Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

forest change

forest change | Global Forest Watch Blog

mapping

mapping | Global Forest Watch Blog

Michelle Sims

Liz Goldman

Elise Mazur

<p>It’s well established that forests are important for people, nature and climate, but not all forests are created equal. Natural forests tend to <a href="https://zenodo.org/records/3553579#.ZFU8GnbMJwI" target="_blank" rel="noreferrer noopener">harbor more biodiversity</a> and offer a wider array of ecosystem services than planted forests, and therefore keeping natural forests standing is particularly critical.</p>



<p>The tree cover data available on Global Forest Watch (GFW) includes all types of tree cover, including natural forests and planted trees such tree crops and wood fiber plantations. Although data on primary forests are available for the humid tropics, distinguishing natural forests globally from other types of tree cover previously required overlaying multiple data sets to exclude areas that are not natural forests.</p>



<p>Now, new <a href="https://gfw.global/4fQfl03">natural forest data on GFW</a> from the <a href="https://sciencebasedtargetsnetwork.org/" target="_blank" rel="noreferrer noopener">Science Based Target Network (SBTN)</a> Natural Lands Map can help shed light on where the world’s natural forests are located and provide a baseline for monitoring loss within them since 2020.</p>



<h2 class="wp-block-heading">What is the Science Based Target Network Natural Lands Map?</h2>



<p>Land & Carbon Lab collaborated with WWF and Systemiq to develop the <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> to support companies who have committed to eliminate conversion of natural lands in their supply chains through targets set with the SBTN land sector framework. The map provides a 2020 baseline of natural lands that companies can use to see where production activities have caused conversion since 2020, as well as identify and prevent future risk of conversion.</p>



<p>The Natural Lands Map includes several classes of natural land cover. The natural forest layer on GFW is a subset of the SBTN Natural Lands Map that includes the natural forest class and relevant subclasses, including wetland forests, mangroves and peat forests.</p>



<p>The <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> was created by combining the best available global and local land cover and land use data sets into a single harmonized map at 30-meter resolution circa the year 2020. This includes data sets such as the University of Maryland GLAD Lab’s 2020 <a href="https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.856903/full" target="_blank" rel="noreferrer noopener">land cover data set</a>, various data sets on cropland and <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">planted trees</a>, and local data sets such as <a href="https://brasil.mapbiomas.org/en/" target="_blank" rel="noreferrer noopener">MapBiomas</a> in South America and Indonesia, among others. The various input data sets were selected based on accuracies reported from the data producers when available and alignment with the definitions used by SBTN. Input data sets were combined to create the final Map.</p>



<h2 class="wp-block-heading">How does the Map define natural forests?</h2>



<p>SBTN uses the Accountability Framework Initiative (AFi) <a href="https://accountability-framework.org/use-the-accountability-framework/definitions/" target="_blank" rel="noreferrer noopener">definitions</a> of natural ecosystems and natural forests to align with other standard setting initiatives. AFi adopts the Food and Agriculture Organization (FAO) definition of forests: land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ, not including land that is predominantly under agricultural or other land use. AFi then differentiates <em>natural</em> forests as those where the species composition, structure and ecological function do not show effects of major human impacts.</p>



<p>Because satellite data cannot directly map all the elements of these definitions, the data development team operationalized them using proxies based on available data to exclude tree cover that would not meet the natural forest definition. Non-natural tree cover may include tree crops (e.g., palm oil, orchards, cocoa), planted forests (e.g., eucalyptus, teak, or pine plantations), patches of trees under 0.5 hectares, or trees within areas predominantly under agricultural or urban land use (e.g., trees planted within agricultural fields or within small urban parks). Natural forests may include primary, secondary or naturally regenerating forests, and may include some forms of low-intensity management.</p>



<p>AFi guidance states that quantitative thresholds established by national and sub-national forest definitions may take precedence over the global definitions, so local maps were given priority over global data in select regions.</p>



<h4 class="wp-block-heading">The natural forest map distinguishes natural forests from other tree cover</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/21139033?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/21139033/thumbnail" width="100%" alt="photo-slider visualization" /></noscript></div>



<h2 class="wp-block-heading">What data is available on Global Forest Watch?</h2>



<p>The natural forest layer now available on the GFW map includes all natural forest subclasses from the SBTN Natural Lands Map (<a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 0.3</a>). This natural forest layer has an overall accuracy of 88%.*</p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6Im5hdHVyYWwtZm9yZXN0cyIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWUsImxheWVycyI6WyJuYXR1cmFsLWZvcmVzdHMtMjAyMCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>



<p>Previously, GFW users could analyze tree cover loss within and outside of the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which helped identify which losses were more likely to represent plantation harvesting or tree crop rotation. Now, the GFW dashboard allows users to separate tree cover loss in natural forests and non-natural tree cover from 2021 onwards using the natural forest map, meaning users can specifically monitor loss of natural forests around the world. This provides users with a more comprehensive and improved method to estimate loss in natural forests that is more aligned with widely accepted definitions.</p>



<iframe loading="lazy" width="630" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeLossPlantations/country/BRA"></iframe>



<p></p>



<p>Users can also now calculate the area** of natural forest, non-natural tree cover and other land cover within administrative boundaries and other land use categories, such as protected areas.</p>



<iframe loading="lazy" width="315" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/naturalForest/global"></iframe>



<p></p>



<h2 class="wp-block-heading">How can this data be used?</h2>



<p>The natural forest layer can be used to assess the extent of natural forest in 2020, and can be combined with additional data, such as tree cover loss, to monitor loss of natural forests from 2021 onward in a user’s area of interest. For example, companies can use the data to assess whether their production or sourcing activities have caused deforestation since 2020, or if their future plans pose a risk of deforestation, for tracking progress under voluntary commitments or the European Union Deforestation Regulation (EUDR).</p>



<p>For any use case however, users should consider the definitions used for the natural forest layer and determine whether these definitions align with their monitoring needs. For example, there is a key difference in how AFi and EUDR define deforestation that users must consider: while AFi defines deforestation as conversion of natural forest to non-natural tree cover or another land cover, <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32023R1115&qid=1686611894470" target="_blank" rel="noreferrer noopener">EUDR defines deforestation</a> as the conversion of any type of forest — including planted forest — to agricultural use. Because the natural forest layer does not include planted forest, users may need to use additional data to include planted forests in their forest baseline to monitor conversion of natural and planted forest to agriculture for EUDR purposes.</p>



<p>For a full description of how the natural forest map relates to EUDR, see <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">this blog</a>. For more information on EUDR definitions and how GFW can support EUDR monitoring, read <a href="https://www.globalforestwatch.org/blog/data-and-tools/satellite-data-eu-regulation-deforestation-free-supply-chains/">part one</a> and <a href="https://www.globalforestwatch.org/blog/data-and-tools/monitoring-forest-degradation-eudr/">two</a> of our blog series.</p>



<p>Whenever using the SBTN Natural Lands Map, including the natural forests map on GFW, users should also be aware of a few limitations of the data:</p>



<ul type="1">
<li>Overestimation of natural forest in agroforestry systems and in temperate and boreal forests: Agroforestry systems, especially those where crops are grown under shade trees (such as some types of coffee and cocoa systems), are difficult to differentiate from natural forests in satellite imagery, and therefore available data on their extent is limited. Similarly, publicly available data on planted forests is limited in many temperate and boreal regions. As a result, these areas may be included as natural forest in the Map.</li>



<li>Inconsistent forest definitions in certain regions: Because the SBTN Natural Lands Map team prioritized local and regional input data over global data during the compilation and harmonization process when the Map was developed, there may be inconsistencies in the height and canopy density thresholds used to define forests in some regions where local data was used, including Brazil, the Amazon, Atlantic Forest, Pampa, Chaco, Indonesia, South Africa and New Zealand.</li>



<li>Temporal inconsistencies: The Map is as close to the year 2020 as possible, but due to limited data availability, some input data is from earlier time periods (e.g., 2015).</li>



<li>Resolution inconsistencies: Most input data used to create the Map is at least 30-meter resolution, but in some cases lower resolution data was used when higher resolution data was unavailable. These inconsistencies create data artifacts (sharp boundary lines that are only due to resolutions) in some areas.</li>
</ul>



<p>See more information on these limitations in the <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">technical documentation</a>.</p>



<h2 class="wp-block-heading">What’s next for the natural forest data on GFW?</h2>



<p>Currently, <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 0.3</a> of the natural forest map is available on GFW. In the coming months, we will update GFW to <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 1</a>, which is now available on the <a href="https://datasets.wri.org/datasets/sbtn-natural-lands-map?map=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" target="_blank" rel="noreferrer noopener">WRI Open Data Portal</a>, <a href="https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1" target="_blank" rel="noreferrer noopener">Google Earth Engine</a> and <a href="https://pro-news.globalforestwatch.org/news-events/how-gfw-pro-supports-eudr-compliance" target="_blank" rel="noreferrer noopener">GFW Pro</a>. We plan to continue to update the data on GFW as future versions of the SBTN Natural Lands Map become available.</p>



<p>We also plan to release additional analysis capabilities with the data, such as the option to filter deforestation and fire alerts to within natural forests. Stay tuned for future updates!</p>



<p></p>



<p>* <em>Note that the validation points were labeled based on definitions of natural forests from v1 of the SBTN Natural Lands Map, which includes more regional data sets and their corresponding definitions, as well as an additional filtering of local savanna classes to remove vegetation shorter than 5 meters from the forest class. This may affect the validation of the v0.3 natural forest layer because of variation in definitions of natural forest where local data was used. See the <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">v1 technical note change log</a> to see the full list of changes.</em></p>



<p><em>** We generally </em><a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener"><em>caution against</em></a><em> using the SBTN Natural Lands Map to quantify the area of natural lands due to data limitations, including overestimation of natural lands. However, data on natural and non-natural forest cover is much more widely available than other land covers, such as natural grassland and pasture, so we consider the natural forest classes to provide a reasonable approximation of natural forest area. Users should still be aware of the limitations of the data when using it to quantify area.</em></p>



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

New Data on Global Forest Watch Distinguishes Natural Forests from Other Tree Cover

<p>It’s well established that forests are important for people, nature and climate, but not all forests are created equal. Natural forests tend to <a href="https://zenodo.org/records/3553579#.ZFU8GnbMJwI" target="_blank" rel="noreferrer noopener">harbor more biodiversity</a> and offer a wider array of ecosystem services than planted forests, and therefore keeping natural forests standing is particularly critical.</p>



<p>The tree cover data available on Global Forest Watch (GFW) includes all types of tree cover, including natural forests and planted trees such tree crops and wood fiber plantations. Although data on primary forests are available for the humid tropics, distinguishing natural forests globally from other types of tree cover previously required overlaying multiple data sets to exclude areas that are not natural forests.</p>



<p>Now, new <a href="https://gfw.global/4fQfl03">natural forest data on GFW</a> from the <a href="https://sciencebasedtargetsnetwork.org/" target="_blank" rel="noreferrer noopener">Science Based Target Network (SBTN)</a> Natural Lands Map can help shed light on where the world’s natural forests are located and provide a baseline for monitoring loss within them since 2020.</p>



<h2 class="wp-block-heading">What is the Science Based Target Network Natural Lands Map?</h2>



<p>Land &amp; Carbon Lab collaborated with WWF and Systemiq to develop the <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> to support companies who have committed to eliminate conversion of natural lands in their supply chains through targets set with the SBTN land sector framework. The map provides a 2020 baseline of natural lands that companies can use to see where production activities have caused conversion since 2020, as well as identify and prevent future risk of conversion.</p>



<p>The Natural Lands Map includes several classes of natural land cover. The natural forest layer on GFW is a subset of the SBTN Natural Lands Map that includes the natural forest class and relevant subclasses, including wetland forests, mangroves and peat forests.</p>



<p>The <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> was created by combining the best available global and local land cover and land use data sets into a single harmonized map at 30-meter resolution circa the year 2020. This includes data sets such as the University of Maryland GLAD Lab’s 2020 <a href="https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.856903/full" target="_blank" rel="noreferrer noopener">land cover data set</a>, various data sets on cropland and <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">planted trees</a>, and local data sets such as <a href="https://brasil.mapbiomas.org/en/" target="_blank" rel="noreferrer noopener">MapBiomas</a> in South America and Indonesia, among others. The various input data sets were selected based on accuracies reported from the data producers when available and alignment with the definitions used by SBTN. Input data sets were combined to create the final Map.</p>



<h2 class="wp-block-heading">How does the Map define natural forests?</h2>



<p>SBTN uses the Accountability Framework Initiative (AFi) <a href="https://accountability-framework.org/use-the-accountability-framework/definitions/" target="_blank" rel="noreferrer noopener">definitions</a> of natural ecosystems and natural forests to align with other standard setting initiatives. AFi adopts the Food and Agriculture Organization (FAO) definition of forests: land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ, not including land that is predominantly under agricultural or other land use. AFi then differentiates <em>natural</em> forests as those where the species composition, structure and ecological function do not show effects of major human impacts.</p>



<p>Because satellite data cannot directly map all the elements of these definitions, the data development team operationalized them using proxies based on available data to exclude tree cover that would not meet the natural forest definition. Non-natural tree cover may include tree crops (e.g., palm oil, orchards, cocoa), planted forests (e.g., eucalyptus, teak, or pine plantations), patches of trees under 0.5 hectares, or trees within areas predominantly under agricultural or urban land use (e.g., trees planted within agricultural fields or within small urban parks). Natural forests may include primary, secondary or naturally regenerating forests, and may include some forms of low-intensity management.</p>



<p>AFi guidance states that quantitative thresholds established by national and sub-national forest definitions may take precedence over the global definitions, so local maps were given priority over global data in select regions.</p>



<h4 class="wp-block-heading">The natural forest map distinguishes natural forests from other tree cover</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/21139033?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/21139033/thumbnail" width="100%" alt="photo-slider visualization" /></noscript></div>



<h2 class="wp-block-heading">What data is available on Global Forest Watch?</h2>



<p>The natural forest layer now available on the GFW map includes all natural forest subclasses from the SBTN Natural Lands Map (<a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 0.3</a>). This natural forest layer has an overall accuracy of 88%.*</p>



<iframe width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6Im5hdHVyYWwtZm9yZXN0cyIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWUsImxheWVycyI6WyJuYXR1cmFsLWZvcmVzdHMtMjAyMCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>



