39qbPdC">tree cover loss by dominant driver,</a> the latter of which can be used to better align with the definition of deforestation used by FRA. GFW uses the tree cover loss by dominant driver data to create a “<a href="https://research.wri.org/gfr/data-methods#forestchange1" target="_blank" rel="noreferrer noopener">deforestation proxy</a>” consisting of tree cover loss due to shifting agricultural expansion into primary forests, commodity-driven deforestation and urbanization. GFW users can also filter the tree cover loss data by primary forest and natural forest to view and download statistics for just these areas.</p>



<p>FRA deforestation data is available for the 5- or 10-year reporting periods, while GFW tree cover loss data is available annually.</p>



<h3 class="wp-block-heading">Forest expansion, tree cover gain and reforestation</h3>



<p>As with the deforestation data, information on forest expansion is available for the FRA reporting periods of 5 or 10 years. In FRA, forest expansion can be due to afforestation or a natural expansion of forest area.</p>



<p>GFW data on tree cover gain is available for 5-year time periods between 2000-2020. Like with the tree cover data, GFW gain data includes regrowth in plantations following harvesting, which would not be included in forest expansion according to FRA.</p>



<p>FRA also provides reforestation data, which does not influence forest area or their net change figure. FRA defines reforestation as the planting of trees in areas already considered as forests by FAO’s land use criterion.</p>



<h3 class="wp-block-heading">Forest area and tree cover net change</h3>



<p>FRA and GFW report net change in forest and tree cover extent respectively. GFW net change data currently only compares 2000 to 2020. FRA data is available for their 5- or 10-year reporting periods.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img decoding="async" loading="lazy" width="1583" height="2048" src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-1583x2048.png" alt="" class="wp-image-19767" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-1583x2048.png 1583w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-791x1024.png 791w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-768x994.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-1187x1536.png 1187w" sizes="(max-width: 1583px) 100vw, 1583px" /></figure></div>


<h2 class="wp-block-heading">Where is the data from?</h2>



<p>FRA compiles official government statistics on forest-related information. This data is self-reported by countries and can come from different sources using a variety of methods, including national forest inventories (NFIs), academic studies, government registries or district forest offices. Remote sensing (RS) is becoming more frequently used as an input into some nationally reported data. Although FAO provides guidelines on what and how to report, in reality, many countries employ their own definitions and methods.</p>



<p>GFW features data that is based on satellite data, providing an independent, transparent and globally consistent record of Earth’s land surface and how it is changing. This record enables consistent comparisons across countries and over time.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img decoding="async" loading="lazy" width="2048" height="1612" src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-2048x1612.png" alt="Where is the data from for GFW and FRA?" class="wp-image-16211" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-2048x1612.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-1024x806.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-768x605.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-1536x1209.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure></div>


<h2 class="wp-block-heading">When is the data collected and reported?</h2>



<p>FRA reports are released every 5 years based on countries’ reported information. Some countries update their forest information systems frequently, while others update them only every 10-20 years, meaning some FRA reports are based on older data. When country reports are unavailable, the FAO fills in gaps using available literature and expert estimates. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b/meta" target="_blank" rel="noreferrer noopener">A study examining the 2020 FRA data sources</a> showed the most recent years in which RS and NFI data were collected and found that although many countries use data that are only a few years old, there are a number of countries using data over 30 years old.</p>



<p>GFW’s tree cover loss data set is updated each year with the previous year’s data. GFW’s net change and gain data covers the 20-year period from 2000 to 2020, with plans underway for more frequent updates.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img decoding="async" loading="lazy" width="2048" height="1188" src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-2048x1188.png" alt="" class="wp-image-19766" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-2048x1188.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-1024x594.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-768x445.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-1536x891.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure></div>


<h2 class="wp-block-heading">What is the unit of analysis?</h2>



<p>FRA collects statistics at the national scale, which are aggregated to provide insights about forest change dynamics regionally and globally.</p>



<p>Satellite-based data on GFW records changes in forest area by dividing Earth’s land surface into pixels. These pixels — which for most data on forest area and change are 30 m by 30 m in size — can then be aggregated to show changes over larger areas of interest. This modular format means that in addition to understanding forest dynamics at global, regional and national scales (as with the FRA), users can also monitor changes within custom areas of interest, such as a specific national park in their country, a provincial district, an Indigenous territory or even their own backyards.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img decoding="async" loading="lazy" width="1275" height="1223" src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/GFW_vs_FAO_graphics_final-05.png" alt="What is the unit of analysis for GFW vs FRA?" class="wp-image-16213" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/07/GFW_vs_FAO_graphics_final-05.png 1275w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/GFW_vs_FAO_graphics_final-05-1024x982.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/07/GFW_vs_FAO_graphics_final-05-768x737.png 768w" sizes="(max-width: 1275px) 100vw, 1275px" /></figure></div>


<h2 class="wp-block-heading">What does it all mean?</h2>



<p>How one defines forest, deforestation, reforestation and forest expansion is at the heart of the confusion and controversies surrounding these two data sources. Critics of FRA point out that country-reported data may be outdated, inconsistent, incomplete or based on non-transparent methods. In addition, FAO’s net forest change statistic (which is the only forest change variable presented for many countries) provides an overly optimistic view of global forest trends since by their definition, the loss of natural forests — rich in biodiversity and carbon — can be offset by expansion of tree plantation monocultures. The same applies for GFW’s net change information when viewed without the contextual data sets.</p>



<p>Meanwhile, critics of GFW claim that satellite-based monitoring has fundamental limitations on what can be observed from satellites, and that tree cover loss creates an overly pessimistic view since these data lump together permanent and temporary loss of tree cover within natural forests and tree plantations. In the case of FRA, temporary loss is not included in deforestation information.</p>



<p>Ultimately, neither GFW nor FRA is perfect or complete, but they both have important, and complementary, applications.</p>



<h2 class="wp-block-heading">When might I use GFW or FRA?</h2>



<p>Bottom-up approaches like FRA benefit from locally appropriate methodologies, customization of data and use of country definitions — although many countries follow the FAO’s standard definitions. FRA is more comprehensive in scope, ranging from biophysical to socio-economic and legislative variables. FRA’s data will be relevant for those that have a broad interest in the state of forests in a country and how they are being managed.</p>



<p>However, FRA’s data is only reported on a national level and cannot be used for assessing regional differences within countries. GFW’s spatially precise updates on tree cover loss allow users to track where changes in tree cover are happening on a sub-national level. GFW also offers more consistency in methods and definitions across geographies for making comparisons across countries.</p>



<p>GFW currently provides more frequent tree cover loss information (annually, and in near-real-time) than FRA does on deforestation, while FRA currently provides more up-to-date information on forest expansion than GFW does on tree cover gain — though GFW will eventually provide annual tree cover gain data. FRA’s figures also reflect tree planting initiatives that are too recent to be picked up by satellite data, although not all of these projects are successful.</p>



<p>In the end, the differences between GFW and FRA reveal strengths and shortcomings of both sources that make them appropriate for different applications, meaning that both are useful to understand what is happening with the world’s forests.</p>



