<p>Total tropical primary forest loss in 2023 totaled 3.7 million hectares, the equivalent of losing almost 10 football (soccer) fields of forest per minute. <a href="https://gfw.global/3TPufvy" rel="noopener" target="_blank">View on the Global Forest Review.</a></p>


2023 Tree Cover Loss Data Analysis

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

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

Brazil

Brazil | Global Forest Watch Blog

forest change

forest change | Global Forest Watch Blog

Indonesia

Indonesia | Global Forest Watch Blog

Liz Goldman

Sarah Carter

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



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



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



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



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



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



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



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



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



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



<p>To address these inconsistencies, we:</p>



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



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



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



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



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



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



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



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



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



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



<p><em>Brazil</em></p>



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



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



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



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



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



<p><em>Indonesia</em></p>



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



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



<p></p>



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



<p></p>



<p></p>

Global Forest Watch’s 2023 Tree Cover Loss Data Explained

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



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



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



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



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



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



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



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



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



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



<p>To address these inconsistencies, we:</p>



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



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



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



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



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



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



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



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



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



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



<p><em>Brazil</em></p>



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



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



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



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



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



<p><em>Indonesia</em></p>



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



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



<p></p>



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



<p></p>



<p></p>

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



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



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



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



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



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



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



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



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



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



<p>Para resolver essas inconsistências, nós:</p>



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



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



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



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



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



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



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



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



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



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



<p><em>Brasil</em>&nbsp;</p>



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



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



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



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



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



<p><em>Indonésia</em></p>



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



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



<p></p>



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<p></p>



<p></p>

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

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

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

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Paige Johnson

Ripe Cocoa pods from a cocoa farm in Ghana.

Cocoa

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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

<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>



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<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>



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<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>



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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>



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<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>



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<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

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> 

 

<hr>



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<p></p>



<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> 

 

<hr>



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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 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>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16365261?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<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>



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

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>



<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 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>



<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>



<div class="flourish-embed flourish-photo-slider" data-src="visualisation/16365261?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<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

logging

logging | Global Forest Watch Blog

policy

policy | Global Forest Watch Blog

Fred Stolle

Tina Schneider

Ruth Nogueron

Monitoring Forest Degradation for the EU Deforestation Regulation Using GFW

Under the EUDR, wood products entering the EU market must be degradation-free. Tools like GFW can help companies monitor for degradation.

Monitoring Forest Degradation for the EUDR | GFW Blog

Mikaela Weisse

<p>New tree cover loss data from the University of Maryland (UMD) and available on Global Forest Watch (GFW) shows high rates of primary forest loss in 2022 despite <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">political ambitions to curb loss</a>. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.</p>



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



<p>The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2022. Because optical satellite data collection can often be obscured by cloud cover in the humid tropics, some late-in-the year loss can be picked up in the next year following persistent cloud cover. This is a common occurrence in many tropical countries where cloud cover is prevalent.</p>



<p>The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops. Loss of this tree cover can be due to human or natural causes and can be permanent or temporary. Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Read more about the differences here</a>.</p>



<p>A new tool available on the Global Forest Review, the <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">Targets Tracker</a>, enables us to use tree cover loss data to measure progress toward deforestation commitments on a global level by using a coarse resolution <a href="https://research.wri.org/gfr/forest-targets-tracker#how-is-deforestation-estimated" target="_blank" rel="noreferrer noopener">proxy of deforestation</a>. To do this, the Targets Tracker identifies losses that are likely to represent deforestation in and outside of the tropics by only including tree cover loss from certain drivers. Specifically, it includes the expansion of small-scale agriculture into humid tropical primary forests, as well as all loss in commodity-driven deforestation and urbanization. It excludes temporary losses, such as from fires or forestry activities.</p>



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



<p>In our <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&utm_medium=bitly&utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world’s deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.</p>



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



<h3 class="wp-block-heading">Brazil&nbsp;</h3>



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



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



<p>Separating out tree cover loss from fire in the UMD data has resulted in a much closer match between these two data sets. However, the two seem to show different trends for 2022 in the Brazilian Amazon: PRODES shows an 11% decrease in deforestation while UMD shows a 21% increase in non-fire primary forest loss.</p>



