<p>You can now easily adjust access to your voice recordings and images within Forest Watcher reports. For step-by-step instructions on how to do this, please refer to our <a href="https://www.globalforestwatch.org/help/forest-watcher/guides/managing-access-to-voice-recordings-and-images-in-reports-on-forest-watcher-web/">new tutorial</a>.</p>


Manage Media Access in Forest Watcher Web Reports

Ripe Cocoa pods from a cocoa farm in Ghana.

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

Cocoa

Cocoa | Global Forest Watch Blog

commodities

commodities | Global Forest Watch Blog

mapping

mapping | Global Forest Watch Blog

<p>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.</p>



<p>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.</p>



<p>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.</p>



<p>She lives in Baltimore, MD with her husband and dog.</p>

Martina Schneider

Caroline Winchester

Sofia Tenorio Fenton

Liz Goldman

<p>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.</p>



<p>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:*</p>



<ul>
<li>The <strong>West Africa Cocoa dataset (WAC)</strong>, 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 <strong>Cocoa Deforestation Risk Assessment (Cocoa DRA)</strong>, 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>



<p>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.</p>



<style>
.css-19vc5o2 aside {
   width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox"><em><b>From Palm Oil to Cocoa</b><p></p>
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>

</em> </aside>



<figure class="wp-block-image size-full"><img decoding="async" loading="lazy" width="1175" height="783" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-pod.jpg" alt="Cocoa pod" class="wp-image-16723" srcset="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-pod.jpg 1175w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-pod-1024x682.jpg 1024w, https://dev-global-forest-watch-blog.pantheonsite.io/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"><strong>What is the West Africa</strong> <strong>Cocoa dataset?</strong></h2>



<p>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.</p>



<p>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.</p>



<p>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.</p>



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

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. <br><br>
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.

</em> </aside>



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



<p>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.</p>



<p>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.</p>



<p>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 <em>all</em> natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.</p>



<p>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.</p>



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



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



<p>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.</p>



<p>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.</p>



<figure class="wp-block-image size-full is-resized"><img decoding="async" loading="lazy" width="1430" height="868" src="https://dev-global-forest-watch-blog.pantheonsite.io/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://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023.png 1430w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-1024x622.png 1024w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/Cocoa-Deforestation-Risk-Assessment-Priority-2023-768x466.png 768w" sizes="(max-width: 1430px) 100vw, 1430px" /></figure>



<p>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.</p>



<p>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.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="2048" height="1448" src="https://dev-global-forest-watch-blog.pantheonsite.io/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://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/tai-and-bia_labels-1-2048x1448.png 2048w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/tai-and-bia_labels-1-1024x724.png 1024w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/tai-and-bia_labels-1-768x543.png 768w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/02/tai-and-bia_labels-1-1536x1086.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p>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;</p>



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



<p>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.</p>



<p>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.</p>



<p>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.</p>



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



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

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

<p>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.</p>



<p>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:*</p>



<ul>
<li>The <strong>West Africa Cocoa dataset (WAC)</strong>, 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 <strong>Cocoa Deforestation Risk Assessment (Cocoa DRA)</strong>, 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>



<p>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.</p>



<style>
.css-19vc5o2 aside {
   width: 100% !important;
float: none !important;
margin-left:
0px
!important;
}
</style>
<aside id="textbox"><em><b>From Palm Oil to Cocoa</b><p></p>
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>

</em> </aside>



<figure class="wp-block-image size-full"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/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"><strong>What is the West Africa</strong> <strong>Cocoa dataset?</strong></h2>



<p>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.</p>



<p>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.</p>



<p>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.</p>



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

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. <br><br>
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.

</em> </aside>



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



<p>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.</p>



<p>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.</p>



<p>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 <em>all</em> natural forests, its scope includes natural forests both inside and outside of protected areas as well as naturally regenerating forests.</p>



<p>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.</p>



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



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



<p>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.</p>



<p>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.</p>



<figure class="wp-block-image size-full is-resized"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/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>



<p>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.</p>



<p>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.</p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/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>



<p>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;</p>



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



<p>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.</p>



<p>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.</p>



<p>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.</p>



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



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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

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Global Forest Watch Content

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Paige Johnson

Sarah Carter

Jan Hooijmans

<p>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>.</p>



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



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<aside id="textbox"><em><b>Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance </b><p></p>
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>).<br><br>
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. <br><br>
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.<br><br>
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. 

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

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

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

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

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

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;"><strong>+3.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;"><strong>+1.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;"><strong>-11.5%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.2%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;"><strong>-24.8%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.02%</strong></span></p>

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



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%"><small><em>*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. </em></small></div>



<p>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).</p>



<p>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.</p>



<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>



<p>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.</p>



<p>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.</p>



<p>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).</p>



<p>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).</p>



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<aside id="textbox"><em><b>Improvements in Monitoring Mean More Similar Numbers</b><p></p>
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.<br><br>
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.<br><br>
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).

<br><br><Strong>GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*</strong>

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

</em> </aside>



<p>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.</p>



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



<p>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. </p>



<p>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/"><em>Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</em></a><em> </em>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.</p>



<hr> 

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

 

</div> 

 

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<p></p>

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

<p>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>.</p>



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



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<aside id="textbox"><em><b>Tree Cover and Forest Area Definitions on GFW and in the FRA At a Glance </b><p></p>
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>).<br><br>
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. <br><br>
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.<br><br>
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. 

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

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

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

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

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

			<p>&nbsp;</p>
			</td>
			<td style="width:65px;height:20px;">
			<p><span style="font-size:10px;"><strong>+3.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:83px;height:20px;">
			<p><span style="font-size:10px;"><strong>+1.0%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:76px;height:20px;">
			<p><span style="font-size:10px;"><strong>-11.5%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:68px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.2%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:71px;height:20px;">
			<p><span style="font-size:10px;"><strong>-24.8%</strong></span></p>

			<p>&nbsp;</p>
			</td>
			<td style="width:112px;height:20px;">
			<p><span style="font-size:10px;"><strong>-1.02%</strong></span></p>

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



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" width="100%"><small><em>*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. </em></small></div>



<p>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).</p>



<p>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.</p>



<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>



<p>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.</p>



<p>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.</p>



<p>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).</p>



<p>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).</p>



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<aside id="textbox"><em><b>Improvements in Monitoring Mean More Similar Numbers</b><p></p>
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.<br><br>
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.<br><br>
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).

<br><br><Strong>GLOBAL FOREST AREA/TREE COVER EXTENT FOR 2000 AND 2020 IN THE FRA AND UMD DATA*</strong>

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

</em> </aside>



<p>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.</p>



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



<p>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. </p>



<p>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/"><em>Global Forest Watch and the Forest Resources Assessment, Explained in 5 Graphics</em></a><em> </em>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.</p>



<hr> 

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

 

</div> 

 

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<p></p>



<p></p>

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

Side by side comparison of UMD tree cover loss and JRC Tropical Moist Forest data

deforestation

deforestation | Global Forest Watch Blog

forest change

forest change | Global Forest Watch Blog

forest quality

forest quality | Global Forest Watch Blog

Michelle Sims

Peter Potapov

Christelle Vancutsem

Clément Bourgoin

Fréderic Achard

<p>The wide availability of forest and land cover maps based on satellite data provides more information on the world’s forests than ever before. However, this plethora of information can also be challenging for people trying to understand differences in data sources and decide which data to use for a specific purpose.</p>



<p>In the past, we’ve compared the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a> from the University of Maryland (UMD) and available on Global Forest Watch (GFW) with national data from <a href="https://www.globalforestwatch.org/blog/data-and-research/2022-tree-cover-loss-data-explained/">Brazil and Indonesia</a>, as well as with data from the United Nations Food and Agriculture Organization’s (FAO) <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 Resources Assessment</a>, to shed light on some of these differences.</p>



<p>Another data set that tracks forest change in the tropics is the <a href="https://forobs.jrc.ec.europa.eu/TMF" target="_blank" rel="noreferrer noopener">Tropical Moist Forest (TMF) data set</a> from the European Commission Joint Research Centre (JRC). Like the UMD tree cover loss data, the JRC TMF data is based on Landsat satellite imagery and is updated annually. Yet while both data sets map forest disturbances, the maps and numbers look a little different.</p>



<p>In this blog, we explain some key differences in the definitions and methods used for each data set that impact the resulting maps and discuss how the disturbances mapped by both data sets compare (while the data sets also map forest gain or regrowth, this blog focuses on comparing their mapping of forest disturbances). While understanding these differences can help determine the contexts for which each data set might be useful, note that this analysis does not evaluate the quality or accuracy of either data set.</p>



<h2 class="wp-block-heading">What areas do the JRC and UMD data sets cover, and how do they define forests?</h2>



<p>The UMD data set maps forests globally based on <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree cover extent</a> in 2000. <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">Tree cover</a> is defined as woody vegetation with a minimum height of at least five meters at 30-meter resolution, which includes all types of tree cover — including natural or managed forest and plantations — and is not limited to a specific forest type. The GFW map shows tree cover with a 30% canopy density, which we use in our analyses and in this comparison, although users can adjust the threshold based on their needs.</p>



<p>The JRC data set, on the other hand, <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">maps forests</a> beginning in 1990. The data includes all closed primary and secondary forests in the humid tropics and does not apply a minimum height or canopy density threshold to delineate forest extent.</p>



<p>As a result of these definitional differences, some areas included in the 2000 UMD tree cover extent in the tropics like dry forests, young secondary forests and plantations are not classified as forests in the JRC data set in 2000 — a total of 655 million hectares (Mha), or 34%, of the UMD tree cover extent area that overlaps with the JRC data. Likewise, the JRC forest extent in 2000 includes some areas that do not meet the height and 30% canopy density threshold in the UMD data or are otherwise not mapped as tree cover by the UMD data — 17.2 Mha, or 1% of the JRC forest extent.</p>



<p>Because of the differences in geographic coverage and scope between the two data sets, this blog only compares forest disturbances from 2001-2022 for areas that, in the year 2000, are mapped both as tree cover by UMD and as forest by JRC.</p>



<h2 class="wp-block-heading">What types of forest disturbances do the UMD and JRC data map?</h2>



<h3 class="wp-block-heading"><em>The JRC Tropical Moist Forest data set maps degradation and deforestation in the humid tropics</em></h3>



<p>The JRC TMF data set maps disturbances to tropical moist forests — including degradation and deforestation — from 1990 to 2022. The JRC team maps these changes using an algorithm that detects forest disruptions, meaning an absence of tree foliage cover within a 30-meter pixel for each available Landsat image, and then classifies the status of forest based on how long these disruptions are visible.</p>



<p>The TMF data set <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">defines degradation</a> as temporary disturbances occurring for up to 2.5 years, while it defines deforestation as longer-term disturbances where disruptions to tree cover are visible for more than 2.5 years and no tree regrowth is detected over the last three years. In any given year, a forest is considered disturbed if there is a disruption detected in at least one Landsat image during that year.</p>



<p>Thus, pixels classified as degraded are still considered forest, with existing or regrowing trees, while pixels classified as deforested are undergoing a longer-term loss of forest. Because disruptions are recorded and analyzed for the entire time series, the data set classifies dynamics over time; for example, a forest pixel may be classified as degraded in one year after a short-term disturbance, then after a period of recovery may later be classified as deforested after ongoing disturbances with no tree regrowth. The JRC deforestation class therefore includes direct deforestation (deforestation of undisturbed forest) or deforestation after degradation (deforestation of forest that was previously classified as degraded).</p>



<p>The data set also analyzes the frequency, duration of disruptions and time of forest recovery in between disruption events to identify deforestation following degradation versus multiple degradation events.</p>



<h3 class="wp-block-heading"><em>The UMD tree cover loss data set on GFW maps all types of tree cover loss globally</em></h3>



<p>The UMD tree cover loss data set maps annual tree cover loss from 2001 to 2022. The UMD team uses an algorithm that detects loss using change detection metrics based on comparison of Landsat images between the present and past years. Tree cover loss, <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">defined as</a> a clearing of tree cover of at least a half of a 30-meter Landsat pixel, includes both temporary and permanent losses without defining or differentiating which loss is considered degradation or deforestation. Once classified as loss, pixels are masked from further updates, meaning that any following forest change dynamics — such as regrowth or subsequent disturbances — are not recorded.</p>



<p>The UMD tree cover loss data can be combined with contextual data layers, such as <a href="https://gfw.global/3kqcoqa">UMD’s map of humid tropical primary forest</a>, to see loss within specific forest types.</p>



<h4 class="wp-block-heading">Key differences between the JRC TMF and UMD tree cover loss data</h4>



<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:165px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>JRC TMF</strong></p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>UMD tree cover loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Temporal coverage</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>1990-2022</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>&nbsp;2000-2022</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Geographic coverage and extent</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Tropical moist forest:</strong> closed primary and secondary forests in the humid tropics in 1990.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Global tree cover: </strong>woody vegetation with a minimum height of at least five meters at 30-meter resolution in 2000.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Definition of disturbance</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>An absence of tree foliage cover within a 30-meter pixel.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Clearing of at least half of tree cover within a 30-meter pixel.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Disturbance classes</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Disturbances are classified as <strong>degradation </strong>if they are visible for less than 2.5 years, and <strong>deforestation </strong>if they are visible for more than 2.5 years with no vegetative regrowth over the last three years.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>All disturbances classified as <strong>tree cover loss. </strong>The length of disturbance is not considered.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Methods</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies valid single-date Landsat images and derives disturbance classes by analyzing the single-date classifications. Dynamics of disturbance events over time are recorded.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies change using change detection metrics derived from all valid Landsat images over the present and past years. Only the first loss event that is detected is recorded.</p>
			</td>
		</tr>
	</tbody>
</table>



<h2 class="wp-block-heading">What do the UMD and JRC data show about forest disturbances in the tropics?</h2>



