A new high-resolution dataset on Global Forest Watch reveals where forest roads are opening up in the Congo Basin. Using satellite imagery and AI, it maps roads first detected from 2019 onward and combines them with historical data back to 1976, offering an unprecedented view of road expansion across the region. Explore the <a href="https://gfw.global/82nKz4">dataset</a> and read <a href="https://www.globalforestwatch.org/blog/data-and-tools/congo-basin-forest-road-mapping/?utm_medium=notification&#038;utm_source=gfwnoticeboard&#038;utm_campaign=loggingroads">our blog</a> to learn about the data and how tracking these roads can help monitor forest usage and fragmentation.

New on GFW: Congo Basin forest roads update

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Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

Congo Basin

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logging

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mapping

mapping | Global Forest Watch Blog

Anika Berger

Bart Slagter

Sarah Carter

In Congo Basin forests, a new road can cause big changes. What may start as a single narrow path can start a cascade of disturbances by opening up access to intact forest, making it vulnerable to agricultural expansion, mining, logging, poaching and other threats. Tracking these roads is an important step in reducing their impact on forests, yet these roads — often unpaved, temporary and quickly overgrown — can be difficult to detect.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="2048" height="1363" src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard.jpg" alt="Wood truck near the village of Ngon. District of Ebolowa, Cameroon. Photo credit: Ollivier Girard/CIFOR." class="wp-image-20073" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard-1024x682.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard-768x511.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard-1536x1022.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Wood truck near the village of Ngon. District of Ebolowa, Cameroon. Photo credit: Ollivier Girard/CIFOR.</figcaption></figure>



A <a href="https://gfw.global/kkkquy">new, high-resolution data set</a> on Global Forest Watch (GFW) helps to close this gap by providing the most comprehensive view to date of forest road development in the Congo Basin. This updated data set builds on recent research that uses <a href="https://www.sciencedirect.com/science/article/pii/S0034425724004061#bb0225" target="_blank" rel="noreferrer noopener">satellite imagery and AI to map forest roads</a> in unprecedented detail, identifying roads first detected from 2019 onward and integrating them with a historical archive of road data extending from 2018 back to 1976. Together, this offers decades of insight into how roads have expanded across one of the world’s most vital tropical forest regions.



This release comes at a critical moment. Road expansion, particularly associated with selective logging, can lead to serious <a href="https://gfr.wri.org/forest-condition-indicators/forest-degradation" target="_blank" rel="noreferrer noopener">forest degradation</a> by fragmenting habitats, increasing carbon emissions and opening previously intact areas to further clearing. Data tracking road development can support verification of logging legality, as well as monitor mining encroachment and agricultural expansion.



In the Congo Basin, there is strong and growing demand for this type of data from government ministries, civil society organizations and forest monitoring initiatives. With technological developments such as this AI-powered approach, we are able to automatically track in high detail where and when road networks expand, meeting demands for valuable information needed by those who monitor forest change in near-real-time.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1920" height="1080" src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_fast.gif" alt="Congo Basin forest roads data set overlaid on satellite imagery in Republic of the Congo" class="wp-image-20078"/><figcaption class="wp-element-caption">Forest roads visible in Sentinel-2 satellite imagery can later disappear as vegetation regrows, but the Congo Basin forest roads data set&nbsp;retains&nbsp;a record of these roads even after they are no longer visible in imagery.</figcaption></figure>



<h2 class="wp-block-heading">Why do forest roads matter?</h2>



Forest roads are often the first physical footprint of industrial activity in tropical forests. Most of the roads in the Congo Basin are unpaved, and only temporarily used for selective logging, which entails harvesting valuable and mature trees while leaving the majority of the forest intact. While forest products benefit regional economies, some extractive practices are more detrimental than others. In tropical forests, selective logging can be part of a sustainable management regime if done with care, compared to clear cutting and establishing timber plantations.