<h4 class="wp-block-heading">PRODES vs. UMD data in the Brazilian Amazon&nbsp;</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/13629875?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>The reason for this discrepancy may be the difference in reporting periods. PRODES uses the period between August and July (in this case, August 2021 to July 2022), while UMD uses the calendar year (January to December 2022). The Brazilian government’s DETER alerts — which track deforestation on a daily basis — suggest that deforestation rates increased in the second half of 2022 compared to previous years. DETER alerts detected a 2% decrease in deforestation between August 2021 and July 2022 compared to August 2020 to July 2021, but detected a 25% increase between the calendar year 2021 and 2022.</p>



<h4 class="wp-block-heading">DETER Alerts in the Brazilian Amazon&nbsp;</h4>



<div class="flourish-embed" data-src="story/1907320?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h3 class="wp-block-heading">Indonesia</h3>



<p>This year, WRI and Indonesia’s Ministry of Environment and Forestry (MoEF) are working together on a joint analysis to better understand the UMD/GFW primary forest loss findings through the MoEF’s data and definitions. Overlaying the UMD/GFW primary forest loss data for 2022 with the MoEF land cover map shows that 67% occurs within Indonesia’s official forest cover classes, with the remaining 33% taking place in mixed dry land agriculture, shrub, swamp shrub and other land cover types. Approximately 107 thousand hectares of loss was found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 107 thousand hectares, about 12 thousand hectares were within areas legally classified as primary forest in Indonesia. The upcoming joint analysis will improve our understanding of these issues.</p>



<figure class="wp-block-image size-large"><img decoding="async" loading="lazy" width="1348" height="1438" src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded.png" alt="Table showing UMD/GFW primary forest loss in hectares per MoEF forest classes in 2022" class="wp-image-16098" srcset="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded.png 1348w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-960x1024.png 960w, https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-768x819.png 768w" sizes="(max-width: 1348px) 100vw, 1348px" /></figure>



<h2 class="wp-block-heading">Data improvements over time result in inconsistencies in the data before 2015&nbsp;&nbsp;</h2>



<p>Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>. Changes to the tree cover loss detection algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013 make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture.&nbsp;Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.</p>



<p>To address these issues, we:&nbsp;&nbsp;</p>



<ul><li>Focus our analysis on trends post-2015.&nbsp;&nbsp;</li><li>Assess the three-year moving average to interpret longer-term trends.&nbsp;</li><li>Disregard the increase in loss post 2012 in places dominated by small-scale agriculture, like Central Africa. This increase is likely due to data improvements that better detect the loss later in the time series.&nbsp;&nbsp;</li></ul>



<h2 class="wp-block-heading">How will the data change over time?&nbsp;</h2>



<p>UMD is working on improvements to the tree cover extent and change data, which will result in changes to the data in the coming years. The new data will track the annual extent of tree cover and offer annual loss and gain information using a consistent algorithm across the time series. It will also track multiple gain and loss events in a single location, allowing us to better track dynamic areas of forest change such as forestry activities and wildfires. Incorporating annual gain and repeated loss events will provide a more complete picture of global forest change.</p>



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Global Forest Watch’s 2022 Tree Cover Loss Data Explained

<p>New tree cover loss data from the University of Maryland (UMD) and available on Global Forest Watch (GFW) shows high rates of primary forest loss in 2022 despite <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">political ambitions to curb loss</a>. What does the data measure, what is different this year and how does it compare to other official estimates of deforestation? Here is what you should know about the new data.</p>



<h2>What tree cover loss measures and how this differs from deforestation&nbsp;</h2>



<p>The tree cover loss data from UMD captures the annual loss of all trees taller than five meters for the calendar years between 2001 and 2022. Because optical satellite data collection can often be obscured by cloud cover in the humid tropics, some late-in-the year loss can be picked up in the next year following persistent cloud cover. This is a common occurrence in many tropical countries where cloud cover is prevalent.</p>