<p></p>



<p><em>This blog was originally published in 2023. It was updated in October 2025 to reflect the latest updates to GFW and the FRA.</em> <em>Rachael Petersen and Octavia Payne contributed to a previous version of this blog.</em></p>



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Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics

<p>Today, we have more data about forests than ever before, but we still can’t seem to agree on where, when and why forests are changing around the world. Even two prominent global data sources appear to disagree, at least on the surface. “Deforestation continues to decline” was <a href="https://openknowledge.fao.org/server/api/core/bitstreams/2dee6e93-1988-4659-aa89-30dd20b43b15/content/FRA-2025/key-findings.html" target="_blank" rel="noreferrer noopener">a headline</a> of the <a href="https://www.fao.org/forest-resources-assessment/en/" target="_blank" rel="noreferrer noopener">2025 Global Forest Resources Assessment (FRA)</a> published by the United Nations Food and Agriculture Organization (FAO). At the same time, Global Forest Watch (GFW) <a href="https://gfr.wri.org/latest-analysis-deforestation-trends" target="_blank" rel="noreferrer noopener">reported that</a> “the tropics lost a record-shattering 6.7 million hectares of primary rainforest in 2024 ... nearly double that of 2023.”</p>



<p>What gives? Unfortunately, the answer is not simple. Both GFW and FRA provide valuable information on the state of global forests, but they differ substantially in terms of purpose, scope and approach. By answering questions on how and why these two global data sources differ, we can begin to see them as complementary rather than contradictory. Ultimately, our knowledge of global forests remains incomplete, and we need all the information we can get.</p>



<div class="textboxright"><strong>WHAT ARE GLOBAL FOREST WATCH (GFW) AND THE GLOBAL FOREST RESOURCES ASSESSMENT (FRA)?</strong> <br><br>GFW is an independent online forest monitoring platform launched in 2014 by a consortium of partners led by World Resources Institute. GFW seeks to empower forest managers, law enforcement officers and other forest stakeholders with free access to timely and high-resolution data about the current status of forests and recent forest change. GFW uses satellite technology to monitor tree cover loss worldwide with weekly and annual updates, as well as to provide longer term tree cover gain and net change data. Gain, net change and annual loss data are GFW’s core data sets and are provided by the University of Maryland. Other data sets on GFW provide useful contextual information related to forests and forest change.<br><br>

The FRA, which the FAO has published at 5- to 10-year intervals since 1946, seeks to provide a consistent and comprehensive approach to describing the world’s forests and how they are changing. Countries provide reports to FRA based on data from National Forest Inventories, satellite data and extrapolation based on past data. The FAO combines the data submitted by countries into global reports and fills in any gaps where data is not available from the countries. The most recent edition of the FRA in 2025 included national-level statistics on around 50 broad ecological and socio-economic variables reported by 236 countries and territories. </div>



<h2 class="wp-block-heading">What is considered a “forest”?</h2>



<p>GFW and FRA both talk about “forests,” but what are they actually measuring and monitoring? There are nearly&nbsp;<a href="https://www.nrs.fs.usda.gov/pubs/gtr/gtr_wo077/gtr_wo077_011.pdf" target="_blank" rel="noreferrer noopener">800 definitions</a>&nbsp;of “forest” in use, which only adds to the confusion.</p>



<p>Every five years, FRA publishes forest area globally and per country. These area measurements are based on a <a href="https://openknowledge.fao.org/server/api/core/bitstreams/a6e225da-4a31-4e06-818d-ca3aeadfd635/content" target="_blank" rel="noreferrer noopener">standardized&nbsp;definition</a>&nbsp;of “forest” adopted by the FAO in 2000, which takes into account biophysical and land use criteria. The biophysical criterion establishes minimum thresholds for the height, canopy cover and extent of trees. The land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or harvest. 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. At the same time, 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.</p>



<p>GFW provides data on “tree cover,” which is derived from satellite data and therefore cannot discern intended land use. Thus, GFW’s monitoring systems rely entirely on biophysical criteria — related to height, canopy cover and extent of trees. GFW does not adopt a specific definition of forest, but rather monitors all forms of tree cover including natural forests and tree plantations. Using GFW’s interactive online platform, users can filter this data based on their preferred definition of forest. Users can select by canopy cover percent, as well as tree height. Users can also view and derive statistics using other data sets that give additional context on the land use, such as <a href="https://gfw.global/3jyrlta">primary forests</a>, <a href="http://gfw.global/4fQfl03">natural forests</a> and <a href="https://gfw.global/2Qbv7fM">planted trees</a>.</p>



<figure class="wp-block-image aligncenter size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_What-is-considered-a-forest-1-2048x1867.png" alt="What is considered a &quot;Forest&quot; by GFW and FRA?" class="wp-image-16209"/></figure>



<h2 class="wp-block-heading">How is “forest change” reported?</h2>



<p>Both FRA and GFW aggregate monitoring data into global statistics about forest change. Like the different ways they measure forests, GFW and FRA apply different lenses to monitor <em>changes to</em> forests, which subsequently influence how both forest expansion and deforestation are reported. GFW reports annual gross loss of tree cover, as well as gross gain for four 5-year periods and net change for a 20-year period. FRA reports on deforestation, net change and forest expansion as separate statistics, though many countries only report forest area, from which net change can be derived.</p>



<h3 class="wp-block-heading">Deforestation and tree cover loss</h3>



<p>Using its land use criterion, the FAO’s definition of deforestation requires permanent land use change to occur. Losses of tree cover considered to be temporary (e.g., forests that have been clear cut but will be left to regrow) are not counted as deforestation.</p>



<p>Conversely, GFW detects and reports all instances of tree cover loss, regardless of whether the loss will be temporary or permanent (e.g., clear cut harvest followed by agriculture). As with forests, GFW does not seek to define deforestation but rather allows users to filter tree cover loss data based on their own preferred definition.</p>



<p>Though GFW data includes all forms of tree cover loss, data on certain drivers of loss is available on GFW, such as <a href="https://gfw.global/3eO7ZjW">tree cover loss due to fires</a> and <a href="https://gfw.global/39qbPdC">tree cover loss by dominant driver,</a> the latter of which can be used to better align with the definition of deforestation used by FRA. GFW uses the tree cover loss by dominant driver data to create a “<a href="https://research.wri.org/gfr/data-methods#forestchange1" target="_blank" rel="noreferrer noopener">deforestation proxy</a>” consisting of tree cover loss due to shifting agricultural expansion into primary forests, commodity-driven deforestation and urbanization. GFW users can also filter the tree cover loss data by primary forest and natural forest to view and download statistics for just these areas.</p>



<p>FRA deforestation data is available for the 5- or 10-year reporting periods, while GFW tree cover loss data is available annually.</p>



<h3 class="wp-block-heading">Forest expansion, tree cover gain and reforestation</h3>



<p>As with the deforestation data, information on forest expansion is available for the FRA reporting periods of 5 or 10 years. In FRA, forest expansion can be due to afforestation or a natural expansion of forest area.</p>



<p>GFW data on tree cover gain is available for 5-year time periods between 2000-2020. Like with the tree cover data, GFW gain data includes regrowth in plantations following harvesting, which would not be included in forest expansion according to FRA.</p>



