Stay atop the latest forest research and news! The Global Forest Watch blog uses data to illuminate the state of forests worldwide and tells the stories of people dedicated to protecting them. Read about rainforests, deforestation, fires, sustainable agriculture, forest management and other topics critical to the future of forests.

Global Forest Watch Blog

Stay atop the latest forest research and news! The Global Forest Watch blog uses data to illuminate the state of forests worldwide and tells the stories of people dedicated to protecting them. Read about rainforests, deforestation, fires, sustainable agriculture, forest management and other topics critical to the future of forests.

Read about data-backed topics critical to the future of forests, including rainforests, deforestation, fires, sustainable agriculture, forest monitoring and management.

Forest News, Research & Monitoring | Global Forest Watch Blog

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The 10-meter cloud-penetrating deforestation alerts are now available in five additional countries in mainland Southeast Asia: Cambodia, Vietnam, Laos, Myanmar, and Thailand. Check them out in the <a href="https://gfw.global/46gapP0">integrated deforestation alerts layer</a>!

NEW: RADD alerts expand to Southeast Asia

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

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

Deforestation Alerts

Deforestation Alerts | Global Forest Watch Blog

Anika Berger

Teresa Schofield

Amy Pickens

Johannes Reiche

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



Global Forest Watch (GFW) brings the most accurate forest data to users as quickly as possible. As part of this, we have continued to expand the near-real-time deforestation alerts available on the GFW platform. In 2022, we introduced the “integrated deforestation alerts” layer, which combines the power of GLAD-L, GLAD-S2 and RADD deforestation alerts to provide a faster, more confident view of forest disturbances than any one individual system. Prompted by user feedback, this update simplifies workflows and harnesses the best of each alert type to support a variety of monitoring purposes.



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



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



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



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



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



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



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



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



Combining the alerts offers several benefits:



1. Users Will Always Have the Earliest Information Available



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



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 indicate real disturbance. For individual systems, there is a delay before a first detection can be verified by additional satellite passes and thus reach “high confidence.” The integrated layer displays where multiple systems overlap, in some cases providing increased confidence faster than waiting for individual systems to reach high confidence through additional satellite images, which can take weeks or months. False positives are effectively eliminated in the highest confidence class as it’s uncommon for two systems to commit the same error since they use different data streams and algorithms. &nbsp;



3. Different Tree Cover Loss Types Are Captured Across a Variety of Landscapes 



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



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



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



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



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



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



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



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



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 July 2024 to reflect the latest developments with the integrated deforestation alerts. 
</div>



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

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

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



Global Forest Watch (GFW) brings the most accurate forest data to users as quickly as possible. As part of this, we have continued to expand the near-real-time deforestation alerts available on the GFW platform. In 2022, we introduced the “integrated deforestation alerts” layer, which combines the power of GLAD-L, GLAD-S2 and RADD deforestation alerts to provide a faster, more confident view of forest disturbances than any one individual system. Prompted by user feedback, this update simplifies workflows and harnesses the best of each alert type to support a variety of monitoring purposes.



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



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



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



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



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



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



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



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



Combining the alerts offers several benefits:



1. Users Will Always Have the Earliest Information Available



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



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 indicate real disturbance. For individual systems, there is a delay before a first detection can be verified by additional satellite passes and thus reach "high confidence." The integrated layer displays where multiple systems overlap, in some cases providing increased confidence faster than waiting for individual systems to reach high confidence through additional satellite images, which can take weeks or months. False positives are effectively eliminated in the highest confidence class as it's uncommon for two systems to commit the same error since they use different data streams and algorithms. &nbsp;



3. Different Tree Cover Loss Types Are Captured Across a Variety of Landscapes 



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



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



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



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



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



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



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



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



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 July 2024 to reflect the latest developments with the integrated deforestation alerts. 
</div>



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

New integrated deforestation alerts layer combines the analytical power of multiple alert systems for an impoved view of forest disturbances.

Explore GFW's New Integrated Deforestation Alerts

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To meet the global demand for Arabica coffee, effective monitoring systems are essential to both protect forests and ensure we can continue to enjoy Gayo coffee.

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This year's tree cover loss data shows that Europe’s forests face increasing pressures including logging, wildfires, insect outbreaks and wood harvesting to meet changing energy demands.

4 Growing Threats to Europe’s Forests: Logging, Bioenergy, Wildfires and Pests | Forest Insights | Global Forest Watch Blog

Users In Action

Hear stories from the forest defenders working in the field.

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Brazil

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Indigenous Peoples &amp; Local Communities

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Isabela Barriga

Inge Snip

A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our Voices of Global Forest Watch series will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.



<iframe loading="lazy" width="560" height="450" src="https://www.youtube.com/embed/IipacbHyMGU?si=O7qrSPWsK16t1gcT" 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>







“Forest, for me, is life. Forest is water, it’s land, it’s people.” – Vinicius Silgueiro, Territorial Intelligence Coordinator, Instituto Centro de Vida



Vinicius Silgueiro’s story begins with a deep-seated connection to the Brazilian Amazon, inspiring his efforts to protect it through his work at the <a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Instituto Centro de Vida (ICV)</a>, a nonprofit organization that focuses on sustainable land use and preserving natural resources in the state of Mato Grosso in Brazil, which is known for its rich biodiversity.



“I was born and grew up in an environment with a diversity of vegetation between the Cerrado and the Pantanal biomes, and then I lived in the Amazon. Having a forest is having life,” said Vinicius, who has been working at ICV since he was an intern 14 years ago.



ICV uses GFW’s extensive satellite imagery and deforestation alert capabilities in its environmental protection work.



As a <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Small Grants Fund (SGF)</a> recipient from 2020-2022, ICV helped expand GFW’s use within the Mato Grosso Public Prosecutor’s Office to hold landholders accountable for environmental crimes. ICV trained the prosecutor’s technicians on how to use GFW tools and data to fight illegal deforestation and fires, and helped develop a methodology and process for prosecutors and technical staff to identify and act on illegal deforestation alerts.



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1536" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-2048x1536.jpg" alt="" class="wp-image-17484" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-2048x1536.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-1024x768.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-768x576.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-1536x1152.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



ICV also developed a series of <a href="https://www.youtube.com/watch?v=pw4SshvYITQ&list=PLH0XsN2wGrLATqJesckApo9S9OHd-1r94">tutorial videos</a> to help prosecutors, technicians, consultants, researchers, academics and others monitor changes in land cover caused by fires and deforestation using GFW.



“We have noticed an increase in environmental inspection efforts both by the public prosecutor’s office and the State Secretariat of the Environment to carry out field inspections and investigate environmental damages and infractions,” said Vinicius.



ICV’s work set a precedent for integrating remote sensing technology in environmental conservation efforts across Mato Grosso. Vinicius believes that involving communities such as Indigenous Peoples in these efforts is crucial for sustainability.



“An essential part of this work is people’s sense of ability [to use forest monitoring tools],” said Vinicius, explaining how ICV has fostered a community-based approach to environmental monitoring, equipping individuals with the tools to gather and report data on forest crimes. “We have more than 40 Indigenous Peoples in [Mato Grosso] today who have the potential to use monitoring tools to monitor their territories and protect them directly.”



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1371" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-2048x1371.jpg" alt="" class="wp-image-17486" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-2048x1371.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-1024x685.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-768x514.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-1536x1028.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



These activities have enabled prosecutors, technicians and the State Secretariat for the Environment to assemble detailed evidence, including geotagged photos and GPS coordinates of illegal activities, which are instrumental in pinpointing and addressing deforestation hotspots. The data collected has significantly enhanced legal enforcement and conservation efforts.



Since 2020, ICV has expanded its use of GFW tools beyond the Amazon to monitor other critical biomes. A <a href="https://www.cambridge.org/core/journals/environmental-conservation/article/abs/public-prosecutors-offices-experience-using-global-forest-watch-to-monitor-and-deter-deforestation-in-the-cerrado/767687C40EF80993741FB500D52196DB" target="_blank" rel="noreferrer noopener">recently published scientific article</a> addresses the experience and results with the use of GFW by the Public Prosecutor’s Office in Mato Grosso to monitor and deter deforestation in the Cerrado biome.



“Biomes such as the Pantanal and the Cerrado do not often have as much visibility as the Amazon, but they are very important to climate and hydrological balance, and house enormous biodiversity,” said Vinicius, explaining that Mato Grosso includes areas of three of Brazil’s six biomes. “A state with all this natural wealth, with a diversity of Indigenous Peoples and traditional communities, as well as a leader in agricultural and livestock productivity, presents itself as an excellent laboratory for finding the sustainability solutions our planet needs.”



<a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Learn more about Instituto Centro de Vida here.</a>



You can also follow Instituto Centro de Vida’s work to protect and restore forest landscapes on <a href="https://www.facebook.com/InstitutoCentrodeVida/" target="_blank" rel="noreferrer noopener">Facebook</a>, <a href="https://www.linkedin.com/company/instituto-centro-de-vida/" target="_blank" rel="noreferrer noopener">LinkedIn</a>, <a href="https://www.instagram.com/institutocentrodevida/" target="_blank" rel="noreferrer noopener">Instagram</a> and <a href="https://twitter.com/icvcentrodevida" target="_blank" rel="noreferrer noopener">Twitter (X)</a>.



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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

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Voices of Global Forest Watch: Vinicius Silgueiro, Instituto Centro de Vida

A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our Voices of Global Forest Watch series will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.



<iframe width="560" height="450" src="https://www.youtube.com/embed/IipacbHyMGU?si=O7qrSPWsK16t1gcT" 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>







“Forest, for me, is life. Forest is water, it's land, it's people.” - Vinicius Silgueiro, Territorial Intelligence Coordinator, Instituto Centro de Vida



Vinicius Silgueiro's story begins with a deep-seated connection to the Brazilian Amazon, inspiring his efforts to protect it through his work at the <a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Instituto Centro de Vida (ICV)</a>, a nonprofit organization that focuses on sustainable land use and preserving natural resources in the state of Mato Grosso in Brazil, which is known for its rich biodiversity.



“I was born and grew up in an environment with a diversity of vegetation between the Cerrado and the Pantanal biomes, and then I lived in the Amazon. Having a forest is having life,” said Vinicius, who has been working at ICV since he was an intern 14 years ago.



ICV uses GFW’s extensive satellite imagery and deforestation alert capabilities in its environmental protection work.



As a <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Small Grants Fund (SGF)</a> recipient from 2020-2022, ICV helped expand GFW’s use within the Mato Grosso Public Prosecutor’s Office to hold landholders accountable for environmental crimes. ICV trained the prosecutor’s technicians on how to use GFW tools and data to fight illegal deforestation and fires, and helped develop a methodology and process for prosecutors and technical staff to identify and act on illegal deforestation alerts.



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-2048x1536.jpg" alt="" class="wp-image-17484"/><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



ICV also developed a series of <a href="https://www.youtube.com/watch?v=pw4SshvYITQ&amp;list=PLH0XsN2wGrLATqJesckApo9S9OHd-1r94">tutorial videos</a> to help prosecutors, technicians, consultants, researchers, academics and others monitor changes in land cover caused by fires and deforestation using GFW.



“We have noticed an increase in environmental inspection efforts both by the public prosecutor’s office and the State Secretariat of the Environment to carry out field inspections and investigate environmental damages and infractions,” said Vinicius.



ICV’s work set a precedent for integrating remote sensing technology in environmental conservation efforts across Mato Grosso. Vinicius believes that involving communities such as Indigenous Peoples in these efforts is crucial for sustainability.



"An essential part of this work is people’s sense of ability [to use forest monitoring tools]," said Vinicius, explaining how ICV has fostered a community-based approach to environmental monitoring, equipping individuals with the tools to gather and report data on forest crimes. “We have more than 40 Indigenous Peoples in [Mato Grosso] today who have the potential to use monitoring tools to monitor their territories and protect them directly.”



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-2048x1371.jpg" alt="" class="wp-image-17486"/><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



These activities have enabled prosecutors, technicians and the State Secretariat for the Environment to assemble detailed evidence, including geotagged photos and GPS coordinates of illegal activities, which are instrumental in pinpointing and addressing deforestation hotspots. The data collected has significantly enhanced legal enforcement and conservation efforts.



Since 2020, ICV has expanded its use of GFW tools beyond the Amazon to monitor other critical biomes. A <a href="https://www.cambridge.org/core/journals/environmental-conservation/article/abs/public-prosecutors-offices-experience-using-global-forest-watch-to-monitor-and-deter-deforestation-in-the-cerrado/767687C40EF80993741FB500D52196DB" target="_blank" rel="noreferrer noopener">recently published scientific article</a> addresses the experience and results with the use of GFW by the Public Prosecutor’s Office in Mato Grosso to monitor and deter deforestation in the Cerrado biome.



“Biomes such as the Pantanal and the Cerrado do not often have as much visibility as the Amazon, but they are very important to climate and hydrological balance, and house enormous biodiversity,” said Vinicius, explaining that Mato Grosso includes areas of three of Brazil’s six biomes. “A state with all this natural wealth, with a diversity of Indigenous Peoples and traditional communities, as well as a leader in agricultural and livestock productivity, presents itself as an excellent laboratory for finding the sustainability solutions our planet needs.”



<a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Learn more about Instituto Centro de Vida here.</a>



You can also follow Instituto Centro de Vida’s work to protect and restore forest landscapes on <a href="https://www.facebook.com/InstitutoCentrodeVida/" target="_blank" rel="noreferrer noopener">Facebook</a>, <a href="https://www.linkedin.com/company/instituto-centro-de-vida/" target="_blank" rel="noreferrer noopener">LinkedIn</a>, <a href="https://www.instagram.com/institutocentrodevida/" target="_blank" rel="noreferrer noopener">Instagram</a> and <a href="https://twitter.com/icvcentrodevida" target="_blank" rel="noreferrer noopener">Twitter (X)</a>.



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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

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Há uma década, o lançamento do Global Forest Watch (GFW) inaugurou uma nova era de responsabilização e transparência sobre o monitoramento e proteção das florestas globais. Para celebrar esses 10 anos de impacto, nossa série Voices of Global Forest Watch destacará os casos de sucessos dos usuários, parceiros e membros da comunidade do GFW ao longo do ano. Nós convidamos você a ler, ver e compartilhar as histórias de líderes excepcionais no monitoramento que desempenharam um papel fundamental na proteção das florestas em todo o mundo.



<iframe width="560" height="450" src="https://www.youtube.com/embed/IipacbHyMGU?si=O7qrSPWsK16t1gcT" 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>







“Floresta, para mim, é vida. Floresta é água, é terra, são as pessoas.” - Vinicius Silgueiro, Coordenador de Inteligência Territorial, Instituto Centro de Vida



A história de Vinicius Silgueiro começa com uma conexão profunda com a Amazônia brasileira, inspirando seus esforços para protegê-la por meio de seu trabalho no <a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Instituto Centro de Vida (ICV)</a>, uma organização sem fins lucrativos que se concentra no uso sustentável da terra e na preservação dos recursos naturais no estado de Mato Grosso, que é conhecido por sua rica biodiversidade.



“Nasci e cresci em um ambiente com uma diversidade de vegetação entre os biomas Cerrado e Pantanal, e depois vim morar na Amazônia. Ter floresta é ter vida”, disse Vinicius, que trabalha no ICV desde que era estagiário, há 14 anos.



O ICV utiliza as extensas imagens de satélite e capacidades de alerta de desmatamento do GFW em seu trabalho de proteção ambiental.



Como beneficiário do <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/">Small Grants Fund (SGF)</a> de 2020 a 2022, o ICV ajudou a expandir o uso do GFW no Ministério Público de Mato Grosso para responsabilizar os proprietários de terras por crimes ambientais. O ICV treinoutécnicos do Ministério Público sobre como usar as ferramentas e dados do GFW para combater o desmatamento ilegal e os incêndios, e ajudou a desenvolver uma metodologia e um processo para que os promotores e a equipe técnica identifiquem e ajam em caso de alertas de desmatamento ilegal.



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2018_Oficina_mapeamento_castanhais_2-1-2048x1536.jpg" alt="" class="wp-image-17488"/><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



O ICV também desenvolveu uma série de vídeos tutoriais para ajudar promotores, técnicos, consultores, pesquisadores, acadêmicos e outros a monitorar as mudanças na cobertura da terra causadas por incêndios e desmatamento, usando o GFW.



“Percebemos um aumento nos esforços de fiscalização ambiental, tanto do Ministério Público quanto da Secretaria de Estado do Meio Ambiente, para realizar fiscalizações em campo e apurar danos e infrações ambientais”, disse Vinicius.



O trabalho do ICV abriu um precedente para a integração da tecnologia de sensoriamento remoto nos esforços de conservação ambiental em Mato Grosso. Vinicius acredita que envolver comunidades como os Povos Indígenas nesses esforços é crucial para a sustentabilidade.



“Uma parte essencial deste trabalho é o sentido de capacidade das pessoas [para usar ferramentas de monitoramento]”, disse Vinicius, explicando como o ICV promoveu uma abordagem comunitária para o monitoramento ambiental, equipando os indivíduos com as ferramentas para recolher e reportar dados sobre crimes contra a floresta. “Temos hoje mais de 40 Povos Indígenas em [Mato Grosso] que têm o potencial para usar ferramentas de monitoramento para monitorar seus territórios e protegê-los diretamente.”



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2015_mapeamento_participativo_babacuais_2-1-2048x1371.jpg" alt="" class="wp-image-17490"/><figcaption class="wp-element-caption">Credit: ICV</figcaption></figure>



Estas atividades permitiram que promotores, técnicos e a Secretaria de Estado do Meio Ambiente reunissem evidências detalhadas, incluindo fotos georreferenciadas e coordenadas geográficas de atividades ilegais, que são fundamentais para identificar e abordar focos de desmatamento. Os dados recolhidos melhoraram significativamente a aplicação da lei e os esforços de conservação.



Desde 2020, o ICV expandiu o uso das ferramentas do GFW para além da Amazônia para monitorar outros biomas críticos. Um <a href="https://www.cambridge.org/core/journals/environmental-conservation/article/abs/public-prosecutors-offices-experience-using-global-forest-watch-to-monitor-and-deter-deforestation-in-the-cerrado/767687C40EF80993741FB500D52196DB" target="_blank" rel="noreferrer noopener">artigo científico publicado recentemente</a> aborda a experiência e resultados com o uso do GFW pelo MPMT para monitorar e deter o desmatamento no bioma Cerrado.



“Biomas como o Pantanal e o Cerrado nem sempre têm tanta visibilidade quanto a Amazônia, mas são muito importantes para o equilíbrio climático e hidrológico e abrigam uma enorme biodiversidade”, disse Vinicius, explicando que Mato Grosso inclui áreas de três dos seis biomas do Brasil.“Um estado com toda essa riqueza natural, com diversidade de Povos Indígenas e comunidades tradicionais, além de líder em produtividade agrícola e pecuária, apresenta-se como um excelente laboratório para encontrar as soluções de sustentabilidade que nosso planeta necessita.”



<a href="https://www.icv.org.br/" target="_blank" rel="noreferrer noopener">Saiba mais sobre o Instituto Centro de Vida aqui.</a>



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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

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Vozes do Global Forest Watch: Vinicius Silgueiro, Instituto Centro de Vida

As part of our Voices of Global Forest Watch series, learn about Vinicius Silgueiro's work with Brazilian nonprofit Instituto Centro de Vida.

Voices of GFW: Vinicius Silgueiro, Instituto Centro de Vida

carbon

carbon | Global Forest Watch Blog

David Gibbs

Melissa Rose

Nancy Harris

What’s New With GFW’s Forest Carbon Monitoring

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

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

Aerial footage of palm oil and the forest in Sentabai Village, West Kalimantan, 2017.

Liz Goldman

New tree cover loss data from the University of Maryland’s GLAD lab (UMD) and available on Global Forest Watch (GFW) shows declines in primary forest loss in some countries but a persistent rate overall in 2023. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.



<h2 class="wp-block-heading">What tree cover loss measures and how this differs from deforestation</h2>



The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2023. The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops (though notably, the primary forest data we use to filter the loss in the tropics excludes plantations, tree crops and regrowing forests). Loss of this tree cover can be due to human or natural causes and can be permanent or temporary.



Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">read more about the differences here</a>. Loss in tree cover, whether deforestation or not, can be legal or illegal.



In some cases, such as in the <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Deforestation and Restoration Targets Tracker</a> on the Global Forest Review, we use a proxy for deforestation. That proxy includes losses that are from small-scale agriculture in humid tropical primary forests and all commodity-driven deforestation and urbanization (inside and outside of humid tropical primary forests). Temporary losses, such as from fires or forestry activities, are not included. We don’t use this deforestation proxy in all of our reporting because it is based on coarse resolution data that is designed to limit the inclusion of temporary losses or losses in planted forests at the global scale only. Deforestation continues to be difficult to track with globally consistent data sets and there are inherent delays in confirming whether loss detected in satellite imagery is temporary or permanent.