<p>The tree cover loss data includes the loss of trees in natural forests as well as in plantations and tree crops. Loss of this tree cover can be due to human or natural causes and can be permanent or temporary. Deforestation differs in that it only refers to a human-caused, permanent change from forest to another land use. Some forms of tree cover loss, such as conversion of a natural forest to agricultural land, are considered deforestation, while other forms of tree cover loss, such as timber harvesting in plantation forests or natural disturbances, are not. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Read more about the differences here</a>.</p>



<p>A new tool available on the Global Forest Review, the <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">Targets Tracker</a>, enables us to use tree cover loss data to measure progress toward deforestation commitments on a global level by using a coarse resolution <a href="https://research.wri.org/gfr/forest-targets-tracker#how-is-deforestation-estimated" target="_blank" rel="noreferrer noopener">proxy of deforestation</a>. To do this, the Targets Tracker identifies losses that are likely to represent deforestation in and outside of the tropics by only including tree cover loss from certain drivers. Specifically, it includes the expansion of small-scale agriculture into humid tropical primary forests, as well as all loss in commodity-driven deforestation and urbanization. It excludes temporary losses, such as from fires or forestry activities.</p>



<h2>Why we focus on the tropics&nbsp;</h2>



<p>In our <a href="https://gfw.global/3LkdrbQ" target="_blank" rel="noreferrer noopener">analysis of the UMD data</a>, we largely focus on tree cover loss in tropical primary forests because tropical forests experience <a href="https://research.wri.org/gfr/forest-extent-indicators/forest-loss?utm_campaign=treecoverloss2021&amp;utm_medium=bitly&amp;utm_source=GFWBlog#how-much-tree-cover-is-lost-in-tropical-versus-temperate-and-boreal-forests" target="_blank" rel="noreferrer noopener">the vast majority</a> (over 96%) of the world's deforestation, and loss in those areas has huge impacts on biodiversity and carbon storage. Even if these losses are eventually reversed, it will <a href="https://www.science.org/doi/10.1126/science.abh3629" target="_blank" rel="noreferrer noopener">take decades</a> for these habitats and carbon stocks to recover, and permanent biodiversity loss may occur.</p>



<h2>How does tree cover loss compare with official country data in Brazil and Indonesia?&nbsp;&nbsp;</h2>



<h3>Brazil&nbsp;</h3>



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



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/05/23-05-04-gfr-technical-blog_UMDvsProdes-EN.png" alt="UMD and PRODES differences explained" class="wp-image-15866"/></figure>



<p>Separating out tree cover loss from fire in the UMD data has resulted in a much closer match between these two data sets. However, the two seem to show different trends for 2022 in the Brazilian Amazon: PRODES shows an 11% decrease in deforestation while UMD shows a 21% increase in non-fire primary forest loss.</p>



<h4>PRODES vs. UMD data in the Brazilian Amazon&nbsp;</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/13629875?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>The reason for this discrepancy may be the difference in reporting periods. PRODES uses the period between August and July (in this case, August 2021 to July 2022), while UMD uses the calendar year (January to December 2022). The Brazilian government’s DETER alerts — which track deforestation on a daily basis — suggest that deforestation rates increased in the second half of 2022 compared to previous years. DETER alerts detected a 2% decrease in deforestation between August 2021 and July 2022 compared to August 2020 to July 2021, but detected a 25% increase between the calendar year 2021 and 2022.</p>



<h4>DETER Alerts in the Brazilian Amazon&nbsp;</h4>



<div class="flourish-embed" data-src="story/1907320?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h3>Indonesia</h3>



<p>This year, WRI and Indonesia’s Ministry of Environment and Forestry (MoEF) are working together on a joint analysis to better understand the UMD/GFW primary forest loss findings through the MoEF’s data and definitions. Overlaying the UMD/GFW primary forest loss data for 2022 with the MoEF land cover map shows that 67% occurs within Indonesia’s official forest cover classes, with the remaining 33% taking place in mixed dry land agriculture, shrub, swamp shrub and other land cover types. Approximately 107 thousand hectares of loss was found to be both within Indonesia’s official forest land cover classes and with a patch size larger than two hectares. Of these 107 thousand hectares, about 12 thousand hectares were within areas legally classified as primary forest in Indonesia. The upcoming joint analysis will improve our understanding of these issues.</p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded.png" alt="Table showing UMD/GFW primary forest loss in hectares per MoEF forest classes in 2022" class="wp-image-16098"/></figure>