However, the impact of logging roads<a href="https://ethz.ch/content/dam/ethz/special-interest/usys/ites/ecosystem-management-dam/documents/EducationDOC/TRF_DOC/Reading/Laurance_2009.pdf" target="_blank" rel="noreferrer noopener"> can be long-lasting</a>. Clearing forest corridors for road construction is the <a href="https://www.researchgate.net/publication/331479711_Reduced-impact_logging_for_climate_change_mitigation_RIL-C_can_halve_selective_logging_emissions_from_tropical_forests" target="_blank" rel="noreferrer noopener">largest source of carbon emissions</a> associated with selective logging in Congo Basin countries, and also contributes to habitat fragmentation, soil disturbance, edge effects and increased fire risk.



The impacts of logging roads often extend well beyond the initial harvest. When roads remain accessible after logging, they can leave remote forests vulnerable to <a href="https://www.researchgate.net/publication/317802710_Impacts_of_logging_roads_on_tropical_forests" target="_blank" rel="noreferrer noopener">unauthorized activities</a> such as agricultural expansion, mining, hunting and unregulated logging. In recent decades, road networks have increasingly expanded into <a href="https://www.wri.org/insights/worlds-last-intact-forests-increasingly-fragmented" target="_blank" rel="noreferrer noopener">intact forest landscapes</a>, which are identified as having little or no prior human influence. The <a href="https://www.wur.nl/en/news/road-networks-stretch-congo-basin-forests-satellites-and-ai-help-monitor" target="_blank" rel="noreferrer noopener">latest data indicates</a> that almost a quarter of Congo Basin forest roads are located in forests that were mapped as intact as of 2016.



<h2 class="wp-block-heading">What does the new Congo Basin forest roads data show?</h2>



Monitoring forest roads is important but challenging. Without knowing where these roads are and how they’re changing over time, it’s impossible to assess their impacts or know what actions need to be taken. Many logging roads are narrow and vegetation grows back quickly after harvest, making them difficult or impossible to detect without the use of radar and optical satellite imagery. Especially in the densely cloud-covered Congo Basin, transient roads going unnoticed pose a major risk.



The new forest roads data set provides highly detailed mapping of road development in tropical rainforests across six Congo Basin countries: Cameroon, Central African Republic, the Democratic Republic of the Congo, Equatorial Guinea, Gabon and Republic of Congo.



Key features include:



<ul>
<li>Updated road detection from 2019 onward, using AI and both optical and radar-based satellite imagery to map transient forest roads that often go undetected in conventional road data sets, with roads attributed by detection year and viewable by time range on GFW.</li>



<li>A long-term historical archive, combining the new data with earlier road maps created from satellite imagery going from 2018 back to 1976.</li>



<li>Improved coverage and consistency, offering users a single, integrated data set rather than multiple sources.</li>
</ul>



Prior to this change, GFW hosted a Congo Basin logging roads data set that was last updated in 2014. That data set was produced through a crowdsourcing effort from the Logging Roads initiative led by Moabi, GFW and the European Commission’s Joint Research Centre. Volunteers used satellite imagery and OpenStreetMap-based tools to manually trace roads and estimate when they appeared. While groundbreaking at the time, the data set covered a limited time period and was difficult to update.



In subsequent years, researchers <a href="https://www.nature.com/articles/s41893-019-0310-6" target="_blank" rel="noreferrer noopener">manually updated</a> the data set through 2018 and integrated it with an earlier road inventory that had <a href="https://www.science.org/doi/10.1126/science.1141057" target="_blank" rel="noreferrer noopener">digitized roads</a> from more than 300 Landsat images collected between 1976 and 2003, creating a comprehensive archive of roads data.



The new data combines the hand-digitized data with a new AI-based approach to offer a more complete and current picture of forest road expansion across the region.



