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Integrated disturbance alerts layer on GFW

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

Anika Berger

Sarah Carter

Teresa Schofield

Amy Pickens

Johannes Reiche

<p>Global Forest Watch (GFW) provides near-real-time information on where vegetation disturbances are happening around the world. The <a href="https://gfw.global/3CrDkAs"><strong>integrated disturbance alerts layer</strong></a> — originally introduced in 2022 and previously called “integrated deforestation alerts” — brings together four independent alert systems, optical and radar-based, to offer the earliest, most confident and most frequent signal of vegetation loss available on the GFW platform. In addition to GLAD-L, GLAD-S2 and RADD alerts, the integrated disturbance alerts layer now includes global vegetation disturbance alerts (DIST-ALERT), which expands the alert coverage to all vegetation both inside and outside of forests globally and detects repeated disturbances. By combining these data sources, users can monitor deforestation and other disturbances more effectively than by relying on any one system alone.</p>



<p>This layer is designed for users who need fast, reliable information for monitoring forests and other ecosystems, responding to illegal activity or prioritizing areas for follow-up.</p>



<h2 class="wp-block-heading"><strong>Which alert systems are included in the integrated disturbance alerts layer?</strong></h2>



<p>Previously, the integrated alerts layer showed disturbances only within tropical forests. The addition of the 30-meter resolution <a href="https://landcarbonlab.org/insights/new-monitoring-system-land-ecosystem-disturbances/" target="_blank" rel="noreferrer noopener"><strong>global vegetation disturbance alerts (DIST-ALERT)</strong></a> system from the University of Maryland (UMD) and NASA expands monitoring beyond forests to all vegetation types — capturing all vegetation disturbances, not only in tree cover — and brings global coverage to the integrated alerts layer for the first time.</p>



<p>With the addition of DIST-ALERT, the integrated alerts layer now includes four alert systems: GLAD-L, GLAD-S2, RADD and DIST-ALERT.</p>



<p><a href="https://www.globalforestwatch.org/blog/data-and-research/glad-deforestation-alerts/">GLAD-L</a> is GFW’s longest-standing alert product from the UMD Global Land Analysis and Discovery (GLAD) lab and uses imagery from NASA’s Landsat satellites to detect tree cover loss at 30-meter resolution.</p>



<p>UMD introduced the higher resolution (10-meter) <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-s2-offers-high-resolution-deforestation-alerts/">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. Radar for Detecting Deforestation (<a href="https://www.globalforestwatch.org/blog/data-and-research/radd-radar-alerts/">RADD</a>) alerts produced by Wageningen University use 10-meter 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>



<p>One of the greatest strengths of higher resolution (10-meter) RADD and GLAD-S2 alerts is their ability to capture smaller changes in the forest than the 30-meter products. This information has been vital for detecting small canopy gaps from logging activities.</p>



<p>We integrated DIST-ALERT in January 2026, which combines Landsat and Sentinel-2 data to cover disturbances both inside forests as well as in other vegetation. Users who want to focus exclusively on forests can still toggle the option on the map legend to display only alerts within areas of tree cover. This flexibility allows the integrated alerts layer to support both monitoring forests specifically, as well as the broader landscape.</p>



<div class="flourish-embed flourish-table" data-src="visualisation/26576189?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/26576189/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<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="1575" height="1079" src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_extent.png" alt="" class="wp-image-19907" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_extent.png 1575w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_extent-1024x702.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_extent-768x526.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_extent-1536x1052.png 1536w" sizes="(max-width: 1575px) 100vw, 1575px" /><figcaption class="wp-element-caption"><em>Map shows the geographic coverage of the integrated disturbance alerts, with brighter pink representing where multiple alert systems are available within tree cover.</em></figcaption></figure>



<p>These systems use satellite imagery of different spatial resolution and are updated at varying frequencies. The integrated option (resampled to 10-meter resolution) 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-full"><img decoding="async" loading="lazy" width="1575" height="716" src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence.png" alt="" class="wp-image-19908" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence.png 1575w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence-1024x466.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence-768x349.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence-1536x698.png 1536w" sizes="(max-width: 1575px) 100vw, 1575px" /><figcaption class="wp-element-caption"><em>Note that the low confidence category is not currently available for DIST-ALERT.</em></figcaption></figure>



<p>Each individual alert system has its own method of determining confidence based on the number of satellite observations that register a loss of vegetation or tree cover. Across all systems, an alert begins as low confidence when the first signs of potential disturbance appear and is upgraded to high confidence only after additional satellite observations provide further evidence or probability of loss. Highest confidence is only reached when multiple alerts systems detect a disturbance in the same pixel location. To learn more about what the confidence levels mean, <a href="https://gfw.global/3CrDkAs">click the “Layer info” icon here</a>.</p>



<p>In an important change from the previous versions of the layer, the addition of DIST-ALERT means the integrated alerts layer now detects repeated disturbances. 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 DIST-ALERT layer on GFW shows the date of the most recent alert, keeping users informed of the most up-to-date losses of forest and vegetation cover, meaning repeat alerts are captured in the integrated alerts layer for the first time. Alerts from multiple systems that occur within 180 days (six months) of each other are considered a single event and reach highest confidence. In this case, the earliest date within that 180-day window will appear as the alert date in the integrated alerts layer. The date of the alert remains until another new alert outside of the six-month timeframe is registered in the same pixel.</p>



<p>Capturing repeat alerts means that areas where tree cover has regrown (such as areas where restoration has taken place) can be monitored for further disturbances, and areas that increasingly face recurring disturbances like fire can also be tracked.</p>



<h2 class="wp-block-heading"><strong>What are the benefits of combining alerts?</strong></h2>



<strong>1. Users will always have the earliest information available on new alerts</strong>



<p>By combining&nbsp;multiple&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 tropical forest and therefore detect change faster, as well as inform users of vegetation loss anywhere on the planet. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82#erlad2d82s4"﷟HYPERLINK "https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82" 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;were alerted&nbsp;to forest loss&nbsp;on average nine days earlier than by referring to any one system alone. This improvement in timeliness varies by region and across seasons, as the alert systems complement each other by providing their own 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 one-four days, particularly in areas with a dry season; whereas the radar system&nbsp;may take up to 12 days to detect change. This research was done with just three systems integrated, so we expect even more improved results with a fourth system integrated.</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 are real vegetation disturbances, rather than errors in the data (known as false positives). 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 loss types are captured across a variety of landscapes </strong></p>