<h2>Data improvements over time result in inconsistencies in the data before 2015&nbsp;&nbsp;</h2>



<p>Algorithm adjustments and better satellite data have <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">improved the tree cover loss data set over time</a>. Changes to the tree cover loss detection algorithm for the years 2011-2014 and 2015 onwards and the incorporation of Landsat 8 data starting in 2013 make it easier to detect smaller scale changes, such as loss due to fires, selective logging and shifting agriculture.&nbsp;Variations in satellite image availability also mean there are inconsistencies with the quality and number of images available to capture data each year.</p>



<p>To address these issues, we:&nbsp;&nbsp;</p>



<ul><li>Focus our analysis on trends post-2015.&nbsp;&nbsp;</li><li>Assess the three-year moving average to interpret longer-term trends.&nbsp;</li><li>Disregard the increase in loss post 2012 in places dominated by small-scale agriculture, like Central Africa. This increase is likely due to data improvements that better detect the loss later in the time series.&nbsp;&nbsp;</li></ul>



<h2>How will the data change over time?&nbsp;</h2>



<p>UMD is working on improvements to the tree cover extent and change data, which will result in changes to the data in the coming years. The new data will track the annual extent of tree cover and offer annual loss and gain information using a consistent algorithm across the time series. It will also track multiple gain and loss events in a single location, allowing us to better track dynamic areas of forest change such as forestry activities and wildfires. Incorporating annual gain and repeated loss events will provide a more complete picture of global forest change.</p>



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<p>De nouvelles données sur la perte de la couverture arborée, fournies par l’Université du Maryland (UMD) et disponibles sur le site Global Forest Watch (GFW), révèlent des taux élevés de perte de forêts primaires en 2022, en dépit des&nbsp;<a href="https://gfw.global/3L1xFpo">ambitions politiques visant à freiner cette perte</a>. Qu’est-ce qui est mesuré par ces données, quelles sont les différences cette années et comment se comparent-elles aux autres estimations officielles de la déforestation&nbsp;? Voici ce qu'il faut savoir sur ces nouvelles données.</p>



<h2>Qu’est-ce que la perte du couverture arborée et en quoi cela diffère-t-elle de la déforestation ?</h2>



<p>Les données recueillies par l'UMD concernant la perte du couvert arboré comprennent les pertes annuelles de tous les arbres d'une hauteur supérieure à cinq mètres entre 2001 et 2022. Dans les régions tropicales humides, où la couverture nuageuse est fréquente, la collecte de données par satellite optique peut être entravée, ce qui peut entraîner une récupération partielle des pertes en fin d'année lorsqu'il y a une couverture nuageuse persistante. Ce phénomène est courant dans de nombreux pays tropicaux où les nuages sont prédominants.</p>



<p>Ces données sur la perte du couvert arboré englobent les pertes d'arbres dans les forêts naturelles, les plantations et les cultures arboricoles. La perte de ce couvert arboré peut être attribuée à des facteurs humains ou naturels et peut être permanente ou temporaire. La déforestation se distingue de la perte de couvert arboré dans le sens où elle se réfère uniquement à un changement permanent de la forêt vers une autre utilisation des terres, causé par l'homme. Certaines formes de perte de couvert arboré, telles que la conversion d'une forêt naturelle en terres agricoles, sont considérées comme de la déforestation, tandis que d'autres formes de perte de couvert arboré, comme l'exploitation du bois dans les plantations forestières ou les perturbations naturelles, ne sont pas considérées comme de la déforestation. Pour en savoir plus sur les différences, vous pouvez cliquer <a href="https://research.wri.org/gfr/key-terms-definitions">ici</a>.</p>