<p>Previously, GFW users could analyze tree cover loss within and outside of the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which helped identify which losses were more likely to represent plantation harvesting or tree crop rotation. Now, the GFW dashboard allows users to separate tree cover loss in natural forests and non-natural tree cover from 2021 onwards using the natural forest map, meaning users can specifically monitor loss of natural forests around the world. This provides users with a more comprehensive and improved method to estimate loss in natural forests that is more aligned with widely accepted definitions.</p>



<iframe width="630" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeLossPlantations/country/BRA"></iframe>



<p></p>



<p>Users can also now calculate the area** of natural forest, non-natural tree cover and other land cover within administrative boundaries and other land use categories, such as protected areas.</p>



<iframe width="315" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/naturalForest/global"></iframe>



<p></p>



<h2 class="wp-block-heading">How can this data be used?</h2>



<p>The natural forest layer can be used to assess the extent of natural forest in 2020, and can be combined with additional data, such as tree cover loss, to monitor loss of natural forests from 2021 onward in a user’s area of interest. For example, companies can use the data to assess whether their production or sourcing activities have caused deforestation since 2020, or if their future plans pose a risk of deforestation, for tracking progress under voluntary commitments or the European Union Deforestation Regulation (EUDR).</p>



<p>For any use case however, users should consider the definitions used for the natural forest layer and determine whether these definitions align with their monitoring needs. For example, there is a key difference in how AFi and EUDR define deforestation that users must consider: while AFi defines deforestation as conversion of natural forest to non-natural tree cover or another land cover, <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32023R1115&amp;qid=1686611894470" target="_blank" rel="noreferrer noopener">EUDR defines deforestation</a> as the conversion of any type of forest — including planted forest — to agricultural use. Because the natural forest layer does not include planted forest, users may need to use additional data to include planted forests in their forest baseline to monitor conversion of natural and planted forest to agriculture for EUDR purposes.</p>



<p>For a full description of how the natural forest map relates to EUDR, see <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">this blog</a>. For more information on EUDR definitions and how GFW can support EUDR monitoring, read <a href="https://www.globalforestwatch.org/blog/data-and-tools/satellite-data-eu-regulation-deforestation-free-supply-chains/">part one</a> and <a href="https://www.globalforestwatch.org/blog/data-and-tools/monitoring-forest-degradation-eudr/">two</a> of our blog series.</p>



<p>Whenever using the SBTN Natural Lands Map, including the natural forests map on GFW, users should also be aware of a few limitations of the data:</p>



<ul type="1">
<li>Overestimation of natural forest in agroforestry systems and in temperate and boreal forests: Agroforestry systems, especially those where crops are grown under shade trees (such as some types of coffee and cocoa systems), are difficult to differentiate from natural forests in satellite imagery, and therefore available data on their extent is limited. Similarly, publicly available data on planted forests is limited in many temperate and boreal regions. As a result, these areas may be included as natural forest in the Map.</li>



<li>Inconsistent forest definitions in certain regions: Because the SBTN Natural Lands Map team prioritized local and regional input data over global data during the compilation and harmonization process when the Map was developed, there may be inconsistencies in the height and canopy density thresholds used to define forests in some regions where local data was used, including Brazil, the Amazon, Atlantic Forest, Pampa, Chaco, Indonesia, South Africa and New Zealand.</li>



<li>Temporal inconsistencies: The Map is as close to the year 2020 as possible, but due to limited data availability, some input data is from earlier time periods (e.g., 2015).</li>



<li>Resolution inconsistencies: Most input data used to create the Map is at least 30-meter resolution, but in some cases lower resolution data was used when higher resolution data was unavailable. These inconsistencies create data artifacts (sharp boundary lines that are only due to resolutions) in some areas.</li>
</ul>



<p>See more information on these limitations in the <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">technical documentation</a>.</p>



<h2 class="wp-block-heading">What’s next for the natural forest data on GFW?</h2>



<p>Currently, <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 0.3</a> of the natural forest map is available on GFW. In the coming months, we will update GFW to <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">version 1</a>, which is now available on the <a href="https://datasets.wri.org/datasets/sbtn-natural-lands-map?map=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" target="_blank" rel="noreferrer noopener">WRI Open Data Portal</a>, <a href="https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1" target="_blank" rel="noreferrer noopener">Google Earth Engine</a> and <a href="https://pro-news.globalforestwatch.org/news-events/how-gfw-pro-supports-eudr-compliance" target="_blank" rel="noreferrer noopener">GFW Pro</a>. We plan to continue to update the data on GFW as future versions of the SBTN Natural Lands Map become available.</p>



<p>We also plan to release additional analysis capabilities with the data, such as the option to filter deforestation and fire alerts to within natural forests. Stay tuned for future updates!</p>



<p></p>



<p>* <em>Note that the validation points were labeled based on definitions of natural forests from v1 of the SBTN Natural Lands Map, which includes more regional data sets and their corresponding definitions, as well as an additional filtering of local savanna classes to remove vegetation shorter than 5 meters from the forest class. This may affect the validation of the v0.3 natural forest layer because of variation in definitions of natural forest where local data was used. See the <a href="https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf" target="_blank" rel="noreferrer noopener">v1 technical note change log</a> to see the full list of changes.</em></p>



<p><em>** We generally </em><a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener"><em>caution against</em></a><em> using the SBTN Natural Lands Map to quantify the area of natural lands due to data limitations, including overestimation of natural lands. However, data on natural and non-natural forest cover is much more widely available than other land covers, such as natural grassland and pasture, so we consider the natural forest classes to provide a reasonable approximation of natural forest area. Users should still be aware of the limitations of the data when using it to quantify area.</em></p>



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New natural forest data on GFW reveals where the world’s natural forests are located and provides a baseline for monitoring loss within them.

New Data Distinguishes Natural Forests from Other Tree Cover

Comment

Global Forest Watch Content

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https://secure.gravatar.com/avatar/be19f67e91ba81d93d32ae47706dcbb8?s=96&d=mm&r=g

Paige Johnson

Tree cover gain 2000-2020 visualized on the GFW map

Mikaela Weisse

Katie Reytar

Peter Potapov

Sarah Carter

<p>In 2022, researchers at the University of Maryland (UMD) <a href="https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full" target="_blank" rel="noreferrer noopener">published the first results</a> of global tree cover height and change monitoring between 2000 and 2020, providing an updated look at tree cover <strong>height</strong>, tree cover <strong>gain</strong>, and <strong>net change</strong> in tree cover worldwide. This data set provided much-needed information to complement data on tree cover loss, illustrating all the components of forest change. The data shows that though the world lost more tree cover than it gained from 2000-2020, <a href="https://www.wri.org/insights/tracking-global-tree-cover-gain" target="_blank" rel="noreferrer noopener">36 countries</a> experienced a net gain in tree cover.</p>



<p>Since then, UMD calculated tree height and change in the intervening five-year periods — from 2005, 2010 and 2015. We’ve now made the five-year gain <a href="https://gfw.global/3ZJS5L6">available on Global Forest Watch (GFW)</a> — read on to learn more about how to access it and how it differs from data you may have used in the past.</p>



<h2 class="wp-block-heading">What’s available on GFW, and how can I access it?</h2>



<p>Three different data sets on the GFW platform are based on the height data:</p>



<p>1.<strong> Tree cover height in 2000 and 2020: </strong>This data shows the height in meters of tree canopy cover in each 30-meter pixel. Users can set the minimum height threshold starting at three meters to view the extent of tree cover above a specific height.</p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://gfw.global/3rrHOm7"></iframe>



<p></p>



<p>2. <strong>Tree cover gain between 2000, 2005, 2010, 2015 and 2020: </strong>This data shows areas of tree cover that grew from a height of less than five meters in the base year (either 2000, 2005, 2010 or 2015) to a height of greater than or equal to five meters in 2020. <a href="https://research.wri.org/gfr/key-terms-definitions#tree-cover-gain-and-restoration" target="_blank" rel="noreferrer noopener">Tree cover gain</a> can include regeneration of natural forests, regrowth after harvesting of planted trees or restoration activities. This layer replaces previous data on tree cover gain between 2000 and 2012.</p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://gfw.global/3ZJS5L6"></iframe>



<p></p>



<p>In addition to viewing the data on the map, users can access statistics on tree cover gain between an adjustable baseline year — either 2000, 2005, 2010 or 2015 — and end year 2020 on the analysis pane and dashboards. In the widget, access the settings to open up the options for adjusting the baseline.</p>



<iframe loading="lazy" width="315" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeCoverGain/global"></iframe>



<p></p>



<p>3. <strong>Net change in tree cover between 2000 and 2020: </strong>This data shows how much more or less tree cover a given location (country, state/province or county/municipality) had in 2020 compared to 2000. This data layer was newly added to GFW in 2022.</p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://gfw.global/3CJo9TN"></iframe>



<p></p>



<p>In addition to viewing the data on the map, users can access statistics on net change in tree cover between 2000 and 2020 on the analysis pane and dashboards.</p>



<iframe loading="lazy" width="630" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/netChange/global"></iframe>



<p></p>



<p>Annual data for all three of these data sets will eventually be available. Read more about what we’re learning from these three data sets in the <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-extent" target="_blank" rel="noreferrer noopener">Forest Extent</a> and <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-gain" target="_blank" rel="noreferrer noopener">Forest Gain</a> Indicators in the Global Forest Review.</p>



<h2 class="wp-block-heading">Why is GFW using tree cover height as well as canopy cover?</h2>



<p>The first iteration of the tree cover change data on GFW, including the current annual tree cover loss data and the previous tree cover gain between 2000 and 2012, used definitions based on the percent canopy cover. The three data sets discussed above are all based instead on tree cover height.</p>



<p>The main reason for this switch is because the tree cover height data is more accurate. <a href="https://gedi.umd.edu/mission/mission-overview/" target="_blank" rel="noreferrer noopener">NASA’s GEDI mission</a>, which uses a lidar sensor aboard the International Space Station, provides point-based measurements of vegetation structure, including forest canopy height, which UMD uses as training data to model tree height globally. The data defining tree cover in 2000 using a minimum height of five meters resulted in an accuracy of 97%, compared to 95% accuracy when tree cover is defined as a minimum canopy cover of 30%.</p>



<p>Additionally, tree cover height allows for improved detection of tree cover gain. Unlike tree canopy cover, which reaches its maximum soon after tree growth begins, tree height changes gradually.</p>



<p>Another value of tree cover height is that it better highlights changes in forest structure related to biomass, carbon storage and wildlife habitat. For example, tree height is linearly related to the increase of forest biomass and carbon storage, and therefore allows us to more accurately monitor carbon sequestration in trees over time.</p>



<h2 class="wp-block-heading">What’s different about the updated tree cover gain data?</h2>



<p>Like the previous tree cover gain data based on canopy cover, the data based on height shows cumulative gain between a base year and an end year. However, the updated data shows tree cover gain in five-year increments between 2000 and 2020, where the previous layer showed gain between 2000 and 2012. With eight additional years of data and a higher temporal cadence, we are now able to see a more recent view of gain and its trajectory since the start of the millennium.</p>



<p>The old data based on canopy cover and updated data based on height also feature slightly different definitions of gain due to the methodological shift from measuring canopy cover to measuring tree cover height, as described above. The old data defined gain as an increase in tree cover to at least 50% canopy cover, while the updated data defines gain as an increase in tree cover to at least five meters tall.</p>



<p>Like the previous data, the updated gain data is available at 30-meter resolution and was produced by the GLAD lab at the University of Maryland based on Landsat satellite images. Future versions of this data will show annual tree cover gain.</p>



<h2 class="wp-block-heading">What do the new 5-year intervals of tree cover gain data show?</h2>



<p>Data from the new intervals provides valuable information on the <a href="https://research.wri.org/gfr/forest-targets-tracker" target="_blank" rel="noreferrer noopener">global trajectory and pace of gain</a> and can help better reveal forest dynamics.</p>



<p>Between 2000 and 2020, a total of 130 million hectares (Mha) of tree cover was gained globally, meaning that 130 Mha of land that had no or very low tree cover in 2000 had trees at least five meters tall in 2020. But what can we say about the trajectory over time? </p>



<p>With the new five-year interval information, we now know that the rate of gain was not consistent over that 20-year time period, ranging from 41.5 Mha gained between 2000-2005 to 22.6 Mha gained between 2015-2020. While the overall trajectory shows a decline in the pace of gain, the decline in the last five-year period for which data are available is less severe. It is also important to consider the lag time between when seedlings are established and when they can be detected by satellite, with a longer time horizon needed for slower growing species. In other words, seedling establishment that occurred at the middle or end of the last five-year period (such as 2017 or later) is likely not captured in the data.&nbsp;&nbsp;</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/20323036?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/20323036/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



<p>Future annual tree cover height change products will improve our understanding of tree cover gain and allow for more precise and detailed measurements of forest changes over time.</p>



<h2 class="wp-block-heading">What is net change, and why is it important?</h2>



<p>The net tree cover change data tells us how much more or less tree cover there was in 2020 than in 2000 in a particular administrative area such as a country, province or district. This is different from the current annual tree cover loss data or the updated tree cover gain data, both of which focus on gross changes in tree cover. The net change corresponds to the area of gross gain minus the area of gross loss to show the overall change (which can be negative or positive).</p>



<p>Net change is important because loss and gain data by themselves show just one side of the coin. If we consider loss on its own, we‘re not accounting for tree cover that grows back due to restoration efforts, tree plantation establishment or natural regeneration (e.g. after a fire). If we consider gain on its own, we’re focusing only on new forest growth while turning a blind eye to any forests that are cut down next door.</p>



<p>Looking at net change in addition to gross gains and losses gives us a more holistic picture. Net change data is also important for better estimates of <a href="https://www.globalforestwatch.org/blog/data-and-research/forest-carbon-flux-data-explained/">carbon fluxes from forests</a> and aligns better with other data such as the FAO’s “forest area net change” statistics in the <a href="https://www.globalforestwatch.org/blog/data/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/?ap3c=IGc_RkUBX32y-fYCAGc_RkU8wbYWz5JGo6zjLQ9HiOVomSzZ_g">Forest Resources Assessment</a>.</p>



<p>However, net change alone doesn’t tell us about the quality of tree cover that was lost or gained. For example, primary forests in Southeast Asia that have been replaced by rubber plantations are considered to have no net change if that location had tree cover in both 2000 and 2020. It is important to look at other data sets available on GFW that can help us better understand these dynamics, such as those on <a href="https://gfw.global/3y9CGGQ">primary forests</a>, Intact Forest Landscapes and <a href="https://gfw.global/3ya4v1Q">tree plantations</a>.</p>