<p>Because of DIST-ALERT, the integrated layer covers not only forests globally but also disturbances in other ecosystems such as grasslands, savannas, shrublands, wetlands and crops. Users who are interested only in forest disturbances can use the tree cover filter to show only alerts within tree cover (defined as all vegetation greater than <a href="https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNi45MjQ1NzkwMDE2NTkzOSwibG5nIjotMTUuNzE3NTU5NzU4MjQ4NjE5fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJ1bWQtdHJlZS1oZWlnaHQiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidW1kLXRyZWUtaGVpZ2h0LTIwMjAiXSwicGFyYW1zIjp7ImhlaWdodCI6IjUifX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJsYW5kQ292ZXIifQ%3D%3D&modalMeta=umd_tree_cover_height_2020">5 meters in height (2020)</a> with greater than <a href="https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNi45MjQ1NzkwMDE2NTkzOSwibG5nIjotMTUuNzE3NTU5NzU4MjQ4NjE5fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJsYW5kQ292ZXIifQ%3D%3D&modalMeta=umd_tree_cover_density_2000">30% canopy cover (2010)</a> and where no <a href="https://www.globalforestwatch.org/map/?map=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%3D&mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoiZm9yZXN0Q2hhbmdlIn0%3D&modalMeta=umd_tree_cover_loss">tree cover loss</a> has been recorded since 2021, and may take the form of natural forests or plantations).</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1310" height="788" src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot_wildfires.png" alt="" class="wp-image-19909" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot_wildfires.png 1310w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot_wildfires-1024x616.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot_wildfires-768x462.png 768w" sizes="(max-width: 1310px) 100vw, 1310px" /><figcaption class="wp-element-caption"><em>Now that alerts are available globally, they can capture disturbances beyond the tropics, such as&nbsp;wildfire activity&nbsp;in Alberta, Canada.</em><strong>&nbsp;</strong></figcaption></figure>



<p>In the tropics, where multiple alert systems are available, the alert systems 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.</p>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1310" height="788" src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot.png" alt="" class="wp-image-19910" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot.png 1310w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot-1024x616.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot-768x462.png 768w" sizes="(max-width: 1310px) 100vw, 1310px" /><figcaption class="wp-element-caption"><em>The integrated disturbance alerts layer demonstrates the ability of multiple alert systems to detect small changes, such as tree removal along logging roads in the Peruvian Amazon.</em></figcaption></figure>



<p>Note that the benefits of increased confidence and more rapid detection resulting from multiple monitoring systems are still limited to tropical forests, as areas outside of forests and outside of the tropics are only captured by one system (DIST-ALERT).</p>



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



<p>Information on <a href="https://gfw.global/Jioe3c">drivers of alerts is now available on GFW</a> for the tropics, and is based on the integrated alert layer. This information supports users in prioritizing alerts by distinguishing natural from human-caused disturbances since users may have more interest in investigating human-caused disturbances, such as to assess the legality of forest clearing. In the future, this layer will be updated monthly, based on the last month’s integrated alerts, giving users a quicker picture of the cause of alerts. Stay tuned for future updates to the integrated alert layer 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 several alert systems to choose from, we aim to simplify users’ workflows in identifying priority alerts. The integrated layer provides the most information on recent forest disturbances for users who want to be notified of forest loss with the highest confidence and provides up-to-date information on other vegetation losses. 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><br><strong><em>For an in-depth look at how the integrated layer was designed and how to use it, see the </em></strong><a href="https://www.globalforestwatch.org/help/forest-watcher/webinars/visualizing-analyzing-integrated-deforestation-alerts/"><strong><em>Help Center</em></strong></a><strong><em>.</em></strong></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 January 2026 to reflect the latest developments with the integrated alerts. 
</div>



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Looking for the Quickest Signal of Deforestation or Vegetation Disturbance Globally? Turn to GFW’s Integrated Disturbance Alerts

<p>Global Forest Watch (GFW) provides near-real-time information on where vegetation disturbances are happening around the world. The <a href="https://gfw.global/3CrDkAs"><strong>integrated disturbance alerts layer</strong></a> — originally introduced in 2022 and previously called “integrated deforestation alerts” — brings together four independent alert systems, optical and radar-based, to offer the earliest, most confident and most frequent signal of vegetation loss available on the GFW platform. In addition to GLAD-L, GLAD-S2 and RADD alerts, the integrated disturbance alerts layer now includes global vegetation disturbance alerts (DIST-ALERT), which expands the alert coverage to all vegetation both inside and outside of forests globally and detects repeated disturbances. By combining these data sources, users can monitor deforestation and other disturbances more effectively than by relying on any one system alone.</p>



<p>This layer is designed for users who need fast, reliable information for monitoring forests and other ecosystems, responding to illegal activity or prioritizing areas for follow-up.</p>



<h2 class="wp-block-heading"><strong>Which alert systems are included in the integrated disturbance alerts layer?</strong></h2>



<p>Previously, the integrated alerts layer showed disturbances only within tropical forests. The addition of the 30-meter resolution <a href="https://landcarbonlab.org/insights/new-monitoring-system-land-ecosystem-disturbances/" target="_blank" rel="noreferrer noopener"><strong>global vegetation disturbance alerts (DIST-ALERT)</strong></a> system from the University of Maryland (UMD) and NASA expands monitoring beyond forests to all vegetation types — capturing all vegetation disturbances, not only in tree cover — and brings global coverage to the integrated alerts layer for the first time.</p>



<p>With the addition of DIST-ALERT, the integrated alerts layer now includes four alert systems: GLAD-L, GLAD-S2, RADD and DIST-ALERT.</p>



<p><a href="https://www.globalforestwatch.org/blog/data-and-research/glad-deforestation-alerts/">GLAD-L</a> is GFW’s longest-standing alert product from the UMD Global Land Analysis and Discovery (GLAD) lab and uses imagery from NASA's Landsat satellites to detect tree cover loss at 30-meter resolution.</p>



<p>UMD introduced the higher resolution (10-meter) <a href="https://www.globalforestwatch.org/blog/data-and-research/glad-s2-offers-high-resolution-deforestation-alerts/">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. Radar for Detecting Deforestation (<a href="https://www.globalforestwatch.org/blog/data-and-research/radd-radar-alerts/">RADD</a>) alerts produced by Wageningen University use 10-meter 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>