Users can view the Congo Basin forest roads data on the GFW map by selecting the <a href="https://www.globalforestwatch.org/map/?map=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%3D&mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZFVzZSJ9">Congo Basin forest roads layer</a> in the Land Use data sets section under Infrastructure.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1310" height="788" src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads.png" alt="Screenshot of Forest roads in the Congo Basin (1976 to 2024)" class="wp-image-20079" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads.png 1310w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads-1024x616.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads-768x462.png 768w" sizes="(max-width: 1310px) 100vw, 1310px" /><figcaption class="wp-element-caption">Forest roads in the Congo Basin (1976 to 2024), with older data shown in darker brown and newly detected roads highlighted in bright gold.</figcaption></figure>



Additionally, the data will be integrated into national <a href="https://www.wri.org/initiatives/forest-atlases" target="_blank" rel="noreferrer noopener">Forest Atlases</a>, which combine government information with independent monitoring tools to support transparent forest management.&nbsp;



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



We mapped roads from 2019 onward using an advanced deep learning model <a href="https://www.wur.nl/en/news/road-networks-stretch-congo-basin-forests-satellites-and-ai-help-monitor" target="_blank" rel="noreferrer noopener">developed by Wageningen University & Research and The Nature Conservancy</a>, based on both Sentinel-1 and Sentinel-2 satellite imagery. The imagery uses radar sensors that can “see through” clouds, which allows for road detections even during the Congo Basin’s frequent rainy seasons. The automated algorithm processed thousands of satellite images and was designed to be continuously updated.



To extend the data set further back in time, the new road data was merged with <a href="https://www.nature.com/articles/s41893-019-0310-6" target="_blank" rel="noreferrer noopener">historical maps</a> that include the <a href="https://www.science.org/doi/10.1126/science.1141057" target="_blank" rel="noreferrer noopener">manually digitized roads</a> from Landsat imagery covering 1976–2003 and crowdsourced mapping efforts. While detection methods differ, together they provide a valuable long-term archive of forest access and fragmentation.



As with most large-scale data sets, there are important caveats that users should be aware of:



<ul>
<li>Not all forest roads are associated with selective logging, although many mapped roads are linked to logging activities. The <a href="https://gfw.global/OQbmTK">logging concessions layer</a> on the GFW map can provide further context.</li>



<li>The data set does not indicate the legality of roads or associated activities.</li>



<li>Road widths are not shown to scale, and the data does not show whether a road is still in use (for example, some roads may have overgrown since they were detected).</li>



<li>Additionally, overlap between older and newer roads may indicate the reopening of older roads. This may be of interest to users investigating selective logging activities because a road reopening can indicate that a new harvest cycle has started at a location where old, overgrown logging roads are being reopened and reused (which generally has <a href="https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2664.12661" target="_blank" rel="noreferrer noopener">less environmental impact</a> than constructing entirely new ones). However, note that reopened roads may not perfectly align with new roads on the map, since new roads (2019 onward) were mapped with more spatial precision than old roads.</li>
</ul>



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



The updated forest roads data set supports a wide range of applications:



<ul>
<li>Forest monitoring and law enforcement: Users can track where new roads appear inside or outside <a href="https://gfw.global/OQbmTK">logging concessions</a>, helping to flag potential illegal activity and support patrol planning.</li>



<li>Concession oversight: Governments and civil society organizations can assess whether road development aligns with approved logging plans and schedules. Detailed logging road maps can also be used to evaluate and potentially reduce the impact of selective logging activities.</li>



<li>Research and analysis: Researchers can use the long-term archive to study logging trends, infrastructure persistence and the carbon implications of new versus reopened roads, as well as the relationship between roads, degradation and deforestation.</li>



<li>Conservation and land-use planning: By revealing patterns of access and fragmentation, the data set can inform broader efforts to protect intact forest landscapes and protected areas.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1310" height="788" src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_v2.png" alt="Screenshot of Forest roads in the Congo Basin in Republic of Congo" class="wp-image-20080" srcset="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_v2.png 1310w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_v2-1024x616.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_v2-768x462.png 768w" sizes="(max-width: 1310px) 100vw, 1310px" /><figcaption class="wp-element-caption">The forest roads data set provides context to forest change data such as near-real-time disturbance alerts, informing users what may be driving forest change. For example, recent disturbance alerts occurring near forest roads in intact forest landscapes highlight areas where access infrastructure and potential logging operations may be contributing to the reduction of previously intact forest.</figcaption></figure>



<h2 class="wp-block-heading">What’s next for forest road monitoring data?</h2>



By combining cutting-edge AI with decades of historical mapping, this new forest roads data set offers an unprecedented view of how access has shaped the Congo Basin’s forests over time. As road expansion continues to impact forests, having timely, transparent and accessible data is essential for effective monitoring and action.