<p>Overall, the JRC data detects a larger area of disturbances across the tropics from 2001-2022 compared with the UMD data. The JRC data detects 258 Mha of disturbances, including 113 Mha of degradation and 145 Mha of deforestation, while the UMD data detects 143 Mha of tree cover loss. However, trends in total deforestation mapped by JRC and tree cover loss mapped by UMD are generally aligned: They are largely similar for the first decade; then from 2013-2015, JRC detects a larger area as deforestation than UMD detects as loss, and after 2016, UMD detects a larger area.</p>



<div class="flourish-embed" data-src="story/2112582?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>We further separated out the disturbance types mapped by the JRC data and overlaid the data sets to evaluate how the two data sets compare at each pixel.</p>



<p>Among all the JRC data’s disturbance classes, the JRC direct deforestation class aligned most closely with UMD tree cover loss. Across the tropics from 2001-2022, 46.8 Mha, or 68%, of the area classified as direct deforestation by the JRC data was also classified as tree cover loss in the UMD data. This varied by region and was highest in Asia/Oceania and the Americas (66% and 75% respectively) and lowest in Africa (50%). The UMD data less often classified areas that JRC identified as deforestation after degradation as tree cover loss (38.1 Mha, or 50% across the tropics).</p>



<p>In some areas, disturbances classified as degradation by the JRC TMF data set were classified as tree cover loss by the UMD data. Across the tropics from 2001-2022, 32.6 Mha, or 29%, of the area the JRC data classified as degradation was also classified as tree cover loss in the UMD data. The JRC degradation class includes three types of degradation: short-duration (less than one year), long-duration (1-2.5 years) and short-duration over 2-3 periods (short-duration degradation that occurs two to three separate times throughout the time period). The JRC data also measures “intensity” of degradation based on the total number of Landsat images where disruptions in tree cover were visible. We found that, in general, the UMD data:</p>



<ul>
<li>More often detected longer duration degradation as tree cover loss. Short-duration degradation (including events occurring just once or over 2-3 periods), which represents 85% of the forest area mapped as degraded by JRC since 2001, is usually not shown as tree cover loss by the UMD data.</li>



<li>Classified degradation events as tree cover loss that had an intensity of greater than six images on average, while those UMD did not classify as loss had approximately three images or less on average.</li>



<li>Detected a larger proportion of the area JRC classified as degradation from 2015 onwards, which may be <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">due to the algorithm changes</a> in the UMD data that increase forest loss detection sensitivity in case of fires, selective logging and other disturbance types that JRC classified as degradation.</li>
</ul>



<p>In other areas across the tropics, the UMD data detected tree cover loss for 6.4 Mha, or 7%, of areas where there the JRC data detected no disturbance events.</p>



<figure class="wp-block-image size-full is-resized"><img decoding="async" loading="lazy" width="1222" height="1163" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png" alt="Comparison of JRC TMF and UMD tree cover loss disturbances across the tropics" class="wp-image-16572" style="aspect-ratio:1.0507308684436802;width:771px;height:auto" srcset="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png 1222w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table-1024x975.png 1024w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table-768x731.png 768w" sizes="(max-width: 1222px) 100vw, 1222px" /></figure>



<h4 class="wp-block-heading">Forest disturbances related to shifting cultivation in Mongala, DRC</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16055613?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h4 class="wp-block-heading">Large scale deforestation along BR-230 in Amazonas, Brazil</h4>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16055698?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h4 class="wp-block-heading">Selective logging in Pará, Brazil</h4>



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<h2 class="wp-block-heading">Why does the UMD and JRC data align in some ways and differ in others?</h2>



<p>The two data sets align more closely for more abrupt disturbances that are likely to meet the definitions used in both data sets, such as intense fires (which the JRC data may classify as long-duration degradation) or clearance of tree cover for agricultural expansion (usually classified as deforestation in the JRC data). The two data sets are less likely to detect the same disturbance when it is followed by a rapid recovery (such as short-duration degradation in the JRC data, including disturbances like drought, selective logging or low-intensity fires, which may only be detected in as few as one Landsat image) or disturbances resulting in gradual reduction of tree cover over a longer time period (such as degradation followed by deforestation in the JRC data). In these cases, the disturbance may not meet the UMD definition of tree cover loss and therefore is less likely to be detected in the UMD data.</p>



<p>Both data sets have different strengths. The UMD data is global in scope, allowing for comparative analysis across regions due to global consistency in definitions and methods. Within the tropics, the UMD data includes broader coverage of forest types beyond tropical moist forests, including tropical dry forests.<a href="https://gfw.global/3GEmSQh"></a> The UMD data also provides flexibility for users to adjust the canopy density threshold according to their preference or needs, which can be used to align with a specific forest definition or forest characteristics of a specific biome.</p>



<p>Meanwhile, the JRC data detects degradation and less intense disturbances, which can be helpful for users who want to understand where forests have experienced various types of stresses that have not resulted in complete loss. The JRC data also monitors forests after initial disturbances to track forest recovery and subsequent disturbances, which is helpful for users who would like to understand yearly change dynamics and identify where there are repeated disturbances.</p>



<p>However, it is important to note that we are not evaluating the accuracy of either data set. Assessing which data set is more accurate for a specific use case or geography would require a formal validation exercise.</p>



<p>Ultimately, these two data sets present complementary information that can help us better understand the various types of changes occurring in tropical forests.</p>



<hr> 

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

Note: This blog investigates the JRC TMF map, which is a separate product from the JRC global map of forest cover for year 2020, released on December 7, 2023.

 

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Differences Between Global Forest Watch’s Tree Cover Loss Data and JRC’s Tropical Moist Forest Data Explained

<p>The wide availability of forest and land cover maps based on satellite data provides more information on the world’s forests than ever before. However, this plethora of information can also be challenging for people trying to understand differences in data sources and decide which data to use for a specific purpose.</p>



<p>In the past, we’ve compared the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a> from the University of Maryland (UMD) and available on Global Forest Watch (GFW) with national data from <a href="https://www.globalforestwatch.org/blog/data-and-research/2022-tree-cover-loss-data-explained/">Brazil and Indonesia</a>, as well as with data from the United Nations Food and Agriculture Organization’s (FAO) <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 Resources Assessment</a>, to shed light on some of these differences.</p>



<p>Another data set that tracks forest change in the tropics is the <a href="https://forobs.jrc.ec.europa.eu/TMF" target="_blank" rel="noreferrer noopener">Tropical Moist Forest (TMF) data set</a> from the European Commission Joint Research Centre (JRC). Like the UMD tree cover loss data, the JRC TMF data is based on Landsat satellite imagery and is updated annually. Yet while both data sets map forest disturbances, the maps and numbers look a little different.</p>



<p>In this blog, we explain some key differences in the definitions and methods used for each data set that impact the resulting maps and discuss how the disturbances mapped by both data sets compare (while the data sets also map forest gain or regrowth, this blog focuses on comparing their mapping of forest disturbances). While understanding these differences can help determine the contexts for which each data set might be useful, note that this analysis does not evaluate the quality or accuracy of either data set.</p>



<h2 class="wp-block-heading">What areas do the JRC and UMD data sets cover, and how do they define forests?</h2>



<p>The UMD data set maps forests globally based on <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">tree cover extent</a> in 2000. <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">Tree cover</a> is defined as woody vegetation with a minimum height of at least five meters at 30-meter resolution, which includes all types of tree cover — including natural or managed forest and plantations — and is not limited to a specific forest type. The GFW map shows tree cover with a 30% canopy density, which we use in our analyses and in this comparison, although users can adjust the threshold based on their needs.</p>



<p>The JRC data set, on the other hand, <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">maps forests</a> beginning in 1990. The data includes all closed primary and secondary forests in the humid tropics and does not apply a minimum height or canopy density threshold to delineate forest extent.</p>



<p>As a result of these definitional differences, some areas included in the 2000 UMD tree cover extent in the tropics like dry forests, young secondary forests and plantations are not classified as forests in the JRC data set in 2000 — a total of 655 million hectares (Mha), or 34%, of the UMD tree cover extent area that overlaps with the JRC data. Likewise, the JRC forest extent in 2000 includes some areas that do not meet the height and 30% canopy density threshold in the UMD data or are otherwise not mapped as tree cover by the UMD data — 17.2 Mha, or 1% of the JRC forest extent.</p>



<p>Because of the differences in geographic coverage and scope between the two data sets, this blog only compares forest disturbances from 2001-2022 for areas that, in the year 2000, are mapped both as tree cover by UMD and as forest by JRC.</p>



<h2 class="wp-block-heading">What types of forest disturbances do the UMD and JRC data map?</h2>



<h3 class="wp-block-heading"><em>The JRC Tropical Moist Forest data set maps degradation and deforestation in the humid tropics</em></h3>



<p>The JRC TMF data set maps disturbances to tropical moist forests — including degradation and deforestation — from 1990 to 2022. The JRC team maps these changes using an algorithm that detects forest disruptions, meaning an absence of tree foliage cover within a 30-meter pixel for each available Landsat image, and then classifies the status of forest based on how long these disruptions are visible.</p>



<p>The TMF data set <a href="https://www.science.org/doi/10.1126/sciadv.abe1603" target="_blank" rel="noreferrer noopener">defines degradation</a> as temporary disturbances occurring for up to 2.5 years, while it defines deforestation as longer-term disturbances where disruptions to tree cover are visible for more than 2.5 years and no tree regrowth is detected over the last three years. In any given year, a forest is considered disturbed if there is a disruption detected in at least one Landsat image during that year.</p>



<p>Thus, pixels classified as degraded are still considered forest, with existing or regrowing trees, while pixels classified as deforested are undergoing a longer-term loss of forest. Because disruptions are recorded and analyzed for the entire time series, the data set classifies dynamics over time; for example, a forest pixel may be classified as degraded in one year after a short-term disturbance, then after a period of recovery may later be classified as deforested after ongoing disturbances with no tree regrowth. The JRC deforestation class therefore includes direct deforestation (deforestation of undisturbed forest) or deforestation after degradation (deforestation of forest that was previously classified as degraded).</p>



<p>The data set also analyzes the frequency, duration of disruptions and time of forest recovery in between disruption events to identify deforestation following degradation versus multiple degradation events.</p>



<h3 class="wp-block-heading"><em>The UMD tree cover loss data set on GFW maps all types of tree cover loss globally</em></h3>



<p>The UMD tree cover loss data set maps annual tree cover loss from 2001 to 2022. The UMD team uses an algorithm that detects loss using change detection metrics based on comparison of Landsat images between the present and past years. Tree cover loss, <a href="https://www.science.org/doi/10.1126/science.1244693" target="_blank" rel="noreferrer noopener">defined as</a> a clearing of tree cover of at least a half of a 30-meter Landsat pixel, includes both temporary and permanent losses without defining or differentiating which loss is considered degradation or deforestation. Once classified as loss, pixels are masked from further updates, meaning that any following forest change dynamics — such as regrowth or subsequent disturbances — are not recorded.</p>



<p>The UMD tree cover loss data can be combined with contextual data layers, such as <a href="https://gfw.global/3kqcoqa">UMD’s map of humid tropical primary forest</a>, to see loss within specific forest types.</p>



<h4 class="wp-block-heading">Key differences between the JRC TMF and UMD tree cover loss data</h4>



<table border="1" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td style="width:165px;height:20px;">
			<p>&nbsp;</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>JRC TMF</strong></p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>UMD tree cover loss</strong></p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Temporal coverage</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>1990-2022</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>&nbsp;2000-2022</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Geographic coverage and extent</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Tropical moist forest:</strong> closed primary and secondary forests in the humid tropics in 1990.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p><strong>Global tree cover: </strong>woody vegetation with a minimum height of at least five meters at 30-meter resolution in 2000.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Definition of disturbance</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>An absence of tree foliage cover within a 30-meter pixel.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Clearing of at least half of tree cover within a 30-meter pixel.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Disturbance classes</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Disturbances are classified as <strong>degradation </strong>if they are visible for less than 2.5 years, and <strong>deforestation </strong>if they are visible for more than 2.5 years with no vegetative regrowth over the last three years.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>All disturbances classified as <strong>tree cover loss. </strong>The length of disturbance is not considered.</p>
			</td>
		</tr>
		<tr>
			<td style="width:165px;height:20px;">
			<p>Methods</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies valid single-date Landsat images and derives disturbance classes by analyzing the single-date classifications. Dynamics of disturbance events over time are recorded.</p>
			</td>
			<td style="width:320px;height:20px;">
			<p>Classifies change using change detection metrics derived from all valid Landsat images over the present and past years. Only the first loss event that is detected is recorded.</p>
			</td>
		</tr>
	</tbody>
</table>



<h2 class="wp-block-heading">What do the UMD and JRC data show about forest disturbances in the tropics?</h2>



<p>Overall, the JRC data detects a larger area of disturbances across the tropics from 2001-2022 compared with the UMD data. The JRC data detects 258 Mha of disturbances, including 113 Mha of degradation and 145 Mha of deforestation, while the UMD data detects 143 Mha of tree cover loss. However, trends in total deforestation mapped by JRC and tree cover loss mapped by UMD are generally aligned: They are largely similar for the first decade; then from 2013-2015, JRC detects a larger area as deforestation than UMD detects as loss, and after 2016, UMD detects a larger area.</p>



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<p>We further separated out the disturbance types mapped by the JRC data and overlaid the data sets to evaluate how the two data sets compare at each pixel.</p>



<p>Among all the JRC data’s disturbance classes, the JRC direct deforestation class aligned most closely with UMD tree cover loss. Across the tropics from 2001-2022, 46.8 Mha, or 68%, of the area classified as direct deforestation by the JRC data was also classified as tree cover loss in the UMD data. This varied by region and was highest in Asia/Oceania and the Americas (66% and 75% respectively) and lowest in Africa (50%). The UMD data less often classified areas that JRC identified as deforestation after degradation as tree cover loss (38.1 Mha, or 50% across the tropics).</p>



<p>In some areas, disturbances classified as degradation by the JRC TMF data set were classified as tree cover loss by the UMD data. Across the tropics from 2001-2022, 32.6 Mha, or 29%, of the area the JRC data classified as degradation was also classified as tree cover loss in the UMD data. The JRC degradation class includes three types of degradation: short-duration (less than one year), long-duration (1-2.5 years) and short-duration over 2-3 periods (short-duration degradation that occurs two to three separate times throughout the time period). The JRC data also measures “intensity” of degradation based on the total number of Landsat images where disruptions in tree cover were visible. We found that, in general, the UMD data:</p>