<h2 class="wp-block-heading">Data improvements over time result in some inconsistencies in the data timeseries</h2>



Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>.



This year, the loss detection algorithm used by the University of Maryland to create the tree cover loss data set was supplemented by information from the <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">DIST-ALERT land disturbance data set</a>. Late season changes are sometimes missed in the tree cover loss data due to insufficient satellite data and cloud cover and are attributed to the following year. DIST-ALERT showed patches of tree cover loss that were originally missed by the tree cover loss data, and these were manually reviewed and included for 2023.



Employing DIST-ALERT helped especially to detect loss from late season forest fires in boreal forests and late season clearing in the tropics that would have otherwise appeared in the 2024 data. The DIST-ALERT data also helped detect some tree cover loss due to flooding and mining. The use of alerts resulted in a 3.7% increase in global tree cover loss detected. Most of the changes affected the boreal regions, while for the rest of the globe, the increase was only 1.7%. It is important to note that since most of the added areas would have otherwise been detected in 2024, we do not believe this change in the mapping approach for 2023 will have any impact on long-term trends in the data.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;



Additional changes to the time series include adjustments to the algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013. These changes make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture. Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.



To address these inconsistencies, we:



<ul>
<li>Focus our analysis on trends post-2015.</li>



<li>Assess the three-year moving average to interpret longer-term trends.</li>



<li>Disregard the increase in loss post-2012 in places dominated by small-scale agriculture, like Central Africa.</li>
</ul>



<h2 class="wp-block-heading">What does tree cover loss from fire capture?</h2>



The tree cover loss from fire data from UMD allows us to distinguish fire-driven loss from other tree cover loss by assigning likelihood of loss due to fire to each 30-meter tree cover loss pixel, breaking the data into loss due to fire and loss due to other drivers, such as agriculture or logging. The tree cover loss due to fire data includes natural or human-ignited fires that result in direct loss of tree canopy cover. The data captures wildfires, fires used to clear land for another use and intentionally set fires that result in tree cover loss (including escaped fires started by humans for purposes related to agriculture, hunting, recreation or arson). Instances where trees are felled and later burned are not included, however, since the initial driver of loss is mechanical removal.



This data allows us to better understand how fire impacted tree cover loss in 2023. For example, fire played especially large roles in Bolivia and Canada — <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">learn more about our findings from the 2023 data here</a>.



Even though fires often do not result in a permanent change in land use, they are still an important source of carbon emissions and can <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">lead to feedback loops</a> of increasing emissions, hotter temperatures, drier forests and more fires.



<h2 class="wp-block-heading">Why we focus on the tropics</h2>



In our <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&utm_medium=bitly&utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world’s deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.



<h2 class="wp-block-heading">How does tree cover loss compare with official country data in Brazil and Indonesia?</h2>



Brazil



PRODES — the official forest monitoring system for the Amazon from Brazil’s National Institute for Space Research (INPE) — and the UMD data have methodological and definitional differences that are important to understand when comparing these two data sources. PRODES measures clear cut deforestation and anthropogenic forest losses by fire larger than 6.25 hectares, whereas UMD captures loss larger than 0.09 hectares of all trees taller than five meters. These are both important measures that help us understand how forests are changing since deforestation, forest fires and small disturbances to forest canopy can all lead to impacts on climate, biodiversity and ecosystem services.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1348" height="1042" src="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png" alt="UMD and PRODES differences explained" class="wp-image-15866" srcset="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png 1348w, https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN-1024x792.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN-768x594.png 768w" sizes="(max-width: 1348px) 100vw, 1348px" /></figure>



Both systems show a downward trend in loss for 2023 — 22% decrease for PRODES and 39% decrease for UMD (42% decrease for non-fire loss) in the Amazon biome, confirming the positive news for the Amazon.



<h4 class="wp-block-heading">PRODES VS. UMD DATA IN THE BRAZILIAN AMAZON</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17268908?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonesia



Overlaying the UMD/GFW primary forest loss data for 2023 with the official Indonesia land cover map from the Ministry of Environment and Forestry (MoEF) shows that 70% occurred within Indonesia’s official forest cover classes, with the remaining 30% taking place in mixed dry land agriculture, shrubland, swamp shrub and other land cover types. Approximately 144 thousand hectares of loss were found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 144 thousand hectares, about 15 thousand hectares were within areas legally classified as primary forest in Indonesia.



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			MoEF class
			</td>
			<td style="width:550px;height:39px;">
			Percent of UMD/GFW 2023 Primary Forest Loss
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Primary forest
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Secondary forest
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Plantation forest
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			Non-forest
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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

Global Forest Watch’s 2023 Tree Cover Loss Data Explained

New tree cover loss data from the University of Maryland’s GLAD lab (UMD) and available on Global Forest Watch (GFW) shows declines in primary forest loss in some countries but a persistent rate overall in 2023. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.



<h2 class="wp-block-heading">What tree cover loss measures and how this differs from deforestation</h2>



The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2023. The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops (though notably, the primary forest data we use to filter the loss in the tropics excludes plantations, tree crops and regrowing forests). Loss of this tree cover can be due to human or natural causes and can be permanent or temporary.



Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">read more about the differences here</a>. Loss in tree cover, whether deforestation or not, can be legal or illegal.



In some cases, such as in the <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Deforestation and Restoration Targets Tracker</a> on the Global Forest Review, we use a proxy for deforestation. That proxy includes losses that are from small-scale agriculture in humid tropical primary forests and all commodity-driven deforestation and urbanization (inside and outside of humid tropical primary forests). Temporary losses, such as from fires or forestry activities, are not included. We don’t use this deforestation proxy in all of our reporting because it is based on coarse resolution data that is designed to limit the inclusion of temporary losses or losses in planted forests at the global scale only. Deforestation continues to be difficult to track with globally consistent data sets and there are inherent delays in confirming whether loss detected in satellite imagery is temporary or permanent.



<h2 class="wp-block-heading">Data improvements over time result in some inconsistencies in the data timeseries</h2>



Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>.



This year, the loss detection algorithm used by the University of Maryland to create the tree cover loss data set was supplemented by information from the <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">DIST-ALERT land disturbance data set</a>. Late season changes are sometimes missed in the tree cover loss data due to insufficient satellite data and cloud cover and are attributed to the following year. DIST-ALERT showed patches of tree cover loss that were originally missed by the tree cover loss data, and these were manually reviewed and included for 2023.



Employing DIST-ALERT helped especially to detect loss from late season forest fires in boreal forests and late season clearing in the tropics that would have otherwise appeared in the 2024 data. The DIST-ALERT data also helped detect some tree cover loss due to flooding and mining. The use of alerts resulted in a 3.7% increase in global tree cover loss detected. Most of the changes affected the boreal regions, while for the rest of the globe, the increase was only 1.7%. It is important to note that since most of the added areas would have otherwise been detected in 2024, we do not believe this change in the mapping approach for 2023 will have any impact on long-term trends in the data.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;



Additional changes to the time series include adjustments to the algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013. These changes make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture. Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.



To address these inconsistencies, we:



<ul>
<li>Focus our analysis on trends post-2015.</li>



<li>Assess the three-year moving average to interpret longer-term trends.</li>



<li>Disregard the increase in loss post-2012 in places dominated by small-scale agriculture, like Central Africa.</li>
</ul>



<h2 class="wp-block-heading">What does tree cover loss from fire capture?</h2>



The tree cover loss from fire data from UMD allows us to distinguish fire-driven loss from other tree cover loss by assigning likelihood of loss due to fire to each 30-meter tree cover loss pixel, breaking the data into loss due to fire and loss due to other drivers, such as agriculture or logging. The tree cover loss due to fire data includes natural or human-ignited fires that result in direct loss of tree canopy cover. The data captures wildfires, fires used to clear land for another use and intentionally set fires that result in tree cover loss (including escaped fires started by humans for purposes related to agriculture, hunting, recreation or arson). Instances where trees are felled and later burned are not included, however, since the initial driver of loss is mechanical removal.



This data allows us to better understand how fire impacted tree cover loss in 2023. For example, fire played especially large roles in Bolivia and Canada — <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">learn more about our findings from the 2023 data here</a>.



Even though fires often do not result in a permanent change in land use, they are still an important source of carbon emissions and can <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">lead to feedback loops</a> of increasing emissions, hotter temperatures, drier forests and more fires.



<h2 class="wp-block-heading">Why we focus on the tropics</h2>



In our <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world’s deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.



<h2 class="wp-block-heading">How does tree cover loss compare with official country data in Brazil and Indonesia?</h2>



Brazil



PRODES — the official forest monitoring system for the Amazon from Brazil’s National Institute for Space Research (INPE) — and the UMD data have methodological and definitional differences that are important to understand when comparing these two data sources. PRODES measures clear cut deforestation and anthropogenic forest losses by fire larger than 6.25 hectares, whereas UMD captures loss larger than 0.09 hectares of all trees taller than five meters. These are both important measures that help us understand how forests are changing since deforestation, forest fires and small disturbances to forest canopy can all lead to impacts on climate, biodiversity and ecosystem services.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png" alt="UMD and PRODES differences explained" class="wp-image-15866"/></figure>



Both systems show a downward trend in loss for 2023 — 22% decrease for PRODES and 39% decrease for UMD (42% decrease for non-fire loss) in the Amazon biome, confirming the positive news for the Amazon.



<h4 class="wp-block-heading">PRODES VS. UMD DATA IN THE BRAZILIAN AMAZON</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17268908?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonesia



Overlaying the UMD/GFW primary forest loss data for 2023 with the official Indonesia land cover map from the Ministry of Environment and Forestry (MoEF) shows that 70% occurred within Indonesia’s official forest cover classes, with the remaining 30% taking place in mixed dry land agriculture, shrubland, swamp shrub and other land cover types. Approximately 144 thousand hectares of loss were found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 144 thousand hectares, about 15 thousand hectares were within areas legally classified as primary forest in Indonesia.



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			MoEF class
			</td>
			<td style="width:550px;height:39px;">
			Percent of UMD/GFW 2023 Primary Forest Loss
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Primary forest
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Secondary forest
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Plantation forest
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			Non-forest
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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

Novos dados de perda de cobertura arbórea do GLAD Lab da University of Maryland (UMD) e disponíveis na plataforma Global Forest Watch (GFW) mostram uma redução na perda de floresta primária em alguns países, mas uma taxa geral persistente em 2023. O que os dados medem, o que há de diferente este ano e como eles se comparam a outras estimativas oficiais de desmatamento? Aqui está o que você precisa saber sobre os novos dados.



<h2 class="wp-block-heading">O que a perda de cobertura arbórea mede e como ela difere do desmatamento </h2>



Os dados de perda de cobertura arbórea da UMD capturam a perda anual de todas as árvores com mais de cinco metros de altura entre os anos de 2001 e 2023. Os dados sobre perda de cobertura arbórea incluem a perda de árvores em florestas naturais, bem como em plantações e cultivo arbóreo (embora seja importante notar que os dados sobre florestas que usamos para filtrar a perda nos trópicos excluem plantações, cultivos de árvores e florestas em regeneração). A perda dessa cobertura pode ser devido a causas humanas ou naturais, permanente ou temporária.



O desmatamento difere por se referir apenas a uma mudança permanente e causada pelo homem, partindo do estado de floresta para outros usos da terra. Algumas formas de perda de cobertura arbórea, como a conversão de uma floresta natural em terras agrícolas, são consideradas desmatamento, enquanto outras formas, como a extração de madeira em florestas plantadas ou distúrbios naturais, não são. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Leia mais sobre as diferenças aqui</a>. A perda de cobertura arbórea, seja desmatamento ou não, pode ser legal ou ilegal.



Em alguns casos, como no <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Targets Tracker de Desmatamento e Restauração</a> no Global Forest Review, usamos um indicador para desmatamento. Esse indicador inclui as perdas que são de agricultura de pequena escala em florestas tropicais primárias úmidas e todo desmatamento e urbanização impulsionados por commodities (dentro e fora de florestas tropicais primárias úmidas). Perdas temporárias, como as decorrentes de incêndios ou atividades florestais, não foram incluídas. Não usamos esse indicador de desmatamento em todos os nossos relatórios porque é baseado em dados de baixa resolução que são concebidos para limitar a inclusão de perdas temporárias ou perdas em florestas plantadas apenas em escala global. O desmatamento continua a ser difícil de rastrear com conjuntos de dados consistentes globalmente e há atrasos inerentes para confirmar se as perdas detectadas em imagens de satélite são temporárias ou permanentes.



<h2 class="wp-block-heading">Melhorias de dados ao longo do tempo resultam em algumas inconsistências na série temporal de dados</h2>



Ajustes de algoritmo e melhores dados de satélite <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">melhoraram o conjunto de dados de perda de cobertura arbórea ao longo do tempo</a>.



Este ano, o algoritmo de detecção de perda usado pela University of Maryland para criar o conjunto de dados de perda de cobertura arbórea foi suplementado pelas informações do <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">conjunto de dados de alteração do solo do DIST-ALERT</a>. Mudanças da última estação às vezes são perdidas nos dados de perda de cobertura arbórea devido a dados de satélite insuficientes e cobertura de nuvens e são atribuídos ao ano seguinte. O DIST-ALERT mostrou fragmentos de perda de cobertura arbórea que originalmente foram omitidos pelos dados de perda de cobertura arbórea e incluídos para 2023 e esses foram revisados manualmente e incluídos em 2023.



Utilizar o DIST-ALERT ajudou especialmente a detectar a perda por queimadas da última estação em florestas boreais e desmatamento da última estação nos trópicos que de outro modo apareceriam nos dados de 2024. Os dados do DIST-ALERT também ajudaram a detectar parte da perda de cobertura arbórea devido à inundação e mineração. O uso de alertas resultou em um aumento de 3,7% em perda de cobertura arbórea detectada. A maioria das mudanças afetou as regiões boreais, embora para o restante do globo, o aumento foi de apenas 1,7%. É importante observar que, como a maioria das áreas adicionadas teria sido detectada em 2024 de outra forma, não acreditamos que essa mudança na abordagem de mapeamento em 2023 terá algum impacto nas tendências de longo prazo nos dados.



Mudanças adicionais para a série temporal inclui ajustes no algoritmo para os anos de 2011 a 2014 e a partir de 2015, bem como a incorporação de dados do Landsat 8 com início em 2013. Essas alterações facilitam a detecção de mudanças em escalas maiores, como a perda devido a incêndios, exploração madeireira seletiva e agricultura de rotação. Variações na disponibilidade de imagens por satélite também significam que há inconsistências na qualidade e no número de imagens disponíveis para capturar dados a cada ano.



Para resolver essas inconsistências, nós:



<ul>
<li>concentramos nossa análise em tendências após 2015;</li>



<li>avaliamos a média móvel de três anos para interpretar as tendências de longo prazo; e</li>



<li>desconsideramos o aumento da perda após 2012 em locais dominados pela agricultura de pequena escala, como a África Central.</li>
</ul>



<h2 class="wp-block-heading">O que a perda de cobertura arbórea devido a incêndio capta?</h2>



Os dados de perda de cobertura arbórea devido a incêndio da UMD nos permitem distinguir de outras mudanças na cobertura florestal ao atribuir a probabilidade de perda devido a incêndio para cada pixel de perda de cobertura arbórea de 30 metros, dividindo os dados em perda devido a incêndio e perda devido a outros fatores, como agricultura ou extração de madeira. Esses dados incluem incêndios naturais ou provocados pelo homem que resultam em perda direta de cobertura de copa das árvores. Os dados capturam incêndios florestais, incêndios usados para limpar a terra para outro uso e incêndios intencionais que resultam em perda de cobertura arbórea (incluindo incêndios escapados iniciados por humanos para fins relacionados à agricultura, caça, recreação ou incêndio criminoso). No entanto, os casos em que as árvores são derrubadas e posteriormente queimadas não são incluídos, uma vez que o fator inicial de perda é a remoção mecânica.



Esses dados nos permitem entender melhor como incêndios afetaram a perda de cobertura arbórea em 2023. Por exemplo, incêndios desempenharam papéis especialmente grandes na Bolívia e Canadá. <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">Saiba mais sobre nossas descobertas a partir dos dados de 2023 aqui</a>.



Embora os incêndios muitas vezes não resultem em uma mudança permanente no uso da terra, eles ainda são uma importante fonte de emissões de carbono e podem <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">levar a ciclos de retroalimentação</a> de emissões crescentes, temperaturas mais quentes, florestas mais secas e mais incêndios.



<h2 class="wp-block-heading">Por que nos concentramos nos trópicos? </h2>



Na <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">análise dos dados da UMD</a>, nós nos concentramos principalmente na perda de cobertura arbórea em florestas primárias tropicais, pois as florestas tropicais sofrem <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">a grande maioria</a> (mais de 96%) do desmatamento mundial, e a perda nessas áreas tem impactos enormes sobre a biodiversidade e o armazenamento de carbono. Mesmo que essas perdas acabem sendo revertidas, <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">levará décadas</a> para que esses habitats e estoques de carbono se recuperem, e poderá ocorrer uma perda permanente de biodiversidade.&nbsp;



<h2 class="wp-block-heading">Como a perda de cobertura arbórea se compara aos dados nacionais oficiais no Brasil e na Indonésia?  </h2>



Brasil&nbsp;



O PRODES – o sistema oficial de monitoramento florestal para a Amazônia do Instituto Nacional de Pesquisas Espaciais (INPE) – e os dados da UMD têm diferenças metodológicas e de definição que são importantes de serem compreendidas ao comparar essas duas fontes de dados. O PRODES mede o desmatamento nítido e as perdas florestais antropogênicas por incêndio maiores que 6,25 hectares, enquanto a UMD captura perdas maiores que 0,09 hectare de todas as árvores com altura superior a cinco metros. Ambas são medidas importantes que nos ajudam a entender como as florestas estão mudando, uma vez que o desmatamento, as queimadas e pequenos distúrbios no dossel florestal podem levar a impactos no clima, na biodiversidade e nos serviços ecossistêmicos.&nbsp;



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2023/06/PT-23-05-04-gfr-technical-blog_UMDvsProdes-PT.png" alt="" class="wp-image-16134" style="aspect-ratio:3/2;object-fit:cover"/></figure>



Os sistemas mostram uma tendência de queda na perda para 2023. Uma diminuição de 22% para o PRODES e 39% para a UMD (42% de redução para a perda não relacionada a incêndios) no bioma da Amazônia, confirmando as notícias positivas para a Amazônia.



<h4 class="wp-block-heading">Dados do PRODES vs. UMD sobre a Amazônia brasileira</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17601358?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonésia



A sobreposição dos dados de perda de floresta primária da UMD/GFW de 2023 com o mapa oficial de cobertura terrestre da Indonésia do Ministério do Meio Ambiente e Florestas (Ministry of Environment and Forestry, MoEF) mostra que 70% ocorreu em classes oficiais de cobertura florestal da Indonésia, com os restantes 30% ocorrendo em áreas de agricultura em terras secas mistas, matagais, arbustos de pântanos e outros tipos de cobertura do solo. Cerca de 144 mil hectares de perda revelaram ser tanto classes oficiais de cobertura florestal da Indonésia quanto ter um tamanho de fragmento maior do que dois hectares. Dos 144 mil hectares, cerca de 15 mil hectares estavam em áreas legalmente classificadas como floresta primária na Indonésia.