Moving forward, we plan to update the data set annually to support continued monitoring of road expansion across the region. In the future, we may apply these methods to map roads in other regions such as the Amazon and Southeast Asian rainforests.



<hr class="wp-block-separator has-alpha-channel-opacity"/>



Access the data: The data set is <a href="https://gfw.global/kkkquy">now available on GFW</a> as well as the <a href="https://www.wri.org/initiatives/forest-atlases" target="_blank" rel="noreferrer noopener">Forest Atlases</a> where it complements existing forest disturbance data, such as <a href="https://gfw.global/qfkEaG">drivers of disturbance alerts</a> and <a href="https://gfw.global/RIZqAn">tree cover loss by dominant driver</a>, by adding spatial detail on logging-related access and infrastructure, helping users better understand forest change in the Congo Basin.



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

Mapping Forest Roads in the Congo Basin in Unprecedented Detail with AI

In Congo Basin forests, a new road can cause big changes. What may start as a single narrow path can start a cascade of disturbances by opening up access to intact forest, making it vulnerable to agricultural expansion, mining, logging, poaching and other threats. Tracking these roads is an important step in reducing their impact on forests, yet these roads — often unpaved, temporary and quickly overgrown — can be difficult to detect.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/CIFOR-ICRAF-Wood-truck-Cameroon_Ollivier-Girard.jpg" alt="Wood truck near the village of Ngon. District of Ebolowa, Cameroon. Photo credit: Ollivier Girard/CIFOR." class="wp-image-20073"/><figcaption class="wp-element-caption">Wood truck near the village of Ngon. District of Ebolowa, Cameroon. Photo credit: Ollivier Girard/CIFOR.</figcaption></figure>



A <a href="https://gfw.global/kkkquy">new, high-resolution data set</a> on Global Forest Watch (GFW) helps to close this gap by providing the most comprehensive view to date of forest road development in the Congo Basin. This updated data set builds on recent research that uses <a href="https://www.sciencedirect.com/science/article/pii/S0034425724004061#bb0225" target="_blank" rel="noreferrer noopener">satellite imagery and AI to map forest roads</a> in unprecedented detail, identifying roads first detected from 2019 onward and integrating them with a historical archive of road data extending from 2018 back to 1976. Together, this offers decades of insight into how roads have expanded across one of the world’s most vital tropical forest regions.



This release comes at a critical moment. Road expansion, particularly associated with selective logging, can lead to serious <a href="https://gfr.wri.org/forest-condition-indicators/forest-degradation" target="_blank" rel="noreferrer noopener">forest degradation</a> by fragmenting habitats, increasing carbon emissions and opening previously intact areas to further clearing. Data tracking road development can support verification of logging legality, as well as monitor mining encroachment and agricultural expansion.



In the Congo Basin, there is strong and growing demand for this type of data from government ministries, civil society organizations and forest monitoring initiatives. With technological developments such as this AI-powered approach, we are able to automatically track in high detail where and when road networks expand, meeting demands for valuable information needed by those who monitor forest change in near-real-time.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_fast.gif" alt="Congo Basin forest roads data set overlaid on satellite imagery in Republic of the Congo" class="wp-image-20078"/><figcaption class="wp-element-caption">Forest roads visible in Sentinel-2 satellite imagery can later disappear as vegetation regrows, but the Congo Basin forest roads data set&nbsp;retains&nbsp;a record of these roads even after they are no longer visible in imagery.</figcaption></figure>



<h2 class="wp-block-heading">Why do forest roads matter?</h2>



Forest roads are often the first physical footprint of industrial activity in tropical forests. Most of the roads in the Congo Basin are unpaved, and only temporarily used for selective logging, which entails harvesting valuable and mature trees while leaving the majority of the forest intact. While forest products benefit regional economies, some extractive practices are more detrimental than others. In tropical forests, selective logging can be part of a sustainable management regime if done with care, compared to clear cutting and establishing timber plantations.