<ul>
<li>More often detected longer duration degradation as tree cover loss. Short-duration degradation (including events occurring just once or over 2-3 periods), which represents 85% of the forest area mapped as degraded by JRC since 2001, is usually not shown as tree cover loss by the UMD data.</li>



<li>Classified degradation events as tree cover loss that had an intensity of greater than six images on average, while those UMD did not classify as loss had approximately three images or less on average.</li>



<li>Detected a larger proportion of the area JRC classified as degradation from 2015 onwards, which may be <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">due to the algorithm changes</a> in the UMD data that increase forest loss detection sensitivity in case of fires, selective logging and other disturbance types that JRC classified as degradation.</li>
</ul>



<p>In other areas across the tropics, the UMD data detected tree cover loss for 6.4 Mha, or 7%, of areas where there the JRC data detected no disturbance events.</p>



<figure class="wp-block-image size-full is-resized"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2024/01/Comparison-JRC-TMF-and-UMD-table.png" alt="Comparison of JRC TMF and UMD tree cover loss disturbances across the tropics" class="wp-image-16572" style="aspect-ratio:1.0507308684436802;width:771px;height:auto"/></figure>



<h4 class="wp-block-heading">Forest disturbances related to shifting cultivation in Mongala, DRC</h4>



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<h4 class="wp-block-heading">Large scale deforestation along BR-230 in Amazonas, Brazil</h4>



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<h4 class="wp-block-heading">Selective logging in Pará, Brazil</h4>



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<h2 class="wp-block-heading">Why does the UMD and JRC data align in some ways and differ in others?</h2>



<p>The two data sets align more closely for more abrupt disturbances that are likely to meet the definitions used in both data sets, such as intense fires (which the JRC data may classify as long-duration degradation) or clearance of tree cover for agricultural expansion (usually classified as deforestation in the JRC data). The two data sets are less likely to detect the same disturbance when it is followed by a rapid recovery (such as short-duration degradation in the JRC data, including disturbances like drought, selective logging or low-intensity fires, which may only be detected in as few as one Landsat image) or disturbances resulting in gradual reduction of tree cover over a longer time period (such as degradation followed by deforestation in the JRC data). In these cases, the disturbance may not meet the UMD definition of tree cover loss and therefore is less likely to be detected in the UMD data.</p>



<p>Both data sets have different strengths. The UMD data is global in scope, allowing for comparative analysis across regions due to global consistency in definitions and methods. Within the tropics, the UMD data includes broader coverage of forest types beyond tropical moist forests, including tropical dry forests.<a href="https://gfw.global/3GEmSQh"></a> The UMD data also provides flexibility for users to adjust the canopy density threshold according to their preference or needs, which can be used to align with a specific forest definition or forest characteristics of a specific biome.</p>



<p>Meanwhile, the JRC data detects degradation and less intense disturbances, which can be helpful for users who want to understand where forests have experienced various types of stresses that have not resulted in complete loss. The JRC data also monitors forests after initial disturbances to track forest recovery and subsequent disturbances, which is helpful for users who would like to understand yearly change dynamics and identify where there are repeated disturbances.</p>



<p>However, it is important to note that we are not evaluating the accuracy of either data set. Assessing which data set is more accurate for a specific use case or geography would require a formal validation exercise.</p>



<p>Ultimately, these two data sets present complementary information that can help us better understand the various types of changes occurring in tropical forests.</p>



<hr> 

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

Note: This blog investigates the JRC TMF map, which is a separate product from the JRC global map of forest cover for year 2020, released on December 7, 2023.

 

</div> 

 

<hr>

We explain key differences between two data sets that track forest change in the tropics: UMD tree cover loss and JRC Tropical Moist Forest.

Tree Cover Loss and Tropical Moist Forest Differences, Explained

Trees outside of dense forests in Ethiopia

Jessica Ertel

Justine Spore

John Brandt

<p>The emergence of new Earth observation data has radically improved our ability to map tree cover, while automated methods and lower computing costs have accelerated the production of data sets to inform our understanding of the global distribution of trees. Global Forest Watch (GFW) hosts three tree cover data sets, including World Resources Institute’s (WRI) newly updated Tropical Tree Cover data (formerly called Trees in Mosaic Landscapes).</p>



<p>These data sets vary in geographic and temporal coverage, resolution, how they define tree cover and other characteristics that can make each best suited for different use cases. Here, we explain these differences and how all three data sets provide valuable information on the extent of forests across the tropics and around the globe.&nbsp;</p>



<h2 class="wp-block-heading">What tree cover data sets are available on GFW?</h2>



<table>
	<tbody>
		<tr>
			<td rowspan="2" width="13%">
			<p><small><strong>Data Set&nbsp;</strong></small></p>
			</td>
			<td rowspan="2" width="11%">
			<p style="text-align: center;"><small><strong>Source</strong></small></p>
			</td>
			<td rowspan="2" width="14%">
			<p><small><strong>Geographic Coverage</strong></small></p>
			</td>
			<td rowspan="2" width="10%">
			<p style="text-align: center;"><small><strong>Spatial Resolution</strong></small></p>
			</td>
			<td rowspan="2" width="12%">
			<p style="text-align: center;"><small><strong>Years of Coverage</strong></small></p>
			</td>
			<td rowspan="2" width="12%">
			<p style="text-align: center;"><small><strong>Variable</strong></small></p>
			</td>
			<td colspan="2">
			<p style="text-align: center;"><small><strong>Tree Definition&nbsp;</strong></small></p>
			</td>
		</tr>
		<tr>
			<td width="14%">
			<p style="text-align: center;"><small><strong>Height criteria</strong></small></p>
			</td>
			<td width="14%">
			<p style="text-align: center;"><small><strong>Canopy Criteria</strong></small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tree Canopy Cover</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.science.org/doi/10.1126/science.1244693" style="color: rgb(7, 130, 193);">Hansen et al., 2013</a></small></p>
			</td>
			<td>
			<p><small>Global</small></p>
			</td>
			<td>
			<p><small>30m</small></p>
			</td>
			<td>
			<p><small>2000, 2010</small></p>
			</td>
			<td>
			<p><small>Tree cover (%)</small></p>
			</td>
			<td>
			<p><small>All vegetation ≥5m in height.</small></p>
			</td>
			<td>
			<p><small>n/a</small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tree Cover Height</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" style="color: rgb(7, 130, 193);">Potapov et al., 2020</a></small></p>
			</td>
			<td>
			<p><small>Global</small></p>
			</td>
			<td>
			<p><small>30m</small></p>
			</td>
			<td>
			<p><small>2000,&nbsp; 2020</small></p>
			</td>
			<td>
			<p><small>Tree height (m)</small></p>
			</td>
			<td>
			<p><small>Woody vegetation ≥3m in height</small></p>
			</td>
			<td>
			<p><small>20-25%&nbsp;</small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tropical Tree Cover*</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.researchsquare.com/article/rs-2109093/v1" style="color: rgb(7, 130, 193);">Brandt et al., 2023</a></small></p>
			</td>
			<td>
			<p><small>Tropics</small></p>
			</td>
			<td>
			<p><small>10m</small></p>
			</td>
			<td>
			<p><small>2020</small></p>
			</td>
			<td>
			<p><small>Tree extent&nbsp;</small></p>
			</td>
			<td>
			<p><small>Woody vegetation ≥5m in height, or ≥3m in height with canopy requirement.</small></p>
			</td>
			<td>
			<p><small>Woody vegetation 3-5m&nbsp;in height must have a ≥5m crown diameter.</small></p>
			</td>
		</tr>
	</tbody>
</table>



<p style="font-size: small"><i>* This data set is available as tree extent at 10 m resolution or tree cover percent at 0.5 ha resolution</i></p>



<h3 class="wp-block-heading">Tree Canopy Cover</h3>



<p>In 2013, experts at the University of Maryland’s (UMD) GLAD Lab published the <a href="https://gfw.global/3K5tHxc"><strong>Tree Canopy Cover</strong></a> data set, which maps tree canopy cover across the globe at a 30-meter resolution and is available on GFW for the years 2000 and 2010. This data set was among the first efforts to map tree cover at a global scale using medium resolution imagery. Furthermore, it established a baseline for the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a>; these data sets together served as the basis for the launch of <a href="https://www.globalforestwatch.org/blog/people/global-forest-watch-join-the-movement/">Global Forest Watch in 2014</a>.</p>



<p>The Tree Canopy Cover layer estimates <em>percent tree cover</em>, which measures the density of tree canopy cover within a 30-meter pixel. The data set defines trees as all vegetation of at least five meters in height and is derived from Landsat imagery. &nbsp;</p>



<h3 class="wp-block-heading">Tree Cover Height</h3>



<p>In 2021, experts at the UMD’s GLAD Lab published the <a href="https://gfw.global/3tkvuCt"><strong>Tree Cover Height</strong></a> data set, which maps tree cover height across the globe at a 30-meter resolution and is available on GFW for the years 2000 and 2020. This data set allows for improved detection of tree cover gain and serves as the basis for the <a href="https://www.globalforestwatch.org/blog/data-and-research/new-gfw-tree-cover-gain-net-change-data/">20-year net change data</a> on GFW. Unlike tree canopy cover, which reaches its maximum soon after tree growth begins, tree height changes gradually and is linearly related to forest biomass and carbon storage. &nbsp;</p>



<p>The Tree Cover Height layer estimates <em>tree canopy height</em>, which refers to the height of the tree canopy in meters within a 30-meter pixel. The data set defines trees as woody vegetation of at least three meters in height and is derived from Global Ecosystem Dynamics Investigation (GEDI) LiDAR measurements and Landsat imagery. This data set estimates the 95<sup>th</sup> percentile of tree canopy height, and accordingly will represent the upper bounds of height for pixels with fractional tree cover, or pixels with variable canopy height.</p>



<h3 class="wp-block-heading">Tropical Tree Cover</h3>



<p>This year, WRI updated the <strong><a href="https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6InRyb3BpY2FsLXRyZWUtY292ZXIiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJvcGljYWwtdHJlZS1jb3Zlci1tZXRlcnMiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZENvdmVyIn0%3D">Tropical Tree Cover</a></strong> data set (now available for download), which maps tree cover across the tropics at a 10-meter resolution and is available on GFW for the year 2020. Unlike the other data sets, the Tropical Tree Cover data set is based on imagery from Sentinel satellites, which increased the resolution of freely available remote sensing data to 10 meters when launched in 2015. This data set capitalized on the advancement in resolution to improve tree cover detection in non-forested areas, especially in drylands, urban areas and on cropland, and our ability to monitor trees at small spatial (local) scales.</p>



<p>The Tropical Tree Cover estimates <em>tree extent</em>, which refers to the probability that one or more tree canopies intersect the middle point of a 10-meter pixel (on GFW, we display the tree extent data with a probability threshold of 40%). “Percent tree cover” is derived by averaging tree extent probabilities at a half-hectare scale. Both the 10-meter and the half-hectare versions of the Tropical Tree Cover data set are available to users on GFW. The Tropical Tree Cover data set defines trees as woody vegetation greater than five meters in height, or between three to five meters with a minimum canopy diameter of five meters. The data is derived from Sentinel 1 and Sentinel 2 imagery. <a href="https://www.globalforestwatch.org/help/map/additional-materials/the-tropical-tree-cover-ttc-data-faqs/">Visit the Help Center</a> to learn more about Tropical Tree Cover.</p>



<h2 class="wp-block-heading">How do these data sets differ and what does this mean in practice?</h2>



<h3 class="wp-block-heading">Differences in Spatial Resolution &nbsp;</h3>



<p>The Tropical Tree Cover data set has a higher spatial resolution at 10-meters, while both the Tree Canopy Cover and Tree Cover Height data sets are 30-meter resolution. At 30-meter resolution, it can be difficult to differentiate small patches of trees, making the Tropical Tree Cover data set more appropriate for monitoring tree cover in areas with disperse tree cover, including dryland regions such as the Sahel, forest perimeters and riparian zones, as well as natural trees in agricultural and urban areas. These differences in resolution will result in different estimates of tree cover in an area of interest since trees that are undetected at 30-meter resolution may be identified in at 10-meter resolution.</p>



<h4 class="wp-block-heading">Tree cover detection at 10 and 30-meter resolution</h4>



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<h3 class="wp-block-heading">Differences in Temporal and Geographic Coverage</h3>



<p>A key difference between the three data sets is their annual availability and geographic coverage. The Tree Canopy Cover data set is available for the years 2000 and 2010. The Tree Cover Height data set, originally produced for the year 2019, is now updated and available on GFW for the years 2000 and 2020. The Tropical Tree Cover data set is currently limited to the year 2020. Given the longer historical availability of the Landsat archive, users may wish to prioritize historical record (Tree Canopy Cover and Tree Cover Height) over resolution (Tropical Tree Cover).</p>



<p>For geographic scope, the Tropical Tree Cover data set only covers the tropics, while the Tree Cover Height and Tree Canopy Cover data sets have global coverage. Therefore, the Tree Cover Height and Tree Canopy Cover data sets should be used for applications in temperate and boreal forests, while the Tropical Tree Cover data set provides a higher resolution look at tropical forests.</p>



<h3 class="wp-block-heading">Differences in Change Data</h3>



<p>The Tree Canopy Cover and Tree Cover Height data sets are used as the basis of GFW’s forest change data sets since they are available for multiple years.</p>



<p>The Tree Canopy Cover data for the year 2000 can be used as the baseline for GFW’s annual tree cover loss data — tree cover loss is calculated for various tree canopy density thresholds in the year 2000 to meet definitions of forest that vary by tree canopy density. GFW presents tree cover loss statistics and maps based on a minimum canopy density of 30% in the year 2000, but users can modify that threshold to best suit their purposes. The Tree Cover Height data serves as the baseline for the 20-year tree cover gain and net change data sets available on GFW.</p>