<p>One of the greatest strengths of higher resolution (10-meter) RADD and GLAD-S2 alerts is their ability to capture smaller changes in the forest than the 30-meter products. This information has been vital for detecting small canopy gaps from logging activities.</p>



<p>We integrated DIST-ALERT in January 2026, which combines Landsat and Sentinel-2 data to cover disturbances both inside forests as well as in other vegetation. Users who want to focus exclusively on forests can still toggle the option on the map legend to display only alerts within areas of tree cover. This flexibility allows the integrated alerts layer to support both monitoring forests specifically, as well as the broader landscape.</p>



<div class="flourish-embed flourish-table" data-src="visualisation/26576189?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img src="https://public.flourish.studio/visualisation/26576189/thumbnail" width="100%" alt="table visualization" /></noscript></div>



<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/2026/01/alert_extent.png" alt="" class="wp-image-19907"/><figcaption class="wp-element-caption"><em>Map shows the geographic coverage of the integrated disturbance alerts, with brighter pink representing where multiple alert systems are available within tree cover.</em></figcaption></figure>



<p>These systems use satellite imagery of different spatial resolution and are updated at varying frequencies. The integrated option (resampled to 10-meter resolution) 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-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alert_confidence.png" alt="" class="wp-image-19908"/><figcaption class="wp-element-caption"><em>Note that the low confidence category is not currently available for DIST-ALERT.</em></figcaption></figure>



<p>Each individual alert system has its own method of determining confidence based on the number of satellite observations that register a loss of vegetation or tree cover. Across all systems, an alert begins as low confidence when the first signs of potential disturbance appear and is upgraded to high confidence only after additional satellite observations provide further evidence or probability of loss. Highest confidence is only reached when multiple alerts systems detect a disturbance in the same pixel location. To learn more about what the confidence levels mean, <a href="https://gfw.global/3CrDkAs">click the “Layer info” icon here</a>.</p>



<p>In an important change from the previous versions of the layer, the addition of DIST-ALERT means the integrated alerts layer now detects repeated disturbances. 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 DIST-ALERT layer on GFW shows the date of the most recent alert, keeping users informed of the most up-to-date losses of forest and vegetation cover, meaning repeat alerts are captured in the integrated alerts layer for the first time. Alerts from multiple systems that occur within 180 days (six months) of each other are considered a single event and reach highest confidence. In this case, the earliest date within that 180-day window will appear as the alert date in the integrated alerts layer. The date of the alert remains until another new alert outside of the six-month timeframe is registered in the same pixel.</p>



<p>Capturing repeat alerts means that areas where tree cover has regrown (such as areas where restoration has taken place) can be monitored for further disturbances, and areas that increasingly face recurring disturbances like fire can also be tracked.</p>



<h2 class="wp-block-heading"><strong>What are the benefits of combining alerts?</strong></h2>



<strong>1. Users will always have the earliest information available on new alerts</strong>



<p>By combining&nbsp;multiple&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 tropical forest and therefore detect change faster, as well as inform users of vegetation loss anywhere on the planet. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82#erlad2d82s4&quot;﷟HYPERLINK &quot;https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82" 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;were alerted&nbsp;to forest loss&nbsp;on average nine days earlier than by referring to any one system alone. This improvement in timeliness varies by region and across seasons, as the alert systems complement each other by providing their own 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 one-four days, particularly in areas with a dry season; whereas the radar system&nbsp;may take up to 12 days to detect change. This research was done with just three systems integrated, so we expect even more improved results with a fourth system integrated.</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 are real vegetation disturbances, rather than errors in the data (known as false positives). 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 loss types are captured across a variety of landscapes </strong></p>



<p>Because of DIST-ALERT, the integrated layer covers not only forests globally but also disturbances in other ecosystems such as grasslands, savannas, shrublands, wetlands and crops. Users who are interested only in forest disturbances can use the tree cover filter to show only alerts within tree cover (defined as all vegetation greater than <a href="https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNi45MjQ1NzkwMDE2NTkzOSwibG5nIjotMTUuNzE3NTU5NzU4MjQ4NjE5fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJ1bWQtdHJlZS1oZWlnaHQiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidW1kLXRyZWUtaGVpZ2h0LTIwMjAiXSwicGFyYW1zIjp7ImhlaWdodCI6IjUifX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&amp;mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJsYW5kQ292ZXIifQ%3D%3D&amp;modalMeta=umd_tree_cover_height_2020">5 meters in height (2020)</a> with greater than <a href="https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNi45MjQ1NzkwMDE2NTkzOSwibG5nIjotMTUuNzE3NTU5NzU4MjQ4NjE5fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D&amp;mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJsYW5kQ292ZXIifQ%3D%3D&amp;modalMeta=umd_tree_cover_density_2000">30% canopy cover (2010)</a> and where no <a href="https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNi45MjQ1NzkwMDE2NTkzOSwibG5nIjotMTUuNzE3NTU5NzU4MjQ4NjE5fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJ0cmVlLWNvdmVyLWxvc3MiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJlZS1jb3Zlci1sb3NzIl0sInRpbWVsaW5lUGFyYW1zIjp7InN0YXJ0RGF0ZSI6IjIwMjEtMDEtMDEiLCJlbmREYXRlIjoiMjAyNC0xMi0zMSIsInRyaW1FbmREYXRlIjoiMjAyNC0xMi0zMSJ9fSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D&amp;mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoiZm9yZXN0Q2hhbmdlIn0%3D&amp;modalMeta=umd_tree_cover_loss">tree cover loss</a> has been recorded since 2021, and may take the form of natural forests or plantations).</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot_wildfires.png" alt="" class="wp-image-19909"/><figcaption class="wp-element-caption"><em>Now that alerts are available globally, they can capture disturbances beyond the tropics, such as&nbsp;wildfire activity&nbsp;in Alberta, Canada.</em><strong>&nbsp;</strong></figcaption></figure>



<p>In the tropics, where multiple alert systems are available, the alert systems 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.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/01/alerts_screenshot.png" alt="" class="wp-image-19910"/><figcaption class="wp-element-caption"><em>The integrated disturbance alerts layer demonstrates the ability of multiple alert systems to detect small changes, such as tree removal along logging roads in the Peruvian Amazon.</em></figcaption></figure>



<p>Note that the benefits of increased confidence and more rapid detection resulting from multiple monitoring systems are still limited to tropical forests, as areas outside of forests and outside of the tropics are only captured by one system (DIST-ALERT).</p>