However, the impact of logging roads<a href="https://ethz.ch/content/dam/ethz/special-interest/usys/ites/ecosystem-management-dam/documents/EducationDOC/TRF_DOC/Reading/Laurance_2009.pdf" target="_blank" rel="noreferrer noopener"> can be long-lasting</a>. Clearing forest corridors for road construction is the <a href="https://www.researchgate.net/publication/331479711_Reduced-impact_logging_for_climate_change_mitigation_RIL-C_can_halve_selective_logging_emissions_from_tropical_forests" target="_blank" rel="noreferrer noopener">largest source of carbon emissions</a> associated with selective logging in Congo Basin countries, and also contributes to habitat fragmentation, soil disturbance, edge effects and increased fire risk.



The impacts of logging roads often extend well beyond the initial harvest. When roads remain accessible after logging, they can leave remote forests vulnerable to <a href="https://www.researchgate.net/publication/317802710_Impacts_of_logging_roads_on_tropical_forests" target="_blank" rel="noreferrer noopener">unauthorized activities</a> such as agricultural expansion, mining, hunting and unregulated logging. In recent decades, road networks have increasingly expanded into <a href="https://www.wri.org/insights/worlds-last-intact-forests-increasingly-fragmented" target="_blank" rel="noreferrer noopener">intact forest landscapes</a>, which are identified as having little or no prior human influence. The <a href="https://www.wur.nl/en/news/road-networks-stretch-congo-basin-forests-satellites-and-ai-help-monitor" target="_blank" rel="noreferrer noopener">latest data indicates</a> that almost a quarter of Congo Basin forest roads are located in forests that were mapped as intact as of 2016.



<h2 class="wp-block-heading">What does the new Congo Basin forest roads data show?</h2>



Monitoring forest roads is important but challenging. Without knowing where these roads are and how they’re changing over time, it’s impossible to assess their impacts or know what actions need to be taken. Many logging roads are narrow and vegetation grows back quickly after harvest, making them difficult or impossible to detect without the use of radar and optical satellite imagery. Especially in the densely cloud-covered Congo Basin, transient roads going unnoticed pose a major risk.



The new forest roads data set provides highly detailed mapping of road development in tropical rainforests across six Congo Basin countries: Cameroon, Central African Republic, the Democratic Republic of the Congo, Equatorial Guinea, Gabon and Republic of Congo.



Key features include:



<ul>
<li>Updated road detection from 2019 onward, using AI and both optical and radar-based satellite imagery to map transient forest roads that often go undetected in conventional road data sets, with roads attributed by detection year and viewable by time range on GFW.</li>



<li>A long-term historical archive, combining the new data with earlier road maps created from satellite imagery going from 2018 back to 1976.</li>



<li>Improved coverage and consistency, offering users a single, integrated data set rather than multiple sources.</li>
</ul>



Prior to this change, GFW hosted a Congo Basin logging roads data set that was last updated in 2014. That data set was produced through a crowdsourcing effort from the Logging Roads initiative led by Moabi, GFW and the European Commission’s Joint Research Centre. Volunteers used satellite imagery and OpenStreetMap-based tools to manually trace roads and estimate when they appeared. While groundbreaking at the time, the data set covered a limited time period and was difficult to update.



In subsequent years, researchers <a href="https://www.nature.com/articles/s41893-019-0310-6" target="_blank" rel="noreferrer noopener">manually updated</a> the data set through 2018 and integrated it with an earlier road inventory that had <a href="https://www.science.org/doi/10.1126/science.1141057" target="_blank" rel="noreferrer noopener">digitized roads</a> from more than 300 Landsat images collected between 1976 and 2003, creating a comprehensive archive of roads data.