<p>Currently, Tropical Tree Cover is a static map for 2020 and cannot be used to directly assess change, but WRI is planning to release an annual Tropical Tree Cover product on GFW in the future.</p>



<p>Users whose primary interest is measuring tree cover change should use the baseline data set associated with the relevant forest change product to avoid any mismatches in definitions and methods.</p>



<h3 class="wp-block-heading">Differences in Tree Definitions</h3>



<p>While the Tree Canopy Cover and Tree Cover Height data sets are based on minimum height criteria, the Tropical Tree Cover data set has a variable height requirement of three to five meters based on canopy diameter. This means that the Tropical Tree Cover data set excludes tall herbaceous vegetation such as sugarcane, bananas and cacti that are considered trees in the other data sets.</p>



<p>For users who are measuring tree cover in areas with large presence of these crops, the area of tree cover detected by the three data sets will differ greatly. It is important to note that the Tree Canopy Cover and Tree Cover Height data sets do not measure forest area using the same criteria as National Forest Inventories or the Food and Agriculture Organization’s Forest Resource Assessment (FRA). <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Read our blog</a> to learn more about how the FRA measures forest versus how GFW measures tree cover, and about other differences in purpose, scope and approach between these data sets.</p>



<h2 class="wp-block-heading">Which data set should I use for my specific application?</h2>



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<h2 class="wp-block-heading">How will these data sets be updated in the future?</h2>



<p>Moving forward, UMD will publish annual integrated tree canopy structure data sets that will enable users to assess annual changes in canopy height and cover.</p>



<p>WRI is currently developing annual Tropical Tree Cover data for 2017-2022, which will allow users to analyze tree cover change across the tropics at the 10-meter scale. WRI is also working on additional pilot projects to improve detection of tree crops and derive carbon estimations for small scale restoration projects.</p>



<p>The composition, height and density of forests vary widely from place to place. Depending on the area, timeframe and specific forest type, different tree cover data sets may be better suited to meet your goal. Remote sensing data can help us monitor a variety of forest attributes using new advances in satellite technology and processing methodologies. This results in a variety of data sets that can complement each other in informing our understanding of the global distribution of trees and provide unique opportunities to support restoration and deforestation monitoring.</p>



<p><em>To stay up to date on the latest Tropical Tree Cover news, please <a href="https://www.wri.org/subscribe-tropical-tree-cover-ttc-updates" target="_blank" rel="noreferrer noopener">register for the Tropical Tree Cover mailing list</a>.&nbsp;</em></p>



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Tree Cover Data Sets on Global Forest Watch, Explained

<p>The emergence of new Earth observation data has radically improved our ability to map tree cover, while automated methods and lower computing costs have accelerated the production of data sets to inform our understanding of the global distribution of trees. Global Forest Watch (GFW) hosts three tree cover data sets, including World Resources Institute’s (WRI) newly updated Tropical Tree Cover data (formerly called Trees in Mosaic Landscapes).</p>



<p>These data sets vary in geographic and temporal coverage, resolution, how they define tree cover and other characteristics that can make each best suited for different use cases. Here, we explain these differences and how all three data sets provide valuable information on the extent of forests across the tropics and around the globe.&nbsp;</p>



<h2 class="wp-block-heading">What tree cover data sets are available on GFW?</h2>



<table>
	<tbody>
		<tr>
			<td rowspan="2" width="13%">
			<p><small><strong>Data Set&nbsp;</strong></small></p>
			</td>
			<td rowspan="2" width="11%">
			<p style="text-align: center;"><small><strong>Source</strong></small></p>
			</td>
			<td rowspan="2" width="14%">
			<p><small><strong>Geographic Coverage</strong></small></p>
			</td>
			<td rowspan="2" width="10%">
			<p style="text-align: center;"><small><strong>Spatial Resolution</strong></small></p>
			</td>
			<td rowspan="2" width="12%">
			<p style="text-align: center;"><small><strong>Years of Coverage</strong></small></p>
			</td>
			<td rowspan="2" width="12%">
			<p style="text-align: center;"><small><strong>Variable</strong></small></p>
			</td>
			<td colspan="2">
			<p style="text-align: center;"><small><strong>Tree Definition&nbsp;</strong></small></p>
			</td>
		</tr>
		<tr>
			<td width="14%">
			<p style="text-align: center;"><small><strong>Height criteria</strong></small></p>
			</td>
			<td width="14%">
			<p style="text-align: center;"><small><strong>Canopy Criteria</strong></small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tree Canopy Cover</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.science.org/doi/10.1126/science.1244693" style="color: rgb(7, 130, 193);">Hansen et al., 2013</a></small></p>
			</td>
			<td>
			<p><small>Global</small></p>
			</td>
			<td>
			<p><small>30m</small></p>
			</td>
			<td>
			<p><small>2000, 2010</small></p>
			</td>
			<td>
			<p><small>Tree cover (%)</small></p>
			</td>
			<td>
			<p><small>All vegetation ≥5m in height.</small></p>
			</td>
			<td>
			<p><small>n/a</small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tree Cover Height</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" style="color: rgb(7, 130, 193);">Potapov et al., 2020</a></small></p>
			</td>
			<td>
			<p><small>Global</small></p>
			</td>
			<td>
			<p><small>30m</small></p>
			</td>
			<td>
			<p><small>2000,&nbsp; 2020</small></p>
			</td>
			<td>
			<p><small>Tree height (m)</small></p>
			</td>
			<td>
			<p><small>Woody vegetation ≥3m in height</small></p>
			</td>
			<td>
			<p><small>20-25%&nbsp;</small></p>
			</td>
		</tr>
		<tr>
			<td>
			<p><small><strong>Tropical Tree Cover*</strong></small></p>
			</td>
			<td>
			<p><small><a href="https://www.researchsquare.com/article/rs-2109093/v1" style="color: rgb(7, 130, 193);">Brandt et al., 2023</a></small></p>
			</td>
			<td>
			<p><small>Tropics</small></p>
			</td>
			<td>
			<p><small>10m</small></p>
			</td>
			<td>
			<p><small>2020</small></p>
			</td>
			<td>
			<p><small>Tree extent&nbsp;</small></p>
			</td>
			<td>
			<p><small>Woody vegetation ≥5m in height, or ≥3m in height with canopy requirement.</small></p>
			</td>
			<td>
			<p><small>Woody vegetation 3-5m&nbsp;in height must have a ≥5m crown diameter.</small></p>
			</td>
		</tr>
	</tbody>
</table>



<p style="font-size: small"><i>* This data set is available as tree extent at 10 m resolution or tree cover percent at 0.5 ha resolution</i></p>



<h3 class="wp-block-heading">Tree Canopy Cover</h3>



<p>In 2013, experts at the University of Maryland’s (UMD) GLAD Lab published the <a href="https://gfw.global/3K5tHxc"><strong>Tree Canopy Cover</strong></a> data set, which maps tree canopy cover across the globe at a 30-meter resolution and is available on GFW for the years 2000 and 2010. This data set was among the first efforts to map tree cover at a global scale using medium resolution imagery. Furthermore, it established a baseline for the <a href="https://gfw.global/3BfwQWz">annual tree cover loss data</a>; these data sets together served as the basis for the launch of <a href="https://www.globalforestwatch.org/blog/people/global-forest-watch-join-the-movement/">Global Forest Watch in 2014</a>.</p>



<p>The Tree Canopy Cover layer estimates <em>percent tree cover</em>, which measures the density of tree canopy cover within a 30-meter pixel. The data set defines trees as all vegetation of at least five meters in height and is derived from Landsat imagery. &nbsp;</p>



<h3 class="wp-block-heading">Tree Cover Height</h3>



<p>In 2021, experts at the UMD’s GLAD Lab published the <a href="https://gfw.global/3tkvuCt"><strong>Tree Cover Height</strong></a> data set, which maps tree cover height across the globe at a 30-meter resolution and is available on GFW for the years 2000 and 2020. This data set allows for improved detection of tree cover gain and serves as the basis for the <a href="https://www.globalforestwatch.org/blog/data-and-research/new-gfw-tree-cover-gain-net-change-data/">20-year net change data</a> on GFW. Unlike tree canopy cover, which reaches its maximum soon after tree growth begins, tree height changes gradually and is linearly related to forest biomass and carbon storage. &nbsp;</p>



<p>The Tree Cover Height layer estimates <em>tree canopy height</em>, which refers to the height of the tree canopy in meters within a 30-meter pixel. The data set defines trees as woody vegetation of at least three meters in height and is derived from Global Ecosystem Dynamics Investigation (GEDI) LiDAR measurements and Landsat imagery. This data set estimates the 95<sup>th</sup> percentile of tree canopy height, and accordingly will represent the upper bounds of height for pixels with fractional tree cover, or pixels with variable canopy height.</p>



<h3 class="wp-block-heading">Tropical Tree Cover</h3>



<p>This year, WRI updated the <strong><a href="https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6InRyb3BpY2FsLXRyZWUtY292ZXIiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJvcGljYWwtdHJlZS1jb3Zlci1tZXRlcnMiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&amp;mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZENvdmVyIn0%3D">Tropical Tree Cover</a></strong> data set (now available for download), which maps tree cover across the tropics at a 10-meter resolution and is available on GFW for the year 2020. Unlike the other data sets, the Tropical Tree Cover data set is based on imagery from Sentinel satellites, which increased the resolution of freely available remote sensing data to 10 meters when launched in 2015. This data set capitalized on the advancement in resolution to improve tree cover detection in non-forested areas, especially in drylands, urban areas and on cropland, and our ability to monitor trees at small spatial (local) scales.</p>



<p>The Tropical Tree Cover estimates <em>tree extent</em>, which refers to the probability that one or more tree canopies intersect the middle point of a 10-meter pixel (on GFW, we display the tree extent data with a probability threshold of 40%). “Percent tree cover” is derived by averaging tree extent probabilities at a half-hectare scale. Both the 10-meter and the half-hectare versions of the Tropical Tree Cover data set are available to users on GFW. The Tropical Tree Cover data set defines trees as woody vegetation greater than five meters in height, or between three to five meters with a minimum canopy diameter of five meters. The data is derived from Sentinel 1 and Sentinel 2 imagery. <a href="https://www.globalforestwatch.org/help/map/additional-materials/the-tropical-tree-cover-ttc-data-faqs/">Visit the Help Center</a> to learn more about Tropical Tree Cover.</p>



<h2 class="wp-block-heading">How do these data sets differ and what does this mean in practice?</h2>



<h3 class="wp-block-heading">Differences in Spatial Resolution &nbsp;</h3>



<p>The Tropical Tree Cover data set has a higher spatial resolution at 10-meters, while both the Tree Canopy Cover and Tree Cover Height data sets are 30-meter resolution. At 30-meter resolution, it can be difficult to differentiate small patches of trees, making the Tropical Tree Cover data set more appropriate for monitoring tree cover in areas with disperse tree cover, including dryland regions such as the Sahel, forest perimeters and riparian zones, as well as natural trees in agricultural and urban areas. These differences in resolution will result in different estimates of tree cover in an area of interest since trees that are undetected at 30-meter resolution may be identified in at 10-meter resolution.</p>



<h4 class="wp-block-heading">Tree cover detection at 10 and 30-meter resolution</h4>



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<h3 class="wp-block-heading">Differences in Temporal and Geographic Coverage</h3>



<p>A key difference between the three data sets is their annual availability and geographic coverage. The Tree Canopy Cover data set is available for the years 2000 and 2010. The Tree Cover Height data set, originally produced for the year 2019, is now updated and available on GFW for the years 2000 and 2020. The Tropical Tree Cover data set is currently limited to the year 2020. Given the longer historical availability of the Landsat archive, users may wish to prioritize historical record (Tree Canopy Cover and Tree Cover Height) over resolution (Tropical Tree Cover).</p>



<p>For geographic scope, the Tropical Tree Cover data set only covers the tropics, while the Tree Cover Height and Tree Canopy Cover data sets have global coverage. Therefore, the Tree Cover Height and Tree Canopy Cover data sets should be used for applications in temperate and boreal forests, while the Tropical Tree Cover data set provides a higher resolution look at tropical forests.</p>



<h3 class="wp-block-heading">Differences in Change Data</h3>



<p>The Tree Canopy Cover and Tree Cover Height data sets are used as the basis of GFW’s forest change data sets since they are available for multiple years.</p>



<p>The Tree Canopy Cover data for the year 2000 can be used as the baseline for GFW’s annual tree cover loss data — tree cover loss is calculated for various tree canopy density thresholds in the year 2000 to meet definitions of forest that vary by tree canopy density. GFW presents tree cover loss statistics and maps based on a minimum canopy density of 30% in the year 2000, but users can modify that threshold to best suit their purposes. The Tree Cover Height data serves as the baseline for the 20-year tree cover gain and net change data sets available on GFW.</p>



<p>Currently, Tropical Tree Cover is a static map for 2020 and cannot be used to directly assess change, but WRI is planning to release an annual Tropical Tree Cover product on GFW in the future.</p>



<p>Users whose primary interest is measuring tree cover change should use the baseline data set associated with the relevant forest change product to avoid any mismatches in definitions and methods.</p>



<h3 class="wp-block-heading">Differences in Tree Definitions</h3>



<p>While the Tree Canopy Cover and Tree Cover Height data sets are based on minimum height criteria, the Tropical Tree Cover data set has a variable height requirement of three to five meters based on canopy diameter. This means that the Tropical Tree Cover data set excludes tall herbaceous vegetation such as sugarcane, bananas and cacti that are considered trees in the other data sets.</p>



<p>For users who are measuring tree cover in areas with large presence of these crops, the area of tree cover detected by the three data sets will differ greatly. It is important to note that the Tree Canopy Cover and Tree Cover Height data sets do not measure forest area using the same criteria as National Forest Inventories or the Food and Agriculture Organization’s Forest Resource Assessment (FRA). <a href="https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/">Read our blog</a> to learn more about how the FRA measures forest versus how GFW measures tree cover, and about other differences in purpose, scope and approach between these data sets.</p>