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			Classe do MoEF
			</td>
			<td style="width:550px;height:39px;">
			Porcentagem de perda florestal primária de 2023 da UMD/GFW
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Floresta primária
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Floresta secundária
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Floresta plantada
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			Não florestal
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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Explicação dos dados do Global Forest Watch sobre a perda de cobertura arbórea de 2023

Los nuevos datos de pérdida de cobertura arbórea del laboratorio GLAD de la Universidad de Maryland (UMD) y disponibles en Global Forest Watch (GFW) muestran disminuciones en la pérdida de bosques primarios en algunos países, pero una tasa persistente en general en 2023. ¿Qué miden los datos, qué ha cambiado este año y cómo se comparan los datos a otras estimaciones oficiales de deforestación? Esto es lo que usted debe saber sobre los nuevos datos.&nbsp;



<h2 class="wp-block-heading">Qué mide la pérdida de cobertura arbórea y cuál es su diferencia con la deforestación &nbsp;</h2>



Los datos de pérdida de cobertura arbórea de UMD capturan la pérdida anual de todos los árboles de más de cinco metros de altura entre el 2001 y el 2023. Los datos de pérdida de cobertura arbórea incluyen la pérdida de árboles en bosques naturales, así como en plantaciones y cultivos arbóreos (aunque es importante recalcar que los datos de bosques primarios que utilizamos para filtrar la pérdida en los trópicos excluyen las plantaciones, los cultivos arbóreos y los bosques en regeneración). La pérdida de esta cobertura arbórea puede ser de origen humano o natural y puede ser permanente o temporal.&nbsp;&nbsp;



La deforestación se diferencia en que solo se refiere a un cambio de cobertura arbórea permanente de un bosque a otro uso de la tierra provocado por seres humanos (y no por causas naturales). Algunas formas de pérdida de cobertura arbórea, como la conversión de un bosque natural a tierras agrícolas, se consideran deforestación, mientras que otras formas de pérdida de cobertura arbórea, como la tala de madera en plantaciones forestales o las alteraciones naturales, no; <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">lea más sobre las diferencias aquí</a>. La pérdida de cobertura arbórea, ya sea deforestación o no, puede ser legal o ilegal.&nbsp;&nbsp;



En algunos casos, como en el <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Seguimiento de Objetivos de Deforestación y Restauración</a> de Global Forest Review, utilizamos un indicador que representa la deforestación. Ese indicador incluye las pérdidas derivadas de la agricultura a pequeña escala en los bosques primarios tropicales húmedos y toda la deforestación y urbanización impulsada por los productos básicos de consumo (dentro y fuera de los bosques primarios tropicales húmedos). Las pérdidas temporales, como aquellas provocadas por incendios o actividades forestales, no están incluidas. No utilizamos este indicador de deforestación en todos nuestros informes porque está basado en datos de resolución aproximada que están diseñados para limitar la inclusión de pérdidas temporales o pérdidas en bosques plantados solo a escala global. La deforestación sigue siendo difícil de rastrear con conjuntos de datos consistentes a nivel mundial y existen retrasos inherentes para confirmar si la pérdida detectada en las imágenes satelitales es temporal o permanente.&nbsp;



<h2 class="wp-block-heading">Las mejoras en los datos a lo largo del tiempo generan algunas incoherencias en las series temporales de datos </h2>



Los ajustes del algoritmo y la mejora de los datos satelitales han <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">mejorado el conjunto de datos de pérdida de cobertura arbórea a lo largo del tiempo</a>.&nbsp;&nbsp;



Este año, el algoritmo de detección de pérdidas utilizado por la Universidad de Maryland para crear los datos de pérdida de cobertura arbórea se complementó con información del <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">conjunto de datos de perturbaciones terrestres DIST-ALERT</a>. A veces los cambios de final de temporada de un año se omiten en los datos de pérdida de cobertura arbórea (debido a datos satelitales insuficientes y la cobertura de nubes) y se atribuyen en vez al año siguiente. DIST-ALERT mostró áreas de pérdida de cobertura arbórea que el algoritmo anual no detectó. Estas áreas se revisaron e incluyeron manualmente para el 2023.&nbsp;&nbsp;



El uso de DIST-ALERT ayudó especialmente a detectar pérdidas por incendios forestales de final de temporada en los bosques boreales y la tala de final de temporada en los trópicos que de otro modo habrían aparecido en los datos del 2024. Los datos de DIST-ALERT también ayudaron a detectar pérdida de cobertura arbórea debido a inundaciones y minería. El uso de estas alertas resultó en un aumento del 3,7 % en la pérdida de cobertura arbórea global detectada. La mayoría de los cambios afectaron a las regiones boreales, mientras que para el resto del mundo, el aumento fue de solo el 1,7 %. Es importante tener en cuenta que, dado que la mayoría de las áreas añadidas se habrían detectado de otro modo en 2024, no creemos que este cambio en el enfoque de mapeo para el 2023 tenga ningún impacto en las tendencias a largo plazo de los datos.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;



Los cambios adicionales a la serie temporal incluyen ajustes al algoritmo para los años 2011-2014 y para los años del 2015 en adelante y la incorporación de datos del Landsat 8 a partir del 2013. Estos cambios facilitan la detección de cambios a menor escala, como pérdidas por incendios, tala selectiva y agricultura migratoria. Las variaciones en la disponibilidad de imágenes satelitales también implican inconsistencias en la calidad y el número de imágenes disponibles para capturar la perdida cada año.&nbsp;



Para abordar estas inconsistencias, nosotros:&nbsp;&nbsp;



<ul>
<li>Centramos nuestros análisis en tendencias después del 2015.&nbsp;&nbsp;</li>



<li>Evaluamos el promedio móvil de tres años para interpretar las tendencias a más largo plazo.&nbsp;&nbsp;</li>



<li>Ignoramos el aumento de las pérdidas después de 2012 en lugares donde predomina la agricultura a pequeña escala, como África Central.&nbsp;&nbsp;</li>
</ul>



<h2 class="wp-block-heading">¿Qué captura la pérdida de cobertura arbórea por incendios? </h2>



Los datos de pérdida de cobertura arbórea a debida a incendios de la UMD nos permiten distinguir la pérdida provocada por incendios de otras pérdidas de cobertura arbórea. Estos datos atribuyen la probabilidad de pérdida debida al fuego de cada píxel de pérdida de cobertura arbórea de 30 metros. Usando estos datos, podemos distinguirla pérdida debida al fuego de la pérdida debida a otros factores, como la agricultura o la explotación forestal. Los datos sobre la pérdida de cobertura arbórea debida a los incendios incluyen los incendios naturales y aquellos provocados por seres humanos, que generan una pérdida directa del dosel de árbol. Los datos capturan los incendios forestales, los incendios utilizados para despejar el terreno para otro uso y los incendios provocados intencionadamente que dan lugar a la pérdida de cobertura arbórea (incluidos los incendios fugitivos iniciados por los seres humanos con fines relacionados con la agricultura, la caza, las actividades recreativas o los incendios provocados). Sin embargo, no se incluyen los casos en los que los árboles se talan y posteriormente se queman, ya que en estos casos el impulsor inicial de la pérdida es la eliminación mecánica.&nbsp;&nbsp;



Estos datos nos permiten comprender mejor el impacto del fuego en la pérdida de cobertura arbórea en el 2023. Por ejemplo, los incendios desempeñaron un papel especialmente importante en Bolivia y Canadá; <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">obtenga más información sobre nuestros hallazgos a partir de los datos de 2023 aquí</a>.&nbsp;



Aunque los incendios no suelen provocar un cambio permanente en el uso del suelo, siguen siendo una fuente importante de emisiones de carbono y pueden <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">dar lugar a bucles de retroalimentación</a> de aumento de emisiones, temperaturas más altas, bosques más secos y más incendios.&nbsp;&nbsp;



<h2 class="wp-block-heading">¿Por qué nos centramos en los trópicos?  </h2>



En nuestro <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">análisis de</a> los datos de la Universidad de Maryland (UMD), nos centramos principalmente en la pérdida de cobertura arbórea en los bosques tropicales primarios, ya que en ellos se registra <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">la mayor parte</a> (más del 96%) de la deforestación mundial, y la pérdida en esas áreas tiene enormes repercusiones sobre la biodiversidad y el almacenamiento de carbono. Aunque estas pérdidas puedan revertirse eventualmente, los hábitats y las reservas de carbono <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">tardarán décadas</a> en recuperarse y podría producirse una pérdida permanente de biodiversidad. &nbsp;



<h2 class="wp-block-heading">¿Cómo se compara la pérdida de cobertura arbórea con los datos oficiales de Brasil e Indonesia?   </h2>



Brasil &nbsp;



PRODES (el sistema oficial de monitoreo forestal para la Amazonia del Instituto Nacional De Investigación Espacial de Brasil (INPE)) y los datos de la Universidad de Maryland (UMD) tienen diferencias metodológicas y de definición que son importantes de entender cuando se comparan estas dos fuentes de datos. PRODES mide la deforestación total de más de 6.25 hectáreas y pérdidas de bosques antropogénicos por incendios, mientras que la Universidad de Maryland (UMD) registra la pérdida de más de 0.09 hectáreas de todos los árboles de más de cinco metros de altura. Ambas son medidas importantes que nos ayudan a entender cómo cambian los bosques, ya que la deforestación, los incendios forestales y las pequeñas alteraciones del dosel forestal pueden tener impactos en el clima, la biodiversidad y los servicios de los ecosistemas. &nbsp;



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/04/ES-Tech-Blog-Graphic_UMDvsProdes-2023.png" alt="" class="wp-image-17442"/></figure>



Ambos sistemas muestran una tendencia a la baja en pérdidas para 2023 en el bioma amazónico: una disminución del 22 % según PRODES y una disminución del 39 % según UMD (disminución del 42 % para pérdidas sin incendios), lo que confirma las noticias positivas para la Amazonia.&nbsp;



<h4 class="wp-block-heading">Datos de PRODES vs. UMD en la Amazonia brasileña</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17690036?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonesia



La superposición de los datos de pérdida de bosques primarios de UMD/GFW para el 2023 con el mapa oficial de cobertura terrestre de Indonesia del Ministerio de Medioambiente y Silvicultura (Ministry of Environment and Forestry, MoEF) muestra que el 70 % de la pérdida ocurrió dentro de las clases oficiales de cobertura arbórea de Indonesia, y el 30 % restante tuvo lugar en agricultura mixta de tierras secas, matorrales, arbustos pantanosos y otros tipos de cobertura de tierra. Aproximadamente 144 mil hectáreas de pérdida con un tamaño de parche superior a dos hectáreas se encontraron dentro de las clases oficiales de cubierta forestal de Indonesia. De estas 144 mil hectáreas, alrededor de 15 mil hectáreas se encontraron dentro de áreas legalmente clasificadas como bosques primarios en Indonesia.&nbsp;&nbsp;



<table border="0" cellpadding="0" cellspacing="0">
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		<tr>
			<td style="width:350px;height:39px;">
			Clase según el MoEF
			</td>
			<td style="width:550px;height:39px;">
			Porcentaje de pérdida de bosques primarios de UMD/GFW en 2023
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Bosques primarios
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Bosques secundarios
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Bosques de plantaciones
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			No forestal
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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Explicación de los datos de Global Forest Watch sobre la pérdida de cobertura arbórea en 2023  

Les nouvelles données du laboratoire GLAD de l’Université du Maryland (UMD) sur la perte de la couverture arborée, disponibles sur Global Forest Watch (GFW), indiquent une réduction des pertes de forêts primaires dans certains pays. Le taux global reste cependant constant en 2023. Que mesurent les données, quelles sont les différences cette année et quelle comparaison peut être faite par rapport aux autres estimations officielles de déforestation ? Voici ce qu'il faut savoir sur les nouvelles données.&nbsp;



<h2 class="wp-block-heading">Qu’est-ce que la perte de couverture arborée et en quoi cela diffère-t-elle de la déforestation?   </h2>



Les données de l’UMD sur la perte de la couverture arborée compilent les pertes annuelles d’arbres mesurant plus de 5 mètres au cours des années civiles 2001 à 2023. Ces données comprennent la perte d’arbres dans les forêts naturelles ainsi que dans les plantations et les cultures arbustives (même s’il convient de préciser que les données sur les forêts primaires que nous utilisons pour filtrer les pertes dans les régions tropicales ne prennent pas en compte les plantations, les cultures arbustives et les forêts en cours de reboisement). La perte de cette couverture arborée peut être due à des causes humaines ou naturelles et peut être permanente ou temporaire.&nbsp;&nbsp;



La déforestation diffère en ce sens qu’elle ne fait référence qu’à un changement permanent, causé par l’homme, de la forêt à une autre utilisation des terres. Certaines formes de perte de couverture arborée, comme la conversion d’une forêt naturelle en terres agricoles, sont considérées comme une déforestation, tandis que d’autres formes de perte de couverture arborée, comme la récolte de bois dans les forêts de plantation ou les perturbations naturelles, ne le sont pas — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">pour en savoir plus sur les différences, cliquez ici</a>. La perte de couverture arborée, qu’elle soit due à la déforestation ou non, peut être légale ou illégale.&nbsp;&nbsp;



Dans certains cas, comme avec le <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Targets Tracker, l’outil de suivi de la déforestation et de la restauration</a> utilisé par Global Forest Review, nous utilisons un proxy pour estimer la déforestation. Ce proxy prend en compte les pertes dues à l’agriculture à petite échelle dans les forêts primaires tropicales humides et toute la déforestation liée aux commodités et à l’urbanisation (au sein des forêts primaires tropicales humides et en dehors). Les pertes temporaires, telles que résultant des incendies ou des activités forestières ne sont pas incluses. Nous n’utilisons pas le proxy de la déforestation dans tous nos rapports, car elle repose sur des données de résolution brute conçues pour limiter l’inclusion de pertes temporaires ou pertes dans les forêts plantées à l’échelle mondiale.&nbsp;&nbsp;



Il reste difficile de suivre la déforestation à l'aide d'ensembles de données cohérents à l'échelle mondiale et il y a des retards inhérents à la confirmation du caractère temporaire ou permanent des pertes détectées par l'imagerie satellitaire.&nbsp;



<h2 class="wp-block-heading">L’amélioration des données au fil du temps entraîne des incohérences dans les séries de données chronologiques&nbsp;</h2>



Les ajustements des algorithmes et de meilleures données satellitaires ont <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">amélioré les données de perte de la couverture arborée au cours du temps</a>.&nbsp;&nbsp;



Cette année, à l’algorithme de détection des pertes utilisé par l’Université du Maryland pour créer l’ensemble de données sur la perte de couverture arborée s’est ajouté <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">l’ensemble de données sur les perturbations de terres DIST-ALERT</a>. Les changements observés en fin de saison ne figurent parfois pas dans les données de perte de couverture arborée en raison d’un manque de données satellitaires ou de la couverture nuageuse. Ils sont alors attribués à l’année suivante. Les données DIST-ALERT ont montré des zones de perte de couverture arborée qui, à l’origine, n’avaient pas été relevées dans les données de perte de couverture arborée. Ces zones ont été étudiées et incluses manuellement dans les estimations de 2023.&nbsp;&nbsp;



L’utilisation des données DIST-ALERT a en particulier permis de détecter les pertes survenues en fin de saison dues aux feux de forêts boréales et au déboisement dans les régions tropicales, pertes qui auraient autrement été comptabilisées dans les données 2024. Les données DIST-ALERT ont également contribué à détecter certaines pertes de couverture arborée dues aux inondations et à l’exploitation minière. Grâce aux alertes, il a été possible d’améliorer de 3,7 % la détection des pertes de couverture arborée à l’échelle mondiale. La plupart des changements ont affecté les régions boréales, l’augmentation n’ayant été que de 1,7 % pour le reste de la planète. Il est important de noter que la plupart des zones ajoutées auraient été détectées en 2024, nous ne pensons pas que ce changement dans l'approche cartographique pour 2023 aura un impact sur les tendances à long terme des données.&nbsp;



Les modifications supplémentaires apportées à la série temporelle incluent des ajustements de l’algorithme pour les années 2011 à 2014 et 2015 et suivantes et l’incorporation des données Landsat 8 à partir de 2013. Ces modifications permettent de détecter plus facilement les changements à petite échelle, comme les pertes dues aux incendies, à la coupe sélective et à l’agriculture itinérante. Les variations dans la disponibilité des images satellites signifient également qu'il y a des incohérences dans la qualité et le nombre d'images disponibles pour capturer les données chaque année.&nbsp;&nbsp;



Pour gérer ces incohérences, nous :&nbsp;&nbsp;



<ul>
<li>Concentrons nos analyses sur les tendances après 2015.&nbsp;&nbsp;</li>



<li>Évaluons la moyenne mobile sur trois ans pour interpréter les tendances à long terme.&nbsp;&nbsp;</li>



<li>Ne tenons pas compte de l’augmentation des pertes après 2012 dans les régions dominées par l’agriculture à petite échelle, comme l’Afrique centrale.&nbsp;&nbsp;</li>
</ul>



<h2 class="wp-block-heading">Qu’indique la perte de couverture arborée due aux incendies? </h2>



En attribuant la probabilité de perte due aux incendies à chaque pixel de 30 mètres de perte de couverture arborée, les données de l’UMD sur la perte de couverture arborée nous permettent de distinguer les pertes dues aux incendies des autres pertes, séparant ainsi les données concernant les pertes dues aux incendies et celles dues à d’autres facteurs comme l’agriculture et l’exploitation forestière. Les données de perte de couverture arborée due aux incendies incluent les feux d’origine naturelle ou humaine entraînant une perte directe du couvert arboré. Les données capturent les incendies, les feux utilisés pour défricher la terre à d’autres fins et les incendies intentionnels entraînant une perte de la couverture arborée (ce qui inclut les feux allumés par des humains à des fins agricoles et de chasse, de loisirs ou les incendies criminels qui se sont propagés). Cependant, les cas dans lesquels les arbres sont tombés et ont ensuite été brûlés ne sont pas inclus étant donné que le facteur initial de la perte implique un enlèvement mécanique.&nbsp;&nbsp;



Ces données nous permettent de mieux comprendre l’impact des incendies sur les données de perte de couverture arborée en 2023. Par exemple, les incendies ont joué un rôle particulièrement important en Bolivie et au Canada - <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">pour en savoir plus sur nos conclusions sur les données 2023, cliquez ici</a>.&nbsp;



Même si les incendies entraînent rarement des changements permanents dans l’occupation des sols, ils sont néanmoins une source importante d’émissions de CO2 susceptibles <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">d’entraîner des boucles de rétroaction</a> créant une augmentation des émissions, une hausse des températures, des forêts plus sèches et plus de feux.&nbsp;&nbsp;



<h2 class="wp-block-heading">Pourquoi nous concentrons-nous sur les tropiques?  </h2>



Dans notre <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">analyse des données de l’UMD</a>, nous nous concentrons principalement sur la perte du couvert arboré dans les forêts primaires tropicales. Cette focalisation est justifiée par le fait que les forêts tropicales représentent la <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">grande majorité</a> (plus de 96 %) des zones touchées par la déforestation dans le monde. La perte de ces forêts a un impact considérable sur la biodiversité et le stockage du carbone. Même si ces pertes sont finalement inversées, il faudra <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">des décennies</a> pour que ces habitats et ces stocks de carbone se reconstituent. De plus, une perte permanente de biodiversité est susceptible de se produire. &nbsp;



<h2 class="wp-block-heading">Comment la perte du couvert arboré se compare-t-elle aux données officielles du Brésil et de l’Indonésie?  </h2>



Brésil &nbsp;



Il est essentiel de comprendre les différences méthodologiques et définitionnelles entre PRODES, le système officiel de surveillance des forêts amazoniennes de l'Institut national brésilien de recherche spatiale (INPE), et les données de l'UMD lors de la comparaison de ces deux sources d'information. PRODES mesure la déforestation par coupes rases et les pertes forestières anthropiques résultant d'incendies sur des surfaces de plus de 6,25 hectares. En revanche, l'UMD enregistre les pertes du couvert forestier de plus de 0,09 hectare, incluant tous les arbres de plus de cinq mètres de haut. Ces deux mesures sont importantes pour comprendre l'évolution des forêts, car la déforestation, les incendies de forêt et les perturbations mineures du couvert forestier peuvent tous avoir un impact sur le climat, la biodiversité et les services écosystémiques.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/04/FR-Tech-Blog-Graphic_UMDvsProdes-2023.png" alt="" class="wp-image-17409"/></figure>



En 2023, les deux systèmes indiquent une tendance à la baisse pour les pertes : 22 % de réduction pour PRODES et 39 % pour l’UMD (42 % pour les pertes qui ne résultent pas d’incendies) dans le biome amazonien, confirmant les nouvelles positives concernant l’Amazonie.&nbsp;



<h4 class="wp-block-heading">Données PRODES vs UMD dans l’Amazonie brésilienne</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17690123?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonésie&nbsp;



En superposant les données de perte de forêts primaires de l’UMD/GFW en 2023 à la carte officielle de la couverture terrestre en Indonésie, publiée par le ministère de l’Environnement et de la Foresterie (MoEF), on observe que 70 % des pertes sont survenues au sein des classes officielles de couverture forestière en Indonésie. Les 30 % restant concernent un mélange d’agriculture dans des zones arides, de zones arbustives, d’arbustes marécageux, et d’autres types de couverture terrestre. Environ 144 000 hectares de perte ont été trouvés à la fois dans les classes officielles de couverture forestière de l'Indonésie et avec une taille de parcelle supérieure à deux hectares. Sur ces 144 000 hectares, environ 15 000 étaient situés dans des zones légalement classées comme forêts primaires en Indonésie.&nbsp;&nbsp;



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			Classe MoEF
			</td>
			<td style="width:550px;height:39px;">
			Pourcentage de perte de forêts primaires selon les données UMD/GFW 2023
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Forêt primaire
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Forêt secondaire
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Forêt de plantation
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			Zones non forestières
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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