<h2 class="wp-block-heading">How does the annual tree cover loss data fit in?</h2>



<p>Future updates to the annual tree cover loss data will also be based on tree cover height data to allow direct comparisons with the updated tree cover height and gain layers going forward. Watch for more information on this change and what it means for tree cover loss resources on GFW and in the Global Forest Review.</p>



<p>Previous years of loss data will also be eventually reprocessed. However, note that until then, the gain and loss data cannot be compared to each other because they are based on different methods and definitions.</p>



<h2 class="wp-block-heading">What are the plans for updating this data?</h2>



<p>GFW is working with UMD to deliver annual data on tree height, including tree cover loss and gain data, which will eventually replace the 20-year data we have now. This will allow us to provide annual updates on global tree cover gain and net change in addition to our annual tree cover loss updates.</p>



<p>In the meantime, you can explore key takeaways about 2000 and 2020 extent, gain and progress toward restoration goals in the <a href="https://research.wri.org/gfr/global-forest-review" target="_blank" rel="noreferrer noopener">Global Forest Review</a>; learn more about what the gain data <a href="https://www.globalforestwatch.org/blog/forest-insights/tree-cover-gain-restoration-brazil">can and can’t show us about restoration</a>; and explore the data on the GFW <a href="https://gfw.global/3ZJS5L6">map</a> and <a href="https://www.globalforestwatch.org/dashboards/global/?category=forest-change&location=WyJnbG9iYWwiXQ%3D%3D&scrollTo=forest-gain">dashboards</a>.</p>



<p><em>This piece was originally published in October 2022. It was updated in December 2024 to reflect the latest developments on GFW.</em></p>



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Exploring Tree Cover Gain and Net Change on Global Forest Watch

<p>In 2022, researchers at the University of Maryland (UMD) <a href="https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full" target="_blank" rel="noreferrer noopener">published the first results</a> of global tree cover height and change monitoring between 2000 and 2020, providing an updated look at tree cover <strong>height</strong>, tree cover <strong>gain</strong>, and <strong>net change</strong> in tree cover worldwide. This data set provided much-needed information to complement data on tree cover loss, illustrating all the components of forest change. The data shows that though the world lost more tree cover than it gained from 2000-2020, <a href="https://www.wri.org/insights/tracking-global-tree-cover-gain" target="_blank" rel="noreferrer noopener">36 countries</a> experienced a net gain in tree cover.</p>



<p>Since then, UMD calculated tree height and change in the intervening five-year periods — from 2005, 2010 and 2015. We’ve now made the five-year gain <a href="https://gfw.global/3ZJS5L6">available on Global Forest Watch (GFW)</a> — read on to learn more about how to access it and how it differs from data you may have used in the past.</p>



<h2 class="wp-block-heading">What’s available on GFW, and how can I access it?</h2>



<p>Three different data sets on the GFW platform are based on the height data:</p>



<p>1.<strong> Tree cover height in 2000 and 2020: </strong>This data shows the height in meters of tree canopy cover in each 30-meter pixel. Users can set the minimum height threshold starting at three meters to view the extent of tree cover above a specific height.</p>



<iframe width="670" height="490" frameborder="0" src="https://gfw.global/3rrHOm7"></iframe>



<p></p>



<p>2. <strong>Tree cover gain between 2000, 2005, 2010, 2015 and 2020: </strong>This data shows areas of tree cover that grew from a height of less than five meters in the base year (either 2000, 2005, 2010 or 2015) to a height of greater than or equal to five meters in 2020. <a href="https://research.wri.org/gfr/key-terms-definitions#tree-cover-gain-and-restoration" target="_blank" rel="noreferrer noopener">Tree cover gain</a> can include regeneration of natural forests, regrowth after harvesting of planted trees or restoration activities. This layer replaces previous data on tree cover gain between 2000 and 2012.</p>



<iframe width="670" height="490" frameborder="0" src="https://gfw.global/3ZJS5L6"></iframe>



<p></p>



<p>In addition to viewing the data on the map, users can access statistics on tree cover gain between an adjustable baseline year — either 2000, 2005, 2010 or 2015 — and end year 2020 on the analysis pane and dashboards. In the widget, access the settings to open up the options for adjusting the baseline.</p>



<iframe width="315" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeCoverGain/global"></iframe>



<p></p>



<p>3. <strong>Net change in tree cover between 2000 and 2020: </strong>This data shows how much more or less tree cover a given location (country, state/province or county/municipality) had in 2020 compared to 2000. This data layer was newly added to GFW in 2022.</p>



<iframe width="670" height="490" frameborder="0" src="https://gfw.global/3CJo9TN"></iframe>



<p></p>



<p>In addition to viewing the data on the map, users can access statistics on net change in tree cover between 2000 and 2020 on the analysis pane and dashboards.</p>



<iframe width="630" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/netChange/global"></iframe>



<p></p>



<p>Annual data for all three of these data sets will eventually be available. Read more about what we’re learning from these three data sets in the <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-extent" target="_blank" rel="noreferrer noopener">Forest Extent</a> and <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-gain" target="_blank" rel="noreferrer noopener">Forest Gain</a> Indicators in the Global Forest Review.</p>



<h2 class="wp-block-heading">Why is GFW using tree cover height as well as canopy cover?</h2>



<p>The first iteration of the tree cover change data on GFW, including the current annual tree cover loss data and the previous tree cover gain between 2000 and 2012, used definitions based on the percent canopy cover. The three data sets discussed above are all based instead on tree cover height.</p>



<p>The main reason for this switch is because the tree cover height data is more accurate. <a href="https://gedi.umd.edu/mission/mission-overview/" target="_blank" rel="noreferrer noopener">NASA’s GEDI mission</a>, which uses a lidar sensor aboard the International Space Station, provides point-based measurements of vegetation structure, including forest canopy height, which UMD uses as training data to model tree height globally. The data defining tree cover in 2000 using a minimum height of five meters resulted in an accuracy of 97%, compared to 95% accuracy when tree cover is defined as a minimum canopy cover of 30%.</p>



<p>Additionally, tree cover height allows for improved detection of tree cover gain. Unlike tree canopy cover, which reaches its maximum soon after tree growth begins, tree height changes gradually.</p>



<p>Another value of tree cover height is that it better highlights changes in forest structure related to biomass, carbon storage and wildlife habitat. For example, tree height is linearly related to the increase of forest biomass and carbon storage, and therefore allows us to more accurately monitor carbon sequestration in trees over time.</p>



<h2 class="wp-block-heading">What’s different about the updated tree cover gain data?</h2>



<p>Like the previous tree cover gain data based on canopy cover, the data based on height shows cumulative gain between a base year and an end year. However, the updated data shows tree cover gain in five-year increments between 2000 and 2020, where the previous layer showed gain between 2000 and 2012. With eight additional years of data and a higher temporal cadence, we are now able to see a more recent view of gain and its trajectory since the start of the millennium.</p>



<p>The old data based on canopy cover and updated data based on height also feature slightly different definitions of gain due to the methodological shift from measuring canopy cover to measuring tree cover height, as described above. The old data defined gain as an increase in tree cover to at least 50% canopy cover, while the updated data defines gain as an increase in tree cover to at least five meters tall.</p>



<p>Like the previous data, the updated gain data is available at 30-meter resolution and was produced by the GLAD lab at the University of Maryland based on Landsat satellite images. Future versions of this data will show annual tree cover gain.</p>



<h2 class="wp-block-heading">What do the new 5-year intervals of tree cover gain data show?</h2>



<p>Data from the new intervals provides valuable information on the <a href="https://research.wri.org/gfr/forest-targets-tracker" target="_blank" rel="noreferrer noopener">global trajectory and pace of gain</a> and can help better reveal forest dynamics.</p>



<p>Between 2000 and 2020, a total of 130 million hectares (Mha) of tree cover was gained globally, meaning that 130 Mha of land that had no or very low tree cover in 2000 had trees at least five meters tall in 2020. But what can we say about the trajectory over time? </p>



<p>With the new five-year interval information, we now know that the rate of gain was not consistent over that 20-year time period, ranging from 41.5 Mha gained between 2000-2005 to 22.6 Mha gained between 2015-2020. While the overall trajectory shows a decline in the pace of gain, the decline in the last five-year period for which data are available is less severe. It is also important to consider the lag time between when seedlings are established and when they can be detected by satellite, with a longer time horizon needed for slower growing species. In other words, seedling establishment that occurred at the middle or end of the last five-year period (such as 2017 or later) is likely not captured in the data.&nbsp;&nbsp;</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/20323036?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/20323036/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



<p>Future annual tree cover height change products will improve our understanding of tree cover gain and allow for more precise and detailed measurements of forest changes over time.</p>



<h2 class="wp-block-heading">What is net change, and why is it important?</h2>



<p>The net tree cover change data tells us how much more or less tree cover there was in 2020 than in 2000 in a particular administrative area such as a country, province or district. This is different from the current annual tree cover loss data or the updated tree cover gain data, both of which focus on gross changes in tree cover. The net change corresponds to the area of gross gain minus the area of gross loss to show the overall change (which can be negative or positive).</p>



<p>Net change is important because loss and gain data by themselves show just one side of the coin. If we consider loss on its own, we‘re not accounting for tree cover that grows back due to restoration efforts, tree plantation establishment or natural regeneration (e.g. after a fire). If we consider gain on its own, we’re focusing only on new forest growth while turning a blind eye to any forests that are cut down next door.</p>



<p>Looking at net change in addition to gross gains and losses gives us a more holistic picture. Net change data is also important for better estimates of <a href="https://www.globalforestwatch.org/blog/data-and-research/forest-carbon-flux-data-explained/">carbon fluxes from forests</a> and aligns better with other data such as the FAO’s "forest area net change” statistics in the <a href="https://www.globalforestwatch.org/blog/data/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/?ap3c=IGc_RkUBX32y-fYCAGc_RkU8wbYWz5JGo6zjLQ9HiOVomSzZ_g">Forest Resources Assessment</a>.</p>



<p>However, net change alone doesn’t tell us about the quality of tree cover that was lost or gained. For example, primary forests in Southeast Asia that have been replaced by rubber plantations are considered to have no net change if that location had tree cover in both 2000 and 2020. It is important to look at other data sets available on GFW that can help us better understand these dynamics, such as those on <a href="https://gfw.global/3y9CGGQ">primary forests</a>, Intact Forest Landscapes and <a href="https://gfw.global/3ya4v1Q">tree plantations</a>.</p>



<h2 class="wp-block-heading">How does the annual tree cover loss data fit in?</h2>



<p>Future updates to the annual tree cover loss data will also be based on tree cover height data to allow direct comparisons with the updated tree cover height and gain layers going forward. Watch for more information on this change and what it means for tree cover loss resources on GFW and in the Global Forest Review.</p>



<p>Previous years of loss data will also be eventually reprocessed. However, note that until then, the gain and loss data cannot be compared to each other because they are based on different methods and definitions.</p>



<h2 class="wp-block-heading">What are the plans for updating this data?</h2>



<p>GFW is working with UMD to deliver annual data on tree height, including tree cover loss and gain data, which will eventually replace the 20-year data we have now. This will allow us to provide annual updates on global tree cover gain and net change in addition to our annual tree cover loss updates.</p>



<p>In the meantime, you can explore key takeaways about 2000 and 2020 extent, gain and progress toward restoration goals in the <a href="https://research.wri.org/gfr/global-forest-review" target="_blank" rel="noreferrer noopener">Global Forest Review</a>; learn more about what the gain data <a href="https://www.globalforestwatch.org/blog/forest-insights/tree-cover-gain-restoration-brazil">can and can’t show us about restoration</a>; and explore the data on the GFW <a href="https://gfw.global/3ZJS5L6">map</a> and <a href="https://www.globalforestwatch.org/dashboards/global/?category=forest-change&amp;location=WyJnbG9iYWwiXQ%3D%3D&amp;scrollTo=forest-gain">dashboards</a>.</p>



<p><em>This piece was originally published in October 2022. It was updated in December 2024 to reflect the latest developments on GFW.</em></p>



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Updated data on GFW provides a more detailed look at tree cover height, tree cover gain and net change in tree cover worldwide.

Exploring New Tree Cover Gain and Net Change Data | GFW

Oil palm plantation in Sentabai Village, West Kalimantan, Indonesia.