<h2 class="wp-block-heading">Which data set should I use for my specific application?</h2>



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<h2 class="wp-block-heading">How will these data sets be updated in the future?</h2>



<p>Moving forward, UMD will publish annual integrated tree canopy structure data sets that will enable users to assess annual changes in canopy height and cover.</p>



<p>WRI is currently developing annual Tropical Tree Cover data for 2017-2022, which will allow users to analyze tree cover change across the tropics at the 10-meter scale. WRI is also working on additional pilot projects to improve detection of tree crops and derive carbon estimations for small scale restoration projects.</p>



<p>The composition, height and density of forests vary widely from place to place. Depending on the area, timeframe and specific forest type, different tree cover data sets may be better suited to meet your goal. Remote sensing data can help us monitor a variety of forest attributes using new advances in satellite technology and processing methodologies. This results in a variety of data sets that can complement each other in informing our understanding of the global distribution of trees and provide unique opportunities to support restoration and deforestation monitoring.</p>



<p><em>To stay up to date on the latest Tropical Tree Cover news, please <a href="https://www.wri.org/subscribe-tropical-tree-cover-ttc-updates" target="_blank" rel="noreferrer noopener">register for the Tropical Tree Cover mailing list</a>.&nbsp;</em></p>



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Explicación de los conjuntos de datos sobre la cubierta arbórea de Global Forest Watch

<p>O surgimento de novos dados de observação da Terra melhorou radicalmente nossa capacidade de mapear a cobertura de árvores, enquanto métodos automatizados e custos de computação mais baixos aceleraram a produção de conjuntos de dados para informar nossa compreensão da distribuição global de árvores. O Global Forest Watch (GFW) oferece três conjuntos de dados de cobertura de árvores, incluindo os dados atualizados do Tropical Tree Cover (cobertura de árvores tropicais) do World Resources Institute (WRI), anteriormente chamado de Trees in Mosaic Landscapes (árvores em paisagens-mosaico).&nbsp;&nbsp;</p>



<p>Esses conjuntos de dados variam em cobertura geográfica, temporal e resolução, como definem a cobertura de árvores e outras características que podem tornar cada um mais adequado para diferentes casos de uso. Aqui, explicamos essas diferenças e como todos os três conjuntos de dados fornecem informações valiosas sobre a extensão das florestas nos trópicos e ao redor do globo. &nbsp;</p>



<h2 class="wp-block-heading">Quais conjuntos de dados de cobertura de árvores estão disponíveis no GFW?&nbsp;</h2>



<table>
    <tbody>
        <tr>
            <td rowspan="2" width="14%">
                <p style="text-align: center;"><strong>Conjunto de dados</strong></p>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Fuente</strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Cobertura geográfica </strong></p>
            </td>
            <td rowspan="2" width="10%">
                <p style="text-align: center;"><strong>Resolução espacial </strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Anos de cobertura  </strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Variável</strong></p>
            </td>
            <td colspan="2">
                <p style="text-align: center;"><strong>Definição de árvore&nbsp;</strong></p>
            </td>
        </tr>
        <tr>
            <td width="14%">
                <p style="text-align: center;"><strong>Critérios de altura</strong></p>
            </td>
            <td width="14%">
                <p style="text-align: center;"><strong>Critérios de cobertura de dossel</strong></p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Cobertura de dossel</strong></p>
            </td>
            <td>
                <p><a href="https://www.science.org/doi/10.1126/science.1244693" style="color: rgb(7, 130, 193);">Hansen et al., 2013</a></p>
            </td>
            <td>
                <p>Global</p>
            </td>
            <td>
                <p>30m</p>
            </td>
            <td>
                <p>2000, 2010</p>
            </td>
            <td>
                <p>Cobertura arbórea (%) </p>
            </td>
            <td>
                <p>Toda a vegetação &ge;5m de altura. </p>
            </td>
            <td>
                <p>n/a</p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Altura da cobertura arbórea</strong></p>
            </td>
            <td>
                <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" style="color: rgb(7, 130, 193);">Potapov et al., 2020</a></p>
            </td>
            <td>
                <p>Global</p>
            </td>
            <td>
                <p>30m</p>
            </td>
            <td>
                <p>2000,&nbsp; 2020</p>
            </td>
            <td>
                <p>Altura das árvores (m)</p>
            </td>
            <td>
                <p>Vegetação lenhosa &ge;3m de altura </p>
            </td>
            <td>
                <p>20-25%&nbsp;</p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Cobertura de árvores tropicais *  </strong></p>
            </td>
            <td>
                <p><a href="https://www.researchsquare.com/article/rs-2109093/v1" style="color: rgb(7, 130, 193);">Brandt et al., 2023</a></p>
            </td>
            <td>
                <p>Trópicos</p>
            </td>
            <td>
                <p>10m</p>
            </td>
            <td>
                <p>2020</p>
            </td>
            <td>
                <p>Extensão das árvores&nbsp;</p>
            </td>
            <td>
                <p>Vegetação lenhosa de &ge;5m de altura, ou &ge;3m de altura com exigência de dossel. </p>
            </td>
            <td>
                <p>A vegetação lenhosa de 3-5m&nbsp; de altura deve ter um diâmetro de copa de &ge;5m.</p>
            </td>
        </tr>
    </tbody>
</table>



<p style="font-size: small"><i>* Este conjunto de dados está disponível como extensão de árvore em resolução de 10 m ou porcentagem de cobertura de árvore em resolução de 0,5 ha.</i></p>



<h3 class="wp-block-heading">Cobertura de dossel&nbsp;</h3>



<p>Em 2013, especialistas do Laboratório GLAD da Universidade de Maryland (UMD) publicaram o conjunto de dados <a href="https://gfw.global/3K5tHxc" target="_blank" rel="noreferrer noopener"><strong>cobertura de dossel</strong></a> que mapeia a cobertura das copas das árvores em todo o mundo com uma resolução de 30 metros e está disponível no GFW para os anos de 2000 e 2010. Este conjunto de dados foi um dos primeiros esforços para mapear a cobertura de árvores em escala global usando imagens de média resolução. Além disso, estabeleceu uma linha de base para os <a href="https://gfw.global/3BfwQWz" target="_blank" rel="noreferrer noopener">dados anuais de perda de cobertura arbórea</a>. Esses conjuntos de dados serviram de base para o <a href="https://www.globalforestwatch.org/blog/people/global-forest-watch-join-the-movement/" target="_blank" rel="noreferrer noopener">lançamento do Global Forest Watch em 2014</a>.&nbsp;&nbsp;</p>



<p>A camada cobertura de dossel estima a <em>porcentagem de cobertura arbórea</em>, que mede a densidade da cobertura de dossel com uma resolução de 30 metros. O conjunto de dados define árvores como toda a vegetação de pelo menos cinco metros de altura e é derivado de imagens Landsat.&nbsp;</p>



<h3 class="wp-block-heading">Altura da cobertura da arbórea&nbsp;</h3>



<p>Em 2021, especialistas do Laboratório GLAD da UMD publicaram o conjunto de dados <a href="https://gfw.global/3tkvuCt" target="_blank" rel="noreferrer noopener"><strong>Altura da cobertura arbórea</strong></a> que mapeia a cobertura das copas das árvores em todo o mundo com uma resolução de 30 metros e está disponível no GFW para os anos de 2000 e 2020. Este conjunto de dados permite uma melhor detecção do ganho de cobertura arbórea e serve como base para os <a href="https://www.globalforestwatch.org/blog/data-and-research/new-gfw-tree-cover-gain-net-change-data/" target="_blank" rel="noreferrer noopener">dados de mudança líquida de 20 anos</a> no GFW. Ao contrário da cobertura do dossel das árvores, que atinge seu máximo logo após o início do crescimento das árvores, a altura das árvores muda gradualmente e está linearmente relacionada à biomassa florestal e ao armazenamento de carbono.&nbsp; &nbsp;</p>



<p>A camada altura da cobertura arbórea estima a <em>altura da cobertura de dossel,</em> que se refere à altura da copa das árvores em metros com uma resolução de 30 metros. O conjunto de dados define uma árvore como vegetação lenhosa de pelo menos três metros de altura e é derivado das medições LiDAR da Global Ecosystem Dynamics Investigation (GEDI) e imagens Landsat. Este conjunto de dados estima o percentil 95 da altura da cobertura de dossel e, consequentemente, representará os limites superiores de altura para pixels com cobertura de árvore fracionada ou pixels com altura variável da copa.&nbsp;</p>



<h3 class="wp-block-heading">Cobertura de árvores tropicais</h3>



<p>Este ano, o WRI atualizou o conjunto de dados <a href="https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6InRyb3BpY2FsLXRyZWUtY292ZXIiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJvcGljYWwtdHJlZS1jb3Zlci1tZXRlcnMiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&amp;mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZENvdmVyIn0%3D" target="_blank" rel="noreferrer noopener">cobertura de árvores tropicais</a> (agora disponível para download), que mapeia a cobertura arbórea nos trópicos em uma resolução de 10 metros e está disponível no GFW para o ano de 2020. Ao contrário dos outros conjuntos de dados, o conjunto de dados cobertura de árvores tropicais é baseado em imagens dos satélites Sentinel, que aumentaram a resolução dos dados de sensoriamento remoto disponíveis gratuitamente para 10 metros quando lançados em 2015. Este conjunto de dados capitalizou o avanço na resolução para melhorar a detecção de cobertura de árvores em áreas não florestais, especialmente em terras áridas, áreas urbanas e em terras de cultivo, e nossa capacidade de monitorar árvores em pequenas escalas espaciais (locais).</p>



<p>A cobertura de árvores tropicais estima a <em>extensão das árvores</em>, que se refere à probabilidade de que uma ou mais copas das árvores interceptem o ponto médio de um pixel de 10 metros (no GFW, exibimos os dados de extensão das árvores com um limite de probabilidade de 40%). A “cobertura percentual de árvores” é derivada da média das probabilidades de extensão das árvores em uma escala de meio hectare. Ambas as versões de 10 metros e 0.5 hectare do conjunto de dados cobertura de árvores tropicais estão disponíveis para os usuários no GFW. O conjunto de dados cobertura de árvores tropicais data define as árvores como vegetação lenhosa com mais de cinco metros de altura, ou entre três a cinco metros com um diâmetro mínimo de copa de cinco metros. Os dados são derivados das imagens do Sentinel 1 e do Sentinel 2.&nbsp;</p>



<h2 class="wp-block-heading">Como esses conjuntos de dados diferem e o que isso significa na prática?&nbsp;</h2>



<h3 class="wp-block-heading">Diferenças na resolução espacial&nbsp;&nbsp;&nbsp;</h3>



<p>O conjunto de dados de cobertura de árvores tropicais tem uma resolução espacial maior a 10 metros, enquanto os conjuntos de dados de cobertura de dossel e altura da cobertura têm uma resolução de 30 metros. Com resolução de 30 metros, pode ser difícil diferenciar pequenos trechos de árvores, tornando o conjunto de dados cobertura de árvores tropicais mais apropriado para fazer o monitoramento em áreas com cobertura arbórea dispersa, incluindo regiões secas como o Sahel, perímetros florestais e zonas ribeirinhas, bem como árvores naturais em áreas agrícolas e urbanas. Essas diferenças na resolução resultarão em diferentes estimativas de cobertura arbórea em uma área de interesse, uma vez que as árvores não detectadas na resolução de 30 metros podem ser identificadas na resolução de 10 metros.&nbsp;</p>



<h4 class="wp-block-heading">Detecção de cobertura arbórea em resolução de 10 e 30 metros</h4>



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<h3 class="wp-block-heading">Diferenças na cobertura temporal e geográfica</h3>



<p>Uma diferença fundamental entre os três conjuntos de dados é sua disponibilidade anual e cobertura geográfica. O conjunto de dados de cobertura de dossel está disponível para os anos de 2000 e 2010. O conjunto de dados de altura da cobertura, originalmente produzido para o ano de 2019, agora está atualizado e disponível no GFW para os anos de 2000 e 2020. O conjunto de dados de cobertura de árvores tropicais está atualmente limitado ao ano de 2020. Dada a disponibilidade histórica mais longa do arquivo Landsat, os usuários podem querer priorizar o registro histórico (cobertura de dossel e altura da cobertura) sobre a resolução (cobertura de árvores tropicais).</p>



<p>Com relação ao escopo geográfico, o conjunto de dados cobertura de árvores tropicais cobre apenas os trópicos, enquanto os conjuntos de dados altura da cobertura e cobertura de dossel têm cobertura global. Portanto, os conjuntos de dados de altura da cobertura arbórea e cobertura de dossel devem ser usados para aplicações em florestas temperadas e boreais, enquanto o conjunto de dados de cobertura de árvores tropicais fornece uma visão de maior resolução em florestas tropicais.&nbsp;</p>



<h3 class="wp-block-heading">Diferenças nos dados sobre mudanças&nbsp;</h3>



<p>Os conjuntos de dados de cobertura de dossel e altura da cobertura arbórea são usados como base para os conjuntos de dados de mudanças florestais do GFW, pois estão disponíveis por vários anos.&nbsp;</p>



<p>Os dados de cobertura de dossel para o ano 2000 podem ser usados como base para os dados anuais de perda de cobertura arbórea do GFW — a perda de cobertura arbórea é calculada para vários limiares de densidade de copa arbórea no ano 2000 para atender às definições de floresta que variam de acordo com a densidade da copa das árvores. O GFW apresenta estatísticas e mapas de perda de cobertura de árvores com base em uma densidade mínima de copa de 30% no ano 2000, mas os usuários podem modificar esse limite para melhor atender a seus interesses de monitoramento. Os dados de altura da cobertura arbórea servem como linha de base para o ganho de cobertura arbórea de 20 anos e conjuntos de dados de mudança líquida disponíveis no GFW.&nbsp;&nbsp;</p>



<p>Atualmente, a cobertura de árvores tropicais é um mapa estático para 2020 e não pode ser usado para avaliar diretamente as mudanças, mas o WRI planeja lançar um produto anual de cobertura de árvores tropicais no GFW no futuro.&nbsp;</p>