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



<p>Information on <a href="https://gfw.global/Jioe3c">drivers of alerts is now available on GFW</a> for the tropics, and is based on the integrated alert layer. This information supports users in prioritizing alerts by distinguishing natural from human-caused disturbances since users may have more interest in investigating human-caused disturbances, such as to assess the legality of forest clearing. In the future, this layer will be updated monthly, based on the last month's integrated alerts, giving users a quicker picture of the cause of alerts. Stay tuned for future updates to the integrated alert layer 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 several alert systems to choose from, we aim to simplify users’ workflows in identifying priority alerts. The integrated layer provides the most information on recent forest disturbances for users who want to be notified of forest loss with the highest confidence and provides up-to-date information on other vegetation losses. 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><br><strong><em>For an in-depth look at how the integrated layer was designed and how to use it, see the </em></strong><a href="https://www.globalforestwatch.org/help/forest-watcher/webinars/visualizing-analyzing-integrated-deforestation-alerts/"><strong><em>Help Center</em></strong></a><strong><em>.</em></strong></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 January 2026 to reflect the latest developments with the integrated alerts. 
</div>



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

GFW's integrated alerts layer combines the analytical power of four alert systems for an improved view deforestation and other vegetation disturbances globally.

Explore GFW's Updated Integrated Disturbance Alerts

Bart Slagter

Laura Cué la Rosa

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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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

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

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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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<p>Les alertes de déforestation de Global Forest Watch (GFW) fournissent des informations sur la perte de couverture forestière en temps quasi réel, ce qui en fait un outil essentiel pour ceux qui luttent contre la déforestation. Les alertes existantes sur GFW ont été utilisées pour aider les communautés autochtones et locales à protéger leurs forêts, à mettre fin aux crimes environnementaux et à poursuivre leurs auteurs, ainsi qu'à protéger la faune sauvage menacée.</p>



<p>Cependant, par le passé, les alertes se contentaient d'informer les utilisateurs qu'un incident s'était produit, sans en identifier la cause, à l'exception de quelques cas particuliers tels que les incendies.</p>



<p>Désormais, de nouvelles données sur les facteurs de déforestation&nbsp;&nbsp; sont disponibles sur GFW et montrent les causes des alertes dans les régions tropicales. Cet ensemble de données innovant utilise une méthode d'intelligence artificielle pour attribuer des facteurs aux alertes de déforestation intégrées de GFW dans les trois principales régions forestières des tropiques, permettant ainsi aux utilisateurs de voir pour la première fois en temps quasi réel ce qui cause la perte de couverture forestière dans ces régions.&nbsp; Ces informations aident les utilisateurs à distinguer la perte de couverture forestière due à des causes naturelles de celle due à des causes humaines, ce qui permet aux gestionnaires forestiers de prendre des mesures plus ciblées et réduit le besoin de visites coûteuses sur le terrain. En outre, les données fournissent également des informations plus rapides pour estimer les impacts écologiques et permettent de mieux comprendre les émissions de carbone liées aux perturbations forestières.</p>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2025/11/fr_drivers_app.png" alt="" class="wp-image-19831"/></figure>



<h2 class="wp-block-heading"><strong>Que révèlent les données sur les facteurs déclencheurs des alertes de déforestation?</strong></h2>



<h3 class="wp-block-heading"><em>Classifications des alertes de déforestation</em></h3>



<p>Les données classent les alertes de déforestation intégrées en temps quasi réel de GFW en 11 catégories de facteurs naturels et anthropiques à une résolution de 10 mètres :</p>



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<h3 class="wp-block-heading"><em>Couverture géographique </em></h3>



<p>Les données couvrent les trois principaux bassins forestiers tropicaux de l'Amazonie, du Congo et de l'Indonésie, qui représentent environ un tiers de la superficie terrestre mondial et abritent 80 % des forêts tropicales de la planète. Les forêts tropicales comptent parmi les écosystèmes forestiers les plus importants. Elles jouent un rôle essentiel pour les moyens de subsistance, le stockage du carbone, l'approvisionnement en eau, la biodiversité et bien d'autres aspects, mais elles sont menacées par la déforestation. </p>



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



<h3 class="wp-block-heading"><em><em>Couverture temporelle et fréquence de mise à jour</em></em></h3>



<p>Actuellement, les données sont disponibles pour les alertes détectées entre 2022 et 2024, mais l'algorithme sera bientôt adapté pour fonctionner sur une base mensuelle. Dans cette configuration entemps quasi réel, chaque nouvelle alerte est classée dans les 31jourssuivant sa détection et chaque mois, le modèle traite à nouveau les alertes des trois mois précédents sur une base continue. Les classifications ne sont affichées que si elles sont classées avec certitude par l'algorithme et les étiquettes peuvent être mises à jour si une classification plus fiable devient disponible. En conséquence, chaque alerte peut être reclassée jusqu'à trois fois, la couche affichant la classification la plus fiable. Ce processus permet d'améliorer la précision au fil du temps, à mesure que des images satellites et des informations contextuelles supplémentaires deviennent disponibles. En outre, certains facteurs (par exemple, l'exploitation forestière sélective et le développement routier) sont mieux identifiés peu après la perturbation de la canopée, tandis que d'autres (par exemple, l'exploitation minière ou l'agriculture) peuvent prendre plus de temps avant que leurs signaux caractéristiques n'apparaissent.</p>



<h2 class="wp-block-heading">Comment les données ont-elles été développées ?</h2>



<p>Les alertes de déforestation intégrées à une résolution de 10 mètres constituent la base de la classification des facteurs. Les alertes intégrées combinent la puissance des alertes de déforestation GLAD-L, GLAD-S2 et RADD pour fournir une vue plus rapide et plus fiable des perturbations forestières que n'importe quel système individuel.</p>



<p>Pour appliquer les classifications des facteurs à l'alerte intégrée de déforestation, nous avons utilisé un modèle d'apprentissage profond initialement développé pour le Suriname, la République du Congo et la République Démocratique du Congo. Le modèle a été entraîné à partir d'un vaste ensemble de données sur les perturbations forestières en Amazonie, au Congo et dans les forêts tropicales d'Asie du Sud-Est, en utilisant l'interprétation visuelle d'images Planet haute-résolution pour attribuer des étiquettes de classification. Le modèle utilise des images satellites Sentinel-1 et Sentinel-2 post-perturbation pour les classifications des facteurs, intégrant les atouts complémentaires des données radar et optiques des satellites. Cela signifie que le modèle peut classer rapidement les facteurs, même par temps nuageux.</p>