Jessica Richter

Nancy Harris

<p>Determining the boundaries of different forest types allows us to better understand how forests are changing over time. A longstanding limitation of the tree cover data available on Global Forest Watch (GFW) has been the inability to differentiate planted forests and tree crops from primary and secondary natural forests. There is no globally consistent data available on the location of planted trees, making it difficult to determine whether tree cover change occurred in natural forest or if it was simply harvesting or management activities in planted trees.</p>



<p>In 2019, GFW and World Resources Institute published the original Spatial Database of Planted Trees (SDPT) to fill this data gap by compiling data sources on planted tree boundaries across the globe. This effort allowed GFW users to view the location of planted trees on the GFW map and focus tree cover loss and gain statistics on change inside or outside planted tree areas.</p>



<p>Now, a new version of the SDPT is <a href="https://gfw.global/2Qbv7fM">available on GFW</a>, which provides broader geographic coverage and several key updates that improve its application to global forest monitoring and management.</p>



<h2 class="wp-block-heading">What is the Spatial Database of Planted Trees (SDPT)?</h2>



<p>The SDPT aims to spatially differentiate planted forests and tree crops from natural and seminatural forests on a global scale. We conducted extensive research and outreach to governments, civil society organizations and researchers to compile, synthesize and integrate national and regional data sources of the world’s planted trees into the SDPT.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1495" src="https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-2048x1495.png" alt="Table showing definitions of planted trees, planted forest and tree crops as defined in the Spatial Database of Planted Trees" class="wp-image-18224" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-2048x1495.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-1024x748.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-768x561.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-1536x1121.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p>The SDPT includes key information on each integrated data set, including the planted tree type (planted forest vs. tree crop), original data source information and carbon removal factors — or the rate at which planted trees remove atmospheric carbon dioxide and store it in biomass. Where available, the SDPT also includes species information, as well as the method and year(s) of imagery used to determine the planted tree boundary.</p>



<p>The SDPT was established as a living database that will continue to evolve as new data becomes available. The recent publication of an updated version of SDPT (<a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">v2.0</a>) reflects this aim and provides significant updates to the initial (<a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">v1.0</a>) database.</p>



<p>Users can download the v2.0 database <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">here</a>. The SDPT is also available as the <a href="https://gfw.global/2Qbv7fM">tree plantation</a> layer on the GFW map and is included in several analyses on the GFW dashboard.</p>



<style>
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   width: 100% !important;
float: none !important;
margin-left:
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<aside id="textbox"><em><b>Comparing the SDPT to Forest Resources Assessment Reporting</b><p></p>
The record of planted forest statistics provided in the <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">Food and Agriculture Organization of the United Nations’ (FAO)</a> <a href="https://www.fao.org/3/ca9825en/ca9825en.pdf" target="_blank" rel="noopener noreferrer">Forest Resources Assessment (FRA)</a> every five years serves as an important baseline of comparison to the SDPT. However, data sets included in the SDPT may not adhere to the definitions used by FAO as there are no universal definitions for the terms “planted trees,” “planted forests” and “tree crops.” In the SDPT, we focused on including data sets, or classes within data sets, that used the terms “plantation,” “plantation forest” or “planted forest,” which we classified under the term planted forest. The SDPT also includes maps of agroforestry systems and various perennial tree crops such as rubber, oil palm, coffee, coconut, cocoa and orchards, which we classified under the term tree crop. FAO instead uses the terms planted forest and plantation, defining planted forest as “forest predominantly composed of trees established through planting and/or deliberate seeding” and plantation as “planted forest that is intensively managed and meet ALL the following criteria at planting and stand maturity: one or two species, even age class, and regular spacing.” FAO’s reporting of planted forests does not include tree crop systems, so we compared FAO’s planted forest statistics to SDPT data classified as planted forest. The SDPT is not intended to resolve definitional debate surrounding these terms but rather to synthesize data.
</em> </aside>



<h2 class="wp-block-heading">What’s new in SDPT version 2.0?</h2>





<p>A range of <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">improvements have been made</a> to the SDPT in v2.0 based on new global and national data sources. These include:</p>



<p><strong>1. Near-global coverage</strong></p>



<p>Coverage in v2.0 has expanded from 82 countries in v1.0 to a total of 158 countries represented. Based on the newly compiled data, v2.0 reports approximately 329 million hectares (Mha) of planted trees worldwide in 2020, made up of 264 Mha of planted forests and 65 Mha of tree crops. This represents 8% of total global tree cover (an area roughly the size of India).</p>



<div class="flourish-embed flourish-map" data-src="visualisation/19737652?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/19737652/thumbnail" width="100%" alt="map visualization" /></noscript></div>



<div class="flourish-embed flourish-chart" data-src="visualisation/16052609?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/16052609/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



<p><strong>2. Tree species information</strong></p>



<p>SDPT v2.0 added or updated tree species information for 39 countries and now includes species-level information for 52 out of the 158 countries covered. Users can view available species information by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing the <em>sciName</em> data attribute.</p>



<p><strong>3. Planting year information</strong></p>



<p>A new planting year attribute has been added to the database that provides the latest approximate year that an individual area was planted. Researchers at Tsinghua University were able to detect the year of planting in SDPT v2.0 using the <a href="https://www.nature.com/articles/s41597-022-01260-2" target="_blank" rel="noreferrer noopener">LandTrendr algorithm</a> on a time series of Landsat images from 1982 to 2020. Results from this analysis were added to each planted tree area and are available by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing the <em>plantedYear </em>data attribute.</p>



<p><strong>4. Carbon removal factors</strong></p>



<p>Carbon removal factors, or carbon sequestration rates, have been assigned to planted trees represented in SDPT v2.0 that were not included in v1.0. In addition to carbon removal factors for aboveground biomass, belowground biomass and the standard deviations of removal factors, the database includes notes on methods and sources for each removal factor. Users can access carbon removal factor information by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing attributes such as <em>removalFactorAGCBGCMgHaYr</em>, <em>removalFactorAGCBGCstdevMgHaYr</em>, <em>removalFactorSource</em> and <em>removalFactorNotes</em>.</p>



<h2 class="wp-block-heading">How can the SDPT be used?</h2>



<p>The SDPT exists first and foremost to differentiate the geographic extent of planted trees from natural forests on a global scale, leading to easier identification of “natural forest” areas. Already, SDPT v2.0 is being used as an input layer into the Science Based Targets Network (SBTN) <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> to filter out planted forests and produce a natural forest class.</p>



<p>Specific SDPT attributes, such as species information, also have important global applications. Enforcement actors, such as United States government customs agencies, can use SDPT species information to expedite inspections of timber shipments. The database can help confirm whether there are other planted timber forests of the same species within the stated country of origin, as this impacts the legality of the import. The removal factors can be used to estimate carbon sequestration rates over large areas of planted trees where plot-level carbon measurements are not available.</p>



<p>Beyond these specific applications, the SDPT has implications for global forest monitoring and management. It can aid in tracking where and when planted trees may be replacing natural forests and help monitor the impact of these planted forests across diverse landscapes. This can lead to more effective tracking of national and global progress towards major international commitments associated with forests, climate and biodiversity.</p>



<h2 class="wp-block-heading">Future plans for the SDPT</h2>



<p>While major challenges remain in mapping planted trees, we expect to see continued, significant advances in large-scale planted tree mapping as well as improvements to national- and regional-scale data over time. The SDPT will continue to integrate these sources across all scales to provide users with the most detailed spatial information available. Incorporating more species-specific data into the SDPT is a key part of this effort. This includes species information taken directly from image-interpreted data sets as well as integrating non-spatial species information derived from literature reviews.</p>



<p>Users can <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">explore the tree plantations layer on GFW</a> and can <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">download the data</a><em>. </em>For more information on the SDPT v2.0 update, <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2">read the technical note here</a>.</p>



<p><em>The SDPT compiles data from a variety of sources. If you have spatial data on planted trees that you think should be incorporated into the data set, </em><a href="https://forms.gle/pUzJy72N7szVMydg9" target="_blank" rel="noreferrer noopener"><em>please let us know here</em></a><em>.</em></p>



<p><em>Additional minor updates have been made to the SDPT in a version 2.1 incorporating new data in Brazil, Cambodia, Cote d’Ivoire and Ghana. </em><a href="https://gfw-files.s3.amazonaws.com/plantations/SDPT_v2.1/sdpt_v21_v09152024_public.gdb.zip"><em>Access the latest version here</em></a><em>.</em></p>



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Updated Planted Trees Map Reaches Near-Global Coverage

<p>Determining the boundaries of different forest types allows us to better understand how forests are changing over time. A longstanding limitation of the tree cover data available on Global Forest Watch (GFW) has been the inability to differentiate planted forests and tree crops from primary and secondary natural forests. There is no globally consistent data available on the location of planted trees, making it difficult to determine whether tree cover change occurred in natural forest or if it was simply harvesting or management activities in planted trees.</p>



<p>In 2019, GFW and World Resources Institute published the original Spatial Database of Planted Trees (SDPT) to fill this data gap by compiling data sources on planted tree boundaries across the globe. This effort allowed GFW users to view the location of planted trees on the GFW map and focus tree cover loss and gain statistics on change inside or outside planted tree areas.</p>



<p>Now, a new version of the SDPT is <a href="https://gfw.global/2Qbv7fM">available on GFW</a>, which provides broader geographic coverage and several key updates that improve its application to global forest monitoring and management.</p>



<h2 class="wp-block-heading">What is the Spatial Database of Planted Trees (SDPT)?</h2>



<p>The SDPT aims to spatially differentiate planted forests and tree crops from natural and seminatural forests on a global scale. We conducted extensive research and outreach to governments, civil society organizations and researchers to compile, synthesize and integrate national and regional data sources of the world’s planted trees into the SDPT.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/11/23_GRAPHIC-GFW-blog-SPDT-spatial-database-planted-trees_V2-2048x1495.png" alt="Table showing definitions of planted trees, planted forest and tree crops as defined in the Spatial Database of Planted Trees" class="wp-image-18224"/></figure>



<p>The SDPT includes key information on each integrated data set, including the planted tree type (planted forest vs. tree crop), original data source information and carbon removal factors — or the rate at which planted trees remove atmospheric carbon dioxide and store it in biomass. Where available, the SDPT also includes species information, as well as the method and year(s) of imagery used to determine the planted tree boundary.</p>



<p>The SDPT was established as a living database that will continue to evolve as new data becomes available. The recent publication of an updated version of SDPT (<a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">v2.0</a>) reflects this aim and provides significant updates to the initial (<a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">v1.0</a>) database.</p>



<p>Users can download the v2.0 database <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">here</a>. The SDPT is also available as the <a href="https://gfw.global/2Qbv7fM">tree plantation</a> layer on the GFW map and is included in several analyses on the GFW dashboard.</p>



<style>
.css-19vc5o2 aside {
   width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox"><em><b>Comparing the SDPT to Forest Resources Assessment Reporting</b><p></p>
The record of planted forest statistics provided in the <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">Food and Agriculture Organization of the United Nations' (FAO)</a> <a href="https://www.fao.org/3/ca9825en/ca9825en.pdf" target="_blank" rel="noopener noreferrer">Forest Resources Assessment (FRA)</a> every five years serves as an important baseline of comparison to the SDPT. However, data sets included in the SDPT may not adhere to the definitions used by FAO as there are no universal definitions for the terms “planted trees,” “planted forests” and “tree crops.” In the SDPT, we focused on including data sets, or classes within data sets, that used the terms “plantation,” “plantation forest” or “planted forest,” which we classified under the term planted forest. The SDPT also includes maps of agroforestry systems and various perennial tree crops such as rubber, oil palm, coffee, coconut, cocoa and orchards, which we classified under the term tree crop. FAO instead uses the terms planted forest and plantation, defining planted forest as “forest predominantly composed of trees established through planting and/or deliberate seeding” and plantation as “planted forest that is intensively managed and meet ALL the following criteria at planting and stand maturity: one or two species, even age class, and regular spacing.” FAO’s reporting of planted forests does not include tree crop systems, so we compared FAO’s planted forest statistics to SDPT data classified as planted forest. The SDPT is not intended to resolve definitional debate surrounding these terms but rather to synthesize data.
</em> </aside>



<h2 class="wp-block-heading">What’s new in SDPT version 2.0?</h2>





<p>A range of <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">improvements have been made</a> to the SDPT in v2.0 based on new global and national data sources. These include:</p>



<p><strong>1. Near-global coverage</strong></p>



<p>Coverage in v2.0 has expanded from 82 countries in v1.0 to a total of 158 countries represented. Based on the newly compiled data, v2.0 reports approximately 329 million hectares (Mha) of planted trees worldwide in 2020, made up of 264 Mha of planted forests and 65 Mha of tree crops. This represents 8% of total global tree cover (an area roughly the size of India).</p>



<div class="flourish-embed flourish-map" data-src="visualisation/19737652?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/19737652/thumbnail" width="100%" alt="map visualization" /></noscript></div>



<div class="flourish-embed flourish-chart" data-src="visualisation/16052609?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/16052609/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



<p><strong>2. Tree species information</strong></p>



<p>SDPT v2.0 added or updated tree species information for 39 countries and now includes species-level information for 52 out of the 158 countries covered. Users can view available species information by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing the <em>sciName</em> data attribute.</p>



<p><strong>3. Planting year information</strong></p>



<p>A new planting year attribute has been added to the database that provides the latest approximate year that an individual area was planted. Researchers at Tsinghua University were able to detect the year of planting in SDPT v2.0 using the <a href="https://www.nature.com/articles/s41597-022-01260-2" target="_blank" rel="noreferrer noopener">LandTrendr algorithm</a> on a time series of Landsat images from 1982 to 2020. Results from this analysis were added to each planted tree area and are available by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing the <em>plantedYear </em>data attribute.</p>



<p><strong>4. Carbon removal factors</strong></p>



<p>Carbon removal factors, or carbon sequestration rates, have been assigned to planted trees represented in SDPT v2.0 that were not included in v1.0. In addition to carbon removal factors for aboveground biomass, belowground biomass and the standard deviations of removal factors, the database includes notes on methods and sources for each removal factor. Users can access carbon removal factor information by <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">downloading the database</a> and referencing attributes such as <em>removalFactorAGCBGCMgHaYr</em>, <em>removalFactorAGCBGCstdevMgHaYr</em>, <em>removalFactorSource</em> and <em>removalFactorNotes</em>.</p>



<h2 class="wp-block-heading">How can the SDPT be used?</h2>



<p>The SDPT exists first and foremost to differentiate the geographic extent of planted trees from natural forests on a global scale, leading to easier identification of “natural forest” areas. Already, SDPT v2.0 is being used as an input layer into the Science Based Targets Network (SBTN) <a href="https://landcarbonlab.org/insights/natural-lands-map-companies-no-conversion-targets/" target="_blank" rel="noreferrer noopener">Natural Lands Map</a> to filter out planted forests and produce a natural forest class.</p>



<p>Specific SDPT attributes, such as species information, also have important global applications. Enforcement actors, such as United States government customs agencies, can use SDPT species information to expedite inspections of timber shipments. The database can help confirm whether there are other planted timber forests of the same species within the stated country of origin, as this impacts the legality of the import. The removal factors can be used to estimate carbon sequestration rates over large areas of planted trees where plot-level carbon measurements are not available.</p>



<p>Beyond these specific applications, the SDPT has implications for global forest monitoring and management. It can aid in tracking where and when planted trees may be replacing natural forests and help monitor the impact of these planted forests across diverse landscapes. This can lead to more effective tracking of national and global progress towards major international commitments associated with forests, climate and biodiversity.</p>



<h2 class="wp-block-heading">Future plans for the SDPT</h2>



<p>While major challenges remain in mapping planted trees, we expect to see continued, significant advances in large-scale planted tree mapping as well as improvements to national- and regional-scale data over time. The SDPT will continue to integrate these sources across all scales to provide users with the most detailed spatial information available. Incorporating more species-specific data into the SDPT is a key part of this effort. This includes species information taken directly from image-interpreted data sets as well as integrating non-spatial species information derived from literature reviews.</p>



<p>Users can <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">explore the tree plantations layer on GFW</a> and can <a href="https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2.zip">download the data</a><em>. </em>For more information on the SDPT v2.0 update, <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2">read the technical note here</a>.</p>



<p><em>The SDPT compiles data from a variety of sources. If you have spatial data on planted trees that you think should be incorporated into the data set, </em><a href="https://forms.gle/pUzJy72N7szVMydg9" target="_blank" rel="noreferrer noopener"><em>please let us know here</em></a><em>.</em></p>



<p><em>Additional minor updates have been made to the SDPT in a version 2.1 incorporating new data in Brazil, Cambodia, Cote d’Ivoire and Ghana. </em><a href="https://gfw-files.s3.amazonaws.com/plantations/SDPT_v2.1/sdpt_v21_v09152024_public.gdb.zip"><em>Access the latest version here</em></a><em>.</em></p>



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A new version of the Spatial Database of Planted Trees provides near-global coverage and other key updates.