<p>FRA also provides reforestation data, which does not influence forest area or their net change figure. FRA defines reforestation as the planting of trees in areas already considered as forests by FAO’s land use criterion.</p>



<h3 class="wp-block-heading">Forest area and tree cover net change</h3>



<p>FRA and GFW report net change in forest and tree cover extent respectively. GFW net change data currently only compares 2000 to 2020. FRA data is available for their 5- or 10-year reporting periods.</p>



<figure class="wp-block-image aligncenter size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_Forest_types_table-1-1583x2048.png" alt="" class="wp-image-19767"/></figure>



<h2 class="wp-block-heading">Where is the data from?</h2>



<p>FRA compiles official government statistics on forest-related information. This data is self-reported by countries and can come from different sources using a variety of methods, including national forest inventories (NFIs), academic studies, government registries or district forest offices. Remote sensing (RS) is becoming more frequently used as an input into some nationally reported data. Although FAO provides guidelines on what and how to report, in reality, many countries employ their own definitions and methods.</p>



<p>GFW features data that is based on satellite data, providing an independent, transparent and globally consistent record of Earth’s land surface and how it is changing. This record enables consistent comparisons across countries and over time.</p>



<figure class="wp-block-image aligncenter size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/23_GFW_vs_FAO_graphics_Where-is-data-from-1-2048x1612.png" alt="Where is the data from for GFW and FRA?" class="wp-image-16211"/></figure>



<h2 class="wp-block-heading">When is the data collected and reported?</h2>



<p>FRA reports are released every 5 years based on countries’ reported information. Some countries update their forest information systems frequently, while others update them only every 10-20 years, meaning some FRA reports are based on older data. When country reports are unavailable, the FAO fills in gaps using available literature and expert estimates. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b/meta" target="_blank" rel="noreferrer noopener">A study examining the 2020 FRA data sources</a> showed the most recent years in which RS and NFI data were collected and found that although many countries use data that are only a few years old, there are a number of countries using data over 30 years old.</p>



<p>GFW’s tree cover loss data set is updated each year with the previous year’s data. GFW’s net change and gain data covers the 20-year period from 2000 to 2020, with plans underway for more frequent updates.</p>



<figure class="wp-block-image aligncenter size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/25_GFW_vs_FAO_graphics_When-is-data-reported-2048x1188.png" alt="" class="wp-image-19766"/></figure>



<h2 class="wp-block-heading">What is the unit of analysis?</h2>



<p>FRA collects statistics at the national scale, which are aggregated to provide insights about forest change dynamics regionally and globally.</p>



<p>Satellite-based data on GFW records changes in forest area by dividing Earth’s land surface into pixels. These pixels — which for most data on forest area and change are 30 m by 30 m in size — can then be aggregated to show changes over larger areas of interest. This modular format means that in addition to understanding forest dynamics at global, regional and national scales (as with the FRA), users can also monitor changes within custom areas of interest, such as a specific national park in their country, a provincial district, an Indigenous territory or even their own backyards.</p>



<figure class="wp-block-image aligncenter size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/07/GFW_vs_FAO_graphics_final-05.png" alt="What is the unit of analysis for GFW vs FRA?" class="wp-image-16213"/></figure>



<h2 class="wp-block-heading">What does it all mean?</h2>



<p>How one defines forest, deforestation, reforestation and forest expansion is at the heart of the confusion and controversies surrounding these two data sources. Critics of FRA point out that country-reported data may be outdated, inconsistent, incomplete or based on non-transparent methods. In addition, FAO’s net forest change statistic (which is the only forest change variable presented for many countries) provides an overly optimistic view of global forest trends since by their definition, the loss of natural forests — rich in biodiversity and carbon — can be offset by expansion of tree plantation monocultures. The same applies for GFW’s net change information when viewed without the contextual data sets.</p>



<p>Meanwhile, critics of GFW claim that satellite-based monitoring has fundamental limitations on what can be observed from satellites, and that tree cover loss creates an overly pessimistic view since these data lump together permanent and temporary loss of tree cover within natural forests and tree plantations. In the case of FRA, temporary loss is not included in deforestation information.</p>



<p>Ultimately, neither GFW nor FRA is perfect or complete, but they both have important, and complementary, applications.</p>



<h2 class="wp-block-heading">When might I use GFW or FRA?</h2>



<p>Bottom-up approaches like FRA benefit from locally appropriate methodologies, customization of data and use of country definitions — although many countries follow the FAO’s standard definitions. FRA is more comprehensive in scope, ranging from biophysical to socio-economic and legislative variables. FRA's data will be relevant for those that have a broad interest in the state of forests in a country and how they are being managed.</p>



<p>However, FRA's data is only reported on a national level and cannot be used for assessing regional differences within countries. GFW’s spatially precise updates on tree cover loss allow users to track where changes in tree cover are happening on a sub-national level. GFW also offers more consistency in methods and definitions across geographies for making comparisons across countries.</p>



<p>GFW currently provides more frequent tree cover loss information (annually, and in near-real-time) than FRA does on deforestation, while FRA currently provides more up-to-date information on forest expansion than GFW does on tree cover gain — though GFW will eventually provide annual tree cover gain data. FRA’s figures also reflect tree planting initiatives that are too recent to be picked up by satellite data, although not all of these projects are successful.</p>



<p>In the end, the differences between GFW and FRA reveal strengths and shortcomings of both sources that make them appropriate for different applications, meaning that both are useful to understand what is happening with the world’s forests.</p>



<p></p>



<p><em>This blog was originally published in 2023. It was updated in October 2025 to reflect the latest updates to GFW and the FRA.</em> <em>Rachael Petersen and Octavia Payne contributed to a previous version of this blog.</em></p>



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



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

Both GFW and FRA provide valuable information on the state of forests, and they both have important, and complementary, applications.

Global Forest Watch and Forest Resources Assessment | GFW Blog

Comment

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James Anderson

Deforestation Alerts

Deforestation Alerts | Global Forest Watch Blog

Anika Berger

Bart Slagter

Laura Cué la Rosa

Johannes Reiche

<p>Global Forest Watch’s (GFW) deforestation alerts provide information about loss of tree cover in near-real-time, making them a critical tool for those working to combat deforestation. Existing alerts on GFW have been used to <a href="https://www.globalforestwatch.org/blog/users-in-action/indigenous-forest-monitors-reduce-deforestation/">help Indigenous and local communities</a> protect their forests, <a href="https://www.wri.org/insights/indigenous-peoples-local-communities-use-satellite-data-deforestation?ap3c=IGbOJV0jomFif30GAGbOJV2IivnpZwia9EpGvhO1OTmkkCABCQ" target="_blank" rel="noreferrer noopener">halt and prosecute</a> environmental crimes and <a href="https://www.globalforestwatch.org/blog/users-in-action/data-protect-cameroon-mengame-gorilla-sanctuary/">protect</a> threatened <a href="https://www.globalforestwatch.org/blog/users-in-action/protecting-forest-habitats-mara-elephant-project/">wildlife</a>.</p>