<p>Os usuários cujo interesse principal é medir a mudança da cobertura florestal devem usar o conjunto de dados de linha de base associado ao produto da mudança florestal relevante para evitar qualquer incompatibilidade nas definições e métodos.</p>



<h3 class="wp-block-heading">Diferenças nas definições de árvore</h3>



<p>Enquanto os conjuntos de dados de cobertura de dossel e altura da cobertura arbórea são baseados em critérios de altura mínima, o conjunto de dados de cobertura de árvores tropicais tem um requisito de altura variável de três a cinco metros com base no diâmetro da copa. Isso significa que o conjunto de dados de cobertura de árvores tropicais exclui vegetação herbácea alta, como cana-de-açúcar, bananas e cactos, que são consideradas árvores nos outros conjuntos de dados.&nbsp;</p>



<p>Para usuários que estão medindo a cobertura arbórea em áreas com grande presença dessas culturas, a área detectada pelos três conjuntos de dados será muito diferente. É importante observar que os conjuntos de dados de cobertura de dossel e altura da cobertura arbórea não medem a área florestal usando os mesmos critérios dos Inventários Florestais Nacionais ou da Avaliação de Recursos Florestais (FRA) da Organização para Alimentação e Agricultura. <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="noreferrer noopener">Leia nosso blog</a> para saber mais sobre como o FRA mede a floresta versus como o GFW mede a cobertura arbórea e sobre outras diferenças de propósito, escopo e abordagem entre esses conjuntos de dados.&nbsp;</p>



<h2 class="wp-block-heading">Qual conjunto de dados devo usar para minha aplicação específica?&nbsp;</h2>



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<h2 class="wp-block-heading">Como esses conjuntos de dados serão atualizados no futuro?</h2>



<p>No futuro, a UMD publicará conjuntos de dados anuais integrados da estrutura da copa das árvores que permitirão aos usuários avaliar as mudanças anuais na altura e cobertura de dossel.&nbsp;</p>



<p>O WRI está atualmente desenvolvendo dados anuais de cobertura de árvores tropicais para 2017-2022, que permitirão aos usuários analisar as mudanças na cobertura arbórea nos trópicos na escala de 10 metros. O WRI também está trabalhando em projetos-piloto adicionais para melhorar a detecção de cultivos de árvores e derivar estimativas de carbono para projetos de restauração de pequena escala.&nbsp;</p>



<p>A composição, altura e densidade das florestas variam muito de um lugar para outro. Dependendo da área, período de análise e tipo de floresta específica, diferentes conjuntos de dados de cobertura arbórea podem ser mais adequados para atingir seu objetivo. Dados de sensoriamento remoto podem nos ajudar a monitorar uma variedade de atributos florestais usando novos avanços em tecnologia de satélite e metodologias de processamento. Isso resulta em uma variedade de conjuntos de dados que podem se complementar para informar nossa compreensão da distribuição global de árvores e fornecer oportunidades únicas para apoiar a restauração e o monitoramento do desmatamento.&nbsp;&nbsp;</p>



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

Explicação dos conjuntos de dados de cobertura de árvores no Global Forest Watch

<p>Munculnya data pengamatan Bumi yang baru telah meningkatkan secara radikal kemampuan kita untuk memetakan tutupan pohon, sementara metode otomatis dan biaya komputasi yang lebih rendah telah mempercepat produksi data set untuk menginformasikan pemahaman kita tentang distribusi pohon secara global. Global Forest Watch (GFW) memiliki tiga data set tutupan pohon, termasuk data Tropical Tree Cover (Tutupan Pohon Tropis) yang baru saja diperbaharui oleh World Resources Institute (WRI) (sebelumnya bernama Pepohonan dalam Lanskap Mosaik / <em>Trees in Mosaic Landscapes</em>).&nbsp;&nbsp;</p>



<p>Data set ini bervariasi dalam cakupan geografis dan temporal, resolusi, serta bagaimana mereka mendefinisikan tutupan pohon dan karakteristik lainnya, sehingga setiap data memiliki peruntukan yang lebih sesuai untuk kebutuhan yang berbeda. Di sini, kami menjelaskan perbedaan-perbedaan tersebut dan bagaimana ketiga data set ini memberikan informasi yang berharga mengenai cakupan hutan di daerah tropis dan di seluruh dunia. &nbsp;</p>



<h2 class="wp-block-heading">Data set tutupan pohon apa saja yang tersedia di GFW?&nbsp;</h2>



<table>
    <tbody>
        <tr>
            <td rowspan="2" width="14%">
                <p style="text-align: center;"><strong>Data Set</strong></p>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Sumber</strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Cakupan Geografis </strong></p>
            </td>
            <td rowspan="2" width="10%">
                <p style="text-align: center;"><strong>Resolusi Spasial </strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Tahun Cakupan </strong></p>
            </td>
            <td rowspan="2" width="12%">
                <p style="text-align: center;"><strong>Variabel</strong></p>
            </td>
            <td colspan="2">
                <p style="text-align: center;"><strong>Definisi Pohon&nbsp;</strong></p>
            </td>
        </tr>
        <tr>
            <td width="14%">
                <p style="text-align: center;"><strong>Kriteria tinggi</strong></p>
            </td>
            <td width="14%">
                <p style="text-align: center;"><strong>Kriteria Kanopi</strong></p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Tutupan Kanopi Pohon</strong></p>
            </td>
            <td>
                <p><a href="https://www.science.org/doi/10.1126/science.1244693" style="color: rgb(7, 130, 193);">Hansen et al., 2013</a></p>
            </td>
            <td>
                <p>Global</p>
            </td>
            <td>
                <p>30m</p>
            </td>
            <td>
                <p>2000, 2010</p>
            </td>
            <td>
                <p>Tutupan pohon (%) </p>
            </td>
            <td>
                <p>Semua vegetasi setinggi &ge;5m. </p>
            </td>
            <td>
                <p>t/a</p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Tinggi Tutupan Pohon</strong></p>
            </td>
            <td>
                <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425720305381?via%3Dihub" style="color: rgb(7, 130, 193);">Potapov et al., 2020</a></p>
            </td>
            <td>
                <p>Global</p>
            </td>
            <td>
                <p>30m</p>
            </td>
            <td>
                <p>2000,&nbsp; 2020</p>
            </td>
            <td>
                <p>Tinggi pohon (m)</p>
            </td>
            <td>
                <p>Vegetasi berkayu setinggi &ge;3m </p>
            </td>
            <td>
                <p>20-25%&nbsp;</p>
            </td>
        </tr>
        <tr>
            <td>
                <p><strong>Tutupan Pohon di Daerah Tropis *   </strong></p>
            </td>
            <td>
                <p><a href="https://www.researchsquare.com/article/rs-2109093/v1" style="color: rgb(7, 130, 193);">Brandt et al., 2023</a></p>
            </td>
            <td>
                <p>Daerah tropis</p>
            </td>
            <td>
                <p>10m</p>
            </td>
            <td>
                <p>2020</p>
            </td>
            <td>
                <p>Luas pohon &nbsp;</p>
            </td>
            <td>
                <p>Vegetasi berkayu setinggi &ge;5m, atau &ge;3m dengan persyaratan kanopi. </p>
            </td>
            <td>
                <p>Vegetasi berkayu setinggi 3-5m&nbsp; harus memiliki diameter tajuk &ge;5m.</p>
            </td>
        </tr>
    </tbody>
</table>



<p style="font-size: small"><i>* Data set ini tersedia dalam bentuk luasan pohon dengan resolusi 10 m atau persentase tutupan pohon dengan resolusi 0,5 ha</i></p>



<h3 class="wp-block-heading">Tutupan Kanopi Pohon&nbsp;</h3>



<p>Pada tahun 2013, para ahli di GLAD Lab University of Maryland (UMD) menerbitkan data set <a href="https://gfw.global/3K5tHxc" target="_blank" rel="noreferrer noopener"><strong>Tutupan Kanopi Pohon</strong></a>, yang memetakan tutupan kanopi pohon di seluruh dunia dengan resolusi 30 meter dan tersedia di GFW untuk tahun 2000 dan 2010. Data set ini merupakan salah satu upaya pertama untuk memetakan tutupan pohon pada skala global dengan menggunakan citra resolusi menengah. Selain itu, data ini juga menentukan <em>baseline</em> untuk <a href="https://gfw.global/3BfwQWz" target="_blank" rel="noreferrer noopener">data kehilangan tutupan pohon tahunan</a>; data set ini menjadi dasar <a href="https://www.globalforestwatch.org/blog/people/global-forest-watch-join-the-movement/" target="_blank" rel="noreferrer noopener">peluncuran Global Forest Watch pada tahun 2014</a>.&nbsp;&nbsp;</p>



<p>Lapisan Tutupan Kanopi Pohon memperkirakan <em>persentase tutupan pohon</em>, yang mengukur kerapatan tutupan kanopi pohon dalam 30 meter piksel. Data set ini mendefinisikan pohon sebagai semua vegetasi dengan tinggi setidaknya lima meter dan berasal dari citra Landsat.&nbsp;&nbsp;&nbsp;</p>



<h3 class="wp-block-heading">Tinggi Tutupan Pohon&nbsp;</h3>



<p>Pada tahun 2021, para ahli di Laboratorium GLAD UMD menerbitkan data set <a href="https://gfw.global/3tkvuCt" target="_blank" rel="noreferrer noopener"><strong>Tinggi Tutupan Pohon</strong></a>, yang memetakan tinggi tutupan pohon di seluruh dunia dengan resolusi 30 meter dan tersedia di GFW untuk tahun 2000 dan 2020. Data set ini dapat membuat pendeteksian yang lebih baik terhadap perolehan tutupan pohon dan menjadi dasar bagi <a href="https://www.globalforestwatch.org/blog/data-and-research/new-gfw-tree-cover-gain-net-change-data/" target="_blank" rel="noreferrer noopener">data perubahan bersih tutupan pohon GFW selama 20 tahun</a>. Tidak seperti tutupan kanopi pohon, yang mencapai maksimum segera setelah pertumbuhan pohon dimulai, tinggi pohon berubah secara bertahap dan berhubungan secara linear dengan biomassa hutan dan penyimpanan karbon.&nbsp;&nbsp;&nbsp; &nbsp;</p>



<p>Lapisan Tinggi Tutupan Pohon memperkirakan <em>tinggi kanopi pohon</em>, yang mengacu pada ketinggian kanopi pohon dalam satuan meter dalam 30 meter piksel. Data set tersebut mendefinisikan pohon sebagai vegetasi berkayu setinggi setidaknya tiga meter dan berasal dari pengukuran Global Ecosystem Dynamics Investigation (GEDI) LiDAR dan citra Landsat. Data set ini memperkirakan persentil ke-95 dari tinggi kanopi pohon, dan oleh karena itu akan mewakili batas atas tinggi piksel dengan tutupan pohon pecahan, atau piksel dengan tinggi kanopi yang bervariasi.&nbsp;</p>



<h3 class="wp-block-heading">Tutupan Pohon di Daerah Tropis&nbsp;</h3>



<p>Tahun ini, WRI memperbarui data set <a href="https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6InRyb3BpY2FsLXRyZWUtY292ZXIiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsidHJvcGljYWwtdHJlZS1jb3Zlci1tZXRlcnMiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&amp;mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoibGFuZENvdmVyIn0%3D" target="_blank" rel="noreferrer noopener"><strong>Tutupan Pohon Tropis</strong></a> (sekarang tersedia untuk diunduh), yang memetakan tutupan pohon di seluruh wilayah tropis dengan resolusi 10 meter dan tersedia di GFW untuk tahun 2020. Berbeda dengan data set lainnya, data set Tutupan Pohon Tropis didasarkan pada citra dari satelit Sentinel, yang meningkatkan resolusi data penginderaan jarak jauh yang tersedia secara gratis menjadi 10 meter ketika diluncurkan pada tahun 2015. Data set ini memanfaatkan kemajuan dalam hal resolusi untuk meningkatkan deteksi tutupan pohon di area tidak berhutan, terutama di lahan kering, area perkotaan, dan lahan pertanian, serta kemampuan kita untuk memonitor pohon pada skala spasial (lokal) yang kecil.&nbsp;</p>



<p>Tutupan Pohon Tropis memperkirakan <em>luas pohon</em>, yang mengacu pada probabilitas bahwa satu atau lebih kanopi pohon memotong titik tengah berukuran 10 meter piksel (pada GFW, kami menampilkan data luas pohon dengan ambang batas probabilitas 40%). "Persentase tutupan pohon" diperoleh dengan merata-ratakan probabilitas luasan pohon pada skala setengah hektar. Baik versi 10 meter maupun setengah hektar dari data set Tutupan Pohon Tropis tersedia bagi para pengguna di GFW. Data set Tutupan Pohon Tropis mendefinisikan pohon sebagai vegetasi berkayu dengan tinggi lebih dari lima meter, atau antara tiga hingga lima meter dengan diameter kanopi minimum lima meter. Data tersebut berasal dari citra satelit Sentinel 1 dan Sentinel 2.&nbsp;</p>



<h2 class="wp-block-heading">Apa perbedaan data set ini dan apa artinya dalam praktiknya?</h2>



<h3 class="wp-block-heading">Perbedaan dalam Resolusi Spasial</h3>



<p>Data set Tutupan Pohon Tropis memiliki resolusi spasial yang lebih tinggi, yaitu 10 meter, sementara data set Tutupan Kanopi Pohon dan Tinggi Tutupan Pohon memiliki resolusi 30 meter. Pada resolusi 30 meter, akan sulit untuk membedakan petak-petak kecil pohon, sehingga data set Tutupan Pohon Tropis lebih sesuai untuk memantau tutupan pohon di wilayah dengan tutupan pohon yang tersebar, termasuk wilayah lahan kering seperti Sahel, pinggiran hutan, dan zona sempadan sungai, serta pepohonan alami di wilayah pertanian dan perkotaan. Perbedaan resolusi ini akan menghasilkan estimasi tutupan pohon yang berbeda di area yang diinginkan karena pohon yang tidak terdeteksi pada resolusi 30 meter dapat diidentifikasi pada resolusi 10 meter.</p>