Deforestation Alerts

Deforestation Alerts | Global Forest Watch Blog

Zhen Song

James MacCarthy

Alyssa Barrett

Anika Berger

<p>Global Forest Watch’s (GFW) deforestation alerts provide information about loss of trees 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" 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>But previously, alerts were only available for tropical forests. Now, a <a href="https://gfw.global/401umrm">new alert system available on GFW called DIST-ALERT</a> provides alerts within forests globally as well as alerts where other ecosystems are disturbed — such as grasslands, savannas, shrublands, wetlands and crops. This new global data set makes it possible to monitor deforestation and other vegetation disturbance events as they happen around the world for the first time.</p>



<p>Read on to learn more about the new DIST-ALERT, how it’s different from other alerts and what’s next for alerts on GFW.</p>



<h2 class="wp-block-heading">What are the new disturbance alerts?</h2>



<p>In 2024, a groundbreaking new land surface disturbance monitoring system known as&nbsp;<a href="https://lpdaac.usgs.gov/products/opera_l3_dist-alert-hls_v1v001/" target="_blank" rel="noreferrer noopener">OPERA Land Surface Disturbance Alert</a>, or DIST-ALERT, was developed by the University of Maryland (UMD) and&nbsp;<a href="https://www.jpl.nasa.gov/go/opera" target="_blank" rel="noreferrer noopener">NASA’s OPERA project</a> to map per pixel vegetation cover loss globally at a 30×30-meter resolution. The DIST-ALERT algorithm uses Harmonized Landsat Sentinel-2 (HLS) satellite data that is updated every 2-4 days for each pixel on the earth, and thus the system is updated every time the satellite data is acquired and the ground is not covered by clouds or snow (<a href="https://landcarbonlab.org/insights/new-monitoring-system-land-ecosystem-disturbances/" target="_blank" rel="noreferrer noopener">read more here</a> about how DIST-ALERT was developed and the methodology behind it).</p>



<p>DIST-ALERT shows an alert when there is an anomaly or notable reduction in vegetation cover, measured as “vegetation fraction” or percent of the ground covered by vegetation. It identifies this by comparing the new vegetation fraction estimate to the minimum fraction for the same time period over the last three years. This means that the algorithm can accommodate changes to seasonal vegetation without triggering an alert.</p>



<h2 class="wp-block-heading">What new alert data is available on GFW?</h2>



<p>The new DIST-ALERT system on GFW updates weekly and tailors the vegetation fraction level to only display alerts in areas that experience at least 30% vegetation cover loss compared to the same period over the last three years. &nbsp;</p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>



<p></p>



<p>DIST-ALERT on GFW also provides information about the level of confidence for alerts. The system determines confidence level by the number of anomalous observations, with more observations meaning a higher confidence level. That is, two to three anomalies detected result in a low confidence alert, whereas four or more mean a high confidence alert. Since the system continues to detect anomalies in a pixel over time and the underlying algorithm is updated every 2-4 days (in cloud free conditions), this means that an alert can quickly move from lower to higher confidence, providing users with more timely and more confident alerting than ever before.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="800" height="155" src="https://content.globalforestwatch.org/wp-content/uploads/2024/12/confidence-levels-DIST-ALERT-GFW.png" alt="" class="wp-image-18301" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/12/confidence-levels-DIST-ALERT-GFW.png 800w, https://content.globalforestwatch.org/wp-content/uploads/2024/12/confidence-levels-DIST-ALERT-GFW-768x149.png 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p>Like other alerts on GFW, users can also explore DIST-ALERT locations with the <a href="https://forestwatcher.globalforestwatch.org/" target="_blank" rel="noreferrer noopener">Forest Watcher mobile app</a>, which allows users to investigate potential deforestation events and other disturbances in the field and offline.</p>



<h2 class="wp-block-heading">How does DIST-ALERT differ from existing alerts on GFW?</h2>



<p>GFW’s existing <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">integrated deforestation alerts</a> are only available in tropical forests. Now, DIST-ALERT makes it possible to view alerts for all types of tree and vegetation cover, including grassland, shrubland, agricultural land, savannas, temperate forests and boreal forests.</p>



<p>Users can also choose to only view alerts within areas of tree cover with at least 30% canopy density across the globe. Disturbances in forests with 10-30% canopy cover are difficult to identify with this product because anomalies can be driven by disturbances of any vegetation in the pixel — for example, disturbances of undergrowth where trees remain intact. For this reason, we provide the option to view alerts where tree cover disturbances are more likely to be driving the anomalies: within areas of 30% tree canopy density or above.</p>



<p>In contrast to many other alert systems available on GFW, DIST-ALERT continues to monitor pixels where it has identified an alert in the past. The layer on GFW shows the date of the most recent alert, keeping users informed of the most up-to-date changes to forest and vegetation cover.</p>



<table>
    <tr>
        <td><strong>System </strong></td>
        <td><td style="width:105px;"><strong>Geographic coverage </strong></td>
        <td><td style="width:89px;"><strong>Resolution </strong></td>
        <td><strong>Revisit time </strong></td>
        <td><strong>Vegetation type </strong></td>
        <td><strong>Alert description</strong></td>
 
    </tr>
    <tr>
        <td><strong>DIST-ALERT</strong> (Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2)  </td>
        <td><td style="width:105px;">Global  </td>
        <td><td style="width:89px;">30 meter </td>
        <td>Every 2-4 days  </td>
        <td>All vegetation (and recommended for forests: tree cover >30%) </td>
        <td>Vegetation cover loss >30% compared to the same period over the previous 3 years in each HLS pixel</td>
    
    </tr>
    <tr>
        <td><strong>Integrated Alerts</strong> (combines GLAD-L, GLAD-S2 and RADD)</td>
        <td><td style="width:105px;">Tropics —30°N to 30°S  </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Upon source systems’ updates (GLAD-L, GLAD-S2 and RADD) </td>
        <td>Tree cover – including primary forests, plantations and secondary forest</td>
        <td>See contributing systems (GLAD-L, GLAD-S2 and RADD) </td>
       
    </tr>
    <tr>
        <td><strong>GLAD-L</strong> (Global Land Analysis and Discovery – Landsat) </td>
        <td><td style="width:105px;">Tropics — 30°N to 30°S </td>
        <td><td style="width:89px;">30 meter </td>
        <td>Every 8 days  </td>
        <td>Tree cover – including primary forest, plantations and secondary forests </td>
        <td>Any pixel that experiences a canopy loss in excess of 50% cover  </td>
     </tr>
    <tr>
        <td><strong>GLAD-S2</strong> (Global Land Analysis and Discovery – Sentinel-2) </td>
        <td><td style="width:105px;">Amazon basin </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Every 5 days </td>
        <td>Humid tropical primary forests  </td>
        <td>Reduction in tree cover compared to the previous 2 years  </td>
     
    </tr>
    <tr>
        <td><strong>RADD</strong> (RAdar for Detecting Deforestation – Sentinel-1) </td>
        <td><td style="width:105px;">Humid tropics </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Every 6-12 days </td>
        <td>Humid tropical primary forests  </td>
        <td>Complete or partial removal of tree cover, with a minimum mapping unit of 0.1 hectares. Use of radar detects disturbances through clouds. </td>
    </tr></table>



<p></p>



<p>GFW also hosts the <a href="https://www.globalforestwatch.org/map/?mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoiZm9yZXN0Q2hhbmdlIn0%3D&modalMeta=viirs_fires">VIIRS fire alerts</a>. Like DIST-ALERT, these monitor all land, but they differ from DIST-ALERT because they only monitor fire activity, have more frequent temporal resolution (2 times daily) and a coarser spatial resolution (375 meter).</p>



<h2 class="wp-block-heading">What can the new alerts tell us?</h2>



<p>The DIST-ALERT data shows changes within both natural and human-managed vegetation cover. These changes may be driven by human activities like agricultural conversion, or natural causes like drought damage in crops, and may be temporary or permanent. Therefore, an alert in tree cover area is not necessarily deforestation, and one in grasslands or savannas is not necessarily conversion to another land use such as agriculture.</p>



<p>In addition, because the alerts are available in near-real-time, they are often available before any change in land use, like conversion to agriculture, has occurred, and before replanting might have happened in areas where forestry activities are taking place, meaning further investigation may be needed to understand the context of the alert.</p>



<p>While this new system is incredibly powerful, detecting any loss of vegetative cover anywhere on earth continuously, not all changes are equally interesting and useful to track. Additional information such as whether the alert is in a natural or non-natural ecosystem can be used to identify alerts that warrant further action. Here are two examples of how the data may be useful:</p>



<p><strong>1.</strong> <strong>Supply chain actors can monitor for no-conversion voluntary commitments and regulatory requirements: </strong>Suppliers looking to ensure and demonstrate sustainability in their supply chains and that have made no-conversion commitments for natural lands (e.g. the <a href="https://accountability-framework.org/news-events/news/the-afi-recommends-a-target-date-no-later-than-2025-to-eliminate-deforestation-and-conversion-in-supply-chains/" target="_blank" rel="noreferrer noopener">Accountability Framework Initiative goal</a>) can use DIST-ALERT to monitor areas outside of forests. The data is also relevant to the European Union’s deforestation regulation (EUDR), providing information on potential deforestation or degradation. In addition, while the EUDR’s scope is currently limited to conversions in forests, it could potentially expand to “Other Wooded Land” (non-agricultural land with few trees that can include shrubs and bushes). This expansion would require identifying conversion of this land cover to cropland, which DIST-ALERT could support.</p>



<p><strong>2. Land managers can identify threats to natural ecosystems: </strong>DIST-ALERT can provide early information about other worrying trends in the world’s natural ecosystems, such as the impacts of droughts and climate change. By tracking alerts within valuable lands such as protected areas, biodiversity hotspots and Indigenous lands, and crosschecking with other information such as weather anomalies or fires, land managers could better tell what’s going on in these areas to identify threats and support their decision making. The global biodiversity framework includes the ambitious “30×30 initiative,” which aims to protect 30% of the planet’s land and water by 2030. Alerts within these protected areas could indicate threats to their long-term conservation and provide an early warning that swift action should be taken to reverse this trend.</p>



<h2 class="wp-block-heading">What’s next for DIST-ALERT on GFW?</h2>



<p>The new DIST-ALERT layer provides more near-real-time information about disturbances in the world’s vegetation than ever before, but there is still more to come.</p>



<p>In future, GFW will have additional features to support DIST-ALERT such as on-the-fly analysis (e.g. number of alerts) in an uploaded or drawn area, the ability to subscribe to email alerts, and the ability to download alert coordinates. We will introduce these items in the coming months, but in the meantime, users can overlay layers available on GFW such as primary forests and protected areas and view together with DIST-ALERT to provide context and support decision making.</p>



<p>We also plan to add information about what is causing alerts. This information will help users understand if the alerts are caused by natural disturbances or human activity, and inform decisions about what kind of action might be required. Land and Carbon Lab is developing an <a href="https://landcarbonlab.org/data/land-disturbance-alert-classification-system/" target="_blank" rel="noreferrer noopener">alert classification system</a> to create targeted alert products that can be used to distinguish between human-caused conversion of natural lands to other land uses like agriculture, mining or urban development, as well as natural disturbances like drought and wildfire. A prototype is currently available for testing and users can register their interest and gain access by <a href="https://survey.alchemer.com/s3/8004701/b7c0ce053cf0" target="_blank" rel="noreferrer noopener">completing this survey</a>.</p>



<p>Lastly, DIST-ALERT is not yet incorporated into GFW’s integrated deforestation alerts, but we are working hard to add it in the near future so users can benefit from improved and expanded alert systems, all in one place. Adding the DIST-ALERT layer to the integrated deforestation alert product means that more timely and confident alerts will be available for users, and will enable having a “highest confidence” category where two or more systems detect an alert in the same pixel, and alerts are therefore more certain.</p>



<p><em>We welcome your input as we continue to improve the new DIST-ALERT data. <a href="https://survey.alchemer.com/s3/8130006/e9ac3f6c1b42" target="_blank" rel="noreferrer noopener">Please fill out a short survey</a> to provide your feedback.&nbsp;</em>&nbsp;</p>



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

Near-Real-Time Disturbance Alerts for All Vegetation Globally are Now Available on GFW

<p>Global Forest Watch’s (GFW) deforestation alerts provide information about loss of trees 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" 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>But previously, alerts were only available for tropical forests. Now, a <a href="https://gfw.global/401umrm">new alert system available on GFW called DIST-ALERT</a> provides alerts within forests globally as well as alerts where other ecosystems are disturbed — such as grasslands, savannas, shrublands, wetlands and crops. This new global data set makes it possible to monitor deforestation and other vegetation disturbance events as they happen around the world for the first time.</p>



<p>Read on to learn more about the new DIST-ALERT, how it’s different from other alerts and what’s next for alerts on GFW.</p>



<h2 class="wp-block-heading">What are the new disturbance alerts?</h2>



<p>In 2024, a groundbreaking new land surface disturbance monitoring system known as&nbsp;<a href="https://lpdaac.usgs.gov/products/opera_l3_dist-alert-hls_v1v001/" target="_blank" rel="noreferrer noopener">OPERA Land Surface Disturbance Alert</a>, or DIST-ALERT, was developed by the University of Maryland (UMD) and&nbsp;<a href="https://www.jpl.nasa.gov/go/opera" target="_blank" rel="noreferrer noopener">NASA’s OPERA project</a> to map per pixel vegetation cover loss globally at a 30x30-meter resolution. The DIST-ALERT algorithm uses Harmonized Landsat Sentinel-2 (HLS) satellite data that is updated every 2-4 days for each pixel on the earth, and thus the system is updated every time the satellite data is acquired and the ground is not covered by clouds or snow (<a href="https://landcarbonlab.org/insights/new-monitoring-system-land-ecosystem-disturbances/" target="_blank" rel="noreferrer noopener">read more here</a> about how DIST-ALERT was developed and the methodology behind it).</p>