Explication des données du Global Forest Watch sur la perte du couvert arboré en 2023  

Data kehilangan tutupan pohon baru dari laboratorium GLAD University of Maryland (UMD) dan tersedia di Global Forest Watch (GFW) menunjukkan penurunan kehilangan hutan primer di beberapa negara, tetapi secara keseluruhan masih tetap tinggi pada tahun 2023. Apa yang diukur oleh data tersebut, apa yang berbeda tahun ini, dan bagaimana perbandingannya dengan perkiraan resmi deforestasi lainnya? Berikut beberapa hal yang perlu Anda ketahui tentang data baru tersebut.&nbsp;



<h2 class="wp-block-heading">Apa yang diukur oleh kehilangan tutupan pohon dan apa perbedaannya dengan deforestasi  </h2>



Data kehilangan tutupan pohon dari UMD mencatat kehilangan tahunan dari semua pohon yang memiliki tinggi lebih dari lima meter selama tahun kalender antara tahun 2001 sampai 2023. Data kehilangan tutupan pohon mencakup kehilangan pohon di hutan alami serta di perkebunan dan tanaman pohon (meskipun perlu diperhatikan bahwa data hutan primer yang kami gunakan untuk menyaring kehilangan di daerah tropis tidak termasuk perkebunan, tanaman pohon, dan hutan yang tumbuh kembali). Kehilangan tutupan pohon ini dapat diakibatkan oleh penyebab manusia atau alam serta dapat bersifat permanen atau sementara.&nbsp;&nbsp;



Hal ini berbeda dengan deforestasi yang hanya mengacu pada perubahan dari hutan menjadi penggunaan lahan lainnya yang bersifat permanen dan disebabkan oleh manusia. Beberapa bentuk kehilangan tutupan pohon, seperti konversi hutan alami menjadi lahan pertanian, dianggap sebagai deforestasi, sedangkan bentuk kehilangan tutupan pohon lainnya, seperti pemanenan kayu di hutan perkebunan atau gangguan alami, tidak termasuk deforestasi — <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">baca selengkapnya tentang perbedaannya di sini</a>. Kehilangan tutupan pohon, baik deforestasi maupun bukan, dapat bersifat legal atau ilegal.&nbsp;&nbsp;



Dalam beberapa kasus, seperti di <a href="https://gfw.global/4apxyiz" target="_blank" rel="noreferrer noopener">Deforestation and Restoration Targets Tracker</a> pada Global Forest Review, kami menggunakan proksi untuk deforestasi. Proksi tersebut mencakup kehilangan yang berasal dari pertanian berskala kecil di hutan primer tropis lembap serta seluruh deforestasi dan urbanisasi yang didorong oleh komoditas (di dalam dan di luar hutan primer tropis lembap). Kehilangan sementara, seperti karena kebakaran atau kegiatan kehutanan, tidak tercakup. Kami tidak menggunakan proksi deforestasi ini pada semua laporan kami karena proksi berasal dari data resolusi kasar yang dirancang untuk membatasi inklusi kehilangan sementara atau kehilangan di hutan yang telah ditanami hanya pada skala global. Deforestasi tetap sulit untuk dilacak dengan kumpulan data yang konsisten secara global dan terdapat penundaan yang melekat dalam mengonfirmasi jika kehilangan yang terdeteksi dalam pencitraan satelit bersifat sementara atau permanen.&nbsp;



<h2 class="wp-block-heading">Perbaikan data dari waktu ke waktu menghasilkan beberapa inkonsistensi dalam rangkaian waktu data </h2>



Penyesuaian algoritma dan data satelit yang lebih baik telah <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">meningkatkan kumpulan data kehilangan tutupan pohon dari waktu ke waktu</a>.&nbsp;&nbsp;



Tahun ini, algoritma pendeteksi kehilangan yang digunakan oleh University of Maryland untuk membuat kumpulan data kehilangan tutupan pohon dilengkapi informasi dari <a href="https://glad.umd.edu/dataset/DIST-ALERT" target="_blank" rel="noreferrer noopener">kumpulan data gangguan lahan DIST-ALERT</a>. Perubahan akhir musim kadang terlewatkan dalam data kehilangan tutupan pohon karena data satelit dan tutupan awan yang tidak mencukupi serta perubahannya dimasukkan pada tahun berikutnya. DIST-ALERT menunjukkan petak kehilangan tutupan pohon yang awalnya terlewatkan oleh data kehilangan tutupan pohon, serta data ini ditinjau secara manual dan dimasukkan untuk tahun 2023.&nbsp;&nbsp;



Penggunaan DIST-ALERT membantu utamanya dalam mendeteksi kehilangan akibat kebakaran hutan pada akhir musim di hutan boreal dan pembukaan lahan pada akhir musim di daerah tropis yang seharusnya muncul dalam data tahun 2024. Data DIST-ALERT ini juga membantu mendeteksi beberapa kehilangan tutupan pohon karena banjir dan pertambangan. Penggunaan data peringatan tersebut menghasilkan peningkatan sebesar 3,7% pada kehilangan tutupan pohon global yang terdeteksi. Sebagian besar perubahan tersebut berdampak pada wilayah boreal, sementara untuk wilayah lainnya di dunia, peningkatannya hanya sebesar 1,7%. Penting untuk diperhatikan bahwa karena sebagian besar area yang ditambahkan akan terdeteksi pada tahun 2024, kami tidak yakin perubahan pendekatan pemetaan untuk tahun 2023 ini akan berdampak pada tren jangka panjang dalam data.&nbsp;



Perubahan tambahan terhadap rangkaian waktu meliputi penyesuaian algoritme untuk tahun 2011-2014 dan 2015 ke depan, serta penggabungan data Landsat 8 mulai tahun 2013. Perubahan ini mempermudah deteksi perubahan berskala lebih kecil, seperti kehilangan akibat kebakaran, tebang pilih, dan pertanian berpindah. Ketersediaan citra satelit yang bervariasi juga berarti terdapat ketidakkonsistenan dalam kualitas dan jumlah citra yang tersedia untuk mencatat data setiap tahunnya.&nbsp;



Untuk menangani ketidakkonsistenan ini, kami:&nbsp;&nbsp;



<ul>
<li>Memfokuskan analisis pada tren pasca-2015&nbsp;&nbsp;</li>



<li>Menilai rata-rata pergerakan selama tiga tahun untuk menginterpretasikan tren dalam jangka waktu yang lebih lama&nbsp;&nbsp;</li>



<li>Mengabaikan peningkatan kehilangan pasca-2012 di tempat yang didominasi oleh pertanian skala kecil, seperti Afrika Tengah&nbsp;&nbsp;</li>
</ul>



<h2 class="wp-block-heading">Apa yang ditunjukkan oleh kehilangan tutupan pohon akibat kebakaran? </h2>



Data kehilangan tutupan pohon akibat kebakaran dari UMD memungkinkan kami membedakan kehilangan yang diakibatkan oleh kebakaran dari kehilangan tutupan pohon lain. Ini dilakukan dengan menetapkan kemungkinan kehilangan akibat kebakaran untuk setiap piksel kehilangan tutupan pohon berukuran 30 meter, memecah data menjadi kehilangan karena kebakaran dan kehilangan karena faktor pendorong lain, seperti pertanian atau penebangan. Data kehilangan tutupan pohon akibat kebakaran termasuk kebakaran alami atau yang dipicu oleh manusia yang mengakibatkan kehilangan tutupan kanopi pohon secara langsung. Data tersebut mencakup kebakaran hutan, kebakaran yang digunakan untuk membuka lahan untuk penggunaan lain, dan kebakaran yang disengaja yang mengakibatkan kehilangan tutupan pohon (termasuk kebakaran yang disebabkan oleh manusia untuk tujuan yang terkait dengan pertanian, berburu, rekreasi, atau pembakaran). Namun demikian, kejadian saat pohon ditebang lalu dibakar tidak termasuk karena pendorong awal kehilangan adalah pemindahan mekanis.&nbsp;&nbsp;



Data ini memungkinkan kami lebih memahami bagaimana kebakaran berdampak pada data kehilangan tutupan pohon pada tahun 2023. Contohnya, kebakaran yang berperan besar khususnya di Bolivia dan Kanada — <a href="https://gfw.global/43tcLbQ" target="_blank" rel="noreferrer noopener">pelajari lebih lanjut tentang temuan kami dari data tahun 2023 di sini</a>.&nbsp;



Meskipun kebakaran sering kali tidak mengakibatkan perubahan permanen dalam penggunaan lahan, kebakaran tetap merupakan sumber emisi karbon yang besar dan dapat <a href="https://www.wri.org/insights/global-trends-forest-fires" target="_blank" rel="noreferrer noopener">menyebabkan putaran umpan balik</a> berupa peningkatan emisi, suhu yang lebih panas, hutan yang lebih kering, dan lebih banyak kebakaran.&nbsp;&nbsp;



<h2 class="wp-block-heading">Mengapa kami berfokus pada daerah tropis &nbsp;</h2>



Dalam <a href="https://gfw.global/3LkdrbQ%22%20/t%20%22_blank" target="_blank" rel="noreferrer noopener">analisis data UMD</a>, kami kebanyakan berfokus pada kehilangan tutupan pohon di hutan primer tropis, sebab hutan tropis menjadi korban dari <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog%22%20\\l%20%22how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests%22%20\t%20%22_blank" target="_blank" rel="noreferrer noopener">sebagian besar</a> (lebih dari 96%) deforestasi di dunia, dan kehilangan di kawasan tersebut berdampak besar pada keanekaragaman hayati dan penyimpanan karbon. Meskipun kehilangan tersebut pada akhirnya dapat dikembalikan, <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">dibutuhkan waktu puluhan tahun</a> agar habitat dan stok karbon ini dapat pulih, dan kehilangan keanekaragaman hayati permanen dapat terjadi. &nbsp;



<h2 class="wp-block-heading">Bagaimana perbandingan kehilangan tutupan pohon dengan data negara resmi di Brasil dan Indonesia?  </h2>



Brasil &nbsp;



PRODES — sistem pemantauan hutan resmi untuk Amazon dari Institut Nasional untuk Penelitian Angkasa Brasil (National Institute for Space Research, INPE) — dan data UMD memiliki perbedaan metodologi dan definisi yang penting untuk dipahami ketika membandingkan kedua sumber data ini. PRODES mengukur deforestasi tebang habis dan kehilangan hutan antropogenik akibat kebakaran yang lebih besar dari 6,25 hektare, sedangkan UMD mencatat kehilangan yang lebih besar dari 0,09 hektare dari semua pohon yang tingginya lebih dari 5 meter. Keduanya merupakan langkah penting yang membantu kita memahami bagaimana hutan berubah karena deforestasi, kebakaran hutan, dan gangguan kecil pada kanopi hutan, semuanya dapat berdampak pada iklim, keanekaragaman hayati, dan jasa ekosistem. &nbsp;



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/04/ID-Tech-Blog-Graphic_UMDvsProdes-2023.png" alt="" class="wp-image-17429"/></figure>



Kedua sistem menunjukkan tren penurunan kehilangan untuk tahun 2023 — penurunan sebesar 22% untuk PRODES dan 39% untuk UMD (penurunan sebesar 42% untuk kehilangan nonkebakaran) di bioma Amazon, yang mengonfirmasi kabar positif untuk Amazon.&nbsp;



<h4 class="wp-block-heading">PRODES vs. Data UMD di Amazon Brasil  </h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/17690090?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



Indonesia&nbsp;



Jika data kehilangan hutan primer UMD/GFW untuk tahun 2023 ditumpangsusunkan dengan peta tutupan lahan resmi Indonesia dari Kementerian Lingkungan Hidup dan Kehutanan (KLHK), terlihat bahwa 70% kehilangan tutupan hutan primer terjadi dalam kelas tutupan hutan resmi Indonesia, dengan 30% sisanya terjadi di lahan pertanian kering campuran, semak belukar, lahan belukar rawa, dan jenis tutupan lahan lainnya. Sekitar 144 ribu hektare kehilangan ditemukan berada dalam kelas tutupan lahan hutan resmi Indonesia dan dengan ukuran petak lebih dari dua hektare. Dari 144 ribu hektare kehilangan hutan ini, 15 ribu hektare di antaranya berada dalam area yang secara sah diklasifikasikan sebagai hutan primer di Indonesia.&nbsp;&nbsp;



<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:350px;height:39px;">
			Kelas KLHK
			</td>
			<td style="width:550px;height:39px;">
			Persentase Kehilangan Hutan Primer Tahun 2023 UMD/GFW
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Hutan primer
			</td>
			<td style="width:550px;height:39px;">
			9%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Hutan sekunder
			</td>
			<td style="width:550px;height:39px;">
			60%
			</td>
		</tr>
		<tr>
			<td style="width:350px;height:39px;">
			Hutan tanaman
			</td>
			<td style="width:550px;height:39px;">
			1%
			</td>
		</tr>
<tr>
			<td style="width:350px;height:39px;">
			Non hutan
			</td>
			<td style="width:550px;height:39px;">
			30%
			</td>
		</tr>
	</tbody>
</table>







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

Penjelasan Data Kehilangan Tutupan Pohon 2023 dari Global Forest Watch

New data shows persistent primary forest loss in 2023. What does the data measure and how does it compare to other official estimates of deforestation?

Global Forest Watch’s 2023 Tree Cover Loss Data Explained | GFW Blog

restoration

restoration | Global Forest Watch Blog

A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our Voices of Global Forest Watch series will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.&nbsp;&nbsp;



<iframe loading="lazy" width="560" height="450" src="https://www.youtube.com/embed/qbCzN9S6iCQ?si=7bfjPsSX-rJt3mGf" 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>







“We believe at Hen Mpoano that conservation without livelihood is a mere conversation.” – Justice Camillus Mensah, Project Manager at Hen Mpoano.&nbsp;



<a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Hen Mpoano</a>, a two-time recipient of GFW’s <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/" target="_blank" rel="noreferrer noopener">Small Grants Fund</a> (SGF), is a nonprofit organization based in Ghana working to protect and restore the Takoradi Forest District in southwestern Ghana, home to two important reserves: Cape Three Points Forest Reserve and Subri River Forest Reserve, which is the largest forest reserve in the country and is designated as a Global Significant Biodiversity Area.&nbsp;



“My passion for forests is from growing up along the coasts and being exposed to mangrove exploitation. But when I got to know the importance of mangroves, I decided to use technology that I love to protect mangroves and any other coastal resource, including the coastal terrestrial forests,” said Justice. “Forest is life. I keep telling the people that indeed when the last tree dies, the last man will also die.”&nbsp;



Justice was able to channel his passion into working with Hen Mpoano in 2013. Over the past decade, he has contributed immensely to ongoing campaigns for coastal ecosystem conservation in Ghana and other African countries.&nbsp;&nbsp;



During Hen Mpoano’s SGF projects, they worked to strengthen effective forest law compliance and enforcement, including pro-conservation action among forest fringing communities — all of which has been effective in reducing deforestation.&nbsp;&nbsp;



According to Justice, forest law enforcement in Ghana is almost impossible without community involvement.&nbsp;&nbsp;



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1365" src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg" alt="Community members training to use GFW tools" class="wp-image-17227" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-768x512.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-1536x1024.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



“We use a community-based approach to forest monitoring and protection. Members from forest fringing communities have been resourced through hands-on training on Global Forest Watch data and tools, in combination with other tools, to collect data on a regular basis,” said Justice.&nbsp;&nbsp;



With smart phones and GPS devices, the community forest monitoring teams collect evidence of forest crimes such as GPS coordinates and geotagged pictures. This data is then used to identify hotspots of forest crimes and used to generate maps using <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> and other software. These maps are shared confidentially with the Forest Services Division to help trigger enforcement action and target limited resources to hotspots.&nbsp;&nbsp;



This work also led to an agreement for the Wildlife Division to provide armed staff support for enforcement in the two reserves. Through this joint effort, more than 70 culprits have been arrested for various infractions, particularly illegal mining.&nbsp;&nbsp;



Hen Mpoano now applies GFW tools and data to monitor mangrove and freshwater swamp in a project funded by the U.S. Forest Service&nbsp;—&nbsp;a landscape also undergoing mangrove restoration through <a href="https://www.africa.terramatch.org/" target="_blank" rel="noreferrer noopener">TerraFund for AFR100</a>. Hen Mpoano’s approach involves not only restoration and planting, but also strengthening conservation efforts in the area by engaging community members to monitor landscape changes using GFW.&nbsp;



Hen Mpoano has faced challenges, particularly with forest patrols where culprits have confronted community monitoring teams, forest officers and even Hen Mpoano staff. However, data and technology such as GFW has helped equip forest monitors with tools to continue their work.&nbsp;



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1365" src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg" alt="Community member using GFW tools" class="wp-image-17226" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-768x512.jpg 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-1536x1024.jpg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



“In my opinion, GFW has cemented the collaborative management approach by providing citizens the tools and skill to be a part of forest protection. The community members feel very much empowered,” said Justice. “If Ghana’s status as the leading country in rainforest loss can be reversed, my team has a great role to play in that process. We will not relent until we see positive change.”&nbsp;&nbsp;



<a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Learn more about Hen Mpoano here.</a>&nbsp;



If you are interested in supporting Hen Mpoano to continue to expand their work to protect and restore forest landscapes, contact <a href="mailto:info@henmpoano.org" target="_blank" rel="noreferrer noopener">info@henmpoano.org</a>.&nbsp;



<hr> 

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

Video filmed at the Land & Carbon Lab Summit in June 2023.

 

</div> 

 

<hr>



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

Voices of Global Forest Watch: Justice Camillus Mensah, Hen Mpoano 

A decade ago, the launch of Global Forest Watch (GFW) ushered in a new era of accountability and transparency around monitoring and protecting the world’s forests. To celebrate 10 years of impact, our Voices of Global Forest Watch series will highlight successes of GFW users, partners and community members throughout the year. We invite you to read, watch and share the stories of exceptional leaders in forest monitoring who have played a pivotal role in protecting forests everywhere.&nbsp;&nbsp;



<iframe width="560" height="450" src="https://www.youtube.com/embed/qbCzN9S6iCQ?si=7bfjPsSX-rJt3mGf" 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>







“We believe at Hen Mpoano that conservation without livelihood is a mere conversation.” - Justice Camillus Mensah, Project Manager at Hen Mpoano.&nbsp;



<a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Hen Mpoano</a>, a two-time recipient of GFW’s <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/" target="_blank" rel="noreferrer noopener">Small Grants Fund</a> (SGF), is a nonprofit organization based in Ghana working to protect and restore the Takoradi Forest District in southwestern Ghana, home to two important reserves: Cape Three Points Forest Reserve and Subri River Forest Reserve, which is the largest forest reserve in the country and is designated as a Global Significant Biodiversity Area.&nbsp;



“My passion for forests is from growing up along the coasts and being exposed to mangrove exploitation. But when I got to know the importance of mangroves, I decided to use technology that I love to protect mangroves and any other coastal resource, including the coastal terrestrial forests,” said Justice. “Forest is life. I keep telling the people that indeed when the last tree dies, the last man will also die.”&nbsp;



Justice was able to channel his passion into working with Hen Mpoano in 2013. Over the past decade, he has contributed immensely to ongoing campaigns for coastal ecosystem conservation in Ghana and other African countries.&nbsp;&nbsp;



During Hen Mpoano’s SGF projects, they worked to strengthen effective forest law compliance and enforcement, including pro-conservation action among forest fringing communities — all of which has been effective in reducing deforestation.&nbsp;&nbsp;



According to Justice, forest law enforcement in Ghana is almost impossible without community involvement.&nbsp;&nbsp;



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_1530-2048x1365.jpg" alt="Community members training to use GFW tools" class="wp-image-17227"/><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



“We use a community-based approach to forest monitoring and protection. Members from forest fringing communities have been resourced through hands-on training on Global Forest Watch data and tools, in combination with other tools, to collect data on a regular basis,” said Justice.&nbsp;&nbsp;



With smart phones and GPS devices, the community forest monitoring teams collect evidence of forest crimes such as GPS coordinates and geotagged pictures. This data is then used to identify hotspots of forest crimes and used to generate maps using <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> and other software. These maps are shared confidentially with the Forest Services Division to help trigger enforcement action and target limited resources to hotspots.&nbsp;&nbsp;



This work also led to an agreement for the Wildlife Division to provide armed staff support for enforcement in the two reserves. Through this joint effort, more than 70 culprits have been arrested for various infractions, particularly illegal mining.&nbsp;&nbsp;



Hen Mpoano now applies GFW tools and data to monitor mangrove and freshwater swamp in a project funded by the U.S. Forest Service&nbsp;—&nbsp;a landscape also undergoing mangrove restoration through <a href="https://www.africa.terramatch.org/" target="_blank" rel="noreferrer noopener">TerraFund for AFR100</a>. Hen Mpoano’s approach involves not only restoration and planting, but also strengthening conservation efforts in the area by engaging community members to monitor landscape changes using GFW.&nbsp;



Hen Mpoano has faced challenges, particularly with forest patrols where culprits have confronted community monitoring teams, forest officers and even Hen Mpoano staff. However, data and technology such as GFW has helped equip forest monitors with tools to continue their work.&nbsp;



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/03/IMG_8842-2048x1365.jpg" alt="Community member using GFW tools" class="wp-image-17226"/><figcaption class="wp-element-caption">Credit: Hen Mpoano</figcaption></figure>



“In my opinion, GFW has cemented the collaborative management approach by providing citizens the tools and skill to be a part of forest protection. The community members feel very much empowered,” said Justice. “If Ghana’s status as the leading country in rainforest loss can be reversed, my team has a great role to play in that process. We will not relent until we see positive change.”&nbsp;&nbsp;



<a href="https://henmpoano.org/" target="_blank" rel="noreferrer noopener">Learn more about Hen Mpoano here.</a>&nbsp;



If you are interested in supporting Hen Mpoano to continue to expand their work to protect and restore forest landscapes, contact <a href="mailto:info@henmpoano.org" target="_blank" rel="noreferrer noopener">info@henmpoano.org</a>.&nbsp;



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Video filmed at the Land & Carbon Lab Summit in June 2023.