<h4 class="wp-block-heading">Deteksi tutupan pohon pada resolusi 10 dan 30 meter</h4>



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<h3 class="wp-block-heading">Perbedaan Cakupan Temporal dan Geografis&nbsp;</h3>



<p>Perbedaan utama antara ketiga data set tersebut adalah ketersediaan tahunan dan cakupan geografisnya. Data set tutupan kanopi pohon tersedia untuk tahun 2000 dan 2010. Data set Tinggi Tutupan Pohon, yang awalnya dibuat untuk tahun 2019, kini telah diperbarui dan tersedia di GFW untuk tahun 2000 dan 2020. Data set tutupan pohon tropis saat ini terbatas pada tahun 2020. Mengingat ketersediaan arsip Landsat yang lebih lama, pengguna dapat memprioritaskan catatan historis (Tutupan Kanopi Pohon dan Tinggi Tutupan Pohon) daripada resolusi (Tutupan Pohon Tropis).&nbsp;</p>



<p>Untuk cakupan geografis, data set Tutupan Pohon Tropis hanya mencakup wilayah tropis, sedangkan data set Tinggi Tutupan Pohon dan Tutupan Kanopi Pohon memiliki cakupan global. Oleh karena itu, data set Tinggi Tutupan Pohon dan Tutupan Kanopi Pohon sebaiknya digunakan untuk aplikasi di hutan beriklim sedang dan hutan boreal, sedangkan data set Tutupan Pohon Tropis memberikan gambaran resolusi yang lebih tinggi pada hutan tropis.&nbsp;&nbsp;</p>



<h3 class="wp-block-heading">Perbedaan dalam Data Perubahan</h3>



<p>Data set Tutupan Kanopi Pohon dan Tinggi Tutupan Pohon digunakan sebagai dasar data set perubahan hutan GFW karena data tersebut tersedia untuk beberapa tahun.</p>



<p>Data Tutupan Kanopi Pohon untuk tahun 2000 dapat digunakan sebagai data dasar untuk data kehilangan tutupan pohon tahunan GFW - kehilangan tutupan pohon dihitung untuk berbagai ambang batas kerapatan kanopi pohon pada tahun 2000 untuk memenuhi definisi hutan yang bervariasi berdasarkan kerapatan kanopi pohon. GFW menyajikan statistik dan peta kehilangan tutupan pohon berdasarkan kerapatan kanopi minimum 30% pada tahun 2000, namun pengguna dapat memodifikasi ambang batas tersebut agar sesuai dengan tujuan mereka. Data Tinggi Tutupan Pohon berfungsi sebagai data dasar untuk data set perolehan tutupan pohon dan perubahan bersih selama 20 tahun yang tersedia di GFW.</p>



<p>Saat ini, Tutupan Pohon Tropis merupakan peta statis untuk tahun 2020 dan tidak dapat digunakan untuk menilai perubahan secara langsung, tetapi WRI berencana untuk merilis produk Tutupan Pohon Tropis tahunan pada GFW di masa mendatang.&nbsp;</p>



<p>Pengguna yang memiliki kepentingan utama untuk mengukur perubahan tutupan pohon sebaiknya menggunakan data set dasar yang terkait dengan produk perubahan hutan yang relevan untuk menghindari ketidaksesuaian definisi dan metode.&nbsp;</p>



<h3 class="wp-block-heading">Perbedaan dalam Definisi Pohon&nbsp;</h3>



<p>Sementara data set Tutupan Kanopi Pohon dan Tinggi Tutupan Pohon didasarkan pada kriteria ketinggian minimum, data set Tutupan Pohon Tropis memiliki persyaratan ketinggian yang bervariasi, yaitu tiga hingga lima meter berdasarkan diameter kanopi. Hal ini berarti bahwa data set Tutupan Pohon Tropis tidak termasuk vegetasi herba tinggi seperti tebu, pisang, dan kaktus yang dianggap sebagai pohon di data set lainnya.</p>



<p>Bagi pengguna yang mengukur tutupan pohon di area dengan banyak tanaman ini, area tutupan pohon yang terdeteksi oleh ketiga data set tersebut akan sangat berbeda. Penting untuk dicatat bahwa data set Tutupan Kanopi Pohon dan Tinggi Tutupan Pohon tidak mengukur area hutan dengan menggunakan kriteria yang sama dengan Inventarisasi Hutan Nasional atau Penilaian Sumber Daya Hutan (FRA) dari Organisasi Pangan dan Pertanian (FAO). <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="noreferrer noopener">Baca blog kami</a> untuk mempelajari lebih lanjut mengenai bagaimana FRA mengukur hutan dibandingkan dengan GFW yang mengukur tutupan pohon, dan mengenai perbedaan tujuan, cakupan, dan pendekatan di antara kedua data set tersebut.&nbsp;</p>



<h2 class="wp-block-heading">Data set mana yang harus digunakan untuk aplikasi spesifik saya?</h2>



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<h2 class="wp-block-heading">Bagaimana data set ini akan diperbarui di masa mendatang?&nbsp;</h2>



<p>Ke depannya, UMD akan mempublikasikan data set struktur kanopi pohon terintegrasi tahunan yang akan memungkinkan pengguna untuk menilai perubahan tahunan dalam tinggi dan tutupan kanopi.</p>



<p>Saat ini, WRI sedang mengembangkan data tutupan pohon tropis tahunan untuk tahun 2017-2022, yang memungkinkan pengguna untuk menganalisis perubahan tutupan pohon di seluruh wilayah tropis pada skala 10 meter. WRI juga sedang mengerjakan proyek percontohan tambahan untuk meningkatkan deteksi tanaman pohon dan memperoleh estimasi karbon untuk proyek restorasi skala kecil.</p>



<p>Komposisi, ketinggian, dan kerapatan hutan sangat bervariasi dari satu tempat ke tempat lain. Tergantung pada area, jangka waktu, dan tipe hutan tertentu, data set tutupan pohon yang berbeda mungkin lebih cocok untuk memenuhi tujuan Anda. Data penginderaan jauh dapat membantu kita memantau berbagai atribut hutan dengan menggunakan kemajuan baru dalam teknologi satelit dan metodologi pemrosesan. Hal ini menghasilkan berbagai data set yang dapat saling melengkapi dalam menginformasikan pemahaman kita mengenai distribusi pohon secara global dan memberikan peluang unik untuk mendukung restorasi dan pemantauan deforestasi.</p>



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Penjelasan Data Set Tutupan Pohon di Global Forest Watch

Les ensembles de données de couverture forestière sur Global Forest Watch, Expliqués

Global Forest Watch hosts three tree cover data sets. We explain the differences between them and how all provide valuable information on forests.

Tree Cover Data Sets on Global Forest Watch, Explained | GFW Blog

Sarah Ruiz

<p>Visitors to the Global Forest Watch map this week may have noticed some changes in the design. These changes go beyond what you see on the surface. The new map is faster, facilitates customization and makes exploring and analyzing forest data even easier.</p>
<h3>Not sure where to start? Use the new search and explore features</h3>
<p>While our diverse collection of data sets still remains, we’ve reorganized the data layers into categories grouped by purpose and scope. If you’re looking for a particular type of data, or data that pertains to a specific country, check out the improved <a href="http://bit.ly/2Teutvg" target="_blank" rel="noopener noreferrer">search tool</a> to quickly find what you’re looking for. If you’re not looking for any specific dataset, our new <a href="http://bit.ly/2TbVzD7" target="_blank" rel="noopener noreferrer">explore</a> tab is designed for the curious user. With curated combinations of layers, you can investigate the map based on a variety of forest topics.</p>
<h3>Analyze forest change for new areas with one click</h3>
<p>The addition of new boundaries for analysis offers new possibilities for the kinds of questions users can ask of our data. Take, for example, the Congo rainforest in central Africa. This rainforest encompasses about <a href="https://globalforestatlas.yale.edu/congo/ecoregions/congo-basin-ecoregion" target="_blank" rel="noopener noreferrer">1.8 million square kilometers</a> — nearly 90 percent of all African rainforest. Though much of this forest falls within the borders of the Democratic Republic of Congo, neither the Congo River that supports the forest ecosystem nor the trees growing along its banks pay heed to political boundaries, which makes getting a precise analysis a tricky task.</p>
<p>With this map update, we’ve added two new ways to analyze areas on the map. The first enables <strong>analyses based on river basins.</strong> This allows us to separate forests based on their natural geography. When we look at tree cover loss in the Congo River Basin, we see a different story than we would using political boundaries.</p>
<p><iframe loading="lazy" src="https://www.globalforestwatch.org/embed/map/country/COD?analysis=eyJzaG93QW5hbHlzaXMiOnRydWUsImhpZGRlbiI6ZmFsc2V9&map=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%3D%3D&menu=eyJtZW51U2VjdGlvbiI6IiIsImRhdGFzZXRDYXRlZ29yeSI6ImZvcmVzdENoYW5nZSJ9" width="800" height="490" frameborder="0"><span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_start">﻿</span></iframe></p>
<p>Despite its unified appearance however, the Congo rainforest is actually comprised of smaller forest ecosystems called “ecoregions.” The new map also enables users to conduct <strong>analyses based on these ecoregions</strong>. When you divide the Congo Basin this way, one giant band of forest becomes six — each of which can be analyzed in one click and easily compared.</p>
<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/11/EcoregionAnalysis.gif" alt="River basin analysis" width="100%" /> The six ecoregions of the Congo rainforest.</div>
<h3>Customize the GFW map experience</h3>
<p>The new map also boasts improved customization. Active layers, for example, can be rearranged to send larger shapes, like tree cover, to the back, and bring more detailed data, like GLAD alerts, to the front. Opacity of various layers can now be adjusted, allowing users to clearly view more data at a time on the map.</p>
<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/11/TransparencyToggle.gif" alt="toggle" width="100%" /> With the customization updates to the GFW map, users can adjust the transparency of data layers.</div>
<p>Basemaps can be customized too. Users can alter labels and boundaries separately to create a basemap that meets their mapping needs.</p>
<p>As GFW grows and changes we welcome your feedback on how to improve the platform. Explore the updated map and let us know your thoughts through this <a href="https://surveys.hotjar.com/s?siteId=1060074&surveyId=119711" target="_blank" rel="noopener noreferrer">survey</a>, or in the comments below.</p>
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Turning Over a New Leaf: GFW’s New Map Makes It Easier to Explore, Analyze and Customize Forest Data

Visitors to the Global Forest Watch map this week may have noticed some changes in the design. These changes go beyond what you see on the surface. The new map is faster, facilitates customization and makes exploring and analyzing forest data even easier.
<h3>Not sure where to start? Use the new search and explore features</h3>
While our diverse collection of data sets still remains, we’ve reorganized the data layers into categories grouped by purpose and scope. If you’re looking for a particular type of data, or data that pertains to a specific country, check out the improved <a href="http://bit.ly/2Teutvg" target="_blank" rel="noopener noreferrer">search tool</a> to quickly find what you’re looking for. If you’re not looking for any specific dataset, our new <a href="http://bit.ly/2TbVzD7" target="_blank" rel="noopener noreferrer">explore</a> tab is designed for the curious user. With curated combinations of layers, you can investigate the map based on a variety of forest topics.
<h3>Analyze forest change for new areas with one click</h3>
The addition of new boundaries for analysis offers new possibilities for the kinds of questions users can ask of our data. Take, for example, the Congo rainforest in central Africa. This rainforest encompasses about <a href="https://globalforestatlas.yale.edu/congo/ecoregions/congo-basin-ecoregion" target="_blank" rel="noopener noreferrer">1.8 million square kilometers</a> — nearly 90 percent of all African rainforest. Though much of this forest falls within the borders of the Democratic Republic of Congo, neither the Congo River that supports the forest ecosystem nor the trees growing along its banks pay heed to political boundaries, which makes getting a precise analysis a tricky task.

With this map update, we’ve added two new ways to analyze areas on the map. The first enables <strong>analyses based on river basins.</strong> This allows us to separate forests based on their natural geography. When we look at tree cover loss in the Congo River Basin, we see a different story than we would using political boundaries.

<iframe src="https://www.globalforestwatch.org/embed/map/country/COD?analysis=eyJzaG93QW5hbHlzaXMiOnRydWUsImhpZGRlbiI6ZmFsc2V9&amp;map=eyJ6b29tIjo2LCJjZW50ZXIiOnsibGF0IjotMy45MzAwMjAxNTcxMTE0NjMsImxuZyI6MjEuMTE1NzIyNjU2MjV9LCJjYW5Cb3VuZCI6ZmFsc2UsImJib3giOm51bGwsImRhdGFzZXRzIjpbeyJkYXRhc2V0IjoiZTY2M2ViMDktMDRkZS00ZjM5LWI4NzEtMzVjNmMyZWQxMGI1Iiwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZSwibGF5ZXJzIjpbImRkNWRmODdmLTM5YzItNGFlYi1hNDYyLTNlZjk2OWIyMGI2NiJdfSx7ImRhdGFzZXQiOiJmZGM4ZGMxYi0yNzI4LTRhNzktYjIzZi1iMDk0ODUwNTJiOGQiLCJsYXllcnMiOlsiNmY2Nzk4ZTYtMzllYy00MTYzLTk3OWUtMTgyYTc0Y2E2NWVlIiwiYzVkMWUwMTAtMzgzYS00NzEzLTlhYWEtNDRmNzI4YzA1NzFjIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9LHsiZGF0YXNldCI6Ijg5N2VjYzc2LTIzMDgtNGM1MS1hZWIzLTQ5NWRlMGJkY2E3OSIsImxheWVycyI6WyJjMzA3NWM1YS01NTY3LTRiMDktYmMwZC05NmVkMTY3M2Y4YjYiXSwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZX1dfQ%3D%3D&amp;menu=eyJtZW51U2VjdGlvbiI6IiIsImRhdGFzZXRDYXRlZ29yeSI6ImZvcmVzdENoYW5nZSJ9" width="800" height="490" frameborder="0"><span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_start">﻿</span></iframe>

Despite its unified appearance however, the Congo rainforest is actually comprised of smaller forest ecosystems called “ecoregions.” The new map also enables users to conduct <strong>analyses based on these ecoregions</strong>. When you divide the Congo Basin this way, one giant band of forest becomes six — each of which can be analyzed in one click and easily compared.
<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/11/EcoregionAnalysis.gif" alt="River basin analysis" width="100%" /> The six ecoregions of the Congo rainforest.</div>
<h3>Customize the GFW map experience</h3>
The new map also boasts improved customization. Active layers, for example, can be rearranged to send larger shapes, like tree cover, to the back, and bring more detailed data, like GLAD alerts, to the front. Opacity of various layers can now be adjusted, allowing users to clearly view more data at a time on the map.
<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/11/TransparencyToggle.gif" alt="toggle" width="100%" /> With the customization updates to the GFW map, users can adjust the transparency of data layers.</div>
Basemaps can be customized too. Users can alter labels and boundaries separately to create a basemap that meets their mapping needs.