<p>DIST-ALERT shows an alert when there is an anomaly or notable reduction in vegetation cover, measured as “vegetation fraction” or percent of the ground covered by vegetation. It identifies this by comparing the new vegetation fraction estimate to the minimum fraction for the same time period over the last three years. This means that the algorithm can accommodate changes to seasonal vegetation without triggering an alert.</p>



<h2 class="wp-block-heading">What new alert data is available on GFW?</h2>



<p>The new DIST-ALERT system on GFW updates weekly and tailors the vegetation fraction level to only display alerts in areas that experience at least 30% vegetation cover loss compared to the same period over the last three years. &nbsp;</p>



<iframe width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>



<p></p>



<p>DIST-ALERT on GFW also provides information about the level of confidence for alerts. The system determines confidence level by the number of anomalous observations, with more observations meaning a higher confidence level. That is, two to three anomalies detected result in a low confidence alert, whereas four or more mean a high confidence alert. Since the system continues to detect anomalies in a pixel over time and the underlying algorithm is updated every 2-4 days (in cloud free conditions), this means that an alert can quickly move from lower to higher confidence, providing users with more timely and more confident alerting than ever before.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/12/confidence-levels-DIST-ALERT-GFW.png" alt="" class="wp-image-18301"/></figure>



<p>Like other alerts on GFW, users can also explore DIST-ALERT locations with the <a href="https://forestwatcher.globalforestwatch.org/" target="_blank" rel="noreferrer noopener">Forest Watcher mobile app</a>, which allows users to investigate potential deforestation events and other disturbances in the field and offline.</p>



<h2 class="wp-block-heading">How does DIST-ALERT differ from existing alerts on GFW?</h2>



<p>GFW’s existing <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">integrated deforestation alerts</a> are only available in tropical forests. Now, DIST-ALERT makes it possible to view alerts for all types of tree and vegetation cover, including grassland, shrubland, agricultural land, savannas, temperate forests and boreal forests.</p>



<p>Users can also choose to only view alerts within areas of tree cover with at least 30% canopy density across the globe. Disturbances in forests with 10-30% canopy cover are difficult to identify with this product because anomalies can be driven by disturbances of any vegetation in the pixel — for example, disturbances of undergrowth where trees remain intact. For this reason, we provide the option to view alerts where tree cover disturbances are more likely to be driving the anomalies: within areas of 30% tree canopy density or above.</p>



<p>In contrast to many other alert systems available on GFW, DIST-ALERT continues to monitor pixels where it has identified an alert in the past. The layer on GFW shows the date of the most recent alert, keeping users informed of the most up-to-date changes to forest and vegetation cover.</p>



<table>
    <tr>
        <td><strong>System </strong></td>
        <td><td style="width:105px;"><strong>Geographic coverage </strong></td>
        <td><td style="width:89px;"><strong>Resolution </strong></td>
        <td><strong>Revisit time </strong></td>
        <td><strong>Vegetation type </strong></td>
        <td><strong>Alert description</strong></td>
 
    </tr>
    <tr>
        <td><strong>DIST-ALERT</strong> (Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2)  </td>
        <td><td style="width:105px;">Global  </td>
        <td><td style="width:89px;">30 meter </td>
        <td>Every 2-4 days  </td>
        <td>All vegetation (and recommended for forests: tree cover &gt;30%) </td>
        <td>Vegetation cover loss &gt;30% compared to the same period over the previous 3 years in each HLS pixel</td>
    
    </tr>
    <tr>
        <td><strong>Integrated Alerts</strong> (combines GLAD-L, GLAD-S2 and RADD)</td>
        <td><td style="width:105px;">Tropics —30°N to 30°S  </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Upon source systems’ updates (GLAD-L, GLAD-S2 and RADD) </td>
        <td>Tree cover - including primary forests, plantations and secondary forest</td>
        <td>See contributing systems (GLAD-L, GLAD-S2 and RADD) </td>
       
    </tr>
    <tr>
        <td><strong>GLAD-L</strong> (Global Land Analysis and Discovery – Landsat) </td>
        <td><td style="width:105px;">Tropics — 30°N to 30°S </td>
        <td><td style="width:89px;">30 meter </td>
        <td>Every 8 days  </td>
        <td>Tree cover - including primary forest, plantations and secondary forests </td>
        <td>Any pixel that experiences a canopy loss in excess of 50% cover  </td>
     </tr>
    <tr>
        <td><strong>GLAD-S2</strong> (Global Land Analysis and Discovery – Sentinel-2) </td>
        <td><td style="width:105px;">Amazon basin </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Every 5 days </td>
        <td>Humid tropical primary forests  </td>
        <td>Reduction in tree cover compared to the previous 2 years  </td>
     
    </tr>
    <tr>
        <td><strong>RADD</strong> (RAdar for Detecting Deforestation – Sentinel-1) </td>
        <td><td style="width:105px;">Humid tropics </td>
        <td><td style="width:89px;">10 meter </td>
        <td>Every 6-12 days </td>
        <td>Humid tropical primary forests  </td>
        <td>Complete or partial removal of tree cover, with a minimum mapping unit of 0.1 hectares. Use of radar detects disturbances through clouds. </td>
    </tr></table>



<p></p>



<p>GFW also hosts the <a href="https://www.globalforestwatch.org/map/?mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoiZm9yZXN0Q2hhbmdlIn0%3D&amp;modalMeta=viirs_fires">VIIRS fire alerts</a>. Like DIST-ALERT, these monitor all land, but they differ from DIST-ALERT because they only monitor fire activity, have more frequent temporal resolution (2 times daily) and a coarser spatial resolution (375 meter).</p>



<h2 class="wp-block-heading">What can the new alerts tell us?</h2>



<p>The DIST-ALERT data shows changes within both natural and human-managed vegetation cover. These changes may be driven by human activities like agricultural conversion, or natural causes like drought damage in crops, and may be temporary or permanent. Therefore, an alert in tree cover area is not necessarily deforestation, and one in grasslands or savannas is not necessarily conversion to another land use such as agriculture.</p>



<p>In addition, because the alerts are available in near-real-time, they are often available before any change in land use, like conversion to agriculture, has occurred, and before replanting might have happened in areas where forestry activities are taking place, meaning further investigation may be needed to understand the context of the alert.</p>



<p>While this new system is incredibly powerful, detecting any loss of vegetative cover anywhere on earth continuously, not all changes are equally interesting and useful to track. Additional information such as whether the alert is in a natural or non-natural ecosystem can be used to identify alerts that warrant further action. Here are two examples of how the data may be useful:</p>



<p><strong>1.</strong> <strong>Supply chain actors can monitor for no-conversion voluntary commitments and regulatory requirements: </strong>Suppliers looking to ensure and demonstrate sustainability in their supply chains and that have made no-conversion commitments for natural lands (e.g. the <a href="https://accountability-framework.org/news-events/news/the-afi-recommends-a-target-date-no-later-than-2025-to-eliminate-deforestation-and-conversion-in-supply-chains/" target="_blank" rel="noreferrer noopener">Accountability Framework Initiative goal</a>) can use DIST-ALERT to monitor areas outside of forests. The data is also relevant to the European Union’s deforestation regulation (EUDR), providing information on potential deforestation or degradation. In addition, while the EUDR’s scope is currently limited to conversions in forests, it could potentially expand to “Other Wooded Land” (non-agricultural land with few trees that can include shrubs and bushes). This expansion would require identifying conversion of this land cover to cropland, which DIST-ALERT could support.</p>



<p><strong>2. Land managers can identify threats to natural ecosystems: </strong>DIST-ALERT can provide early information about other worrying trends in the world’s natural ecosystems, such as the impacts of droughts and climate change. By tracking alerts within valuable lands such as protected areas, biodiversity hotspots and Indigenous lands, and crosschecking with other information such as weather anomalies or fires, land managers could better tell what’s going on in these areas to identify threats and support their decision making. The global biodiversity framework includes the ambitious “30x30 initiative,” which aims to protect 30% of the planet's land and water by 2030. Alerts within these protected areas could indicate threats to their long-term conservation and provide an early warning that swift action should be taken to reverse this trend.</p>



<h2 class="wp-block-heading">What’s next for DIST-ALERT on GFW?</h2>



<p>The new DIST-ALERT layer provides more near-real-time information about disturbances in the world’s vegetation than ever before, but there is still more to come.</p>



<p>In future, GFW will have additional features to support DIST-ALERT such as on-the-fly analysis (e.g. number of alerts) in an uploaded or drawn area, the ability to subscribe to email alerts, and the ability to download alert coordinates. We will introduce these items in the coming months, but in the meantime, users can overlay layers available on GFW such as primary forests and protected areas and view together with DIST-ALERT to provide context and support decision making.</p>



<p>We also plan to add information about what is causing alerts. This information will help users understand if the alerts are caused by natural disturbances or human activity, and inform decisions about what kind of action might be required. Land and Carbon Lab is developing an <a href="https://landcarbonlab.org/data/land-disturbance-alert-classification-system/" target="_blank" rel="noreferrer noopener">alert classification system</a> to create targeted alert products that can be used to distinguish between human-caused conversion of natural lands to other land uses like agriculture, mining or urban development, as well as natural disturbances like drought and wildfire. A prototype is currently available for testing and users can register their interest and gain access by <a href="https://survey.alchemer.com/s3/8004701/b7c0ce053cf0" target="_blank" rel="noreferrer noopener">completing this survey</a>.</p>



<p>Lastly, DIST-ALERT is not yet incorporated into GFW’s integrated deforestation alerts, but we are working hard to add it in the near future so users can benefit from improved and expanded alert systems, all in one place. Adding the DIST-ALERT layer to the integrated deforestation alert product means that more timely and confident alerts will be available for users, and will enable having a “highest confidence” category where two or more systems detect an alert in the same pixel, and alerts are therefore more certain.</p>



<p><em>We welcome your input as we continue to improve the new DIST-ALERT data. <a href="https://survey.alchemer.com/s3/8130006/e9ac3f6c1b42" target="_blank" rel="noreferrer noopener">Please fill out a short survey</a> to provide your feedback.&nbsp;</em>&nbsp;</p>



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

A new global alert system on GFW, DIST-ALERT, provides alerts within forests globally as well where other ecosystems are disturbed.

Disturbance Alerts for All Vegetation Globally Now on GFW

Tree cover loss due to fires visualized on the GFW map, 2001-2021

fires

fires | Global Forest Watch Blog

Sasha Tyukavina

<p><a href="https://www.frontiersin.org/articles/10.3389/frsen.2022.825190/full" target="_blank" rel="noreferrer noopener">The latest data</a> from the University of Maryland (UMD), available on Global Forest Watch (GFW), provides a detailed view of how fires have driven tree cover loss across the globe through 2023. The <a href="https://gfw.global/3d5Ri2B">tree cover loss from fires data</a> offers insights on <a href="https://gfw.global/3Kdqn1n" target="_blank" rel="noreferrer noopener">trends in fire-driven loss since 2001</a> at a higher resolution than other global burned area data sets. Read on to learn what the data measures, how we can use it and how it differs from other fire-related data on GFW.</p>



<h2 class="wp-block-heading">The Latest Insights Into Trends of Fire-Driven Tree Cover Loss</h2>



<p>The tree cover loss from fires data builds on the existing <a href="https://research.wri.org/gfr/latest-analysis-deforestation-trends" target="_blank" rel="noreferrer noopener">annual tree cover loss data</a> from UMD by providing additional context on where fires are driving loss. Each 30-m pixel in the annual tree cover loss data is analyzed using Landsat satellite images to determine whether the loss was likely due to stand-replacing fires (which kill all or most living trees). This data allows fire-driven loss to be separated from all other drivers of tree cover loss, like agriculture and forestry. Using this data, detailed patterns of tree cover loss due to fires can be assessed over a 23-year period (2001-2023) using a globally consistent definition of fire-driven forest loss.</p>



<p>For this data, loss due to fires is defined as natural or human-ignited fires that result in direct loss of tree canopy cover. This could include wildfires, and intentionally set fires (including escaped fires started by humans for purposes related to agriculture, hunting, recreation or arson). Instances where trees are felled and later burned are not included since the initial driver of loss in that case is mechanical removal. Low-intensity and understory forest fires that do not result in substantial tree canopy loss at the scale of a 30-m pixel are also excluded from this definition.&nbsp;</p>



<p>This data is updated on an annual basis and allows us to better understand global, regional and local trends in tree cover loss as they evolve over time.</p>



<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="tree cover loss due to fires 2001-2023" src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/2024-Fires-Final-GIF-W-TITLE-1-1.gif" width="100%"></div>



<h2 class="wp-block-heading">How Can We Use the Tree Cover Loss from Fires Data?</h2>



<h3 class="wp-block-heading"><em>Identifying Areas with the Most Tree Cover Loss Due to Fires</em></h3>



<p>Users can use this data to visualize and analyze the spatial distribution of loss due to fires over time on GFW. For example, the global dashboard can help users identify countries with the most tree cover loss due to fires in the previous year.</p>



<iframe loading="lazy" width="315" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeLossFires/global"></iframe>



<p></p>



<p>Looking at the list, Canada, Russia, and Brazil had the most tree cover loss due to fires in 2023. After analyzing the downloaded data, we can see that these three countries combined lost nearly 10.6 million hectares or 89% of all fire-related loss in 2023.</p>



<p>We can then view how fire activity has changed over time for these areas. For example, in 2023, Canada experienced the largest area of tree cover loss the country has seen in 23 years, 91% of which was due to fires.</p>



<iframe loading="lazy" width="630" height="460" frameborder="0" src="https://www.globalforestwatch.org/embed/widget/treeLossFiresAnnual/country/CAN"></iframe>



<p></p>



<p>Users can also use the GFW map to visualize when and where these fires occurred and select specific years to dig deeper into the data. For instance, looking at the map, it is clear that most of Canada’s fires in 2023 occurred across the western provinces and Quebec.</p>



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



<p></p>



<h3 class="wp-block-heading"><em>Separating Trends in Fire-Related Tree Cover Loss from Other Drivers</em></h3>



<p>In some cases, this data set can also provide insights into trends of forest loss that are <em>not</em> due to fires. Since loss from fires tends to vary significantly year to year depending on climate and other conditions, it can obscure the longer-term trend in tree cover loss from other drivers.</p>