 

</div> 

 

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As part of our Voices of Global Forest Watch series, watch and read about Justice Camillus Mensah's work with Ghana nonprofit Hen Mpoano.

Voices of GFW: Justice Camillus Mensah, Hen Mpoano 

Ripe Cocoa pods from a cocoa farm in Ghana.

Cocoa

Cocoa | Global Forest Watch Blog

mapping

mapping | Global Forest Watch Blog

Martina Schneider is a GIS Analyst with Global Forest Watch. She supports the GFW team in preparing data and developing content for the GFW platform and web application.



As a graduate student, Martina specialized in applying GIS in the fields of environmental science and natural resources management. Specifically, she studied wildfire ecology and environmental factors that contribute to risk of ignition. In addition, Martina worked at Ducks Unlimited, the world's leader in wetland and waterfowl conservation, where she was part of a team updating the National Wetlands Inventory in Minnesota.



Martina holds an M.S. in Geographic Information Systems from Eastern Michigan University and a B.S. in Geography from Texas A&amp;M University.



She lives in Baltimore, MD with her husband and dog.

Martina Schneider

Caroline Winchester

Sofia Tenorio Fenton

The global demand for chocolate has had a significant impact on West African forests. Approximately 70% of the global supply of cocoa originates from Ghana and Côte d’Ivoire, where production sites have replaced millions of hectares of forest. Between 2001 and 2015 alone, <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">cocoa was linked to</a> the loss of a third of Ghana’s forest area and a quarter of Côte d’Ivoire’s.



The <a href="https://www.worldcocoafoundation.org/initiative/cocoa-forests-initiative/" target="_blank" rel="noreferrer noopener">Cocoa & Forests Initiative (CFI)</a>, facilitated by World Cocoa Foundation (WCF) and the <a href="https://www.idhsustainabletrade.com/initiative/cocoa-and-forests/" target="_blank" rel="noreferrer noopener">Sustainable Trade Initiative (IDH)</a>, was established in 2017 in an effort to end cocoa-driven deforestation and restore forest areas. Companies that joined the CFI agreed to map their supply chains, align methodologies on deforestation risk assessment and contribute to transparent monitoring and reporting. To support these efforts, World Resources Institute (WRI) partnered with WCF and 19 major cocoa and chocolate companies to create two new resources:*



<ul>
<li>The West Africa Cocoa dataset (WAC), a database of mapped cocoa plot boundaries in the direct supply chains of participating companies that provides an extensive view of mapped cocoa production in Côte d’Ivoire and Ghana. The public version of the dataset is <a href="https://gfw.global/4bEEES2">available on Global Forest Watch (GFW)</a> as a cocoa plot heat map that further anonymizes data to protect the privacy of farmers and companies.</li>



<li>The Cocoa Deforestation Risk Assessment (Cocoa DRA), a map that provides a standardized view of cocoa-driven deforestation risk across the cocoa growing region of these two countries. The <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> is available for download on GFW.</li>
</ul>



These resources are the result of an unprecedented collaboration among companies to share highly sensitive cocoa plot data, and an extensive ethics and legal review to ensure the protection of farmers’ data throughout the process. For the first time, companies and other stakeholders in the cocoa sector have a shared view of priority areas in Ghana and Côte d’Ivoire for coordinated efforts toward a deforestation-free cocoa sector.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">From Palm Oil to Cocoa
The development of the WAC and the Cocoa DRA builds on the success of similar products developed to support the palm oil sector: the <a href="https://www.globalforestwatch.org/blog/commodities/improved-universal-mill-list-expands-partnerships-improves-transparency-in-palm-oil-industry/" target="_blank" rel="noopener noreferrer">Universal Mill List (UML)</a>, a harmonized database of palm oil mill locations, and the <a href="https://www.wri.org/research/palm-risk-assessment-methodology-prioritizing-areas-landscapes-and-mills" target="_blank" rel="noopener noreferrer">Palm Risk Tool</a>, a method of identifying deforestation risks in their vicinity. However, differing supply chain dynamics between palm oil and cocoa were considered, such as:<li>
The locations of palm oil mills in the UML are represented as points while the cocoa plots in the WAC are represented by polygons. Further, the UML maps industrial scale infrastructure, whereas the WAC maps smallholder production. This raised additional privacy concerns, and steps were taken to avoid violating farmer privacy and safeguarding farmer livelihoods.<li>
The Palm Risk Tool analyzes deforestation risk by creating a buffer around each mill point to estimate a potential sourcing area. Oil palm fruit must be delivered to a mill quickly or the quality of the oil will decrease. This places a geographic constraint on how far oil palm fruit can travel to a mill, and stakeholders in the sector rely on a standardized sourcing radius of 50-km for their risk assessments. Cocoa does not have a geographic constraint nor an agreed upon estimation of a sourcing area, meaning that cocoa collected at a central point of purchase could have been grown anywhere in these two countries.<li>
The Palm Risk Tool was developed to assess deforestation risks in the vicinity of a specific type of infrastructure: palm oil mills. The Cocoa DRA was developed to be more flexible, enabling analysis of multiple areas of interest such as the plots in the WAC, cooperatives or other types of collection points and/or warehouses.<ul>

 </aside>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1175" height="783" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg" alt="Cocoa pod" class="wp-image-16723" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg 1175w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod-1024x682.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod-768x512.jpg 768w" sizes="(max-width: 1175px) 100vw, 1175px" /><figcaption class="wp-element-caption">Cocoa pod. Credit: Caroline Winchester, WRI.</figcaption></figure>



<h2 class="wp-block-heading">What is the West Africa Cocoa dataset?</h2>



The West Africa Cocoa dataset (WAC) aggregates data from 2021 on cocoa plot locations (a cocoa plot is a subset of a cocoa farm, and a single farm may be represented by multiple plots) in the direct supply chains of 19 cocoa and chocolate companies operating in Ghana and Côte d’Ivoire. Project participants developed and agreed on a data collation protocol and data sharing agreement that outlined submission requirements and the procedure for cleaning and aggregating individual company data sets. To be included in the WAC, each plot had to be current, actively growing cocoa and occupying a single parcel of land. WRI then reviewed, cleaned and aggregated the data.



While the cocoa plot polygon data is considered sensitive and will not be available publicly (see more below), we visualized the data as a heat map, <a href="https://gfw.global/4bEEES2">available on GFW</a>, which avoids disclosing the precise boundaries of the plots in the WAC while giving users access to information on the approximate location of cocoa plots. Additionally, we distributed the plot data to impartial research institutions for use as training data to create derivative products; specifically, public cocoa production extent maps.



These strategies make the data publicly available to support companies and other users in more robust cocoa mapping and monitoring while safeguarding the privacy of individual farmers.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">Protecting the Privacy of Cocoa Farmers and Companies

The raw data used to create the WAC included sensitive supply chain information from both companies and farmers. Cocoa is primarily grown by smallholder farmers who live near the cocoa plots they manage. A public release of this raw data would show the boundaries of specific cocoa plots and could allow individual farmers and their homes to be identified. Further, since there are over 2 million farmers across West Africa, it is difficult to know whether they are all fully aware of how data pertaining to them is being used. 
To protect farmer and company privacy, the WAC data collection protocol and data sharing agreement were informed by an ethics review to ensure that potentially sensitive information was respected and handled properly, and by a legal review to ensure the data privacy laws of Ghana and Côte d’Ivoire were followed. Based off these reviews, and in order to protect farmer privacy, no personal data was included in the WAC or shared with WRI, and a secure method of transferring data among all parties was established. With farmer privacy in mind, the parties involved also decided that precise locations of cocoa plot boundaries would not be made publicly available.

 </aside>



<h2 class="wp-block-heading">What is the Cocoa Deforestation Risk Assessment?</h2>



The Cocoa Deforestation Risk Assessment (Cocoa DRA) is a map that assesses the risk of future deforestation due to new or expanding cocoa production. The Cocoa DRA aligns various methods for assessing risk to provide a standardized view that companies and other stakeholders can use to make decisions around reducing deforestation. It can be used to proactively and efficiently identify deforestation risk across a supply chain that includes tens of thousands of geographically dispersed producers. Once risk has been identified, Cocoa DRA users can implement interventions for promoting sustainable production, protecting remaining forest cover and improving farmer livelihoods.



To create the map, we analyzed spatial variables that influence cocoa-linked deforestation including recent forest loss, proximity to cocoa, accessibility and suitability for cocoa production. The present-day extent of natural forest was approximated using the University of Maryland’s <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree canopy cover</a> data set and removing cocoa plots mapped in the WAC, <a href="https://essd.copernicus.org/articles/13/1211/2021/" target="_blank" rel="noreferrer noopener">other data sets</a> that <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">map planted trees</a> and historic tree cover loss.



Primary and intact forests in West Africa are limited, so to ensure the Cocoa DRA comprehensively assesses where future cocoa-linked deforestation is likely to occur in all natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.



The Cocoa DRA translates risk into five priority rankings at a 1-km resolution (high, mid-high, medium, mid-low and low priority) indicating the likelihood of future deforestation associated with cocoa production. Cocoa DRA users can assess risk at different scales, including groups of farms, supply sheds around cocoa bean collection points and other types of landscapes.



Users can download the <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> on GFW.



<h2 class="wp-block-heading">What have we learned from the WAC and Cocoa DRA?</h2>



Across Ghana and Côte d’Ivoire, the WAC maps 840,000 unique cocoa plots in 2021. Though cocoa plots tend to be small (most plots were between 1 to 2 hectares), the plots included in the WAC cover approximately 1.5 million hectares of planted cocoa area.



Cocoa plot data is most comprehensive in western Côte d’Ivoire as well as near the shared border between Côte d’Ivoire and Ghana. The forests in these areas have been <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">particularly impacted by cocoa production</a>. We found that the Cocoa DRA showed that these same regions had a significant amount of high priority areas (areas with higher likelihood of future cocoa-related deforestation). This is most evident in Côte d’Ivoire near the border of Liberia, while in western Ghana the landscape is more mixed.



<figure class="wp-block-image size-full is-resized"><img decoding="async" loading="lazy" width="1430" height="868" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png" alt="Cocoa deforestation risk assessment priority map for 2023" class="wp-image-16731" style="aspect-ratio:1.6474654377880185;width:840px;height:auto" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png 1430w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-1024x622.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-768x466.png 768w" sizes="(max-width: 1430px) 100vw, 1430px" /></figure>



While creating the Cocoa DRA, we found that proximity to recent loss was an important predictor of future deforestation risk. The influence of this variable is most evident in several high-interest protected areas where, presumably due to effective legal protection, forest loss has been prevented in their boundaries. Because these forests have experienced less loss compared to the surrounding landscape, this resulted in a low priority ranking for the areas.



A good example of this is Taï National Park in western Côte d’Ivoire, which contains the only remaining intact forest within the study area. This is notable because the park is located within a region of Côte d’Ivoire where forests have been significantly impacted by cocoa production. A similar example is Bia National Park in western Ghana, which contains primary forest that has experienced low rates of forest loss compared to the surrounding landscape.



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1448" src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png" alt="Cocoa deforestation risk assessment priority ranking map (2023) showing protected areas" class="wp-image-16750" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png 2048w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-1024x724.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-768x543.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-1536x1086.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



That being said, additional data on legal status and conservation value provides important contextual information and we recommend users consider this data when using the Cocoa DRA for prioritizing engagement activities and supply chain interventions. &nbsp;



<h2 class="wp-block-heading">Driving collective action: How companies and others can use the WAC and Cocoa DRA</h2>



Used together, the WAC and the Cocoa DRA can inform decision making and coordinated action to reduce cocoa-driven deforestation. These resources also have utility beyond the scope of the CFI. For example, the public WAC heat map of cocoa plots may be used alongside near-real-time alerts to identify deforestation in cocoa production landscapes, or as a resource to collectively assess the industry’s progress towards ending deforestation. Use of the WAC as training data to develop public cocoa extent maps will also shed light on production across West Africa. Such accessible and holistic extent data is essential for stakeholders striving to tackle cocoa-driven deforestation.



For companies and farmers seeking to comply with the EUDR, the WAC and Cocoa DRA can also be valuable tools. The risk map can enable companies and other stakeholders to identify high-priority landscapes and prevent deforestation before it happens. To achieve this, engagement with farmers in high priority areas will be needed, enabling them to protect remaining forests while safeguarding their livelihoods. The risk map and WAC can also help companies determine where to prioritize further cocoa plot data collection efforts. High priority areas where the WAC shows a lack of mapped cocoa plots is a good indication of where such efforts are needed for enabling compliance with the EUDR’s geolocation requirement. This data will also be key for enabling inclusion of smallholder farmers in the EU market.



As public resources that provide stakeholders with a shared understanding of where it’s most urgent to address deforestation while respecting farmer privacy, the WAC and Cocoa DRA can help realize a more sustainable cocoa sector. Through this collective knowledge, stakeholders can more easily form collaborative approaches to eliminate cocoa-driven deforestation. Furthermore, through ongoing updates based on best available data, both resources can remain a public good and contribute to transparent monitoring of the cocoa sector.



* Development of these products was also informed by preliminary research carried out by Climate Focus on other methods of assessing deforestation risk associated cocoa. 



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Ending Deforestation from Cocoa in West Africa with New Data-Driven Resources

The global demand for chocolate has had a significant impact on West African forests. Approximately 70% of the global supply of cocoa originates from Ghana and Côte d’Ivoire, where production sites have replaced millions of hectares of forest. Between 2001 and 2015 alone, <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">cocoa was linked to</a> the loss of a third of Ghana’s forest area and a quarter of Côte d’Ivoire’s.



The <a href="https://www.worldcocoafoundation.org/initiative/cocoa-forests-initiative/" target="_blank" rel="noreferrer noopener">Cocoa &amp; Forests Initiative (CFI)</a>, facilitated by World Cocoa Foundation (WCF) and the <a href="https://www.idhsustainabletrade.com/initiative/cocoa-and-forests/" target="_blank" rel="noreferrer noopener">Sustainable Trade Initiative (IDH)</a>, was established in 2017 in an effort to end cocoa-driven deforestation and restore forest areas. Companies that joined the CFI agreed to map their supply chains, align methodologies on deforestation risk assessment and contribute to transparent monitoring and reporting. To support these efforts, World Resources Institute (WRI) partnered with WCF and 19 major cocoa and chocolate companies to create two new resources:*



<ul>
<li>The West Africa Cocoa dataset (WAC), a database of mapped cocoa plot boundaries in the direct supply chains of participating companies that provides an extensive view of mapped cocoa production in Côte d’Ivoire and Ghana. The public version of the dataset is <a href="https://gfw.global/4bEEES2">available on Global Forest Watch (GFW)</a> as a cocoa plot heat map that further anonymizes data to protect the privacy of farmers and companies.</li>



<li>The Cocoa Deforestation Risk Assessment (Cocoa DRA), a map that provides a standardized view of cocoa-driven deforestation risk across the cocoa growing region of these two countries. The <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> is available for download on GFW.</li>
</ul>



These resources are the result of an unprecedented collaboration among companies to share highly sensitive cocoa plot data, and an extensive ethics and legal review to ensure the protection of farmers’ data throughout the process. For the first time, companies and other stakeholders in the cocoa sector have a shared view of priority areas in Ghana and Côte d’Ivoire for coordinated efforts toward a deforestation-free cocoa sector.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">From Palm Oil to Cocoa
The development of the WAC and the Cocoa DRA builds on the success of similar products developed to support the palm oil sector: the <a href="https://www.globalforestwatch.org/blog/commodities/improved-universal-mill-list-expands-partnerships-improves-transparency-in-palm-oil-industry/" target="_blank" rel="noopener noreferrer">Universal Mill List (UML)</a>, a harmonized database of palm oil mill locations, and the <a href="https://www.wri.org/research/palm-risk-assessment-methodology-prioritizing-areas-landscapes-and-mills" target="_blank" rel="noopener noreferrer">Palm Risk Tool</a>, a method of identifying deforestation risks in their vicinity. However, differing supply chain dynamics between palm oil and cocoa were considered, such as:<li>
The locations of palm oil mills in the UML are represented as points while the cocoa plots in the WAC are represented by polygons. Further, the UML maps industrial scale infrastructure, whereas the WAC maps smallholder production. This raised additional privacy concerns, and steps were taken to avoid violating farmer privacy and safeguarding farmer livelihoods.<li>
The Palm Risk Tool analyzes deforestation risk by creating a buffer around each mill point to estimate a potential sourcing area. Oil palm fruit must be delivered to a mill quickly or the quality of the oil will decrease. This places a geographic constraint on how far oil palm fruit can travel to a mill, and stakeholders in the sector rely on a standardized sourcing radius of 50-km for their risk assessments. Cocoa does not have a geographic constraint nor an agreed upon estimation of a sourcing area, meaning that cocoa collected at a central point of purchase could have been grown anywhere in these two countries.<li>
The Palm Risk Tool was developed to assess deforestation risks in the vicinity of a specific type of infrastructure: palm oil mills. The Cocoa DRA was developed to be more flexible, enabling analysis of multiple areas of interest such as the plots in the WAC, cooperatives or other types of collection points and/or warehouses.<ul>

 </aside>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-pod.jpg" alt="Cocoa pod" class="wp-image-16723"/><figcaption class="wp-element-caption">Cocoa pod. Credit: Caroline Winchester, WRI.</figcaption></figure>



<h2 class="wp-block-heading">What is the West Africa Cocoa dataset?</h2>



The West Africa Cocoa dataset (WAC) aggregates data from 2021 on cocoa plot locations (a cocoa plot is a subset of a cocoa farm, and a single farm may be represented by multiple plots) in the direct supply chains of 19 cocoa and chocolate companies operating in Ghana and Côte d’Ivoire. Project participants developed and agreed on a data collation protocol and data sharing agreement that outlined submission requirements and the procedure for cleaning and aggregating individual company data sets. To be included in the WAC, each plot had to be current, actively growing cocoa and occupying a single parcel of land. WRI then reviewed, cleaned and aggregated the data.



While the cocoa plot polygon data is considered sensitive and will not be available publicly (see more below), we visualized the data as a heat map, <a href="https://gfw.global/4bEEES2">available on GFW</a>, which avoids disclosing the precise boundaries of the plots in the WAC while giving users access to information on the approximate location of cocoa plots. Additionally, we distributed the plot data to impartial research institutions for use as training data to create derivative products; specifically, public cocoa production extent maps.



These strategies make the data publicly available to support companies and other users in more robust cocoa mapping and monitoring while safeguarding the privacy of individual farmers.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">Protecting the Privacy of Cocoa Farmers and Companies

The raw data used to create the WAC included sensitive supply chain information from both companies and farmers. Cocoa is primarily grown by smallholder farmers who live near the cocoa plots they manage. A public release of this raw data would show the boundaries of specific cocoa plots and could allow individual farmers and their homes to be identified. Further, since there are over 2 million farmers across West Africa, it is difficult to know whether they are all fully aware of how data pertaining to them is being used. 
To protect farmer and company privacy, the WAC data collection protocol and data sharing agreement were informed by an ethics review to ensure that potentially sensitive information was respected and handled properly, and by a legal review to ensure the data privacy laws of Ghana and Côte d’Ivoire were followed. Based off these reviews, and in order to protect farmer privacy, no personal data was included in the WAC or shared with WRI, and a secure method of transferring data among all parties was established. With farmer privacy in mind, the parties involved also decided that precise locations of cocoa plot boundaries would not be made publicly available.