<p>However, in the past, alerts only let users know there’s been a disturbance without identifying a cause, other than a few limited classes such as fires.</p>



<p>Now, <a href="http://gfw.global/43jOCp9">new drivers of deforestation alerts data</a> is available on GFW that shows the causes of alerts across the tropics. This innovative data set uses an AI method to assign drivers to GFW’s integrated deforestation alerts in the three major forested regions in the tropics, allowing users to see what’s driving loss in near-real-time in these regions for the first time.</p>



<p>Critically, this information helps users distinguish between tree cover loss from natural and human causes, enabling more targeted action from those managing forests and reducing the need for costly field visits. Further, the data also provides more rapid information to estimate ecological impacts and supports better understanding of carbon emissions related to forest disturbances.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1310" height="788" src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/drivers_app.png" alt="" class="wp-image-19750" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/10/drivers_app.png 1310w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/drivers_app-1024x616.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/drivers_app-768x462.png 768w" sizes="(max-width: 1310px) 100vw, 1310px" /></figure>



<h2 class="wp-block-heading"><strong>What does the drivers of deforestation alerts data show?</strong></h2>



<h3 class="wp-block-heading"><strong><em>Driver classes</em></strong></h3>



<p>The data classifies GFW’s near-real-time integrated deforestation alerts into 11 natural and human-caused driver classes at 10-meter resolution:</p>



<div class="flourish-embed flourish-cards" data-src="visualisation/25392464?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/25392464/thumbnail" width="100%" alt="cards visualization" /></noscript></div>



<h3 class="wp-block-heading"><strong><em>Geographic coverage</em></strong></h3>



<p>The data covers the three major forest basins of the tropics in the Amazon, Congo and Indonesia, which cover around one third of the world’s land surface and include 80% of the world’s tropical forests. </p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1501" height="482" src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/alerts_coverage_gfw.png" alt="" class="wp-image-19751" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/10/alerts_coverage_gfw.png 1501w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/alerts_coverage_gfw-1024x329.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2025/10/alerts_coverage_gfw-768x247.png 768w" sizes="(max-width: 1501px) 100vw, 1501px" /></figure>



<p>Tropical forests are some of the <a href="https://gfr.wri.org/many-benefits-forests">most important forest ecosystems</a>, critical for livelihoods, carbon storage, water provision, biodiversity and more, and are <a href="https://gfr.wri.org/latest-analysis-deforestation-trends">under threat from deforestation</a>. &nbsp;</p>



<h3 class="wp-block-heading"><strong><em>Temporal coverage and update frequency</em></strong></h3>



<p>Currently, data is available for alerts detected from 2022-2024, but the algorithm will soon be adapted to run on a monthly basis. In this near-real-time setup, each new alert is classified within 31 days of detection, and every month the model reprocesses alerts from the previous three months on a rolling basis. Classifications are only displayed if they are confidently classified by the algorithm, and labels may be updated if a more confident classification becomes available. As a result, each alert can be reclassified up to three times, with the layer showing the most confident classification. This process allows accuracy to improve over time as additional satellite imagery and context become available. Additionally, some drivers (e.g., selective logging and road development) are best identified soon after the canopy is disturbed, while other drivers (e.g., mining or agriculture) may take longer for their characteristic signals to appear.</p>



<h2 class="wp-block-heading"><strong>How was the data developed?</strong></h2>



<p>The <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">integrated deforestation alerts</a> at 10-meter resolution are the basis for the driver classifications. Integrated alerts combine the power of GLAD-L, GLAD-S2 and RADD deforestation alerts to provide a faster, more confident view of forest disturbances than any one individual system.</p>



<p>To apply driver classifications to the integrated deforestation alerts, we used a deep learning model first developed for Suriname, the Republic of Congo and the Democratic Republic of the Congo. The model was trained on an extensive data set of forest disturbance events in the Amazon, Congo and Southeast Asian rainforests using visual interpretation of high-resolution Planet imagery to assign classification labels. The&nbsp;model&nbsp;uses post-disturbance Sentinel-1 and Sentinel-2&nbsp;satellite&nbsp;imagery for the&nbsp;driver&nbsp;classifications,&nbsp;integrating the&nbsp;complementary strengths of radar and optical satellite data. This means that the model can classify drivers&nbsp;rapidly,&nbsp;even&nbsp;in cloudy conditions.&nbsp;&nbsp;</p>



<p>To identify where fire coincided with clearing of agricultural land — a commonly used agricultural management technique in the tropics — we used VIIRS fire alerts and Normalized Burn Ratio (information that identifies burned areas) from Sentinel-2.</p>



<p>To reduce noise in the data,&nbsp;we&nbsp;carried out&nbsp;smoothing:&nbsp;&nbsp;</p>



<ul>
<li>First, alert pixels not exceeding 0.75 confidence were attributed&nbsp;as&nbsp;uncertain classifications and set to ‘unlabeled.’&nbsp;</li>
</ul>



<ul>
<li>Second,&nbsp;for&nbsp;each distinct alert patch (i.e.&nbsp;a spatially connected group of alert pixels),&nbsp;the dominant driver was selected and attributed to the entire patch, with a few exceptions&nbsp;for drivers&nbsp;occurring&nbsp;at a finer, pixel-level scale&nbsp;(road development and selective logging)&nbsp;and for&nbsp;extremely large patches.&nbsp;</li>
</ul>



<p>The accuracy of the classifications varies by region, with the Congo Basin having the highest overall performance. Most misclassifications are related to confusion between agriculture and mining,&nbsp;which often occur in the same area, and&nbsp;between&nbsp;flooding and wildfires&nbsp;in floodplains.&nbsp;</p>



<p><a href="https://www.sciencedirect.com/science/article/pii/S0034425723002067" target="_blank" rel="noreferrer noopener">Learn more here</a> about the methodology and limitations.</p>



<h2 class="wp-block-heading"><strong>How does the data compare to other data showing drivers of loss?</strong></h2>



<p>Other data sets from GFW and Land & Carbon Lab provide information on drivers of vegetation disturbances, but vary in terms of their detail, timeframe and update frequency, and therefore have different applications. For example, the <a href="https://www.globalforestwatch.org/blog/data-and-tools/new-drivers-data-forest-loss/">1-km annual drivers of tree cover loss data</a> available on GFW is more suited for historical analysis over many years, and the Land Disturbance Alert Classification System from Land & Carbon Lab provides information on vegetation disturbances both inside and outside of forests globally.</p>



<div class="flourish-embed flourish-table" data-src="visualisation/25391478?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/25391478/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<p>Some attributes make the data sets more suitable for specific applications. For example, higher resolution allows more insight into co-occurrence of drivers in local areas, and sub-annual information allows insights into the seasonality of different drivers:</p>



<div class="flourish-embed flourish-table" data-src="visualisation/25549662?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/25549662/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<h2 class="wp-block-heading"><strong>How can the data be used? A vital source for forest protectors</strong></h2>



<p>Deforestation alerts are <a href="https://www.globalforestwatch.org/blog/category-and-topics/?category=users-in-action&topic=deforestation-alerts">used around the world</a> by hundreds of Indigenous Peoples and local communities, as well as forest rangers and NGOs, to investigate potential unplanned or illegal disturbances in forests.</p>