<p>Pour identifier les endroits où les incendies coïncidaient avec le défrichage de terres agricoles — une technique de gestion agricole couramment utilisée dans les régions tropicales —, nous avons utilisé les alertes incendie VIIRS et le ratio de combustion normalisée (informations qui identifient les zones brûlées) de Sentinel-2.</p>



<p>Afin de réduire le bruit dans les données, nous avons procédé à un lissage :</p>



<ul>
<li>Tout d'abord, les pixels d'alerte ne dépassant pas un niveau de confiance de 0,75 ont été attribués à des classifications incertaines et définis comme « non étiquetés ».</li>



<li>Ensuite, pour chaque zone d'alerte distincte (c'est-à-dire un groupe de pixels d'alerte connectés spatialement), le facteur dominant a été sélectionné et attribué à l'ensemble de la zone, à quelques exceptions près pour les facteurs se produisant à une échelle plus fine, au niveau des pixels (développement routier et exploitation forestière sélective) et pour les zones extrêmement vastes.</li>
</ul>



<p>La précision des classifications varie selon les régions, avec le bassin du Congo affichant les meilleurs résultats globaux. La plupart des erreurs de classification sont liées à la confusion entre l'agriculture et l'exploitation minière, qui se déroulent souvent dans la même zone et entre les inondations et les incendies de forêt dans les plaines inondables.</p>



<p>Pour en savoir plus sur la méthodologie et les limites, <a href="https://www.sciencedirect.com/science/article/pii/S0034425723002067" target="_blank" rel="noreferrer noopener">cliquez ici</a>.</p>



<h2 class="wp-block-heading">Comment ces données se comparent-elles à d'autres données indiquant les facteurs de perte ?</h2>



<p>D'autres ensembles de données provenant de GFW et de Land &amp; Carbon Lab fournissent des informations sur les facteurs de perturbation de la végétation, mais varient en termes de détail, de période couverte et de fréquence de mise à jour, et ont donc des applications différentes. Par exemple, les données annuelles sur les facteurs de perte de couvert forestier à 1 km disponibles sur GFW sont plus adaptées à une analyse historique sur plusieurs années, tandis que le système de classification des alertes de perturbation des terres de Land &amp; Carbon Lab fournit des informations sur les perturbations de la végétation à l'intérieur et à l'extérieur des forêts à l'échelle mondiale.</p>



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<p>Certains attributs rendent les ensembles de données plus adaptés à des applications spécifiques. Par exemple, une résolution plus élevée permet de mieux comprendre la co-occurrence des facteurs dans les zones locales, et les informations infra-annuelles permettent de mieux comprendre la saisonnalité des différents facteurs :</p>



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<h2 class="wp-block-heading">Comment ces données peuvent-elles être utilisées ? Une source vitale pour les protecteurs de la forêt</h2>



<p>Les alertes de déforestation sont utilisées dans le monde entier par des centaines de peuples autochtones et de communautés locales, ainsi que par des gardes forestiers et des ONG, pour enquêter sur d'éventuelles perturbations imprévues ou illégales dans les forêts.</p>



<p>Cependant, les différentes causes de perturbations nécessitent des réponses différentes. Par exemple, dans le cas d'une conversion agricole, les utilisateurs peuvent vouloir enquêter sur la perte afin d'évaluer la légalité du déboisement. Dans le cas d'une perturbation naturelle telle qu'un glissement de terrain, aucune enquête ou un autre type d'enquête serait nécessaire. Les utilisateurs peuvent se référer aux données relatives aux causes des alertes de déforestation afin de déterminer le type de réponse approprié en fonction de la cause de la perturbation.</p>



<p>Bien que des visites sur le terrain ou l'utilisation d'images satellite haute résolution pour vérifier les classifications puissent encore être nécessaires dans certains cas afin de planifier des mesures appropriées ou de bien comprendre le contexte, l'ajout d'informations sur les facteurs déterminants dans la base de données GFW permet de hiérarchiser les alertes et de mener des actions plus ciblées, ce qui permet d'économiser des ressources et de concentrer les enquêtes sur les pertes les plus préoccupantes.</p>



<p>Les données peuvent également être utilisées pour mieux comprendre les émissions de carbone résultant des perturbations forestières. Certains types de conversion éliminent toute la végétation du terrain, tandis que d'autres laissent des arbres sur place. En outre, des informations sur la cause de la perte sont nécessaires pour estimer les émissions résultant de l'utilisation ultérieure des terres après le déboisement. Elles indiquent également si une repousse est à prévoir, ce qui est probable après des activités d'exploitation forestière, mais pas après une conversion à l'agriculture permanente, par exemple.</p>



<h2 class="wp-block-heading">Quelle est la prochaine étape pour les données des facteurs déclencheurs des alertes de déforestation ?</h2>



<p><strong>Mises à jour mensuelles :</strong> les données de 2025 seront disponibles début 2026, après quoi des mises à jour mensuelles seront disponibles à partir de 2026.</p>



<p><strong>Extension à l'ensemble des régions tropicales :</strong> nous prévoyons prochainement d'étendre les données à l'ensemble de la couverture des alertes de déforestation intégrées (actuellement de 30 degrés nord à 30 degrés sud). À l'avenir, le modèle pourrait également être appliqué aux perturbations dans les forêts tempérées et boréales. Des recherches supplémentaires pourraient également permettre de classer les perturbations d'autres types de végétation à l'aide d'une approche similaire.</p>



<p><strong>Intégration avec d'autres outils GFW :</strong> nous prévoyons d'ajouter les données à Forest Watcher afin de les rendre plus accessibles aux protecteurs des forêts travaillant sur le terrain.</p>



<p>Restez à l'écoute pour plus d'informations, car nous continuons à améliorer les alertes de déforestation afin d'en élargir la couverture, l'accès et l'application. <a href="https://www.globalforestwatch.org/help/datasets/forest-change/">Consultez le Centre d'aide</a> pour plus d'informations sur l'utilisation des données. </p>



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Découvrez pour la première fois ce qui déclenche les alertes de déforestation sur Global Forest Watch

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

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

Zhen Song

James MacCarthy

Alyssa Barrett

Jessica Richter

<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=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%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>



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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=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%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>



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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