 </aside>



<h2 class="wp-block-heading">What is the Cocoa Deforestation Risk Assessment?</h2>



The Cocoa Deforestation Risk Assessment (Cocoa DRA) is a map that assesses the risk of future deforestation due to new or expanding cocoa production. The Cocoa DRA aligns various methods for assessing risk to provide a standardized view that companies and other stakeholders can use to make decisions around reducing deforestation. It can be used to proactively and efficiently identify deforestation risk across a supply chain that includes tens of thousands of geographically dispersed producers. Once risk has been identified, Cocoa DRA users can implement interventions for promoting sustainable production, protecting remaining forest cover and improving farmer livelihoods.



To create the map, we analyzed spatial variables that influence cocoa-linked deforestation including recent forest loss, proximity to cocoa, accessibility and suitability for cocoa production. The present-day extent of natural forest was approximated using the University of Maryland’s <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree canopy cover</a> data set and removing cocoa plots mapped in the WAC, <a href="https://essd.copernicus.org/articles/13/1211/2021/" target="_blank" rel="noreferrer noopener">other data sets</a> that <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10" target="_blank" rel="noreferrer noopener">map planted trees</a> and historic tree cover loss.



Primary and intact forests in West Africa are limited, so to ensure the Cocoa DRA comprehensively assesses where future cocoa-linked deforestation is likely to occur in all natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.



The Cocoa DRA translates risk into five priority rankings at a 1-km resolution (high, mid-high, medium, mid-low and low priority) indicating the likelihood of future deforestation associated with cocoa production. Cocoa DRA users can assess risk at different scales, including groups of farms, supply sheds around cocoa bean collection points and other types of landscapes.



Users can download the <a href="https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about">Cocoa DRA map layer</a> on GFW.



<h2 class="wp-block-heading">What have we learned from the WAC and Cocoa DRA?</h2>



Across Ghana and Côte d’Ivoire, the WAC maps 840,000 unique cocoa plots in 2021. Though cocoa plots tend to be small (most plots were between 1 to 2 hectares), the plots included in the WAC cover approximately 1.5 million hectares of planted cocoa area.



Cocoa plot data is most comprehensive in western Côte d’Ivoire as well as near the shared border between Côte d’Ivoire and Ghana. The forests in these areas have been <a href="https://research.wri.org/gfr/forest-extent-indicators/deforestation-agriculture#how-much-forest-has-been-replaced-by-cocoa" target="_blank" rel="noreferrer noopener">particularly impacted by cocoa production</a>. We found that the Cocoa DRA showed that these same regions had a significant amount of high priority areas (areas with higher likelihood of future cocoa-related deforestation). This is most evident in Côte d’Ivoire near the border of Liberia, while in western Ghana the landscape is more mixed.



<figure class="wp-block-image size-full is-resized"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png" alt="Cocoa deforestation risk assessment priority map for 2023" class="wp-image-16731" style="aspect-ratio:1.6474654377880185;width:840px;height:auto"/></figure>



While creating the Cocoa DRA, we found that proximity to recent loss was an important predictor of future deforestation risk. The influence of this variable is most evident in several high-interest protected areas where, presumably due to effective legal protection, forest loss has been prevented in their boundaries. Because these forests have experienced less loss compared to the surrounding landscape, this resulted in a low priority ranking for the areas.



A good example of this is Taï National Park in western Côte d’Ivoire, which contains the only remaining intact forest within the study area. This is notable because the park is located within a region of Côte d’Ivoire where forests have been significantly impacted by cocoa production. A similar example is Bia National Park in western Ghana, which contains primary forest that has experienced low rates of forest loss compared to the surrounding landscape.



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png" alt="Cocoa deforestation risk assessment priority ranking map (2023) showing protected areas" class="wp-image-16750"/></figure>



That being said, additional data on legal status and conservation value provides important contextual information and we recommend users consider this data when using the Cocoa DRA for prioritizing engagement activities and supply chain interventions. &nbsp;



<h2 class="wp-block-heading">Driving collective action: How companies and others can use the WAC and Cocoa DRA</h2>



Used together, the WAC and the Cocoa DRA can inform decision making and coordinated action to reduce cocoa-driven deforestation. These resources also have utility beyond the scope of the CFI. For example, the public WAC heat map of cocoa plots may be used alongside near-real-time alerts to identify deforestation in cocoa production landscapes, or as a resource to collectively assess the industry’s progress towards ending deforestation. Use of the WAC as training data to develop public cocoa extent maps will also shed light on production across West Africa. Such accessible and holistic extent data is essential for stakeholders striving to tackle cocoa-driven deforestation.



For companies and farmers seeking to comply with the EUDR, the WAC and Cocoa DRA can also be valuable tools. The risk map can enable companies and other stakeholders to identify high-priority landscapes and prevent deforestation before it happens. To achieve this, engagement with farmers in high priority areas will be needed, enabling them to protect remaining forests while safeguarding their livelihoods. The risk map and WAC can also help companies determine where to prioritize further cocoa plot data collection efforts. High priority areas where the WAC shows a lack of mapped cocoa plots is a good indication of where such efforts are needed for enabling compliance with the EUDR’s geolocation requirement. This data will also be key for enabling inclusion of smallholder farmers in the EU market.



As public resources that provide stakeholders with a shared understanding of where it’s most urgent to address deforestation while respecting farmer privacy, the WAC and Cocoa DRA can help realize a more sustainable cocoa sector. Through this collective knowledge, stakeholders can more easily form collaborative approaches to eliminate cocoa-driven deforestation. Furthermore, through ongoing updates based on best available data, both resources can remain a public good and contribute to transparent monitoring of the cocoa sector.



* Development of these products was also informed by preliminary research carried out by Climate Focus on other methods of assessing deforestation risk associated cocoa. 



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Two new data-driven resources provide a shared view of priority areas in West Africa and can help realize a a deforestation-free cocoa sector.

Ending Deforestation from Cocoa in West Africa | GFW Blog

Jan Hooijmans

Global Forest Watch (GFW) and the Global Forest Resources Assessment (FRA) from the Food and Agricultural Organization of the United Nations (FAO) both provide global data sets with information on the world’s forests. The latest FRA was released in 2020, and when compared to 2020 data on forest area from the University of Maryland (UMD), available on GFW, we see similarities and differences. So how similar are the numbers and what could explain the differences? This blog compares the forest extent from these two data sets, discusses why there are differences and explains what this means for users. To learn more about general differences between data on GFW and FRA data, <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">check out this blog</a>.



<h2 class="wp-block-heading">What was the area of trees and forests in 2020 according to UMD and FRA data?</h2>



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance 
Some of the primary distinctions between GFW and FRA are in their data sources and definitions (<a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">read more here</a>). 
For 2020, GFW uses the UMD <a href="https://gfw.global/2Mei4Z0" target="_blank" rel="noopener noreferrer">2020 tree cover height data</a> to report on the extent of tree cover — a purely biophysical definition related to height and extent of trees. This satellite data picks up all trees with a minimum canopy height, which users can set when visualizing the data. For this analysis, we use a minimum height threshold of 5 meters. GFW also has a tree cover extent data set for 2000 and 2010 from UMD based on the percent of canopy density, which is used in many functions on GFW, but this was largely not used in this blog. 
The FRA relies on country-reported data based on a definition of forest that includes both a biophysical and land use component. In the FRA data, forests have a tree canopy height of at least 5 meters and canopy cover of at least 10% or are capable of reaching this threshold. Additionally, the land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or timber production. 
This means that in the FRA data, land that is temporarily devoid of trees — for example, recently logged or burned areas — would still be classified as forest if the official land use stipulates that trees will be allowed to regenerate in the future. Conversely, logged and burned areas are not captured as tree cover by the UMD data. Land that contains trees but is designated for agricultural or urban uses — for example, an oil palm plantation, shade grown cocoa (agroforestry) or a city park — would not be considered a forest by the FRA, but would be included in UMD tree cover data. 

 </aside>



<h4 class="wp-block-heading">Extent of trees and forests globally and by region in 2020 (in million hectares — Mha)</h4>



<table>
	<tbody>
		<tr>
			<td style="width:180px;height:20px;">
			&nbsp;
			</td>
			<td style="width:80px;height:20px;">
			Africa
			</td>
			<td style="width:95px;height:20px;">
			Asia including Russian Federation
			</td>
			<td style="width:95px;height:20px;">
			Europe, excluding Russian Federation
			</td>
			<td style="width:95px;height:20px;">
			North and Central America
			</td>
			<td style="width:80px;height:20px;">
			South America
			</td>
			<td style="width:80px;height:20px;">
			Oceania
			</td>
			<td style="width:80px;height:20px;">
			Global &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			UMD tree cover extent 2020
			</td>
			<td style="width:69px;height:10px;">
			674 Mha

			&nbsp;
			</td>
			<td style="width:65px;height:10px;">
			1,483 Mha

			&nbsp;
			</td>
			<td style="width:83px;height:10px;">
			204 Mha
			</td>
			<td style="width:76px;height:10px;">
			675 Mha
			</td>
			<td style="width:68px;height:10px;">
			834 Mha
			</td>
			<td style="width:71px;height:10px;">
			147 Mha
			</td>
			<td style="width:112px;height:10px;">
			4,018 Mha

			&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			FRA forest area 2020
			</td>
			<td style="width:69px;height:20px;">
			637 Mha
			</td>
			<td style="width:65px;height:20px;">
			1,438 Mha
			</td>
			<td style="width:83px;height:20px;">
			202 Mha
			</td>
			<td style="width:76px;height:20px;">
			753 Mha
			</td>
			<td style="width:68px;height:20px;">
			844 Mha
			</td>
			<td style="width:71px;height:20px;">
			184 Mha
			</td>
			<td style="width:112px;height:20px;">
			4,059 Mha

			&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			Difference of UMD extent as compared to FRA
			</td>
			<td style="width:69px;height:20px;">
			+5.6%

			&nbsp;
			</td>
			<td style="width:65px;height:20px;">
			+3.0%

			&nbsp;
			</td>
			<td style="width:83px;height:20px;">
			+1.0%

			&nbsp;
			</td>
			<td style="width:76px;height:20px;">
			-11.5%

			&nbsp;
			</td>
			<td style="width:68px;height:20px;">
			-1.2%

			&nbsp;
			</td>
			<td style="width:71px;height:20px;">
			-24.8%

			&nbsp;
			</td>
			<td style="width:112px;height:20px;">
			-1.02%

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



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%">*The regional figures compare the same list of countries, with some adjustments made to accommodate different country boundaries in the Database of Global Administrative Areas (GADM) and FRA. </div>



Globally and regionally, tree/forest cover as estimated by UMD data and the FRA are similar. Global estimates of forest area in 2020 only vary by about 1% (4,059 million hectares — Mha — for the FRA and 4,018 Mha for the UMD data).



At the regional level there are many similarities, with very small differences for Asia, Europe and South America. Oceania shows the largest difference at the regional scale, which is almost entirely attributable to the FRA’s greater estimate of forest area in Australia — Australia’s FRA forest area is 52% greater than UMD’s tree cover extent. This may be because Australia’s definition of “forest” specifies a minimum vegetation height of 2 meters, which is used in the FRA estimate. The majority of countries in the FRA follow the FAO definition of a minimum vegetation height of 5 meters, which is also the height threshold used in this blog for analysis of the UMD data.



<h4 class="wp-block-heading">Forest area/tree cover 2020 according to UMD data on GFW and the FRA</h4>



<div class="flourish-embed flourish-map" data-src="visualisation/15325037?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



In central Africa and Southeast Asia, UMD’s tree cover estimates are typically greater than the FRA’s forest area estimations. In Malaysia, for example, UMD’s estimate is 33% higher than the FRA’s (28.6 Mha for UMD vs. 19.1 Mha for FRA). This may be explained by the difference in definitions, for example Southeast Asia typically contains large areas of oil palm plantations, which would be considered tree cover according to UMD but not forest according to the FRA.



For a better comparison, it would be useful subtract the area of tree plantations recorded in the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which incorporates a number of different tree crops and planted trees, from the UMD tree cover estimate to better match the UMD data with the FAO forest definition. In the instance of Malaysia for example, excluding oil palm plantations from UMD tree cover results in an area considerably closer to the forest area reported in the FRA.



For some countries, the UMD tree cover extent matches the FRA forest extent very closely. For example, FRA’s forest area estimate for Chile is only 0.1% less than UMD’s. Another example is Nigeria, where the FRA forest area is just 5% less than UMD’s tree cover (21.6 Mha for FRA and 22.7 Mha for UMD).



The forest area according to the FRA and UMD data have not always matched so well though — the FRA estimate for forest extent in 2000 (reported in the FRA 2015) was 46% less than UMD’s tree cover area (for the year 2000). Nigeria significantly changed their forest area figures for 2000 as reported in the 2020 FRA, based on the availability of more and better data, resulting in the FRA and UMD figures becoming much more similar (3% difference).



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
#textbox table {
 width: 100% !important;
 margin-left: auto;
}
</style>
<aside id="textbox">Improvements in Monitoring Mean More Similar Numbers
Due to the improvements in data quality from both the FRA and UMD, the data is becoming more and more similar. 
A <a href="https://www.sciencedirect.com/science/article/pii/S0048969722048872" target="_blank" rel="noopener noreferrer">scientific study</a> found that in many countries, the quality of data sources used for forest area estimates as reported to the FRA have increased over time. Specifically, the percentage of forest area monitored by satellites with a quality indication of “good” or “very good” in the FRA has increased. To incorporate these data advances, older data points are recalculated if methodologies/data have been revised — for example, the forest extent in 2000 presented in the FRA 2020 is not the same as was presented in the FRA 2000. 
At the same time, the data on GFW has also improved. The original UMD tree cover data on GFW, which is still used as a baseline for the annual tree cover loss data, is based on canopy density (<a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noopener noreferrer">Hansen et al. 2013</a>) for 2000 and 2010 (thresholded in this case to 30% canopy cover). New data, also from UMD, is based on tree height (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" target="_blank" rel="noopener noreferrer">Potapov et al. 2021</a>) for 2000 and 2020. The new data incorporates updated methods, and new data such as GEDI which better captures the structure of trees. 
Thanks to these improvements, the difference estimates of the FRA forest area and UMD height-based tree cover extent has shrunk to less than 1% (when comparing UMD’s height-based data with the most recent FRA estimate).

 GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*

<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:157px;height:20px;">
			FRA 2000
			</td>
			<td style="width:157px;height:20px;">
			FRA 2020
			</td>
			<td style="width:157px;height:20px;">
			UMD canopy density data (2000)
			</td>
			<td style="width:157px;height:20px;">
			UMD tree height data (2020)
			</td>
		</tr>
		<tr>
			<td style="width:157px;height:20px;">
			3,869 Mha
			</td>
			<td style="width:157px;height:20px;">
			4,158 Mha
			</td>
			<td style="width:157px;height:20px;">
			3,993 Mha
			</td>
			<td style="width:157px;height:20px;">
			4,122 Mha
			</td>
		</tr>
	</tbody>
</table>
*These figures are the globally reported figures

 </aside>



Some countries showed higher estimates for FRA forest areas than UMD tree cover extent, for instance, many of the Nordic European countries and Canada. Again, the difference between the definitions of tree cover used in UMD and forest area used by FRA likely explains the differences. According to the FRA 2020, a substantial proportion of forested areas in these countries are production forests, meaning that vast areas of land temporarily unstocked due to timber harvesting are included as forest area for the FRA. UMD does not include these regions, nor areas that have been regenerated but have not yet reached the UMD height threshold, as tree cover.



<h2 class="wp-block-heading">What does this mean for those using UMD and FRA data?</h2>



Estimates by UMD and the FRA of tree cover and forest can vary, in some cases substantially within a country. The same patterns are not seen for all countries; some countries have higher estimates according to UMD, and for others, FRA estimates are higher. In many cases, these can be explained when taking into account the different forest definitions used. 



Because of their differences, both data sets can be useful for different stakeholders. Read our blog <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</a> to learn more about different ways the UMD and FRA data can be used. In the end, it’s important for users to know the differences between the data sets to help them decide which data will best meet their needs.



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> We acknowledge and thank the FAO for their helpful comments on this blog.

 

</div> 

 

<hr>



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Comparing Forest Extent in 2020 from Global Forest Watch and the Forest Resources Assessment

Global Forest Watch (GFW) and the Global Forest Resources Assessment (FRA) from the Food and Agricultural Organization of the United Nations (FAO) both provide global data sets with information on the world's forests. The latest FRA was released in 2020, and when compared to 2020 data on forest area from the University of Maryland (UMD), available on GFW, we see similarities and differences. So how similar are the numbers and what could explain the differences? This blog compares the forest extent from these two data sets, discusses why there are differences and explains what this means for users. To learn more about general differences between data on GFW and FRA data, <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">check out this blog</a>.



<h2 class="wp-block-heading">What was the area of trees and forests in 2020 according to UMD and FRA data?</h2>



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance 
Some of the primary distinctions between GFW and FRA are in their data sources and definitions (<a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/" target="_blank" rel="noopener noreferrer">read more here</a>). 
For 2020, GFW uses the UMD <a href="https://gfw.global/2Mei4Z0" target="_blank" rel="noopener noreferrer">2020 tree cover height data</a> to report on the extent of tree cover — a purely biophysical definition related to height and extent of trees. This satellite data picks up all trees with a minimum canopy height, which users can set when visualizing the data. For this analysis, we use a minimum height threshold of 5 meters. GFW also has a tree cover extent data set for 2000 and 2010 from UMD based on the percent of canopy density, which is used in many functions on GFW, but this was largely not used in this blog. 
The FRA relies on country-reported data based on a definition of forest that includes both a biophysical and land use component. In the FRA data, forests have a tree canopy height of at least 5 meters and canopy cover of at least 10% or are capable of reaching this threshold. Additionally, the land use criterion requires that land be officially or legally designated for a “forest use,” such as conservation or timber production. 
This means that in the FRA data, land that is temporarily devoid of trees — for example, recently logged or burned areas — would still be classified as forest if the official land use stipulates that trees will be allowed to regenerate in the future. Conversely, logged and burned areas are not captured as tree cover by the UMD data. Land that contains trees but is designated for agricultural or urban uses — for example, an oil palm plantation, shade grown cocoa (agroforestry) or a city park — would not be considered a forest by the FRA, but would be included in UMD tree cover data. 

 </aside>



<h4 class="wp-block-heading">Extent of trees and forests globally and by region in 2020 (in million hectares — Mha)</h4>



<table>
	<tbody>
		<tr>
			<td style="width:180px;height:20px;">
			&nbsp;
			</td>
			<td style="width:80px;height:20px;">
			Africa
			</td>
			<td style="width:95px;height:20px;">
			Asia including Russian Federation
			</td>
			<td style="width:95px;height:20px;">
			Europe, excluding Russian Federation
			</td>
			<td style="width:95px;height:20px;">
			North and Central America
			</td>
			<td style="width:80px;height:20px;">
			South America
			</td>
			<td style="width:80px;height:20px;">
			Oceania
			</td>
			<td style="width:80px;height:20px;">
			Global &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			UMD tree cover extent 2020
			</td>
			<td style="width:69px;height:10px;">
			674 Mha

			&nbsp;
			</td>
			<td style="width:65px;height:10px;">
			1,483 Mha

			&nbsp;
			</td>
			<td style="width:83px;height:10px;">
			204 Mha
			</td>
			<td style="width:76px;height:10px;">
			675 Mha
			</td>
			<td style="width:68px;height:10px;">
			834 Mha
			</td>
			<td style="width:71px;height:10px;">
			147 Mha
			</td>
			<td style="width:112px;height:10px;">
			4,018 Mha

			&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			FRA forest area 2020
			</td>
			<td style="width:69px;height:20px;">
			637 Mha
			</td>
			<td style="width:65px;height:20px;">
			1,438 Mha
			</td>
			<td style="width:83px;height:20px;">
			202 Mha
			</td>
			<td style="width:76px;height:20px;">
			753 Mha
			</td>
			<td style="width:68px;height:20px;">
			844 Mha
			</td>
			<td style="width:71px;height:20px;">
			184 Mha
			</td>
			<td style="width:112px;height:20px;">
			4,059 Mha

			&nbsp;
			</td>
		</tr>
		<tr>
			<td style="width:92px;height:10px;">
			Difference of UMD extent as compared to FRA
			</td>
			<td style="width:69px;height:20px;">
			+5.6%

			&nbsp;
			</td>
			<td style="width:65px;height:20px;">
			+3.0%

			&nbsp;
			</td>
			<td style="width:83px;height:20px;">
			+1.0%

			&nbsp;
			</td>
			<td style="width:76px;height:20px;">
			-11.5%

			&nbsp;
			</td>
			<td style="width:68px;height:20px;">
			-1.2%

			&nbsp;
			</td>
			<td style="width:71px;height:20px;">
			-24.8%

			&nbsp;
			</td>
			<td style="width:112px;height:20px;">
			-1.02%

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



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%">*The regional figures compare the same list of countries, with some adjustments made to accommodate different country boundaries in the Database of Global Administrative Areas (GADM) and FRA. </div>



Globally and regionally, tree/forest cover as estimated by UMD data and the FRA are similar. Global estimates of forest area in 2020 only vary by about 1% (4,059 million hectares — Mha — for the FRA and 4,018 Mha for the UMD data).