<p>For example, the annual tree cover loss data on primary forest loss in Brazil shows a large spike in 2016 and 2017 that is not captured by the official deforestation monitoring system for the Brazilian Amazon, PRODES. Using the tree cover loss due to fires data, we can see that much of that spike was due to loss from fires, which are typically <a href="https://www.globalforestwatch.org/blog/data-and-research/2021-tree-cover-loss-data-explained/">not captured by the PRODES system</a>. Understanding where fire drives forest loss and where it does not helps provide more nuance when interpreting the data and allows more direct comparisons with other data sources.</p>



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<h2 class="wp-block-heading">What are the Limitations for Using this Data?</h2>



<p>In a <a href="https://globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">previous blog</a>, we describe changes to the tree cover loss data since 2001 and recommend caution for comparing trends before and after 2015. This data on fires builds on that tree cover loss data set, so the same cautions apply. While the changes to the tree cover loss data have resulted in improved detection of forest loss in recent years, including better detection of fires in the boreal forests and other regions, it has also introduced inconsistencies over time.</p>



<p>Despite these inconsistencies, the data features high levels of <a href="https://www.frontiersin.org/articles/10.3389/frsen.2022.825190/full" target="_blank" rel="noreferrer noopener">spatial and temporal accuracy</a> at the global level (assessed for 2001-2019). Globally, 90% of areas classified as tree cover loss due to fires were classified correctly; the other 10% were false positives.</p>



<p>Spatial accuracy varied by region but remained relatively high for most regions. Europe and North America had the highest levels of accuracy with false positive rates of 7% and 4%, respectively. Africa had the lowest accuracy and the highest number of false positives, with 39% of the area labeled as tree cover loss due to fires incorrectly classified. The high false positive rate in Africa is likely because forest fires there are relatively rare.&nbsp;</p>



<table>
  <tbody><tr>
    <th>Region</th>
    <th>False Positive Rate</th>
  </tr>
 <tr>
    <td>North America</td>
    <td>4%</td>
  </tr>
  <tr>
    <td>Europe</td>
    <td>7%</td>
  </tr>
 <tr>
    <td>Latin America</td>
    <td>26%</td>
  </tr>
<tr>
    <td>Southeast Asia</td>
    <td>27%</td>
  </tr>
<tr>
    <td>Africa</td>
    <td>39%</td>
  </tr>
<tr>
    <td><strong>Global</strong></td>
    <td><strong>10%</strong></td>
  </tr>
</tbody></table>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%">False positive rates assessed across 2001-2019 within tree cover greater than 0%.</div>



<p>There are also cases where there are delays in detecting tree cover loss due to fires, where the loss occurs close to the end of the year but there aren’t enough clear satellite images to detect it until the following year. This is important to note because sometimes spikes we see in loss due to fires can reflect trends from the previous year. Overall, 96% of all fire-related loss events between 2001 and 2019 occurred within one year of the mapped loss date.</p>



<h2 class="wp-block-heading">What Other Fire Data is on GFW, and What’s Different About the Tree Cover Loss from Fire Data?</h2>



<p>The fire data available on GFW falls into two categories: fire activity data and contextual data. <em>Fire activity data</em> allows users to view ongoing fires and analyze the extent of fires in areas of interest, such as a country or user-defined polygon. <em>Contextual data</em> can be used to supplement the fire activity data and help users understand what areas are at risk of fire and the impacts of fire on air quality.&nbsp;&nbsp;</p>



<h4 class="wp-block-heading">Comparison of GFW Fire Data</h4>



<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td rowspan="4" style="width:150px;">
			<p align="left" style="margin-left:1pt;"><strong>Fire Activity</strong></p>

			<p align="center" style="margin-left:1pt;">&nbsp;</p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Data Set</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Resolution</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Data Source</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Update Frequency</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Date Range</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center"><strong>Best Use</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:150px;">
			<p>Tree Cover Loss from Fires</p>
			</td>
			<td style="width:150px;">
			<p align="center">30m</p>
			</td>
			<td style="width:150px;">
			<p align="center">UMD/GLAD</p>
			</td>
			<td style="width:150px;">
			<p align="center">Annual</p>
			</td>
			<td style="width:150px;">
			<p align="center">2001-2023</p>
			</td>
			<td style="width:150px;">
			<p>Calculating extent of stand-replacing forest fires</p>
			</td>
		</tr>
		<tr>
			<td style="width:150px;">
			<p>VIIRS Fire Alerts</p>
			</td>
			<td style="width:150px;">
			<p align="center">375m</p>
			</td>
			<td style="width:150px;">
			<p align="center">NASA</p>
			</td>
			<td style="width:150px;">
			<p align="center">Twice daily</p>
			</td>
			<td style="width:150px;">
			<p align="center">2012-Present</p>
			</td>
			<td style="width:150px;">
			<p>Detecting fire locations in near-real-time (day/night)</p>
			</td>
		</tr>
		<tr>
			<td style="width:150px;">
			<p>MODIS Fire Alerts*</p>
			</td>
			<td style="width:150px;">
			<p align="center">500m</p>
			</td>
			<td style="width:150px;">
			<p align="center">NASA</p>
			</td>
			<td style="width:150px;">
			<p align="center">Twice daily</p>
			</td>
			<td style="width:150px;">
			<p align="center">2000-Present</p>
			</td>
			<td style="width:150px;">
			<p>Detecting fire locations in near-real-time (day)</p>
			</td>
		</tr>
		<tr>
			<td rowspan="2" style="width:150px;">
			<p align="left" style="margin-left:1pt;"><strong>Contextual</strong></p>
			</td>
			<td style="width:150px;">
			<p align="center">Global Fire Weather Index</p>
			</td>
			<td style="width:150px;">
			<p align="center">10km</p>
			</td>
			<td style="width:150px;">
			<p align="center">NASA</p>

			<p align="center">GFWED</p>
			</td>
			<td style="width:150px;">
			<p align="center">Daily</p>
			</td>
			<td style="width:150px;">
			<p align="center">Present</p>
			</td>
			<td style="width:150px;">
			<p>Current fire risk</p>
			</td>
		</tr>
		<tr>
			<td style="width:150px;">
			<p align="center">Global Air Quality Index</p>
			</td>
			<td style="width:150px;">
			<p align="center">n/a</p>
			</td>
			<td style="width:150px;">
			<p align="center">World Air Quality Index</p>

			<p align="center">US EPA</p>
			</td>
			<td style="width:150px;">
			<p align="center">Every 15 minutes</p>
			</td>
			<td style="width:150px;">
			<p align="center">Present</p>
			</td>
			<td style="width:150px;">
			<p>Fire-related effects on air pollution</p>

			<p>&nbsp;</p>

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



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%">*The Terra satellite that carries the MODIS sensor will be decommissioned next year, along with associated products including the MODIS near-real-time alerts and burned areas data set. VIIRS will continue to be the main source of near-real-time fire alerts.</div>



<h3 class="wp-block-heading"><em>Fire Activity Data</em></h3>



<p><strong>Tree Cover Loss from Fires Data</strong></p>



<p>The tree cover loss from fires data differs from other fire activity data on the GFW platform because it focuses solely on stand-replacing forest fires, while other fire data sets do not make a distinction between stand-replacing fires and other fires. This is important because forests act as <a href="https://gfw.global/3JWxECg" target="_blank" rel="noreferrer noopener">carbon sinks</a> and contain most of the land-based carbon on the planet, which is released when they burn.&nbsp;</p>



<p>The tree cover loss from fire data also tracks fires at a much finer resolution than the other fire activity data such as <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod45.php" target="_blank" rel="noreferrer noopener">MODIS burned areas</a>; however, it is also updated less frequently, making it less useful for monitoring ongoing or very recent fire activity.</p>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/10857448?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p><strong>Other Fire Activity Data</strong></p>



<p><strong>Near-real-time alerts (MODIS and VIIRS):<em> </em></strong>MODIS* and VIIRS active fire alerts provide users with near-real-time data that is updated daily and can be used to monitor and respond to ongoing fires. The alerts can detect varying fire sizes depending on the fire temperature, time of day and presence of cloud cover or heavy haze. &nbsp;</p>



<p>Users can analyze fire activity data on the GFW <a href="https://gfw.global/3SmpoiN" target="_blank" rel="noreferrer noopener">map</a> and <a href="https://gfw.global/3ozQGoe" target="_blank" rel="noreferrer noopener">dashboard</a>, and filter the data to only view fires that occur in primary forests or other areas of conservation importance. Signing up for a myGFW account also gives users the option to create and analyze custom areas of interest. Users can also stay up to date on fire activity in their custom areas by <a href="https://www.globalforestwatch.org/help/map/guides/save-area-subscribe-forest-change-notifications/" target="_blank" rel="noreferrer noopener">subscribing to notifications</a> through their myGFW account.</p>



<h3 class="wp-block-heading"><em>Contextual Data</em></h3>



<ul>
<li><strong>Fire risk (Global Fire Weather Index):</strong> The Global Fire Weather Index can be used to identify areas where climate conditions make landscapes more vulnerable to fires, allowing users to understand the current risk of burning.&nbsp;</li>



<li><strong>Health impacts (Global Air Quality Index):</strong> The Global Air Quality Index measures current air quality of particulate matter which is strongly impacted by smoke and haze from fires. With over 15,000 stations worldwide, this helps users measure the risk of health effects related to air pollution. &nbsp;</li>
</ul>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/map/?map=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%3D&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJmb3Jlc3RDaGFuZ2UifQ%3D%3D"></iframe>



<p></p>



<p>The tree cover loss from fires data is a valuable data set in the suite of fire-related data on GFW and enables all users to better track the impact of fires on forests worldwide to develop more effective management responses.</p>



<hr> 

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

To explore fire data on GFW, visit the <a href="https://gfw.global/3SmpoiN" rel="noopener noreferrer">map</a> and <a href="https://www.globalforestwatch.org/dashboards/global/?category=fires&location=WyJnbG9iYWwiXQ%3D%3D&map=eyJkYXRhc2V0cyI6W119&showMap=true" rel="noopener noreferrer">dashboard</a>.
<br><br>
<a href="https://www.globalforestwatch.org/help/map/webinars/using-global-forest-watch-to-explore-active-fires-and-trends/?utm_campaign=treecoverlossfires2022&utm_medium=bitly&utm_source=WRIInsights" rel="noopener noreferrer">View a webinar</a> in English, Spanish, Portuguese and Bahasa Indonesia to learn more about tree cover loss from fires and other fire-related data on Global Forest Watch. <p><p>
This piece was originally published in 2022. It was updated in August 2024 to reflect the latest developments with the tree cover loss due to fires data.
 

</div> 

 

<hr>



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



<p></p>

Over Two Decades of Fire-Driven Loss in Exceptional Detail

Detalhes excepcionais de mais de duas décadas de perda causada por incêndios

Détails exceptionnels sur plus de deux décennies de pertes causées par des incendies

Lebih Dari Dua Dekade Kehilangan Akibat Kebakaran Dilihat dalam Perincian yang Menakjubkan

Más de dos décadas de pérdidas generadas debidas a incendios con el mayor detalle

Tree cover loss from fires data offers a higher-resolution look than ever before at trends in fire-driven loss over more than two decades.

Planet satellite imagery of primary forest removal in Kalimantan Barat, Indonesia on GFW map

Teresa Schofield

Amy Pickens

Johannes Reiche

<p><em>Languages:&nbsp;<a href="https://www.globalforestwatch.org/blog/es/data-and-research/alertas-de-deforestacion-integradas/">Leerlo en español</a>, <a href="https://www.globalforestwatch.org/blog/pt/data-and-research/alertas-de-desmatamento-integrados/">Leia em portugues</a>, <a href="https://www.globalforestwatch.org/blog/id/data-and-research/peringatan-deforestasi-terintegrasi/">Baca dalam bahasa indonesia</a>, <a href="https://www.globalforestwatch.org/blog/fr/data-and-research/alertes-integrees-de-deforestation/">Lire en français</a></em></p>



<p>Global Forest Watch (GFW) brings the most accurate forest data to users as quickly as possible. As part of this, we have continued to expand the near-real-time deforestation alerts available on the GFW platform. In 2022, we introduced the “integrated deforestation alerts” layer, which combines 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. Prompted by user feedback, this update simplifies workflows and harnesses the best of each alert type to support a variety of monitoring purposes.</p>



<h2 class="wp-block-heading"><strong>Which Alert Systems are Being Integrated?</strong></h2>



<p>GFW currently offers three individual deforestation alert systems. <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-deforestation-alerts/" target="_blank" rel="noreferrer noopener">GLAD-L</a> is GFW’s longest-standing alert product from the University of Maryland’s (UMD) Global Land Analysis and Discovery (GLAD) lab and uses imagery from NASA’s Landsat satellites. UMD introduced the higher resolution <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-s2-offers-high-resolution-deforestation-alerts/" target="_blank" rel="noreferrer noopener">GLAD-S2</a> alerts in May 2021, which use a similar methodology to GLAD-L, but are sourced from the European Space Agency’s Sentinel-2 satellites. And finally, Radar for Detecting Deforestation (<a href="https://www.globalforestwatch.org/blog/data-and-research/radd-radar-alerts/" target="_blank" rel="noreferrer noopener">RADD</a>) alerts produced by Wageningen University use Sentinel-1 radar data. These alerts have the additional capability of detecting forest change through cloud cover that often blocks the view of the other satellites.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1759" src="https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-2048x1759.png" alt="" class="wp-image-17704" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-2048x1759.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-1024x880.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-768x660.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-1536x1319.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<h2 class="wp-block-heading"><strong>What Does the Integrated Alerts Layer Offer?</strong></h2>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1099" height="403" src="https://content.globalforestwatch.org/wp-content/uploads/2024/07/integrated-deforestation-alerts-layer-2021.png" alt="" class="wp-image-17705" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/07/integrated-deforestation-alerts-layer-2021.png 1099w, https://content.globalforestwatch.org/wp-content/uploads/2024/07/integrated-deforestation-alerts-layer-2021-1024x375.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/07/integrated-deforestation-alerts-layer-2021-768x282.png 768w" sizes="(max-width: 1099px) 100vw, 1099px" /><figcaption class="wp-element-caption"><em>Map shows the geographic coverage of the integrated deforestation alerts, with brighter pink representing where multiple alert systems are available.</em></figcaption></figure>