Biodiversity

Biodiversity | Global Forest Watch Blog

Congo Basin

Congo Basin | Global Forest Watch Blog

Geneviève Ndjiki, Action for Sustainable Development

Data is Helping Protect Threatened Forests and Biodiversity in Cameroon’s Mengamé Gorilla Sanctuary

<iframe width="450" height="500" src="https://www.youtube.com/embed/Wwsny843ruo?si=5LLawBa7ww7lCvfF" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>



<p></p>



<p>Cameroon has very rich biological diversity, housed within several ecosystems that are representative of the ecosystems of Africa — the reason Cameroon is often described as “Africa in miniature.” The nation's biological diversity underpins its economy and has scientific and medicinal importance for Cameroon’s people.</p>



<p>In order to meet its commitments to protect the environment after the <a href="https://www.un.org/en/conferences/environment/rio1992" target="_blank" rel="noreferrer noopener">1992 United Nations Conference on Environment and Development</a>, and consequently its commitment to the Convention on Biological Diversity, objective 11 of Cameroon’s <a href="https://www.cbd.int/doc/world/cm/cm-nbsap-v2-en.pdf" target="_blank" rel="noreferrer noopener">National Biodiversity Strategy and Action Plan</a> calls for at least 30% of the national territory to be designated as protected areas, which aim to protect nature and biodiversity from human activities and pressures.</p>



<p>To this end, several protected areas (national parks, reserves, etc.) were established, including the Mengamé Gorilla Sanctuary. This sanctuary faces several threats including the loss of forest cover, which leads to fragmentation of the habitat found there.</p>



<p>Our organization, Action for Sustainable Development, works to respond to the increasing threat of deforestation in Mengamé. In 2023, we received a Global Forest Watch (GFW) Small Grants Fund (SGF) grant to help us monitor forests in the sanctuary. We found that forest loss over the last three years is increasing, and that a new management plan for the sanctuary based on better data is needed to protect the sanctuary’s forests and the biodiversity that depends on them.</p>



<h2 class="wp-block-heading"><strong>The Mengamé Gorilla Sanctuary</strong></h2>



<p>The Mengamé Gorilla Sanctuary (MGS) belongs to the Tri-national Dja-Odzala-Minkébé (TRIDOM) complex, a cross-border area encompassing 10% of the Congo Basin’s rainforests and 11 protected areas across Cameroon, Congo and Gabon. TRIDOM is a key area for the conservation of the biodiversity of the Congo Basin, including iconic large mammals such as elephants, chimpanzees and gorillas. The MGS is located in the equatorial forest zone in the southern part of Cameroon on the border with Gabon. It covers 26,780 hectares and straddles the districts of Mvangan in Mvila subdivision and Oveng district in Dja-et Lobo subdivision. The MGS is home to a diversity of wildlife including primates like the Western Lowland Gorilla, which is critically endangered due to human activities that have intensified over the years, such as deforestation leading to loss of habitat as well as poaching.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-2048x1448.png" alt="" class="wp-image-17782"/><figcaption class="wp-element-caption"><em>The area occupied by the MGS in the two districts (Mvangan and Oveng). Both these districts have several small villages surrounding the sanctuary.</em></figcaption></figure>



<h2 class="wp-block-heading"><strong>Satellite data reveals increasing loss of the sanctuary's forest cover</strong></h2>



<p>Action for Sustainable Development (ASD), a Cameroonian civil society organization, has been able to use the tools available from GFW to conduct satellite monitoring of forest cover change in protected areas. As part of our SGF project “Participatory monitoring of forest cover in and around the MGS,” we used GFW’s near-real-time integrated deforestation alerts to identify areas of forest loss, and supported park agents and others in the community to use GFW tools to investigate the alerts.</p>



<p>We analyzed alerts for two time periods over three years (2021-2023) and found that for the first period, from January 1, 2021 to July 20, 2022, more than 937 deforestation alerts were recorded totaling eight hectares of forest loss. The map below shows the distribution of these alerts in the two districts (896 alerts in the part of the sanctuary located in the Mvangan district and 41 alerts in the part of the sanctuary located in the Oveng district).</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-final1-2048x1448.png" alt="" class="wp-image-17784"/><figcaption class="wp-element-caption"><em>Map showing deforestation alerts in the MGS between January 1, 2021 and July 20, 2022.</em></figcaption></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/MGS-field-deployment-ASD.jpg" alt="" class="wp-image-17751"/><figcaption class="wp-element-caption"><em>Field deployment of a mixed team (ecoguards and community) to identify the causes of deforestation alerts. Credit: ASD</em></figcaption></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/MGS-field-deployment-2-ASD.jpg" alt="" class="wp-image-17752"/><figcaption class="wp-element-caption"><em>Field deployment of a mixed team (ecoguards and ASD) to identify the causes of deforestation alerts. Credit: ASD</em></figcaption></figure>



<p>Likewise, for the second period between July 21, 2022 and December 31, 2023, more than 1,436 deforestation alerts were recorded, including 1,292 in the part of the sanctuary located in the Mvangan district and 144 in the part of the sanctuary located in the Oveng district. The total of these deforestation alerts represents a loss of 17 hectares of forest.</p>



<p>The Mengamé gorilla sanctuary has therefore already lost nearly 25 hectares of forest in just three years from 2021 to 2023. These findings indicate that, over time, anthropogenic activity is increasing in the sanctuary. Investigations carried out to determine the main causes of this loss of forest cover showed that this has been caused by the establishment or extension of agricultural plots (cocoa and banana plantations). This pressure is growing due to the increase in the price of cocoa on the international market — and cocoa is the main cash crop in this area. If nothing is done, the sanctuary will be severely depleted and degraded the next several years.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-final2-2048x1448.png" alt="" class="wp-image-17785"/><figcaption class="wp-element-caption"><em>Deforestation alerts in the Mengamé gorilla sanctuary between July 21, 2022 and December 31, 2023.</em></figcaption></figure>



<h2 class="wp-block-heading"><strong>To protect the sanctuary, a new management plan is needed</strong></h2>



<p>To understand the motivations or reasons that lead local communities to set up their cocoa plantations and others in the sanctuary, our team conducted a survey as part of our project.</p>



<p>Two major reasons were mentioned by local communities to justify their actions:</p>



<p>The first reason is the lack of collaboration between local communities and conservation services. Indeed, 38% of the population thinks that they are not involved in the management of the sanctuary. There is almost no conservation activity within the sanctuary or ecotourism, appearing to local communities to have been abandoned for several years. Thus, they thought that the sanctuary was no longer a priority for the State. Our project has since succeeded in bringing together local communities, the conservation service and civil society organizations as part of a more participatory monitoring of the sanctuary.</p>