At the regional level there are many similarities, with very small differences for Asia, Europe and South America. Oceania shows the largest difference at the regional scale, which is almost entirely attributable to the FRA’s greater estimate of forest area in Australia — Australia’s FRA forest area is 52% greater than UMD’s tree cover extent. This may be because Australia’s definition of “forest” specifies a minimum vegetation height of 2 meters, which is used in the FRA estimate. The majority of countries in the FRA follow the FAO definition of a minimum vegetation height of 5 meters, which is also the height threshold used in this blog for analysis of the UMD data.



<h4 class="wp-block-heading">Forest area/tree cover 2020 according to UMD data on GFW and the FRA</h4>



<div class="flourish-embed flourish-map" data-src="visualisation/15325037?240776"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



In central Africa and Southeast Asia, UMD’s tree cover estimates are typically greater than the FRA’s forest area estimations. In Malaysia, for example, UMD's estimate is 33% higher than the FRA's (28.6 Mha for UMD vs. 19.1 Mha for FRA). This may be explained by the difference in definitions, for example Southeast Asia typically contains large areas of oil palm plantations, which would be considered tree cover according to UMD but not forest according to the FRA.



For a better comparison, it would be useful subtract the area of tree plantations recorded in the <a href="https://gfw.global/2Qbv7fM">Spatial Database of Planted Trees</a>, which incorporates a number of different tree crops and planted trees, from the UMD tree cover estimate to better match the UMD data with the FAO forest definition. In the instance of Malaysia for example, excluding oil palm plantations from UMD tree cover results in an area considerably closer to the forest area reported in the FRA.



For some countries, the UMD tree cover extent matches the FRA forest extent very closely. For example, FRA’s forest area estimate for Chile is only 0.1% less than UMD’s. Another example is Nigeria, where the FRA forest area is just 5% less than UMD's tree cover (21.6 Mha for FRA and 22.7 Mha for UMD).



The forest area according to the FRA and UMD data have not always matched so well though — the FRA estimate for forest extent in 2000 (reported in the FRA 2015) was 46% less than UMD’s tree cover area (for the year 2000). Nigeria significantly changed their forest area figures for 2000 as reported in the 2020 FRA, based on the availability of more and better data, resulting in the FRA and UMD figures becoming much more similar (3% difference).



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
#textbox table {
 width: 100% !important;
 margin-left: auto;
}
</style>
<aside id="textbox">Improvements in Monitoring Mean More Similar Numbers
Due to the improvements in data quality from both the FRA and UMD, the data is becoming more and more similar. 
A <a href="https://www.sciencedirect.com/science/article/pii/S0048969722048872" target="_blank" rel="noopener noreferrer">scientific study</a> found that in many countries, the quality of data sources used for forest area estimates as reported to the FRA have increased over time. Specifically, the percentage of forest area monitored by satellites with a quality indication of “good” or “very good” in the FRA has increased. To incorporate these data advances, older data points are recalculated if methodologies/data have been revised — for example, the forest extent in 2000 presented in the FRA 2020 is not the same as was presented in the FRA 2000. 
At the same time, the data on GFW has also improved. The original UMD tree cover data on GFW, which is still used as a baseline for the annual tree cover loss data, is based on canopy density (<a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noopener noreferrer">Hansen et al. 2013</a>) for 2000 and 2010 (thresholded in this case to 30% canopy cover). New data, also from UMD, is based on tree height (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" target="_blank" rel="noopener noreferrer">Potapov et al. 2021</a>) for 2000 and 2020. The new data incorporates updated methods, and new data such as GEDI which better captures the structure of trees. 
Thanks to these improvements, the difference estimates of the FRA forest area and UMD height-based tree cover extent has shrunk to less than 1% (when comparing UMD’s height-based data with the most recent FRA estimate).

 GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*

<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:157px;height:20px;">
			FRA 2000
			</td>
			<td style="width:157px;height:20px;">
			FRA 2020
			</td>
			<td style="width:157px;height:20px;">
			UMD canopy density data (2000)
			</td>
			<td style="width:157px;height:20px;">
			UMD tree height data (2020)
			</td>
		</tr>
		<tr>
			<td style="width:157px;height:20px;">
			3,869 Mha
			</td>
			<td style="width:157px;height:20px;">
			4,158 Mha
			</td>
			<td style="width:157px;height:20px;">
			3,993 Mha
			</td>
			<td style="width:157px;height:20px;">
			4,122 Mha
			</td>
		</tr>
	</tbody>
</table>
*These figures are the globally reported figures

 </aside>



Some countries showed higher estimates for FRA forest areas than UMD tree cover extent, for instance, many of the Nordic European countries and Canada. Again, the difference between the definitions of tree cover used in UMD and forest area used by FRA likely explains the differences. According to the FRA 2020, a substantial proportion of forested areas in these countries are production forests, meaning that vast areas of land temporarily unstocked due to timber harvesting are included as forest area for the FRA. UMD does not include these regions, nor areas that have been regenerated but have not yet reached the UMD height threshold, as tree cover.



<h2 class="wp-block-heading">What does this mean for those using UMD and FRA data?</h2>



Estimates by UMD and the FRA of tree cover and forest can vary, in some cases substantially within a country. The same patterns are not seen for all countries; some countries have higher estimates according to UMD, and for others, FRA estimates are higher. In many cases, these can be explained when taking into account the different forest definitions used. 



Because of their differences, both data sets can be useful for different stakeholders. Read our blog <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</a> to learn more about different ways the UMD and FRA data can be used. In the end, it’s important for users to know the differences between the data sets to help them decide which data will best meet their needs.



<hr> 

<div class="attribute" style="”padding-bottom:" 10px;”=""> We acknowledge and thank the FAO for their helpful comments on this blog.

 

</div> 

 

<hr>



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

This blog compares the forest extent in 2020 for data from UMD on GFW and the FAO Forest Resources Assessment and explains the differences.

Forest Extent from GFW and the Forest Resources Assessment

James MacCarthy

Sasha Tyukavina

Mikaela Weisse

Canada’s Record-breaking 2023 Wildfires Released Nearly 4 Times More Carbon than Global Aviation

Canada's Record-breaking 2023 Wildfires Released Nearly 4 Times More Carbon than Global Aviation

Canada’s 2023 wildfires burned 7.8 Mha of forests, emitting 3 billion tons of carbon, 25% more than from all tropical primary forest loss combined in 2023.

Canada's Record-breaking 2023 Wildfires Released Nearly 4 Times More Carbon than Global Aviation | Forest Insights | Global Forest Watch Blog

Canada&#8217;s Record-breaking 2023 Wildfires Released Nearly 4 Times More Carbon than Global Aviation

Stephanie Borcea

Small Grants, Big Impact: Meet the 2024 Recipients Driving Change with Global Forest Watch

Learn about 11 organizations in 10 countries who will tailor GFW tools and data to confront deforestation within their forests.

Meet the 2024 GFW Small Grants Fund Recipients | GFW Blog

amazon

amazon | Global Forest Watch Blog

Peru

Peru | Global Forest Watch Blog

Nancy Sanchez

Ruth Nogueron

Withman A. Sánchez Hidalgo

“Just for monitoring, just for surveillance, our leaders have been killed,” recounts Ranin Koshi (in Shipibo meaning Strong Man, Hunter), the leader of <a href="https://www.orau.org.pe/" target="_blank" rel="noreferrer noopener">Organización Regional de AIDESEP-Ucayali (ORAU)</a> in Peru. The Indigenous communities ORAU represents have long recognized their territory in the Peruvian Amazon as the very essence of life. It’s the foundation of their culture, history and survival.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">About ORAU
ORAU is a nonprofit civil association founded in 1999 with the purpose of representing and promoting the economic, social, political and cultural development of 13 Indigenous federations representing 20 Indigenous ethnic groups including: Amahuaca, Asháninka, Ashéninka Cashibo-Kakataibo, Cocama – Cocamilla, Shipibo – Conibo, Culina, Isconahua, Machiguenga, Mashco – Piro, Mastanahua, Murunahua, Nahua, Sharanahua, Cashinahua, Yaminahua, Yanesha, Yine, Awajun, and Chitonahua, whose ancestral territories are mostly located in the Ucayali department but also in part of the Loreto region (Ucayali province) and Huanuco department (Puerto Inca province) of Peru. ORAU is the regional office of AIDESEP (Asociación Interétnica de Desarrollo de la Selva Peruana), the governing organization representing Indigenous Peoples of the Peruvian Amazon.
 </aside>



Yet, this deep bond with the land is under constant threat from illegal logging, deforestation, illicit crops, and other forms of intrusion. In 2001, Peru’s primary forest covered 69.1 million hectares, making up 54% of the country’s total land area. By 2023, the country experienced a 4% reduction of these forests.



Forest monitoring in communal territories has been going on for many years and has been embraced strongly, becoming a core part of the culture of Indigenous communities in the Peruvian Amazon. Despite the threats, these communities were able to <a href="https://www.pnas.org/doi/10.1073/pnas.2015171118" target="_blank" rel="noreferrer noopener">decrease deforestation</a> in their areas by more than 50% in just a year when using satellite data from Global Forest Watch (GFW) in their monitoring.



But more tools are needed to both protect forests and keep communities safe — in-person monitoring has led to dangerous confrontations and, tragically, the loss of leaders who dared to defend their sovereignty over their lands.



Now, Indigenous leaders in Peru are working collaboratively to develop new resources using MapBuilder, a tool that is helping them to control the data and narrative around deforestation in their territories and the threats they face to better work together, target solutions, and guard and defend their territories against latent threats to ancestral forests.



<h2 class="wp-block-heading">A history of forest monitoring using satellite data</h2>



With support from Rainforest Foundation US (RFUS) since 2016, and organizations like ORAU, Indigenous communities in the Ucayali and Loreto regions have moved beyond traditional methods of territory monitoring, developing the technical capacity to collect and manage satellite data and analyses themselves. Gone are the days of relying solely on physical patrols and the unaided eye; now, satellite imagery and near real-time data paint a territory-wide picture of their land’s condition.



“Technology allows us to reaffirm and guarantee our rights, especially with regards to the territory, for our future generations,” says Ranin Koshi.



In 2022, ORAU created its Satellite Information Center, “Imenko Tsiroti,” to address deforestation and threats in communal areas with the support of RFUS and to meet the needs of Indigenous peoples for mapping and satellite information for monitoring, surveillance and land management. This center processes data collected by forest monitors, along with relevant GFW data on forest loss in their territories, to address the challenge of monitoring community forests in near real-time to prevent and reduce deforestation.



Since last year, ORAU has been promoting the creation of its Regional Indigenous Environmental Crime Monitoring Network, with the Imenko Tsiroti Center as its base and main hub. With the support of &nbsp;<a href="https://www.usaid.gov/peru/documents/proyecto-prevenir" target="_blank" rel="noreferrer noopener">USAID’s PREVENIR project</a>, it seeks to reduce the incidence of deforestation and crimes faced by Indigenous communities, as well as carry out strategic patrols and minimize the exposure of Indigenous vigilantes, monitors and/or leaders through the analysis of satellite data and technological tools.



<h2 class="wp-block-heading">A collaborative approach to visualizing data with MapBuilder</h2>



To help further advance Indigenous communities’ capacity, Peru’s Indigenous organizations identified MapBuilder as an ideal tool for Indigenous leaders to help visualize the data the communities are gathering from their territories about the threats they face and share it in dynamic format in their discussions and negotiations with external actors (such as government agencies, UN bodies and donors) without having to print and carry large maps. <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> is a tool developed by WRI’s Data Lab that allows users to create interactive maps combining curated, timely, trusted global data — including data from GFW — with the user’s local data.



At two RFUS-organized Tech Camps in 2023 — a reciprocal educational experience designed to train participants in using satellite data and tools, and also to understand the communities’ needs and collaborate on what needs data and tools can address — the GFW team trained technical staff from the Indigenous organizations to customize MapBuilder to create their own online geospatial portals.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1151" height="729" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru" class="wp-image-17509" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg 1151w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS-1024x649.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS-768x486.jpg 768w" sizes="(max-width: 1151px) 100vw, 1151px" /></figure>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1155" height="770" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru." class="wp-image-17510" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg 1155w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS-768x512.jpg 768w" sizes="(max-width: 1155px) 100vw, 1155px" /><figcaption class="wp-element-caption">2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru. Credit: RFUS.</figcaption></figure>



GFW also trained the organizations to create ESRI StoryMaps, which are useful to highlight specific issues such as video testimonials, photo galleries and narrative maps.



A main outcome of the Tech Camps was the creation of the ORAU geospatial portal using MapBuilder. This was possible due to the partnership between GFW, RFUS, the Indigenous organizations and the GIS mapping software company ESRI, participating as equal contributors in a process focused on shared learning and mutual benefit. Each contributor plays an important role in this process: GFW provides tailored monitoring data, the MapBuilder template, and training; RFUS convenes and facilitates the space for collaboration through the Tech Camps and offers crucial technical support on-site; and ORAU, through its Imenko Tsiroti Satellite Information Center, provides the data for the geospatial portal and functions as the data hub to collect field data generated by Indigenous communities as they patrol their territories informed by GFW deforestation alerts. This collaboration has been significantly advanced by the software licenses donations from ESRI, enhancing the project’s capacity for developing and maintaining tools like MapBuilder.



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1095" height="730" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – Indigenous groups demonstrate their maps in Pucallpa, Peru. Credit: RFUS." class="wp-image-17511" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS.jpg 1095w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS-1024x683.jpg 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS-768x512.jpg 768w" sizes="(max-width: 1095px) 100vw, 1095px" /><figcaption class="wp-element-caption">2023 Tech Camp organized by RFUS – Indigenous groups demonstrate their maps in Pucallpa, Peru. Credit: RFUS.</figcaption></figure>



This collective endeavor, centered around joint exploration and development, leverages technology to directly integrate Indigenous partners’ knowledge and needs.



<h2 class="wp-block-heading">Telling the story of forest threats</h2>



Why, though, is visualizing information on a map so transformative? The answer lies in the map’s ability to contextualize data, creating a compelling narrative about the areas and communities it represents. A map tells a visual story, highlighting trends and priorities through the data it cross-references. For Indigenous communities, this means having the ability to prioritize resources and efforts based on timely, evidence-based data. For example, <a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464">maps</a> can leverage data from GFW on deforestation alerts and incorporate local insights on illegal activities, which in turn can help identify which community would benefit most from training monitors to detect illegal logging.



<figure class="wp-block-image size-full"><a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464"><img decoding="async" loading="lazy" width="1860" height="1096" src="https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map.png" alt="" class="wp-image-17530" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map.png 1860w, https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map-1024x603.png 1024w, https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map-768x453.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map-1536x905.png 1536w" sizes="(max-width: 1860px) 100vw, 1860px" /></a></figure>



Through the use of MapBuilder, Indigenous leaders are creating online geospatial portals and StoryMaps, making the reality of their territories visible to the world. It offers an unfiltered, bottom-up perspective on the threats these communities face and their efforts to counteract them.



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="1898" height="2048" src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-1898x2048.png" alt="How indigenous organizations in the Peruvian Amazon monitor their territories" class="wp-image-17512" srcset="https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-1898x2048.png 1898w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-949x1024.png 949w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-768x829.png 768w, https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-1423x1536.png 1423w" sizes="(max-width: 1898px) 100vw, 1898px" /></figure>



With tools like MapBuilder, the Indigenous communities and organizations that represent them control the narrative based on the data they are collecting and releasing to the public.



This shift towards digital storytelling and monitoring represents a significant milestone in forest protection. “There is deforestation; I can only say it. But now, I can demonstrate it physically with the ease of technology,” Ranin Koshi says, adding that this transition from manual to digital is revolutionary for the communities and their leadership. MapBuilder provides them with undeniable evidence of the challenges they face, transforming abstract threats into tangible realities that can be addressed and acted upon.



But that’s not all. With over 33 leaders assassinated in a decade, starting with Edwin Chota in 2014 and Quinto Inuma in 2023, by adding the location of the threats to Indigenous environmental defenders, the maps show the convergence of the threats with the deforestation drivers based on the evidence collected during the patrols.



<h2 class="wp-block-heading">Scaling collaboration to protect Indigenous-held forests</h2>



By integrating GFW data with communities’ field data into interactive applications, organizations like ORAU are fostering trust and enhancing collaborative efforts among community members and external stakeholders. The geospatial portals, developed using the MapBuilder template within ArcGIS Online, facilitate the seamless sharing of crucial environmental data, tailored to each organization’s specific needs and contexts.



Peru is just the starting point, signaling the potential for expansion within the Amazon Basin. Other Indigenous and local communities have begun to adopt monitoring technologies. For example, the <a href="https://sarayaku.org/guardianes-selva/" target="_blank" rel="noreferrer noopener">Sarayaku </a>People in Ecuador use monitoring technology to support the implementation of their territory management plan, and <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/?country=COL&projectId=99" target="_blank" rel="noreferrer noopener">COCOMASUR</a> protects the territory of 33 Afro-descendant communities in Colombia. At the regional level, the AIDESEP-COICA Early Warning and Rapid Response Alert System (<a href="https://aidesep.org.pe/wp-content/uploads/2023/06/Infografia-Sistema-de-Alerta-Temprana-de-COICA.pdf" target="_blank" rel="noreferrer noopener">SAT/RR-COICA</a>) seeks to combine monitoring technology with a platform and protocols to respond to threats to Indigenous territories and peoples.&nbsp;&nbsp;



ORAU’s MapBuilder-based geoportal is more than just a mapping tool; it’s a testament to the power of collaboration between technology experts and Indigenous organizations and communities. The use of MapBuilder in Peru by Indigenous organizations can pave the way for its adoption elsewhere, and shows how technology, when shaped by the very people it aims to serve, can become a powerful tool in the preservation of resources.



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

Indigenous Communities in the Peruvian Amazon Protect their Forest Heritage with MapBuilder 

“Just for monitoring, just for surveillance, our leaders have been killed,” recounts Ranin Koshi (in Shipibo meaning Strong Man, Hunter), the leader of <a href="https://www.orau.org.pe/" target="_blank" rel="noreferrer noopener">Organización Regional de AIDESEP-Ucayali (ORAU)</a> in Peru. The Indigenous communities ORAU represents have long recognized their territory in the Peruvian Amazon as the very essence of life. It's the foundation of their culture, history and survival.



<style>
.css-19vc5o2 aside {
 width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox">About ORAU
ORAU is a nonprofit civil association founded in 1999 with the purpose of representing and promoting the economic, social, political and cultural development of 13 Indigenous federations representing 20 Indigenous ethnic groups including: Amahuaca, Asháninka, Ashéninka Cashibo-Kakataibo, Cocama - Cocamilla, Shipibo - Conibo, Culina, Isconahua, Machiguenga, Mashco - Piro, Mastanahua, Murunahua, Nahua, Sharanahua, Cashinahua, Yaminahua, Yanesha, Yine, Awajun, and Chitonahua, whose ancestral territories are mostly located in the Ucayali department but also in part of the Loreto region (Ucayali province) and Huanuco department (Puerto Inca province) of Peru. ORAU is the regional office of AIDESEP (Asociación Interétnica de Desarrollo de la Selva Peruana), the governing organization representing Indigenous Peoples of the Peruvian Amazon.
 </aside>



Yet, this deep bond with the land is under constant threat from illegal logging, deforestation, illicit crops, and other forms of intrusion. In 2001, Peru’s primary forest covered 69.1 million hectares, making up 54% of the country’s total land area. By 2023, the country experienced a 4% reduction of these forests.



Forest monitoring in communal territories has been going on for many years and has been embraced strongly, becoming a core part of the culture of Indigenous communities in the Peruvian Amazon. Despite the threats, these communities were able to <a href="https://www.pnas.org/doi/10.1073/pnas.2015171118" target="_blank" rel="noreferrer noopener">decrease deforestation</a> in their areas by more than 50% in just a year when using satellite data from Global Forest Watch (GFW) in their monitoring.