<p>However, different causes for disturbances warrant different responses. For example, in the case of agricultural conversion, users may want to investigate the loss to assess the legality of the clearing. In the case of a natural disturbance such as a landslide, either no investigation or a different sort of investigation would be required. Users can reference the drivers of deforestation alerts data to determine what type of response is appropriate depending on the cause of the disturbance.</p>



<p>While field visits or the use of high-resolution satellite imagery to check classifications may still be required in some instances to plan appropriate action or to fully understand the context, the addition of driver information on GFW enables alert prioritization and more targeted action, saving resources and allowing investigations to be focused on more concerning losses.</p>



<p>The data can also be used to improve understanding of carbon emissions from forest disturbances. Some types of conversion clear all the vegetation from the land, whereas others leave trees behind. In addition, information on the cause of loss is required to estimate emissions from the subsequent land use after the forest has been cleared. It also indicates whether regrowth can be expected, which is likely after logging activities but not after conversion to permanent agriculture, for example.</p>



<h2 class="wp-block-heading"><strong>What’s next for the drivers of deforestation alerts data?</strong></h2>



<p><strong>Monthly updates: </strong>The 2025 data will be made available in early 2026, after which monthly updates will be available from 2026 onwards.</p>



<p><strong>Expansion to the whole of the tropics: </strong>We soon plan to expand the data across the full coverage of the integrated deforestation alerts (currently 30 degrees north to 30 degrees south). In the future, the model may be applied to disturbances in temperate and boreal forests as well. Further research could also allow disturbances of other vegetation types to be classified using a similar approach.</p>



<p><strong>Integration with other GFW tools</strong>: We plan to add the data to Forest Watcher to make it more accessible to forest protectors working in the field.</p>



<p>Stay tuned for more updates as we continue to improve drivers of deforestation alerts for greater coverage, access and application, and <a href="https://www.globalforestwatch.org/help/datasets/forest-change/">visit the Help Center</a> to learn more.  </p>



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

See What’s Driving Deforestation Alerts for the First Time on Global Forest Watch

<p>Global Forest Watch’s (GFW) deforestation alerts provide information about loss of tree cover in near-real-time, making them a critical tool for those working to combat deforestation. Existing alerts on GFW have been used to <a href="https://www.globalforestwatch.org/blog/users-in-action/indigenous-forest-monitors-reduce-deforestation/">help Indigenous and local communities</a> protect their forests, <a href="https://www.wri.org/insights/indigenous-peoples-local-communities-use-satellite-data-deforestation?ap3c=IGbOJV0jomFif30GAGbOJV2IivnpZwia9EpGvhO1OTmkkCABCQ" target="_blank" rel="noreferrer noopener">halt and prosecute</a> environmental crimes and <a href="https://www.globalforestwatch.org/blog/users-in-action/data-protect-cameroon-mengame-gorilla-sanctuary/">protect</a> threatened <a href="https://www.globalforestwatch.org/blog/users-in-action/protecting-forest-habitats-mara-elephant-project/">wildlife</a>.</p>



<p>However, in the past, alerts only let users know there’s been a disturbance without identifying a cause, other than a few limited classes such as fires.</p>



<p>Now, <a href="http://gfw.global/43jOCp9">new drivers of deforestation alerts data</a> is available on GFW that shows the causes of alerts across the tropics. This innovative data set uses an AI method to assign drivers to GFW’s integrated deforestation alerts in the three major forested regions in the tropics, allowing users to see what’s driving loss in near-real-time in these regions for the first time.</p>



<p>Critically, this information helps users distinguish between tree cover loss from natural and human causes, enabling more targeted action from those managing forests and reducing the need for costly field visits. Further, the data also provides more rapid information to estimate ecological impacts and supports better understanding of carbon emissions related to forest disturbances.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/drivers_app.png" alt="" class="wp-image-19750"/></figure>



<h2 class="wp-block-heading"><strong>What does the drivers of deforestation alerts data show?</strong></h2>



<h3 class="wp-block-heading"><strong><em>Driver classes</em></strong></h3>



<p>The data classifies GFW’s near-real-time integrated deforestation alerts into 11 natural and human-caused driver classes at 10-meter resolution:</p>



<div class="flourish-embed flourish-cards" data-src="visualisation/25392464?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/25392464/thumbnail" width="100%" alt="cards visualization" /></noscript></div>



<h3 class="wp-block-heading"><strong><em>Geographic coverage</em></strong></h3>



<p>The data covers the three major forest basins of the tropics in the Amazon, Congo and Indonesia, which cover around one third of the world’s land surface and include 80% of the world's tropical forests. </p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2025/10/alerts_coverage_gfw.png" alt="" class="wp-image-19751"/></figure>



<p>Tropical forests are some of the <a href="https://gfr.wri.org/many-benefits-forests">most important forest ecosystems</a>, critical for livelihoods, carbon storage, water provision, biodiversity and more, and are <a href="https://gfr.wri.org/latest-analysis-deforestation-trends">under threat from deforestation</a>. &nbsp;</p>



<h3 class="wp-block-heading"><strong><em>Temporal coverage and update frequency</em></strong></h3>



<p>Currently, data is available for alerts detected from 2022-2024, but the algorithm will soon be adapted to run on a monthly basis. In this near-real-time setup, each new alert is classified within 31 days of detection, and every month the model reprocesses alerts from the previous three months on a rolling basis. Classifications are only displayed if they are confidently classified by the algorithm, and labels may be updated if a more confident classification becomes available. As a result, each alert can be reclassified up to three times, with the layer showing the most confident classification. This process allows accuracy to improve over time as additional satellite imagery and context become available. Additionally, some drivers (e.g., selective logging and road development) are best identified soon after the canopy is disturbed, while other drivers (e.g., mining or agriculture) may take longer for their characteristic signals to appear.</p>



<h2 class="wp-block-heading"><strong>How was the data developed?</strong></h2>



<p>The <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">integrated deforestation alerts</a> at 10-meter resolution are the basis for the driver classifications. Integrated alerts combine the power of GLAD-L, GLAD-S2 and RADD deforestation alerts to provide a faster, more confident view of forest disturbances than any one individual system.</p>



<p>To apply driver classifications to the integrated deforestation alerts, we used a deep learning model first developed for Suriname, the Republic of Congo and the Democratic Republic of the Congo. The model was trained on an extensive data set of forest disturbance events in the Amazon, Congo and Southeast Asian rainforests using visual interpretation of high-resolution Planet imagery to assign classification labels. The&nbsp;model&nbsp;uses post-disturbance Sentinel-1 and Sentinel-2&nbsp;satellite&nbsp;imagery for the&nbsp;driver&nbsp;classifications,&nbsp;integrating the&nbsp;complementary strengths of radar and optical satellite data. This means that the model can classify drivers&nbsp;rapidly,&nbsp;even&nbsp;in cloudy conditions.&nbsp;&nbsp;</p>