<p>The second reason is the fact that the space dedicated to the communities' rural activities is becoming insufficient. 22% of local communities believe that the area dedicated to their activities is becoming insufficient given the rapid population growth. Yet despite these changes, the development and management plan for the sanctuary, which should be renewed every five years to better take into account the realities of all stakeholders, has not been updated since 2007. This plan provides information about the sanctuary based on uses, priorities, threats and more, but not only is it out of date, it is also based partially on data from the neighboring Kom National Park as there is insufficient data on the sanctuary itself. Furthermore, the demarcation of the boundaries of the sanctuary had not been effective in all the riverside villages, and currently the boundaries that had been demarcated are no longer visible.</p>



<p>Therefore, development of a new management plan for the sanctuary is needed. This plan should:</p>



<ul>
<li>Take into account the increase in population and suggest effective ways for the development of alternative income-generating activities for local communities</li>



<li>Place emphasis on promoting ecotourism by developing infrastructure for this purpose</li>



<li>Involve local communities in the participatory management of the sanctuary</li>



<li>Provide updated data on biodiversity (fauna and flora species present and their densities, etc.)</li>



<li>Place emphasis on transboundary management of the sanctuary</li>
</ul>



<p>The MGS’s forests and the valuable biodiversity that depends on them can still be protected if the sanctuary implements a plan and manages the sanctuary based on recent, accurate data. The data collected by ASD using GFW tools is an important part of this and can help us preserve Mengamé Gorilla Sanctuary for years to come.</p>



<p></p>



<p><em>Learn more about ASD: Visit </em><a href="https://www.asd.contact/" target="_blank" rel="noreferrer noopener"><em>their website</em></a><em> and follow them on </em><a href="https://www.facebook.com/ASDCameroun" target="_blank" rel="noreferrer noopener"><em>Facebook</em></a><em>, </em><a href="https://twitter.com/asdcameroun" target="_blank" rel="noreferrer noopener"><em>Twitter/X</em></a><em> and </em><a href="https://www.linkedin.com/company/action-for-sustainable-development-asd/" target="_blank" rel="noreferrer noopener"><em>LinkedIn</em></a><em>.</em>&nbsp;</p>



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<p>Le Cameroun possède une diversité biologique très riche, hébergée dans plusieurs écosystèmes représentatifs des écosystèmes africains – raison pour laquelle le Cameroun est souvent décrit comme « l’Afrique en miniature ». La diversité biologique du pays soutient son économie et revêt une importance scientifique et médicinale pour la population camerounaise.</p>



<p>Afin de respecter ses engagements en matière de protection de l’environnement après la <a href="mailto:https://www.un.org/fr/conferences/environment/rio1992" target="_blank" rel="noreferrer noopener">Conférence des Nations Unies sur l’environnement et le développement en 1992</a>, et par conséquent son engagement envers la Convention sur la diversité biologique, l’objectif 11 de la <a href="https://www.cbd.int/doc/world/cm/cm-nbsap-v2-en.pdf" target="_blank" rel="noreferrer noopener">Stratégie nationale et du plan d’action pour la biodiversité</a> du Cameroun prévoit qu’au moins 30% du territoire national soient désignés comme aires protégées, destinées à protéger la nature et la biodiversité des activités et pressions humaines.</p>



<p>À cet effet, plusieurs aires protégées (parcs nationaux, réserves, etc.) ont été créées, parmi lesquels le Sanctuaire à gorilles de Mengamé. Ce sanctuaire fait face à plusieurs menaces dont la perte du couvert forestier, qui entraîne une fragmentation de l’habitat qui s’y trouve.</p>



<p>Notre organisation, Action for Sustainable Development, s’efforce de répondre à la menace croissante de déforestation à Mengamé. En 2023, nous avons reçu une subvention du Fonds de petites subventions (SGF) de Global Forest Watch (GFW) pour nous aider à surveiller les forêts du sanctuaire. Nous avons constaté que la perte de forêt au cours des trois dernières années est en augmentation et qu’un nouveau plan de gestion du sanctuaire basé sur de meilleures données est nécessaire pour protéger les forêts du sanctuaire et la biodiversité qui en dépend.&nbsp;</p>



<h2 class="wp-block-heading"><strong>Le sanctuaire à gorilles de Mengamé </strong><strong></strong></h2>



<p>Le Sanctuaire à Gorilles de Mengamé (SGM) fait partie du complexe trinational Dja-Odzala-Minkébé (TRIDOM), une zone transfrontalière englobant 10 % des forêts tropicales du bassin du Congo et 11 aires protégées au Cameroun, au Congo et au Gabon. Le TRIDOM est une zone clé pour la conservation de la biodiversité du bassin du Congo, notamment des grands mammifères emblématiques tels que les éléphants, les chimpanzés et les gorilles. Le SGM est situé dans la zone de forêt équatoriale dans la partie Sud du Cameroun à la frontière avec le Gabon. Il couvre 26 780 ha et chevauche les districts de Mvangan dans l'arrondissement de la Mvila et le district d'Oveng dans l'arrondissement du Dja-et Lobo. Le SGM abrite une faune diversifiée, notamment des primates comme le gorille des plaines de l'Ouest, qui est en danger critique d'extinction en raison des activités humaines qui se sont intensifiées au fil des ans, telles que la déforestation entraînant la perte de l’habitat, ainsi que le braconnage.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-2048x1448.png" alt="" class="wp-image-17782"/><figcaption class="wp-element-caption"><em>La zone occupée par le SGM dans les deux districts (Mvangan et Oveng). Ces deux districts comptent plusieurs petits villages entourant le sanctuaire.</em></figcaption></figure>



<h2 class="wp-block-heading"><strong>Les données satellitaires révèlent une perte croissante du couvert forestier du sanctuaire</strong></h2>



<p>Action for Sustainable Development (ASD), une organisation de la société civile camerounaise, a pu utiliser les outils mis à disposition par GFW pour effectuer un suivi satellitaire de l’évolution du couvert forestier dans les aires protégées. Dans le cadre de notre projet SGF « Suivi participatif du couvert forestier dans et autour du SGM », nous avons utilisé les alertes de déforestation intégrées en temps quasi réel de GFW pour identifier les zones de perte de forêt, et avons aidé les écogardes du parc et d’autres membres de la communauté à utiliser les outils du GFW pour enquêter sur les alertes.</p>