The new data combines the hand-digitized data with a new AI-based approach to offer a more complete and current picture of forest road expansion across the region.



Users can view the Congo Basin forest roads data on the GFW map by selecting the <a href="https://www.globalforestwatch.org/map/?map=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%3D&amp;mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZFVzZSJ9">Congo Basin forest roads layer</a> in the Land Use data sets section under Infrastructure.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads.png" alt="Screenshot of Forest roads in the Congo Basin (1976 to 2024)" class="wp-image-20079"/><figcaption class="wp-element-caption">Forest roads in the Congo Basin (1976 to 2024), with older data shown in darker brown and newly detected roads highlighted in bright gold.</figcaption></figure>



Additionally, the data will be integrated into national <a href="https://www.wri.org/initiatives/forest-atlases" target="_blank" rel="noreferrer noopener">Forest Atlases</a>, which combine government information with independent monitoring tools to support transparent forest management.&nbsp;



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



We mapped roads from 2019 onward using an advanced deep learning model <a href="https://www.wur.nl/en/news/road-networks-stretch-congo-basin-forests-satellites-and-ai-help-monitor" target="_blank" rel="noreferrer noopener">developed by Wageningen University &amp; Research and The Nature Conservancy</a>, based on both Sentinel-1 and Sentinel-2 satellite imagery. The imagery uses radar sensors that can “see through” clouds, which allows for road detections even during the Congo Basin’s frequent rainy seasons. The automated algorithm processed thousands of satellite images and was designed to be continuously updated.



To extend the data set further back in time, the new road data was merged with <a href="https://www.nature.com/articles/s41893-019-0310-6" target="_blank" rel="noreferrer noopener">historical maps</a> that include the <a href="https://www.science.org/doi/10.1126/science.1141057" target="_blank" rel="noreferrer noopener">manually digitized roads</a> from Landsat imagery covering 1976–2003 and crowdsourced mapping efforts. While detection methods differ, together they provide a valuable long-term archive of forest access and fragmentation.



As with most large-scale data sets, there are important caveats that users should be aware of:



<ul>
<li>Not all forest roads are associated with selective logging, although many mapped roads are linked to logging activities. The <a href="https://gfw.global/OQbmTK">logging concessions layer</a> on the GFW map can provide further context.</li>



<li>The data set does not indicate the legality of roads or associated activities.</li>



<li>Road widths are not shown to scale, and the data does not show whether a road is still in use (for example, some roads may have overgrown since they were detected).</li>



<li>Additionally, overlap between older and newer roads may indicate the reopening of older roads. This may be of interest to users investigating selective logging activities because a road reopening can indicate that a new harvest cycle has started at a location where old, overgrown logging roads are being reopened and reused (which generally has <a href="https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2664.12661" target="_blank" rel="noreferrer noopener">less environmental impact</a> than constructing entirely new ones). However, note that reopened roads may not perfectly align with new roads on the map, since new roads (2019 onward) were mapped with more spatial precision than old roads.</li>
</ul>



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



The updated forest roads data set supports a wide range of applications:



<ul>
<li>Forest monitoring and law enforcement: Users can track where new roads appear inside or outside <a href="https://gfw.global/OQbmTK">logging concessions</a>, helping to flag potential illegal activity and support patrol planning.</li>



<li>Concession oversight: Governments and civil society organizations can assess whether road development aligns with approved logging plans and schedules. Detailed logging road maps can also be used to evaluate and potentially reduce the impact of selective logging activities.</li>



<li>Research and analysis: Researchers can use the long-term archive to study logging trends, infrastructure persistence and the carbon implications of new versus reopened roads, as well as the relationship between roads, degradation and deforestation.</li>