<p>To identify where fire coincided with clearing of agricultural land — a commonly used agricultural management technique in the tropics — we used VIIRS fire alerts and Normalized Burn Ratio (information that identifies burned areas) from Sentinel-2.</p>



<p>To reduce noise in the data,&nbsp;we&nbsp;carried out&nbsp;smoothing:&nbsp;&nbsp;</p>



<ul>
<li>First, alert pixels not exceeding 0.75 confidence were attributed&nbsp;as&nbsp;uncertain classifications and set to ‘unlabeled.’&nbsp;</li>
</ul>



<ul>
<li>Second,&nbsp;for&nbsp;each distinct alert patch (i.e.&nbsp;a spatially connected group of alert pixels),&nbsp;the dominant driver was selected and attributed to the entire patch, with a few exceptions&nbsp;for drivers&nbsp;occurring&nbsp;at a finer, pixel-level scale&nbsp;(road development and selective logging)&nbsp;and for&nbsp;extremely large patches.&nbsp;</li>
</ul>



<p>The accuracy of the classifications varies by region, with the Congo Basin having the highest overall performance. Most misclassifications are related to confusion between agriculture and mining,&nbsp;which often occur in the same area, and&nbsp;between&nbsp;flooding and wildfires&nbsp;in floodplains.&nbsp;</p>



<p><a href="https://www.sciencedirect.com/science/article/pii/S0034425723002067" target="_blank" rel="noreferrer noopener">Learn more here</a> about the methodology and limitations.</p>



<h2 class="wp-block-heading"><strong>How does the data compare to other data showing drivers of loss?</strong></h2>



<p>Other data sets from GFW and Land &amp; Carbon Lab provide information on drivers of vegetation disturbances, but vary in terms of their detail, timeframe and update frequency, and therefore have different applications. For example, the <a href="https://www.globalforestwatch.org/blog/data-and-tools/new-drivers-data-forest-loss/">1-km annual drivers of tree cover loss data</a> available on GFW is more suited for historical analysis over many years, and the Land Disturbance Alert Classification System from Land &amp; Carbon Lab provides information on vegetation disturbances both inside and outside of forests globally.</p>



<div class="flourish-embed flourish-table" data-src="visualisation/25391478?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/25391478/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<p>Some attributes make the data sets more suitable for specific applications. For example, higher resolution allows more insight into co-occurrence of drivers in local areas, and sub-annual information allows insights into the seasonality of different drivers:</p>



<div class="flourish-embed flourish-table" data-src="visualisation/25549662?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/25549662/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<h2 class="wp-block-heading"><strong>How can the data be used? A vital source for forest protectors</strong></h2>



<p>Deforestation alerts are <a href="https://www.globalforestwatch.org/blog/category-and-topics/?category=users-in-action&amp;topic=deforestation-alerts">used around the world</a> by hundreds of Indigenous Peoples and local communities, as well as forest rangers and NGOs, to investigate potential unplanned or illegal disturbances in forests.</p>



<p>However, different causes for disturbances warrant different responses. For example, in the case of agricultural conversion, users may want to investigate the loss to assess the legality of the clearing. In the case of a natural disturbance such as a landslide, either no investigation or a different sort of investigation would be required. Users can reference the drivers of deforestation alerts data to determine what type of response is appropriate depending on the cause of the disturbance.</p>



<p>While field visits or the use of high-resolution satellite imagery to check classifications may still be required in some instances to plan appropriate action or to fully understand the context, the addition of driver information on GFW enables alert prioritization and more targeted action, saving resources and allowing investigations to be focused on more concerning losses.</p>



<p>The data can also be used to improve understanding of carbon emissions from forest disturbances. Some types of conversion clear all the vegetation from the land, whereas others leave trees behind. In addition, information on the cause of loss is required to estimate emissions from the subsequent land use after the forest has been cleared. It also indicates whether regrowth can be expected, which is likely after logging activities but not after conversion to permanent agriculture, for example.</p>



<h2 class="wp-block-heading"><strong>What’s next for the drivers of deforestation alerts data?</strong></h2>



<p><strong>Monthly updates: </strong>The 2025 data will be made available in early 2026, after which monthly updates will be available from 2026 onwards.</p>



<p><strong>Expansion to the whole of the tropics: </strong>We soon plan to expand the data across the full coverage of the integrated deforestation alerts (currently 30 degrees north to 30 degrees south). In the future, the model may be applied to disturbances in temperate and boreal forests as well. Further research could also allow disturbances of other vegetation types to be classified using a similar approach.</p>



<p><strong>Integration with other GFW tools</strong>: We plan to add the data to Forest Watcher to make it more accessible to forest protectors working in the field.</p>



<p>Stay tuned for more updates as we continue to improve drivers of deforestation alerts for greater coverage, access and application, and <a href="https://www.globalforestwatch.org/help/datasets/forest-change/">visit the Help Center</a> to learn more.  </p>



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

New drivers of deforestation alerts data, now available on GFW, shows the causes of alerts across the tropics for the first time.

See What’s Driving Deforestation Alerts for the First Time on GFW

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Paige Johnson

mapping

mapping | Global Forest Watch Blog

Manuka Khan

A New Satellite Data App Supports Better Monitoring of European Forests

A new European Forest App supports better monitoring of European forests by providing tailored information and insights in an easy-to-use platform.

A New Satellite Data App Supports Better Monitoring of European Forests | Data &amp; Tools | Global Forest Watch Blog

carbon

carbon | Global Forest Watch Blog

David Gibbs

Melissa Rose

What’s New With GFW’s Forest Carbon Monitoring

Carbon monitoring data on GFW provides information on where forests are gaining or losing carbon. We updated the model in 2023 and 2024.

What’s New With GFW’s Forest Carbon Monitoring | GFW Blog

Brazil

Brazil | Global Forest Watch Blog

Indoensia

Indoensia | Global Forest Watch Blog

Michelle Sims

Liz Goldman

Global Forest Watch’s 2024 Tree Cover Loss Data Explained

Explicación de los datos de Global Forest Watch sobre la pérdida de cobertura arbórea en 2024

Explication des données du Global Forest Watch sur la perte de la couverture arborée en 2024  

Penjelasan Data Kehilangan Tutupan Pohon 2024 dari Global Forest Watch  

Explicação dos dados do Global Forest Watch sobre a perda de cobertura arbórea de 2024  

New data shows alarming increases in primary forest loss in 2024. What does the data measure and how does it compare to other official data?