<p>Nous avons analysé les alertes sur deux périodes pendant trois ans (2021-2023) et constaté que pour la première période, du 1er janvier 2021 au 20 juillet 2022, plus de 937 alertes de déforestation ont été enregistrées totalisant huit hectares de perte du couvert forestier. La carte ci-dessous montre la répartition de ces alertes dans les deux districts (896 alertes dans la partie du sanctuaire située dans le district de Mvangan et 41 alertes dans la partie du sanctuaire située dans le district d'Oveng).</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-final1-2048x1448.png" alt="" class="wp-image-17784"/><figcaption class="wp-element-caption"><em>Carte montrant les alertes de déforestation dans le SGM entre le 1er janvier 2021 et le 20 juillet 2022.</em></figcaption></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/MGS-field-deployment-ASD.jpg" alt="" class="wp-image-17751"/><figcaption class="wp-element-caption"><em>Déploiement sur le terrain d'une équipe mixte (écogardes et communauté) pour identifier les causes des alertes de déforestation.</em></figcaption></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/MGS-field-deployment-2-ASD.jpg" alt="" class="wp-image-17752"/><figcaption class="wp-element-caption"><em>Déploiement sur le terrain d'une équipe mixte (écogardes et ASD) pour identifier les causes des alertes de déforestation.</em></figcaption></figure>



<p>De même, pour la deuxième période entre le 21 juillet 2022 et le 31 décembre 2023, plus de 1 436 alertes de déforestation ont été enregistrées, dont 1 292 dans la partie du sanctuaire située dans le district de Mvangan et 144 dans la partie du sanctuaire située dans le district d’Oveng. Le total de ces alertes de déforestation représente une perte de 17 hectares de forêt.</p>



<p>Le sanctuaire à gorilles de Mengamé a donc déjà perdu près de 25 hectares de forêt en seulement trois ans, de 2021 à 2023. Ces résultats indiquent qu’au fil du temps, l’activité anthropique augmente dans le sanctuaire. Les investigations menées pour déterminer les principales causes de cette perte du couvert forestier ont montré que celle-ci est due à l’implantation ou à l’extension des parcelles agricoles (cacaoyères et bananeraies). Cette pression s’accroît en raison de l’augmentation du prix du cacao sur le marché international – et le cacao est la principale culture de rente dans cette zone. Si rien n’est fait, le sanctuaire sera gravement appauvri et dégradé dans les prochaines années.</p>



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/08/Mengame-final2-2048x1448.png" alt="" class="wp-image-17785"/><figcaption class="wp-element-caption"><em>Alertes de déforestation dans le sanctuaire à gorilles de Mengamé entre le 21 juillet 2022 et le 31 décembre 2023.</em></figcaption></figure>



<h2 class="wp-block-heading"><strong>Pour protéger le sanctuaire, un nouveau plan d’aménagement est nécessaire</strong></h2>



<p>Pour comprendre les motivations ou les raisons qui poussent les communautés locales à installer leurs plantations de cacao et d'autres dans le sanctuaire, notre équipe a mené une enquête dans le cadre de notre projet.</p>



<p>Deux raisons majeures ont été évoquées par les communautés locales pour justifier leurs actions :</p>



<p>La première raison est le manque de collaboration entre les communautés locales et les services de la conservation. En effet, 38% de la population pense ne pas être impliquée dans la gestion du sanctuaire. Il n’y a quasiment aucune activité de conservation au sein du sanctuaire ni d’écotourisme, ce qui donne aux communautés locales l’impression d’être abandonnées depuis plusieurs années. Ainsi, elles pensaient que le sanctuaire n’était plus une priorité pour l’État. Notre projet a ainsi réussi à réunir les communautés locales, le service de la conservation et les organisations de la société civile dans le cadre d’un suivi plus participatif du sanctuaire.</p>



<p>La deuxième raison est le fait que l’espace dédié aux activités rurales des communautés devient insuffisant. 22 % des communautés locales estiment que la surface dédiée à leurs activités devient insuffisante compte tenu de la croissance rapide de la population. Pourtant, malgré ces changements, le plan d’aménagement et de gestion du sanctuaire, qui devrait être renouvelé tous les 5 ans pour mieux prendre en compte les réalités de toutes les parties prenantes, n’a pas été mis à jour depuis 2007. Ce plan fournit des informations sur le sanctuaire en fonction des usages, des priorités, des menaces, etc., non seulement il est obsolète, mais il se base également en partie sur les données du parc national voisin de Kom, car il n’existe pas suffisamment de données sur le sanctuaire lui-même. De plus, la délimitation des limites du sanctuaire n’a pas été effective dans tous les villages riverains, et actuellement les limites qui avaient été matérialisées ne sont plus visibles.</p>



<p>Il est donc nécessaire d'élaborer un nouveau plan de gestion pour le sanctuaire. Ce plan devrait :</p>



<ul>
<li>Prendre en compte l’augmentation de la population et proposer des moyens efficaces pour le développement d’activités alternatives génératrices de revenus pour les communautés locales</li>



<li>Mettre l’accent sur la promotion de l’écotourisme en développant des infrastructures à cet effet</li>



<li>Impliquer les communautés locales dans la gestion participative du sanctuaire</li>



<li>Fournir des données actualisées sur la biodiversité (espèces de faune et de flore présentes et leurs densités, etc.)</li>



<li>Mettre l’accent sur la gestion transfrontalière du sanctuaire</li>
</ul>



<p>Les forêts du SGM et la précieuse biodiversité qui en dépend peuvent encore être protégées si le sanctuaire met en œuvre un plan et gère le sanctuaire sur la base de données récentes et précises. Les données collectées par ASD à l’aide des outils du GFW constituent une partie importante de cette démarche et peuvent nous aider à préserver le sanctuaire à gorilles de Mengamé pour les années à venir.</p>



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<p><em>Apprenez-en davantage sur ASD : <a href="https://www.asd.contact/" target="_blank" rel="noreferrer noopener">Visitez leur site Web</a> et suivez-les sur <a href="https://www.facebook.com/ASDCameroun" target="_blank" rel="noreferrer noopener">Facebook</a>, <a href="https://twitter.com/asdcameroun" target="_blank" rel="noreferrer noopener">Twitter/X</a> et <a href="https://www.linkedin.com/company/action-for-sustainable-development-asd/" target="_blank" rel="noreferrer noopener">LinkedIn</a>.</em></p>



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