<li>Conservation and land-use planning: By revealing patterns of access and fragmentation, the data set can inform broader efforts to protect intact forest landscapes and protected areas.</li>
</ul>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2026/03/congobasinroads_v2.png" alt="Screenshot of Forest roads in the Congo Basin in Republic of Congo" class="wp-image-20080"/><figcaption class="wp-element-caption">The forest roads data set provides context to forest change data such as near-real-time disturbance alerts, informing users what may be driving forest change. For example, recent disturbance alerts occurring near forest roads in intact forest landscapes highlight areas where access infrastructure and potential logging operations may be contributing to the reduction of previously intact forest.</figcaption></figure>



<h2 class="wp-block-heading">What’s next for forest road monitoring data?</h2>



By combining cutting-edge AI with decades of historical mapping, this new forest roads data set offers an unprecedented view of how access has shaped the Congo Basin’s forests over time. As road expansion continues to impact forests, having timely, transparent and accessible data is essential for effective monitoring and action.



Moving forward, we plan to update the data set annually to support continued monitoring of road expansion across the region. In the future, we may apply these methods to map roads in other regions such as the Amazon and Southeast Asian rainforests.



<hr class="wp-block-separator has-alpha-channel-opacity"/>



Access the data: The data set is <a href="https://gfw.global/kkkquy">now available on GFW</a> as well as the <a href="https://www.wri.org/initiatives/forest-atlases" target="_blank" rel="noreferrer noopener">Forest Atlases</a> where it complements existing forest disturbance data, such as <a href="https://gfw.global/qfkEaG">drivers of disturbance alerts</a> and <a href="https://gfw.global/RIZqAn">tree cover loss by dominant driver</a>, by adding spatial detail on logging-related access and infrastructure, helping users better understand forest change in the Congo Basin.



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Cartographier les routes forestières du bassin du Congo avec un niveau de détail sans précédent grâce à l'IA  

In Congo Basin forests, roads can cause big changes. A new, high-res data set on GFW provides a detailed view of these forest roads.

Mapping Forest Roads in the Congo Basin Powered by AI

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Integrated disturbance alerts layer on GFW

Teresa Schofield

Amy Pickens

Johannes Reiche

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">integrated disturbance alerts layer</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.



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.



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



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">global vegetation disturbance alerts (DIST-ALERT)</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.



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



<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.



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.



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.



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.



<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">What does the integrated alerts layer offer?</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">Map shows the geographic coverage of the integrated disturbance alerts, with brighter pink representing where multiple alert systems are available within tree cover.</figcaption></figure>



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:



<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">Note that the low confidence category is not currently available for DIST-ALERT.</figcaption></figure>



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>.



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.



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.



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



1. Users will always have the earliest information available on new alerts



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.



2. The overlap of multiple systems increases confidence to prioritize alerts



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;



3. Different loss types are captured across a variety of landscapes 



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).



<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">Now that alerts are available globally, they can capture disturbances beyond the tropics, such as&nbsp;wildfire activity&nbsp;in Alberta, Canada.&nbsp;</figcaption></figure>



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.



<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">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.</figcaption></figure>



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).



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



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.



<h2 class="wp-block-heading">Get started with integrated alerts </h2>



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.



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



<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. 

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

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

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">integrated disturbance alerts layer</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.



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.



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



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">global vegetation disturbance alerts (DIST-ALERT)</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.



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



<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.



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.



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.



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.



<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">What does the integrated alerts layer offer?</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">Map shows the geographic coverage of the integrated disturbance alerts, with brighter pink representing where multiple alert systems are available within tree cover.</figcaption></figure>



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:



<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">Note that the low confidence category is not currently available for DIST-ALERT.</figcaption></figure>



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>.



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.



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.



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



1. Users will always have the earliest information available on new alerts



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.



2. The overlap of multiple systems increases confidence to prioritize alerts



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;



3. Different loss types are captured across a variety of landscapes 



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=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%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).



<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">Now that alerts are available globally, they can capture disturbances beyond the tropics, such as&nbsp;wildfire activity&nbsp;in Alberta, Canada.&nbsp;</figcaption></figure>



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.



<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">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.</figcaption></figure>



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).



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



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.