Radost Stanimirova

Anton Raichuk

Maxim Neumann

<p>Over the past several decades, forest loss has been a <a href="https://research.wri.org/gfr/latest-analysis-deforestation-trends" target="_blank" rel="noreferrer noopener">stubbornly persistent problem</a> that can disrupt ecosystems, biodiversity and climate stability, as well as negatively impact forest-dependent people around the world.</p>



<p>However, <a href="https://research.wri.org/gfr/key-terms-definitions#tree-cover-loss-and-deforestation" target="_blank" rel="noreferrer noopener">not all forest loss is created equal</a>. Deforestation, which typically refers to the permanent conversion of forests to other land uses, often has more severe carbon and ecological impacts compared to temporary forest disturbances such as from logging and other natural and anthropogenic causes.</p>



<p>To better track and differentiate these changes, Global Forest Watch (GFW), Land & Carbon Lab and <a href="https://deepmind.google/" target="_blank" rel="noreferrer noopener">Google DeepMind</a> have developed a <a href="https://doi.org/10.1088/1748-9326/add606" target="_blank" rel="noreferrer noopener">new global data set</a> — <a href="https://gfw.global/39qbPdC">available on GFW</a> — on the drivers of tree cover loss at 1-kilometer (km) resolution from 2001 to 2024. Previously, data on drivers was available at <a href="https://www.science.org/doi/10.1126/science.aau3445" target="_blank" rel="noreferrer noopener">10-km resolution</a>. The development of this data was led by <a href="https://sustainabilityconsortium.org/" target="_blank" rel="noreferrer noopener">The Sustainability Consortium</a> and provided the foundation for the new 1-km data set. The higher resolution of the new data makes it possible to assess drivers in local or regional applications for the first time. The new data also has more driver classes, providing the most detailed view of drivers yet.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1500" height="619" src="https://content.globalforestwatch.org/wp-content/uploads/2025/05/global_drivers_wlogging-1.png" alt="" class="wp-image-19070" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/05/global_drivers_wlogging-1.png 1500w, https://content.globalforestwatch.org/wp-content/uploads/2025/05/global_drivers_wlogging-1-1024x423.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2025/05/global_drivers_wlogging-1-768x317.png 768w" sizes="(max-width: 1500px) 100vw, 1500px" /></figure>



<p>Thanks to this increased granularity, the new data can be used to support the development of forest conservation and management policies, inform supply chain actors and governments bodies about the causes of deforestation, carbon emissions and biodiversity loss within their supply chains and jurisdictions, and track progress towards global goals to end deforestation.</p>



<h2 class="wp-block-heading">How was the new drivers of tree cover loss data developed?&nbsp;</h2>



<p>The new drivers of tree cover loss data, part of a World Resources Institute (WRI) and Google DeepMind collaboration, was developed using deep learning, which is a type of advanced AI model. We developed a <a href="https://arxiv.org/abs/1912.11370" target="_blank" rel="noreferrer noopener">customized</a> <a href="https://arxiv.org/abs/1512.03385" target="_blank" rel="noreferrer noopener">Residual Network</a> (or ResNet, a convolutional neural network model) that uses publicly available satellite observations from Landsat 7 & 8 and Sentinel 2 along with additional satellite-derived biophysical and population data relevant to the driver classes (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/add606" target="_blank" rel="noreferrer noopener">see Table 2</a>) to predict the dominant driver of tree cover loss from 2001 to 2024 at 1-km resolution.</p>



<p>To train the model to make accurate predictions, we visually interpreted nearly 7,000 training samples using very-high resolution imagery. The model uses these labeled samples to learn how to associate the patterns from the input satellite imagery and other supporting data with a driver class. ResNet is a powerful AI algorithm because it uses many data transformations (or “layers”) to extract important features or patterns from an image. It can handle intricate tasks like recognizing objects in images more accurately and efficiently than traditional machine learning approaches.</p>



<p>We also collected an independent stratified random sample of 3,574 1-km plots to estimate the accuracy of the final drivers of tree cover loss map. Both the training and validation data sets are available on <a href="https://zenodo.org/records/15366671" target="_blank" rel="noreferrer noopener">Zenodo</a> and <a href="https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km" target="_blank" rel="noreferrer noopener">WRI’s Data Explorer</a>. You can <a href="https://doi.org/10.1088/1748-9326/add606" target="_blank" rel="noreferrer noopener">learn more here</a> about the methodology behind the new data. </p>



<p>The data distinguishes between seven classes of drivers: permanent agriculture, hard commodities, shifting cultivation, logging, settlements and infrastructure, wildfire, and other natural disturbances. The dominant driver is defined as the direct driver that caused the majority of <a href="https://gfw.global/2HrzxeF">tree cover loss area</a> within each 1-km grid cell from 2001 to 2024.</p>



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<p>In addition to the drivers of tree cover loss classification, we also created seven probability layers, one for each of the seven driver classes. With these layers, users can implement different probability thresholds to customize the driver classification based on their knowledge of a region of interest. For example, a stakeholder in Southeast Asia interested in understanding tree cover loss due to palm oil expansion can select a custom threshold for the permanent agriculture class (e.g., greater than 0.7), and can exclude areas where probabilities of multiple drivers are high (e.g., no single class has a probability higher than 0.6) as those are more uncertain. The probabilities are available on <a href="https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km" target="_blank" rel="noreferrer noopener">WRI’s Data Explorer</a> and <a href="https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024" target="_blank" rel="noreferrer noopener">Google Earth Engine.</a></p>



<p>Despite the improvements the new data provides, there are some limitations:</p>



<ul>
<li>The map does not distinguish between the loss of natural forest and planted trees. For example, logging can occur in both natural forests and plantations. Similarly, permanent agriculture, hard commodities, and settlements and infrastructure classes represent a close approximation of deforestation but do not always represent the conversion of natural forests to other land uses.</li>



<li>We do not disentangle different drivers that are co-located at scales smaller than 1- km or that occur in quick succession in time.</li>
</ul>



<p><a href="https://doi.org/10.1088/1748-9326/add606" target="_blank" rel="noreferrer noopener">Read more details here</a> about the limitations of the data.</p>



<h2 class="wp-block-heading">Where can I find the new drivers of tree cover loss data on GFW?&nbsp;</h2>



<p>Users can access the data on the<a href="https://gfw.global/39qbPdC"> GFW map</a>. Users can also analyze the area and proportion of loss attributable to each driver from 2001 to 2024 within their area of interest.</p>



<iframe loading="lazy" width="630" height="700" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeLossTsc/country/BRA"></iframe>



<p>Note that results from the previous drivers of tree cover loss data were displayed on the GFW dashboard as a bar chart time series, while results from the new data are displayed as a donut chart. We have changed the visualization to better reflect the characteristics of the underlying data set — the dominant driver of tree cover loss for the full time period between 2001 and 2024.</p>



<h2 class="wp-block-heading">How does the new drivers of tree cover loss data compare with the previous data?&nbsp;</h2>



<p>Compared to the <a href="https://www.science.org/doi/10.1126/science.aau3445" target="_blank" rel="noreferrer noopener">previous drivers of tree cover loss data set</a>, the new data has the following improvements:</p>



<ul>
<li>Higher spatial resolution<ul><li>Improves differentiation between small-scale commodity crop production and shifting cultivation</li></ul>
<ul>
<li>Improves detection of small-scale drivers, such as small wildfires, settlements and infrastructure, and selective logging</li>
</ul>
</li>



<li>More driver classes<ul><li>Separates commodity-driven deforestation into two separate classes: permanent agriculture and hard commodities (i.e., mining and energy infrastructure)</li></ul>
<ul>
<li>Adds a new class: other natural disturbances (e.g., storms, flooding, landslides, etc.)</li>
</ul>
</li>