<p>These three systems alert us of forest change happening in distinct parts of the world, use satellite imagery of different spatial resolution, and are updated at varying frequencies. The integrated option enables users to derive meaningful summary statistics that incorporate each of the individual systems. To achieve this, the layer indicates where multiple systems have reported an alert in any given location and displays it through confidence levels:</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="948" src="https://content.globalforestwatch.org/wp-content/uploads/2021/12/21.12.01-Integrated-Alerts-Blog_Graphic-2-2-2048x948.png" alt="confidence levels of integrated deforestation alerts" class="wp-image-12684"/></figure>



<p>This new layer comes with an important change: we are now reporting on the <em>area </em>affected by alerts as opposed to the <em>count of alert pixels.</em> As the integrated alert combines systems with varying spatial resolutions, it is necessary to report areas rather than the number of alert pixels. However, be aware that the alerts are designed to quickly identify forest disturbances rather than precisely delineate forest loss – as a result, the areas presented on GFW may under or overestimate the actual area of forest loss.</p>



<p>Combining the alerts offers several benefits:</p>



<p><strong>1. Users Will Always Have the Earliest Information Available</strong></p>



<p>By combining&nbsp;all three&nbsp;alert systems into one layer, we are harnessing&nbsp;the&nbsp;different&nbsp;timing of&nbsp;the&nbsp;satellites to increase the chances of getting&nbsp;an unobscured&nbsp;look&nbsp;at the forest and therefore detect change faster. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82#erlad2d82s4" target="_blank" rel="noreferrer noopener">A study on alerts</a> found that integrating alert systems leads to quicker detection of new disturbances, sometimes by days or even months. For example, users&nbsp;looking at integrated alerts from 2021 in the Amazon basin&nbsp;would&nbsp;have been alerted&nbsp;to forest loss&nbsp;on average nine days earlier than by referring to any one system alone. This&nbsp;improvement in timeliness&nbsp;varies&nbsp;by&nbsp;region&nbsp;and across&nbsp;seasons, as the alert systems&nbsp;complement&nbsp;each other by providing&nbsp;their own&nbsp;strengths. For example, in consistently cloud-covered areas, the radar-based alert system provides an advantage of detecting&nbsp;forest loss&nbsp;through clouds. However, the higher revisit time of the optical alert systems gives a more frequent opportunity to detect disturbances every five-eight days, particularly in areas with a dry season, whereas the radar system&nbsp;may take up to 12 days to detect change.</p>



<p><strong>2. The Overlap of Multiple Systems Increases Confidence to Prioritize Alerts</strong></p>



<p>Confidence levels help forest monitors prioritize alerts for follow up since satellite-derived data is subject to errors including false alerts. If two or more alert systems detect a change in the same location, we are more confident (“highest confidence”) that these alerts indicate real disturbance. For individual systems, there is a delay before a first detection can be verified by additional satellite passes and thus reach “high confidence.” The integrated layer displays where multiple systems overlap, in some cases providing increased confidence faster than waiting for individual systems to reach high confidence through additional satellite images, which can take weeks or months. False positives are effectively eliminated in the highest confidence class as it’s uncommon for two systems to commit the same error since they use different data streams and algorithms. &nbsp;</p>



<p><strong>3. Different Tree Cover Loss Types Are Captured Across a Variety of Landscapes </strong></p>



<p>The integrated alerts can fill the gaps that the use of any one system alone might present. Although there is a lot of overlap in what the various alert systems detect, there are also some differences. One of the greatest strengths of higher resolution RADD and GLAD-S2 alerts is their ability to capture smaller changes in the forest than the 30-meter GLAD-L product. This information has been vital for detecting small canopy gaps from logging activities.<strong></strong></p>



<p>Now that multiple alert systems are available across the tropics, they can be used in concert to identify illegal activities quickly and with increased confidence, a critical tool for both law enforcement and Indigenous Peoples and local communities working to combat deforestation.<strong></strong></p>



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?mainMap=eyJoaWRlTGVnZW5kIjp0cnVlfQ%3D%3D&map=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&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJmb3Jlc3RDaGFuZ2UifQ%3D%3D"></iframe>



<div class="attribute"> The integrated deforestation alerts layer demonstrates the ability of multiple alert systems to detect small changes, such as tree removal along logging roads in the Peruvian Amazon.</div>



<h2 class="wp-block-heading"><strong>What’s Next?</strong></h2>



<p>As new alert systems become available, they are being considered for integration into our integrated alert layer. The recently available <a href="https://www.landcarbonlab.org/news-updates/new-monitoring-system-land-ecosystem-disturbances" target="_blank" rel="noreferrer noopener">land surface disturbance alerts</a> developed by UMD and NASA have the potential to provide alerts in all forest types, such as dry open forests, as well as beyond the tropics – both of which are limitations for the current version of the integrated alerts. Information on drivers of alerts is also under development, which will support users in prioritizing alerts. Stay tuned for future updates to the integrated alert layer based on these and other advancements in the field of forest monitoring.</p>



<h2 class="wp-block-heading"><strong>Get Started with Integrated Alerts</strong></h2>



<p>With three alert systems to choose from, we aim to simplify users’ workflow in identifying priority alerts. The integrated layer provides the most information on recent forest change for users who want to be notified of forest loss with the highest confidence. Though we see these systems as complimentary to each other, users may still opt to select individual systems for alert notifications and analysis if these suit their needs.</p>



<p><em><em><em><strong>For an in-depth look at how the integrated layer was designed and how to use it, see the <a href="https://www.globalforestwatch.org/help/forest-watcher/webinars/visualizing-analyzing-integrated-deforestation-alerts/">Help Center</a>.</strong></em></em></em></p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> 
Amy Pickens is an assistant research professor at University of Maryland and operates the GLAD alert systems.<br><br>

Johannes Reiche is an associate professor at Wageningen University and operates the RADD alert system.<br><br>

This piece was originally published in 2022. It was updated in July 2024 to reflect the latest developments with the integrated deforestation alerts. 
</div>



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Looking for the Quickest Signal of Deforestation? Turn to GFW’s Integrated Alerts

<p><em>Languages:&nbsp;<a href="https://www.globalforestwatch.org/blog/es/data-and-research/alertas-de-deforestacion-integradas/">Leerlo en español</a>, <a href="https://www.globalforestwatch.org/blog/pt/data-and-research/alertas-de-desmatamento-integrados/">Leia em portugues</a>, <a href="https://www.globalforestwatch.org/blog/id/data-and-research/peringatan-deforestasi-terintegrasi/">Baca dalam bahasa indonesia</a>, <a href="https://www.globalforestwatch.org/blog/fr/data-and-research/alertes-integrees-de-deforestation/">Lire en français</a></em></p>



<p>Global Forest Watch (GFW) brings the most accurate forest data to users as quickly as possible. As part of this, we have continued to expand the near-real-time deforestation alerts available on the GFW platform. In 2022, we introduced the “integrated deforestation alerts” layer, which combines 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. Prompted by user feedback, this update simplifies workflows and harnesses the best of each alert type to support a variety of monitoring purposes.</p>



<h2 class="wp-block-heading"><strong>Which Alert Systems are Being Integrated?</strong></h2>



<p>GFW currently offers three individual deforestation alert systems. <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-deforestation-alerts/" target="_blank" rel="noreferrer noopener">GLAD-L</a> is GFW’s longest-standing alert product from the University of Maryland's (UMD) Global Land Analysis and Discovery (GLAD) lab and uses imagery from NASA's Landsat satellites. UMD introduced the higher resolution <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-s2-offers-high-resolution-deforestation-alerts/" target="_blank" rel="noreferrer noopener">GLAD-S2</a> alerts in May 2021, which use a similar methodology to GLAD-L, but are sourced from the European Space Agency's Sentinel-2 satellites. And finally, Radar for Detecting Deforestation (<a href="https://www.globalforestwatch.org/blog/data-and-research/radd-radar-alerts/" target="_blank" rel="noreferrer noopener">RADD</a>) alerts produced by Wageningen University use Sentinel-1 radar data. These alerts have the additional capability of detecting forest change through cloud cover that often blocks the view of the other satellites.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/07/24.07.22-Integrated-Alerts-Blog-2048x1759.png" alt="" class="wp-image-17704"/></figure>



<h2 class="wp-block-heading"><strong>What Does the Integrated Alerts Layer Offer?</strong></h2>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/07/integrated-deforestation-alerts-layer-2021.png" alt="" class="wp-image-17705"/><figcaption class="wp-element-caption"><em>Map shows the geographic coverage of the integrated deforestation alerts, with brighter pink representing where multiple alert systems are available.</em></figcaption></figure>



<p>These three systems alert us of forest change happening in distinct parts of the world, use satellite imagery of different spatial resolution, and are updated at varying frequencies. The integrated option enables users to derive meaningful summary statistics that incorporate each of the individual systems. To achieve this, the layer indicates where multiple systems have reported an alert in any given location and displays it through confidence levels:</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2021/12/21.12.01-Integrated-Alerts-Blog_Graphic-2-2-2048x948.png" alt="confidence levels of integrated deforestation alerts" class="wp-image-12684"/></figure>



<p>This new layer comes with an important change: we are now reporting on the <em>area </em>affected by alerts as opposed to the <em>count of alert pixels.</em> As the integrated alert combines systems with varying spatial resolutions, it is necessary to report areas rather than the number of alert pixels. However, be aware that the alerts are designed to quickly identify forest disturbances rather than precisely delineate forest loss – as a result, the areas presented on GFW may under or overestimate the actual area of forest loss.</p>



<p>Combining the alerts offers several benefits:</p>



<p><strong>1. Users Will Always Have the Earliest Information Available</strong></p>



<p>By combining&nbsp;all three&nbsp;alert systems into one layer, we are harnessing&nbsp;the&nbsp;different&nbsp;timing of&nbsp;the&nbsp;satellites to increase the chances of getting&nbsp;an unobscured&nbsp;look&nbsp;at the forest and therefore detect change faster. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82#erlad2d82s4" target="_blank" rel="noreferrer noopener">A study on alerts</a> found that integrating alert systems leads to quicker detection of new disturbances, sometimes by days or even months. For example, users&nbsp;looking at integrated alerts from 2021 in the Amazon basin&nbsp;would&nbsp;have been alerted&nbsp;to forest loss&nbsp;on average nine days earlier than by referring to any one system alone. This&nbsp;improvement in timeliness&nbsp;varies&nbsp;by&nbsp;region&nbsp;and across&nbsp;seasons, as the alert systems&nbsp;complement&nbsp;each other by providing&nbsp;their own&nbsp;strengths. For example, in consistently cloud-covered areas, the radar-based alert system provides an advantage of detecting&nbsp;forest loss&nbsp;through clouds. However, the higher revisit time of the optical alert systems gives a more frequent opportunity to detect disturbances every five-eight days, particularly in areas with a dry season, whereas the radar system&nbsp;may take up to 12 days to detect change.</p>



<p><strong>2. The Overlap of Multiple Systems Increases Confidence to Prioritize Alerts</strong></p>



<p>Confidence levels help forest monitors prioritize alerts for follow up since satellite-derived data is subject to errors including false alerts. If two or more alert systems detect a change in the same location, we are more confident (“highest confidence”) that these alerts indicate real disturbance. For individual systems, there is a delay before a first detection can be verified by additional satellite passes and thus reach "high confidence." The integrated layer displays where multiple systems overlap, in some cases providing increased confidence faster than waiting for individual systems to reach high confidence through additional satellite images, which can take weeks or months. False positives are effectively eliminated in the highest confidence class as it's uncommon for two systems to commit the same error since they use different data streams and algorithms. &nbsp;</p>



<p><strong>3. Different Tree Cover Loss Types Are Captured Across a Variety of Landscapes </strong></p>



<p>The integrated alerts can fill the gaps that the use of any one system alone might present. Although there is a lot of overlap in what the various alert systems detect, there are also some differences. One of the greatest strengths of higher resolution RADD and GLAD-S2 alerts is their ability to capture smaller changes in the forest than the 30-meter GLAD-L product. This information has been vital for detecting small canopy gaps from logging activities.<strong></strong></p>



<p>Now that multiple alert systems are available across the tropics, they can be used in concert to identify illegal activities quickly and with increased confidence, a critical tool for both law enforcement and Indigenous Peoples and local communities working to combat deforestation.<strong></strong></p>



<iframe width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?mainMap=eyJoaWRlTGVnZW5kIjp0cnVlfQ%3D%3D&map=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&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJmb3Jlc3RDaGFuZ2UifQ%3D%3D"></iframe>



<div class="attribute"> The integrated deforestation alerts layer demonstrates the ability of multiple alert systems to detect small changes, such as tree removal along logging roads in the Peruvian Amazon.</div>



<h2 class="wp-block-heading"><strong>What's Next?</strong></h2>



<p>As new alert systems become available, they are being considered for integration into our integrated alert layer. The recently available <a href="https://www.landcarbonlab.org/news-updates/new-monitoring-system-land-ecosystem-disturbances" target="_blank" rel="noreferrer noopener">land surface disturbance alerts</a> developed by UMD and NASA have the potential to provide alerts in all forest types, such as dry open forests, as well as beyond the tropics – both of which are limitations for the current version of the integrated alerts. Information on drivers of alerts is also under development, which will support users in prioritizing alerts. Stay tuned for future updates to the integrated alert layer based on these and other advancements in the field of forest monitoring.</p>



<h2 class="wp-block-heading"><strong>Get Started with Integrated Alerts</strong></h2>



<p>With three alert systems to choose from, we aim to simplify users’ workflow in identifying priority alerts. The integrated layer provides the most information on recent forest change for users who want to be notified of forest loss with the highest confidence. Though we see these systems as complimentary to each other, users may still opt to select individual systems for alert notifications and analysis if these suit their needs.</p>



<p><em><em><em><strong>For an in-depth look at how the integrated layer was designed and how to use it, see the <a href="https://www.globalforestwatch.org/help/forest-watcher/webinars/visualizing-analyzing-integrated-deforestation-alerts/">Help Center</a>.</strong></em></em></em></p>



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> 
Amy Pickens is an assistant research professor at University of Maryland and operates the GLAD alert systems.<br><br>

Johannes Reiche is an associate professor at Wageningen University and operates the RADD alert system.<br><br>

This piece was originally published in 2022. It was updated in July 2024 to reflect the latest developments with the integrated deforestation alerts. 
</div>



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