<h2 class="wp-block-heading">Get started with integrated alerts </h2>



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.



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



<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. 

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

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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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

policy

policy | Global Forest Watch Blog

Rod Taylor

Forests Can Be Assets or Liabilities. It Up to Us

The world’s forests are assets that generate priceless benefits, but if neglected, they can quickly turn into liabilities.

Large forest fire to open area for agriculture and cattle ranching in the Amazon rainforest. Credit: Paralaxis / Alamy Stock Photo

3A2KGCM

amazon

amazon | Global Forest Watch Blog

Brazil

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Indonesia

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Malaysia

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mexico

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Peru

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Fires Drove Record-breaking Tropical Forest Loss in 2024

The tropics lost a record-shattering amount of primary rainforest in 2024 driven largely by fires, according to new data from UMD and GFW.

Zhen Song

James MacCarthy

Alyssa Barrett

Jessica Richter

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>.



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.



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.



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



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> and funded by NASA Satellite Needs Working Group 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).



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.



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



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;



<iframe loading="lazy" width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>







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.



<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>



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.



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



GFW’s other alert products are only available for forests. Now, DIST-ALERT makes it possible to view alerts for all types of tree and vegetation cover, including grassland, shrubland, agricultural land and savanna. We have <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">combined DIST-ALERT with other GFW alert products</a> to form <a href="https://gfw.global/3CrDkAs">integrated disturbance alerts</a>, which expands alert coverage to all vegetation both inside and outside of forests globally and detects repeated disturbances.



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.



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.



<div class="flourish-embed flourish-chart" 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="visualization" /></noscript></div>







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).



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



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.



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.



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:



1. Supply chain actors can monitor for no-conversion voluntary commitments and regulatory requirements: 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.



2. Land managers can identify threats to natural ecosystems: 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.



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



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.



In the 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.



We also plan to add more information about what is causing alerts. This information helps 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 & Carbon Lab has developed 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. At the same time, GFW now shows the <a href="https://www.globalforestwatch.org/blog/data-and-tools/drivers-deforestation-alerts/">drivers of historical alerts in three tropical forest basins</a>. Efforts to provide regular updates of these alert driver products are underway.



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This piece was originally published December 17, 2024. It was updated in March 2026 to reflect the latest updates related to DIST-ALERT.



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

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>.



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.



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.



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



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> and funded by NASA Satellite Needs Working Group 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).



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.



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



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;



<iframe width="670" height="490" frameborder="0" src="https://www.globalforestwatch.org/embed/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6IkRJU1RfYWxlcnRzIiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbIkRJU1RfYWxlcnRzX2FsbCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6InRyZWUtY292ZXIiLCJsYXllcnMiOlsidHJlZS1jb3Zlci0yMDEwIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D"></iframe>







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.



<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>



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.



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



GFW’s other alert products are only available for forests. Now, DIST-ALERT makes it possible to view alerts for all types of tree and vegetation cover, including grassland, shrubland, agricultural land and savanna. We have <a href="https://www.globalforestwatch.org/blog/data-and-tools/integrated-deforestation-alerts/">combined DIST-ALERT with other GFW alert products</a> to form <a href="https://gfw.global/3CrDkAs">integrated disturbance alerts</a>, which expands alert coverage to all vegetation both inside and outside of forests globally and detects repeated disturbances.



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.



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.



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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).



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



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.



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.



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:



1. Supply chain actors can monitor for no-conversion voluntary commitments and regulatory requirements: 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.



2. Land managers can identify threats to natural ecosystems: 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.



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



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.



In the 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.



We also plan to add more information about what is causing alerts. This information helps 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 &amp; Carbon Lab has developed 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. At the same time, GFW now shows the <a href="https://www.globalforestwatch.org/blog/data-and-tools/drivers-deforestation-alerts/">drivers of historical alerts in three tropical forest basins</a>. Efforts to provide regular updates of these alert driver products are underway.



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This piece was originally published December 17, 2024. It was updated in March 2026 to reflect the latest updates related to DIST-ALERT.



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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.