As GFW grows and changes we welcome your feedback on how to improve the platform. Explore the updated map and let us know your thoughts through this <a href="https://surveys.hotjar.com/s?siteId=1060074&amp;surveyId=119711" target="_blank" rel="noopener noreferrer">survey</a>, or in the comments below.

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Turning Over a New Leaf: GFW's New Map Makes It Easier to Explore, Analyze and Customize Forest Data

Visitors to the Global Forest Watch map this week may have noticed some changes in the design. These changes go beyond what you see on the surface. The

Turning Over a New Leaf: GFW's New Map Makes It Easier to Explore, Analyze and Customize Forest Data | Global Forest Watch Blog

Turning Over a New Leaf: GFW&#8217;s New Map Makes It Easier to Explore, Analyze and Customize Forest Data

https://secure.gravatar.com/avatar/3dc298e411f65b199fc3734919d6934e?s=96&d=mm&r=g

carbon

carbon | Global Forest Watch Blog

Indonesia

Indonesia | Global Forest Watch Blog

Katie Lyons

<p>Twenty years ago, Dr. Florian Siegert, a German-trained biologist, was conducting ecology research in Borneo when a series of fires blazed across Indonesia. The <a href="https://rainforests.mongabay.com/08indo_fires.htm" target="_blank" rel="noopener noreferrer">Asian Forest Fires of 1997-1998</a> burned thousands of miles of rainforest, creating a haze and air quality disaster that caused $5-6 billion in economic losses and endangered the long-term health of <a href="https://rainforests.mongabay.com/08indo_fires.htm" target="_blank" rel="noopener noreferrer">70 million people</a>.</p>
<p>The rampant spread of the fires through the vast tropical rainforest struck Siegert. Why were these fires happening, and why were they so aggressive?</p>
<p>Seeking answers to those questions led Siegert to where he is today, the winner of a $1 million prize for mapping Indonesia’s peat, boggy lands composed of decaying organic matter. It’s just the latest achievement in a long career exploring peat, an often overlooked yet vitally important ecosystem in the global battle to fight climate change.</p>
<p>As Siegert and his team studied the 1997-1998 fires, it became clear that rainforests in places like Borneo and Sumatra grow on peat, and there were still vast layers of peatland had not yet been burned.</p>
<p>“Peatlands at that time were basically uninhabited – strange ecosystems with acidic water and many endemic plants,” Siegert recalled. “We wanted to find out more about the ecology of this ecosystem and how it was formed.”</p>
<h3>Peat, Long Buried, Starts to Burn</h3>
<p>In 2002, Siegert and team published a landmark paper exposing a dire problem: When tropical peat burns, <a href="https://www.nature.com/articles/nature01131" target="_blank" rel="noopener noreferrer">it releases massive carbon emissions</a>.</p>
<p>Knowing just how thick these unburned layers of peat were was now critical to understanding how much carbon remained within the peat—and how much damage it would cause if it were to burn.</p>
<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Peat-Swamp.jpg" alt="Peat Swamp" width="100%" />Peat Swamp, Central Kalimantan, Indonesia. Flickr/ WRI</div>
<p>Siegert and researchers began collecting field data in collaboration with Indonesian and European universities, resulting in more than 10 years of concerted research on the specific properties of peat swamps and why exactly they burn. “We started to drill holes in peat swamps to see how thick the layer was and did analysis of the biomass materials to get an understanding of how exactly it was formed,” Siegert explained.</p>
<p>Indonesia’s record-breaking 2015 fire season demonstrated how important this research continues to be today. Half of the fires burned on peatland, igniting a carbon bomb that catapulted the country from the world’s sixth-largest emitter to its fourth-largest. Global pressure for Indonesia to protect and restore its peatlands increased.</p>
<p>Without a precise understanding of the location, type, extent and depth of Indonesia’s peat, though, this task would be nearly impossible. In response, the Indonesian Geospatial Information Agency (BIG) opened the <a href="http://indonesianpeatprize.com/" target="_blank" rel="noopener noreferrer">Indonesian Peat Prize</a> contest. The team that came up with the best way to map peat would be awarded a $1 million prize—and their contribution would form the basis for Indonesia’s national action on peat.</p>
<p><iframe loading="lazy" src="https://player.vimeo.com/video/195836473" width="640" height="360" frameborder="0" allowfullscreen="allowfullscreen"></iframe></p>
<h3>The Perfect Peat Map: Lasers, Planes and Satellites</h3>
<p>When Siegert heard about the Peat Prize, he began hand-selecting a team of partners he’d worked with over the last 15 years.</p>
<p>The team’s diverse experts would have to address both parts of mapping peatlands: distribution and topography.</p>
<div class="imagecenter*"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Peat-Mapping-Team.png" alt="mapping Team" width="100%" /></div>
<p>The first step towards assessing peat distribution was to identify already burned or degraded peat no longer recognizable as peat swamp in recent satellite imagery. To do this, the team used historic Landsat satellite imagery dating back to the 1970s, which enabled them to view peat swamp forest distribution and how it changed over time. Using more recent, higher-resolution Sentinel-2 satellite imagery, the team could identify additional characteristics typical for peatland such as drainage canals  and double-check information gathered from Landsat.</p>
<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Swamp-Graphic.png" alt="peat swamp graphic" width="100%" /></div>
<p>With a better understanding of the peat’s distribution, they moved on to determine the thickness of peat locations—a process also accomplished in two steps. Peat is characteristically formed in dome-like structures; you can guess how much peat there is by mapping the topography of the domes in the landscape. Satellite images taken from the Airbus WorldDEM provided a first look at how tall each peat dome might be. The team then innovatively used LiDAR—which works as sonar does with sound, but with lasers—to create a very detailed topography and digital 3D representation of selected areas. (Flying LiDAR light beam instruments from airplanes cost more than any other part of the peat mapping process, but the team maximized its efficiency by only using this method to cross-check select areas of the topography map produced by WorldDEM.)</p>
<p>Finally, the team hired locals who lived nearby these peat areas to drill several dozens of holes into the soil. They examined each hole to see how deep the peat went—a kind of fact-check for the satellite-based work that could be efficiently replicated by Indonesians across the country.</p>
<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Prize-Method.png" alt="Peat mapping tech" width="100%" /></div>
<p>This combination proved to be the most accurate, efficient and affordable submission in the Indonesian Peat Prize content, and the International Peat Mapping team won the $1 million prize. The Indonesian government will now use the team’s methodology—as well as select elements of other submissions, including a helicopter-based frame for measuring peat thickness with electromagnetic waves—as the standard for mapping the entire country’s peatlands.</p>
<h3>The Potential for Peat Restoration</h3>
<p>After paying back the investment made by Remote Sensing Solutions, his 15-person company that funded the initiative, Siegert plans to apply the winnings to do research on peat restoration. He also intends to enhance academic cooperation among universities to develop a “peat thesis” program that deals specifically with restoration of peat. Finally, Siegert says he’ll also work to expand this effort beyond Indonesia to explore the completely unknown—and luckily, undisturbed—peatlands in Africa.</p>
<hr />
<div class="attribute">BANNER PHOTO: Drainage canals in peatland, Central Kalimantan, Indonesia. Flickr/World Resources Institute.</div>
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How Scientists Used Lasers, Satellites and Planes to Map One of Indonesia’s Biggest Sources of Emissions

Twenty years ago, Dr. Florian Siegert, a German-trained biologist, was conducting ecology research in Borneo when a series of fires blazed across Indonesia. The <a href="https://rainforests.mongabay.com/08indo_fires.htm" target="_blank" rel="noopener noreferrer">Asian Forest Fires of 1997-1998</a> burned thousands of miles of rainforest, creating a haze and air quality disaster that caused $5-6 billion in economic losses and endangered the long-term health of <a href="https://rainforests.mongabay.com/08indo_fires.htm" target="_blank" rel="noopener noreferrer">70 million people</a>.

The rampant spread of the fires through the vast tropical rainforest struck Siegert. Why were these fires happening, and why were they so aggressive?

Seeking answers to those questions led Siegert to where he is today, the winner of a $1 million prize for mapping Indonesia’s peat, boggy lands composed of decaying organic matter. It’s just the latest achievement in a long career exploring peat, an often overlooked yet vitally important ecosystem in the global battle to fight climate change.

As Siegert and his team studied the 1997-1998 fires, it became clear that rainforests in places like Borneo and Sumatra grow on peat, and there were still vast layers of peatland had not yet been burned.

“Peatlands at that time were basically uninhabited – strange ecosystems with acidic water and many endemic plants,” Siegert recalled. “We wanted to find out more about the ecology of this ecosystem and how it was formed.”
<h3>Peat, Long Buried, Starts to Burn</h3>
In 2002, Siegert and team published a landmark paper exposing a dire problem: When tropical peat burns, <a href="https://www.nature.com/articles/nature01131" target="_blank" rel="noopener noreferrer">it releases massive carbon emissions</a>.

Knowing just how thick these unburned layers of peat were was now critical to understanding how much carbon remained within the peat—and how much damage it would cause if it were to burn.
<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Peat-Swamp.jpg" alt="Peat Swamp" width="100%" />Peat Swamp, Central Kalimantan, Indonesia. Flickr/ WRI</div>
Siegert and researchers began collecting field data in collaboration with Indonesian and European universities, resulting in more than 10 years of concerted research on the specific properties of peat swamps and why exactly they burn. “We started to drill holes in peat swamps to see how thick the layer was and did analysis of the biomass materials to get an understanding of how exactly it was formed,” Siegert explained.

Indonesia’s record-breaking 2015 fire season demonstrated how important this research continues to be today. Half of the fires burned on peatland, igniting a carbon bomb that catapulted the country from the world’s sixth-largest emitter to its fourth-largest. Global pressure for Indonesia to protect and restore its peatlands increased.

Without a precise understanding of the location, type, extent and depth of Indonesia’s peat, though, this task would be nearly impossible. In response, the Indonesian Geospatial Information Agency (BIG) opened the <a href="http://indonesianpeatprize.com/" target="_blank" rel="noopener noreferrer">Indonesian Peat Prize</a> contest. The team that came up with the best way to map peat would be awarded a $1 million prize—and their contribution would form the basis for Indonesia’s national action on peat.

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<h3>The Perfect Peat Map: Lasers, Planes and Satellites</h3>
When Siegert heard about the Peat Prize, he began hand-selecting a team of partners he’d worked with over the last 15 years.

The team’s diverse experts would have to address both parts of mapping peatlands: distribution and topography.
<div class="imagecenter*"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Peat-Mapping-Team.png" alt="mapping Team" width="100%" /></div>
The first step towards assessing peat distribution was to identify already burned or degraded peat no longer recognizable as peat swamp in recent satellite imagery. To do this, the team used historic Landsat satellite imagery dating back to the 1970s, which enabled them to view peat swamp forest distribution and how it changed over time. Using more recent, higher-resolution Sentinel-2 satellite imagery, the team could identify additional characteristics typical for peatland such as drainage canals  and double-check information gathered from Landsat.
<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Swamp-Graphic.png" alt="peat swamp graphic" width="100%" /></div>
With a better understanding of the peat’s distribution, they moved on to determine the thickness of peat locations—a process also accomplished in two steps. Peat is characteristically formed in dome-like structures; you can guess how much peat there is by mapping the topography of the domes in the landscape. Satellite images taken from the Airbus WorldDEM provided a first look at how tall each peat dome might be. The team then innovatively used LiDAR—which works as sonar does with sound, but with lasers—to create a very detailed topography and digital 3D representation of selected areas. (Flying LiDAR light beam instruments from airplanes cost more than any other part of the peat mapping process, but the team maximized its efficiency by only using this method to cross-check select areas of the topography map produced by WorldDEM.)

Finally, the team hired locals who lived nearby these peat areas to drill several dozens of holes into the soil. They examined each hole to see how deep the peat went—a kind of fact-check for the satellite-based work that could be efficiently replicated by Indonesians across the country.
<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2018/09/Prize-Method.png" alt="Peat mapping tech" width="100%" /></div>
This combination proved to be the most accurate, efficient and affordable submission in the Indonesian Peat Prize content, and the International Peat Mapping team won the $1 million prize. The Indonesian government will now use the team’s methodology—as well as select elements of other submissions, including a helicopter-based frame for measuring peat thickness with electromagnetic waves—as the standard for mapping the entire country’s peatlands.
<h3>The Potential for Peat Restoration</h3>
After paying back the investment made by Remote Sensing Solutions, his 15-person company that funded the initiative, Siegert plans to apply the winnings to do research on peat restoration. He also intends to enhance academic cooperation among universities to develop a “peat thesis” program that deals specifically with restoration of peat. Finally, Siegert says he’ll also work to expand this effort beyond Indonesia to explore the completely unknown—and luckily, undisturbed—peatlands in Africa.

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<div class="attribute">BANNER PHOTO: Drainage canals in peatland, Central Kalimantan, Indonesia. Flickr/World Resources Institute.</div>

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