<li>Higher accuracy and added probability layers<ul><li>Has higher accuracy for most regions (except Europe and Africa) and smaller confidence intervals across all regions</li></ul>
<ul>
<li>Provides probability layers, one for each of the seven driver classes</li>
</ul>
</li>
</ul>



<h4 class="wp-block-heading">Specifications of the previous 10-km data and the new 1-km data&nbsp;</h4>



<table>
    <tr>
        <td><strong>Data set</strong></td>
        <td><strong>Geographic coverage</strong></td>
        <td><strong>Years of coverage</strong></td>
        <td><strong>Spatial resolution</strong></td>
        <td><strong>Number of driver classes</strong></td>
        <td><strong>Number of layers</strong></td>
        <td><strong>Global overall accuracy</strong></td>
    </tr>
    <tr>
        <td>Previous 10-km drivers data 
<a href="https://www.science.org/doi/10.1126/science.aau3445">(Curtis et al., 2018)</a>
 </td>
        <td>Global </td>
        <td>2001-2023 </td>
        <td>10-km  </td>
        <td>5 </td>
        <td>1 </td>
        <td>89 ± 3% </td>
    </tr>
    <tr>
        <td>New 1-km drivers data <a href="https://doi.org/10.1088/1748-9326/add606">(Sims et al. 2025)</a></td>
        <td>Global </td>
        <td>2001-2024, to be updated annually with new tree cover loss data </td>
        <td>1-km  </td>
        <td>7 </td>
        <td>8, 1 for driver classes and 7 layers of probabilities </td>
        <td>91 ± 1% </td>
    </tr>
</table>



<figure class="wp-block-image size-large is-resized"><img decoding="async" loading="lazy" width="2048" height="1638" src="https://content.globalforestwatch.org/wp-content/uploads/2025/05/Drivers-accuracy-comparison-2048x1638.png" alt="" class="wp-image-19053" style="aspect-ratio:1.2503052503052503;width:840px;height:auto" srcset="https://content.globalforestwatch.org/wp-content/uploads/2025/05/Drivers-accuracy-comparison-2048x1638.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2025/05/Drivers-accuracy-comparison-1024x819.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2025/05/Drivers-accuracy-comparison-768x614.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2025/05/Drivers-accuracy-comparison-1536x1229.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p>These improvements can be seen when comparing the two data sets in various places.</p>



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<p>The new data differentiates small scale fires from logging in Oregon, USA. The previous data in the same location classifies all loss as forestry.</p>



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<p>The new data detects mining, smallholder cocoa production, and urban expansion outside Kumasi, Ghana. The previous data in the same location classifies shifting agriculture and urban expansion.</p>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/21452353?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/21452353/thumbnail" width="100%" alt="photo-slider visualization" /></noscript></div>



<p>The new data detects selective logging, fires and flooding for hydropower in the Ouésso District of the Republic of Congo. The previous data in the same location classifies all loss as shifting agriculture.</p>



<p>Due to the substantial improvements the new data offers, we recommend using the 1-km drivers data set for analysis of tree cover loss drivers going forward. However, the previous 10-km data can still be accessed via the <a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about" target="_blank" rel="noreferrer noopener">GFW open data portal</a>.</p>



<h2 class="wp-block-heading">How can the new data be used and what does it tell us about drivers of tree cover loss?&nbsp;</h2>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6InRyZWUtY292ZXItbG9zcy1ieS1kb21pbmFudC1kcml2ZXIiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJlZS1jb3Zlci1sb3NzLWJ5LWRvbWluYW50LWRyaXZlciJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>



<p>On a global scale, our analysis of the new data reveals that most tree cover loss can be attributed to three main drivers: permanent agriculture, wildfire and logging. But while drivers like other natural disturbances, <a href="https://www.wri.org/insights/how-mining-impacts-forests" target="_blank" rel="noreferrer noopener">hard commodities</a> and settlements and infrastructure represent a relatively small proportion of loss globally, they can play an important role regionally. <a href="https://doi.org/10.1088/1748-9326/add606" target="_blank" rel="noreferrer noopener">Read more here</a> about findings from the data on what’s driving tree cover loss.</p>



<p>This analysis demonstrates how the higher level of spatial detail in the new map provides useful contextual information for users to understand the nature of tree cover loss in their local area of interest. For example, using the map, GFW users can now separate tree cover loss due to expansion of “soft” agricultural commodities from “hard” commodities such as minerals, better characterize selective logging in the tropics and better differentiate permanent agriculture from shifting cultivation. Moreover, this map represents a major advancement in differentiating between natural and anthropogenic losses by detecting events such as bark beetle outbreaks, hurricane damage, meandering rivers and other natural disturbance events. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>



<p>Because these improvements allow us to disentangle deforestation from temporary disturbances in greater detail, the new data improves our ability to track progress towards reducing deforestation such as in the <a href="https://forestdeclaration.org/about/assessment/" target="_blank" rel="noreferrer noopener">Forest Declaration Assessment</a>, the <a href="https://research.wri.org/gfr/forest-targets-tracker" target="_blank" rel="noreferrer noopener">Global Forest Review Targets Tracker</a> and the <a href="https://www.wri.org/research/state-climate-action-2023" target="_blank" rel="noreferrer noopener">State of Climate Action</a> report.</p>



<p>The new data can be used to support the development of forest conservation and management policies by making it possible to pinpoint the causes of deforestation hotspots at finer scales than ever before possible. Detailed insights allow policymakers and conservationists to design tailored interventions to address specific drivers such as logging, for example, and to hold accountable governments and organizations based on their conservation commitments.</p>



<p>Additionally, using this new data, we can refine <a href="https://gfw.global/3jLklJ9" target="_blank" rel="noreferrer noopener">maps</a> of <a href="https://www.wri.org/insights/forests-absorb-twice-much-carbon-they-emit-each-year" target="_blank" rel="noreferrer noopener">greenhouse gas emissions</a> due to tree cover loss by assigning different amounts of emissions to each driver of loss. It also improves our understanding of the causes of emissions from tree cover loss.</p>



<h2 class="wp-block-heading">What’s next for 1-km global drivers of tree cover loss data?&nbsp;</h2>



<p>This new data replaces the previous 10-km data across the whole GFW platform and in all analyses, such as <a href="https://www.nature.com/articles/s41558-020-00976-6" target="_blank" rel="noreferrer noopener">GFW’s carbon flux model</a>. The new data is used in the <a href="https://gfr.wri.org/latest-analysis-deforestation-trends" target="_blank" rel="noreferrer noopener">2024 analysis of tree cover loss</a> and in the updated <a href="https://gfr.wri.org/" target="_blank" rel="noreferrer noopener">Global Forest Review</a>. We will update the new 1-km drivers of tree cover loss data annually. </p>



<p>In the future, we plan to develop an AI model to map drivers of tree cover loss at even higher temporal and spatial resolution to understand how different drivers play out over time. Annual data on the drivers of tree cover loss is critical for assessing deforestation after specific cutoff dates relevant for policy commitments such as European Union Deforestation Regulation (EUDR) and for quantifying trends in drivers such as expansion of commodity agriculture.</p>



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New Data on GFW Provides the Most Detailed Look Yet of What’s Driving Forest Loss Globally