But more tools are needed to both protect forests and keep communities safe — in-person monitoring has led to dangerous confrontations and, tragically, the loss of leaders who dared to defend their sovereignty over their lands.



Now, Indigenous leaders in Peru are working collaboratively to develop new resources using MapBuilder, a tool that is helping them to control the data and narrative around deforestation in their territories and the threats they face to better work together, target solutions, and guard and defend their territories against latent threats to ancestral forests.



<h2 class="wp-block-heading">A history of forest monitoring using satellite data</h2>



With support from Rainforest Foundation US (RFUS) since 2016, and organizations like ORAU, Indigenous communities in the Ucayali and Loreto regions have moved beyond traditional methods of territory monitoring, developing the technical capacity to collect and manage satellite data and analyses themselves. Gone are the days of relying solely on physical patrols and the unaided eye; now, satellite imagery and near real-time data paint a territory-wide picture of their land's condition.



“Technology allows us to reaffirm and guarantee our rights, especially with regards to the territory, for our future generations,” says Ranin Koshi.



In 2022, ORAU created its Satellite Information Center, “Imenko Tsiroti," to address deforestation and threats in communal areas with the support of RFUS and to meet the needs of Indigenous peoples for mapping and satellite information for monitoring, surveillance and land management. This center processes data collected by forest monitors, along with relevant GFW data on forest loss in their territories, to address the challenge of monitoring community forests in near real-time to prevent and reduce deforestation.



Since last year, ORAU has been promoting the creation of its Regional Indigenous Environmental Crime Monitoring Network, with the Imenko Tsiroti Center as its base and main hub. With the support of &nbsp;<a href="https://www.usaid.gov/peru/documents/proyecto-prevenir" target="_blank" rel="noreferrer noopener">USAID's PREVENIR project</a>, it seeks to reduce the incidence of deforestation and crimes faced by Indigenous communities, as well as carry out strategic patrols and minimize the exposure of Indigenous vigilantes, monitors and/or leaders through the analysis of satellite data and technological tools.



<h2 class="wp-block-heading">A collaborative approach to visualizing data with MapBuilder</h2>



To help further advance Indigenous communities’ capacity, Peru's Indigenous organizations identified MapBuilder as an ideal tool for Indigenous leaders to help visualize the data the communities are gathering from their territories about the threats they face and share it in dynamic format in their discussions and negotiations with external actors (such as government agencies, UN bodies and donors) without having to print and carry large maps. <a href="https://mapbuilder.wri.org/" target="_blank" rel="noreferrer noopener">MapBuilder</a> is a tool developed by WRI’s Data Lab that allows users to create interactive maps combining curated, timely, trusted global data — including data from GFW — with the user’s local data.



At two RFUS-organized Tech Camps in 2023 — a reciprocal educational experience designed to train participants in using satellite data and tools, and also to understand the communities’ needs and collaborate on what needs data and tools can address — the GFW team trained technical staff from the Indigenous organizations to customize MapBuilder to create their own online geospatial portals.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru" class="wp-image-17509"/></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru." class="wp-image-17510"/><figcaption class="wp-element-caption">2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru. Credit: RFUS.</figcaption></figure>



GFW also trained the organizations to create ESRI StoryMaps, which are useful to highlight specific issues such as video testimonials, photo galleries and narrative maps.



A main outcome of the Tech Camps was the creation of the ORAU geospatial portal using MapBuilder. This was possible due to the partnership between GFW, RFUS, the Indigenous organizations and the GIS mapping software company ESRI, participating as equal contributors in a process focused on shared learning and mutual benefit. Each contributor plays an important role in this process: GFW provides tailored monitoring data, the MapBuilder template, and training; RFUS convenes and facilitates the space for collaboration through the Tech Camps and offers crucial technical support on-site; and ORAU, through its Imenko Tsiroti Satellite Information Center, provides the data for the geospatial portal and functions as the data hub to collect field data generated by Indigenous communities as they patrol their territories informed by GFW deforestation alerts. This collaboration has been significantly advanced by the software licenses donations from ESRI, enhancing the project's capacity for developing and maintaining tools like MapBuilder.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – Indigenous groups demonstrate their maps in Pucallpa, Peru. Credit: RFUS." class="wp-image-17511"/><figcaption class="wp-element-caption">2023 Tech Camp organized by RFUS – Indigenous groups demonstrate their maps in Pucallpa, Peru. Credit: RFUS.</figcaption></figure>



This collective endeavor, centered around joint exploration and development, leverages technology to directly integrate Indigenous partners' knowledge and needs.



<h2 class="wp-block-heading">Telling the story of forest threats</h2>



Why, though, is visualizing information on a map so transformative? The answer lies in the map's ability to contextualize data, creating a compelling narrative about the areas and communities it represents. A map tells a visual story, highlighting trends and priorities through the data it cross-references. For Indigenous communities, this means having the ability to prioritize resources and efforts based on timely, evidence-based data. For example, <a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464">maps</a> can leverage data from GFW on deforestation alerts and incorporate local insights on illegal activities, which in turn can help identify which community would benefit most from training monitors to detect illegal logging.



<figure class="wp-block-image size-full"><a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map.png" alt="" class="wp-image-17530"/></a></figure>



Through the use of MapBuilder, Indigenous leaders are creating online geospatial portals and StoryMaps, making the reality of their territories visible to the world. It offers an unfiltered, bottom-up perspective on the threats these communities face and their efforts to counteract them.



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_EN-1898x2048.png" alt="How indigenous organizations in the Peruvian Amazon monitor their territories" class="wp-image-17512"/></figure>



With tools like MapBuilder, the Indigenous communities and organizations that represent them control the narrative based on the data they are collecting and releasing to the public.



This shift towards digital storytelling and monitoring represents a significant milestone in forest protection. “There is deforestation; I can only say it. But now, I can demonstrate it physically with the ease of technology,” Ranin Koshi says, adding that this transition from manual to digital is revolutionary for the communities and their leadership. MapBuilder provides them with undeniable evidence of the challenges they face, transforming abstract threats into tangible realities that can be addressed and acted upon.



But that’s not all. With over 33 leaders assassinated in a decade, starting with Edwin Chota in 2014 and Quinto Inuma in 2023, by adding the location of the threats to Indigenous environmental defenders, the maps show the convergence of the threats with the deforestation drivers based on the evidence collected during the patrols.



<h2 class="wp-block-heading">Scaling collaboration to protect Indigenous-held forests</h2>



By integrating GFW data with communities’ field data into interactive applications, organizations like ORAU are fostering trust and enhancing collaborative efforts among community members and external stakeholders. The geospatial portals, developed using the MapBuilder template within ArcGIS Online, facilitate the seamless sharing of crucial environmental data, tailored to each organization's specific needs and contexts.



Peru is just the starting point, signaling the potential for expansion within the Amazon Basin. Other Indigenous and local communities have begun to adopt monitoring technologies. For example, the <a href="https://sarayaku.org/guardianes-selva/" target="_blank" rel="noreferrer noopener">Sarayaku </a>People in Ecuador use monitoring technology to support the implementation of their territory management plan, and <a href="https://www.globalforestwatch.org/grants-and-fellowships/projects/?country=COL&amp;projectId=99" target="_blank" rel="noreferrer noopener">COCOMASUR</a> protects the territory of 33 Afro-descendant communities in Colombia. At the regional level, the AIDESEP-COICA Early Warning and Rapid Response Alert System (<a href="https://aidesep.org.pe/wp-content/uploads/2023/06/Infografia-Sistema-de-Alerta-Temprana-de-COICA.pdf" target="_blank" rel="noreferrer noopener">SAT/RR-COICA</a>) seeks to combine monitoring technology with a platform and protocols to respond to threats to Indigenous territories and peoples.&nbsp;&nbsp;



ORAU’s MapBuilder-based geoportal is more than just a mapping tool; it's a testament to the power of collaboration between technology experts and Indigenous organizations and communities. The use of MapBuilder in Peru by Indigenous organizations can pave the way for its adoption elsewhere, and shows how technology, when shaped by the very people it aims to serve, can become a powerful tool in the preservation of resources.



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"Solo por monitorear, solo por vigilar, han asesinado a nuestros líderes", relata Ranin Koshi (en Shipibo, Hombre Fuerte, Cazador), líder de la Organización Regional de AIDESEP-Ucayali (ORAU) en Perú. Las comunidades indígenas que representa la ORAU reconocen desde hace mucho tiempo su territorio en la Amazonía peruana como la esencia misma de la vida. Es la base de su cultura, historia y supervivencia.



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<aside id="textbox">Acerca de la ORAU
La ORAU es una asociación civil sin fines de lucro fundada en 1999 con el propósito de representar y promover el desarrollo económico, social, político y cultural de 20 pueblos indígenas y 13 bases federativas, como: Amahuaca, Asháninka, Ashéninka Cashibo-Kakataibo, Cocama - Cocamilla, Shipibo – Conibo, Culina, Isconahua, Machiguenga, Mashco – Piro, Mastanahua, Murunahua, Nahua, Sharanahua, Cashinahua, Yaminahua, Yanesha, Yine, Awajun, Chitonahua, cuyos territorios ancestrales se encuentran mayoritariamente en el departamento Ucayali, parte de la región Loreto (provincia de Ucayali) y departamento Huánuco (provincia Puerto Inca). La ORAU es la oficina regional de la AIDESEP (Asociación Interétnica de Desarrollo de la Selva Peruana), la organización vocera de los pueblos indígenas de la Amazonía del Perú.
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Sin embargo, este profundo vínculo con la tierra está constantemente amenazado por la tala ilegal, la deforestación, cultivos ilícitos y otras formas de intrusión. En 2001, los bosques primarios de Perú cubrían 69,1 millones de hectáreas, lo que representaba el 54% de la superficie total del país. Para 2023, el país experimentaba una reducción del 4% de estos bosques.



El monitoreo forestal en los territorios comunales se lleva realizando desde hace muchos años y se está convirtiendo en un elemento central del patrimonio de las comunidades indígenas de la Amazonía peruana. A pesar de las amenazas, estas comunidades <a href="https://www.pnas.org/doi/10.1073/pnas.2015171118">lograron reducir la deforestación</a> en sus áreas en más del 50% en solo un año al utilizar datos satelitales de Global Forest Watch (GFW) en su monitoreo.



Pero se necesitan más herramientas para proteger los bosques y mantener seguras a las comunidades: el monitoreo en persona ha provocado enfrentamientos peligrosos y, trágicamente, la pérdida de líderes que se atrevieron a defender la soberanía sobre sus tierras.



Ahora, los líderes indígenas de Perú están trabajando en colaboración para desarrollar nuevos recursos utilizando MapBuilder, una herramienta que les está ayudando a controlar los datos y la narrativa en torno a la deforestación en sus territorios y las amenazas que enfrentan para trabajar mejor juntos y encontrar soluciones que les permitan monitorear, vigilar y defender sus territorios frente a amenazas latentes en los bosques ancestrales.



<h2 class="wp-block-heading">Una historia de monitoreo forestal utilizando datos satelitales</h2>



Con el apoyo de organizaciones como RFUS desde 2016, y organizaciones como la ORAU, las comunidades indígenas en las regiones de Ucayali y Loreto han ido más allá de los métodos tradicionales de monitoreo territorial, desarrollando la capacidad técnica para recolectar y administrar datos por sí mismas. Atrás quedaron los días de depender únicamente de las patrullas físicas y a simple vista; ahora, las imágenes satelitales y los datos casi en tiempo real brindan una imagen completa del estado de sus territorios.



“La tecnología nos permite reafirmar y garantizar nuestros derechos, especialmente en lo que respecta al territorio, para nuestras generaciones futuras”, dice Ranin Koshi.



En 2022, la ORAU creó su Centro de Información Satelital “Imenko Tsiroti” (CISIT) para abordar la deforestación y las amenazas en territorios comunales con el apoyo de la RFUS y atender la demanda de información cartográfica y satelital de los pueblos indígenas con información para monitoreo, vigilancia y gestión de territorio. Dicho centro procesa datos con información de Global Forest Watch con la gran tarea de monitorear los bosques comunales utilizando tecnología que brinda información en tiempo casi real para prevenir y reducir la deforestación.



Desde el año pasado, la ORAU impulsa la creación de su Red de Monitoreo Regional Indígena de Delitos Ambientales con el CISIT como base y eje principal. Con la asistencia del Proyecto <a href="https://www.usaid.gov/peru/documents/proyecto-prevenir">PREVENIR de USAID</a>, se busca reducir la incidencia de la deforestación y los delitos que afrontan las comunidades nativas, así como la realización de patrullajes estratégicos y minimizar la exposición de los vigilantes, monitores y/o líderes indígenas mediante el análisis de datos satelitales y herramientas tecnológicas.&nbsp;



<h2 class="wp-block-heading">Un enfoque colaborativo para visualizar datos con MapBuilder</h2>



Para ayudar a avanzar aún más la capacidad de las comunidades indígenas, las organizaciones indígenas identificaron a MapBuilder como una herramienta para que los líderes indígenas ayuden a visualizar los datos geográficos sobre sus territorios y las amenazas que enfrentan en sus discusiones y negociaciones con actores externos (como agencias gubernamentales, organismos de la ONU, donantes y organizaciones de la sociedad civil). <a href="https://mapbuilder.wri.org/">MapBuilder</a> es una herramienta desarrollada por el Data Lab de WRI que permite a los usuarios crear mapas interactivos que combinan datos globales oportunos y confiables, incluidos los datos de GFW, con los datos locales del usuario.



En dos campamentos tecnológicos (Tech Camps) organizados por RFUS en 2023 - una experiencia educativa recíproca diseñada para capacitar a los participantes en el uso de datos y herramientas satelitales, y también para comprender las necesidades de las comunidades y colaborar en qué necesidades pueden abordar los datos y las herramientas - el equipo de GFW capacitó al personal técnico de las organizaciones indígenas para crear sus propios portales geoespaciales en línea.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/1_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru" class="wp-image-17509"/></figure>



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2_2023-Tech-Camp-organized-by-RFUS-–-workshop-in-Iquitos-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – workshop in Iquitos, Peru." class="wp-image-17510"/><figcaption class="wp-element-caption">Campamento Tecnológico 2023 organizado por RFUS - taller en Iquitos, Perú. Crédito: RFUS.</figcaption></figure>



GFW también capacitó a las organizaciones para crear ESRI StoryMaps, que son útiles para resaltar materiales multimedia como testimonios en video, galerías de fotos y mapas narrativos.



Un resultado principal de los Tech Camps fue la creación del portal geoespacial la ORAU utilizando MapBuilder. Esto fue posible gracias a la alianza entre GFW, RFUS, la organización indígena y la donación de software de mapeo y sistemas geoespaciales de ESRI, participando como contribuyentes iguales en un proceso enfocado en el aprendizaje compartido y el beneficio mutuo. Cada colaborador juega un papel importante en este proceso:



GFW y WRI proporcionan datos de monitoreo personalizados, la plantilla MapBuilder y capacitación; RFUS convoca y facilita el espacio de colaboración a través de los Tech Camps además de ofrecer apoyo técnico crucial en campo; y la ORAU, a través del CISIT, proporciona los datos para el portal geoespacial y funciona como el centro de datos para recopilar datos de campo generados por las comunidades indígenas mientras patrullan sus territorios informados por las alertas de deforestación de GFW. Esta colaboración ha avanzado significativamente debido a&nbsp; las donaciones de licencias de software de ESRI, mejorando la capacidad del proyecto para desarrollar y mantener herramientas como MapBuilder.



<figure class="wp-block-image size-full"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/2023-Tech-Camp-organized-by-RFUS-–-Indigenous-groups-demonstrate-their-maps-in-Pucallpa-Peru.-Credit-RFUS.jpg" alt="2023 Tech Camp organized by RFUS – Indigenous groups demonstrate their maps in Pucallpa, Peru. Credit: RFUS." class="wp-image-17511"/><figcaption class="wp-element-caption">Campamento Tecnológico 2023 organizado por RFUS - Grupos indígenas muestran sus mapas en Pucallpa, Perú. Crédito: RFUS.</figcaption></figure>



Este esfuerzo colectivo, centrado en la exploración y el desarrollo conjuntos, aprovecha la tecnología para integrar directamente el conocimiento y las necesidades de los socios indígenas.



<h2 class="wp-block-heading">Contando la historia de las amenazas forestales</h2>



¿Por qué, entonces, visualizar información en un mapa es tan transformador? La respuesta radica en la capacidad del mapa para contextualizar los datos, creando una narrativa convincente sobre las áreas y comunidades que representa. Un mapa cuenta una historia visual, destacando tendencias y prioridades a través de los datos que hace referencia cruzada. Para las comunidades indígenas, esto significa tener la capacidad de priorizar recursos y esfuerzos en función de datos oportunos y basados ​​en evidencia. Por ejemplo, <a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464">los mapas</a> pueden aprovechar datos de GFW sobre alertas de deforestación e incorporar información local sobre actividades ilegales, lo que a su vez puede ayudar a identificar qué comunidad se beneficiaría más de la capacitación de monitores para detectar la tala ilegal.



<figure class="wp-block-image size-full"><a href="https://my.gfw-mapbuilder.org/v1.latest/?appid=867894697a6a4bc68c18a2c4b95ae464"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/06/ORAU-mapbuilder-map-1.png" alt="" class="wp-image-17533"/></a></figure>



Mediante el uso de MapBuilder, los líderes indígenas están creando portales geoespaciales y StoryMaps en línea, haciendo visible la realidad de sus territorios para el mundo. Ofrece una perspectiva sin filtros y de abajo hacia arriba sobre las amenazas que enfrentan estas comunidades y sus esfuerzos para contrarrestarlas.



<figure class="wp-block-image size-large"><img src="https://content.globalforestwatch.org/wp-content/uploads/2024/05/24.05.28_MapBuilder-Peru_Insights_ES-1898x2048.png" alt="Cómo las organizaciones indígenas en la Amazonía peruana monitorean y mapean sus territorios" class="wp-image-17527"/></figure>



Con herramientas como MapBuilder, las comunidades indígenas y las organizaciones que las representan controlan la narrativa en función de los datos que recopilan y publican al público.



Este cambio hacia la narración y el monitoreo digitales representa un hito significativo en la protección de sus bosques ancestrales. “Hay deforestación; solo puedo decirlo. Pero ahora, puedo demostrarlo físicamente con la facilidad de la tecnología”, dice Ranin Koshi, y agrega que esta transición de manual a digital es revolucionaria para las comunidades y su liderazgo. MapBuilder les proporciona evidencia innegable de los desafíos que enfrentan, transformando las amenazas abstractas en realidades tangibles que pueden abordarse y sobre las que se puede actuar.



Pero eso no es todo. Con más de 33 líderes asesinados en una década, desde Edwin Chota en 2014 hasta Quinto Inuma en 2023, al agregar la ubicación de las amenazas a los defensores indígenas del medio ambiente, los mapas muestran la convergencia de las amenazas con los factores de deforestación según la evidencia recopilada durante los patrullajes.



<h2 class="wp-block-heading">Escalando la colaboración para proteger los bosques de las comunidades indígenas</h2>



Al integrar los datos de GFW con los datos de campo obtenidos por las comunidades en aplicaciones interactivas, organizaciones como la ORAU fomentan la confianza y fortalecen los esfuerzos de colaboración entre los miembros de la comunidad y las partes interesadas externas. Los portales geoespaciales, desarrollados utilizando la plantilla MapBuilder dentro de ArcGIS Online, facilitan el intercambio fluido de datos ambientales cruciales, adaptados a las necesidades y contextos específicos de cada organización.



Perú es solo el punto de partida, lo que indica el potencial de expansión dentro de la cuenca del Amazonas. Otras comunidades indígenas y locales han comenzado a adoptar tecnologías de monitoreo. Por ejemplo, el Pueblo Sarayaku en Ecuador utiliza tecnología de monitoreo para apoyar la implementación de su plan de manejo territorial, y COCOMASUR protege el territorio de 33 comunidades afrodescendientes en Colombia. A nivel regional, el Sistema de Alerta Temprana y Respuesta Rápida AIDESEP-COICA (SAT/RR-COICA) busca combinar la tecnología de monitoreo con una plataforma y protocolos para responder a las amenazas a los territorios y pueblos indígenas.



El geoportal de la ORAU basado en MapBuilder es más que una simple herramienta de mapeo; es un testimonio del poder de la colaboración entre expertos en tecnología y organizaciones y comunidades indígenas. El uso de MapBuilder en Perú por organizaciones indígenas puede abrir el camino para su adopción en otros lugares, y muestra cómo la tecnología, cuando la moldean las mismas personas a las que pretende servir, puede convertirse en una herramienta poderosa para la preservación de los recursos.



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