<p>Le <a href="https://research.wri.org/gfr/forest-targets-tracker?utm_campaign=treecoverloss2022&amp;utm_medium=bitly&amp;utm_source=GFWBlog">Targets Tracker</a>, un nouvel outil disponible sur la Revue mondiale des forêts, permet de mesurer les progrès réalisés dans la lutte contre la déforestation à l'échelle mondiale en utilisant des données sur la perte du couvert arboré. Cet outil utilise une estimation approximative de la <a href="https://research.wri.org/gfr/forest-targets-tracker#how-is-deforestation-estimated">déforestation à résolution grossière</a> en identifiant les pertes qui pourraient représenter la déforestation, tant dans les régions tropicales que hors de celles-ci. Il se concentre uniquement sur les pertes du couvert arboré liées à certains facteurs, tels que l'expansion de l'agriculture à petite échelle dans les forêts tropicales humides primaires, ainsi que les pertes causées par la déforestation et l'urbanisation liées aux produits de base. Les pertes temporaires, telles que celles causées par les incendies ou les activités forestières, sont exclues de cette évaluation.</p>



<h2>Pourquoi nous concentrons-nous sur les tropiques ?</h2>



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



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



<h3>Brésil</h3>



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



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/07/FR-23-05-04-gfr-technical-blog_UMDvsProdes-FR.png" alt="Explication des différences UMD et PRODES" class="wp-image-16148"/></figure>



<p>Lorsqu'on isole la perte du couvert arboré causée par les incendies dans les données de l'UMD, on observe une correspondance beaucoup plus étroite entre ces deux ensembles de données. Cependant, pour l'année 2022 en Amazonie brésilienne, les deux sources semblent présenter des tendances différentes : PRODES indique une diminution de 11 % de la déforestation, tandis que l'UMD révèle une augmentation de 21 % de la perte de forêt primaire non liée aux incendies.</p>



<h4>Données PRODES vs UMD dans l’Amazonie brésilienne&nbsp;</h4>



<div class="flourish-embed flourish-chart" data-src="visualisation/14261358?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<p>La différence entre les données PRODES et UMD dans la région amazonienne du Brésil peut s'expliquer par l'écart entre les périodes de déclaration utilisées par ces sources. PRODES utilise la période d'août à juillet (par exemple, août 2021 à juillet 2022), tandis que l'UMD se base sur l'année civile (janvier à décembre 2022). Les alertes DETER du gouvernement brésilien, qui surveillent la déforestation quotidiennement, suggèrent que les taux de déforestation ont augmenté au cours du second semestre 2022 par rapport aux années précédentes. Les alertes DETER ont montré une diminution de 2 % de la déforestation entre août 2021 et juillet 2022 par rapport à la période d'août 2020 à juillet 2021, mais ont enregistré une augmentation de 25 % entre l'année civile 2021 et 2022.</p>



<h4>Alertes DETER en Amazonie brésilienne</h4>



<div class="flourish-embed" data-src="story/1962332?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h3>Indonésie</h3>



<p>Cette année, le WRI (World Resources Institute) et le ministère indonésien de l'environnement et des forêts (MoEF) travaillent ensemble sur une analyse commune dans le but de mieux comprendre les résultats de la perte de forêt primaire selon les données et les définitions de l'UMD/GFW (Université du Maryland/Global Forest Watch). En superposant les données de perte de forêt primaire de l'UMD/GFW pour l'année 2022 avec la carte de couverture terrestre du ministère indonésien de l'environnement et des forêts, il a été constaté que 67 % des pertes se produisent dans les classes officielles de couverture forestière de l'Indonésie, tandis que les 33 % restants se produisent dans des zones de terres agricoles mixtes sèches, d'arbustes, d'arbustes marécageux et d'autres types de couverture terrestre. De plus, il a été relevé qu'environ 107 000 hectares de perte se trouvent à la fois dans les classes officielles de couverture forestière de l'Indonésie et ont une taille de parcelle supérieure à deux hectares. Parmi ces 107 000 hectares, environ 12 000 hectares se situent dans des zones légalement désignées comme des forêts primaires en Indonésie. L'analyse conjointe en cours permettra d'améliorer notre compréhension de ces questions.</p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/07/FR-23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-FR.png" alt="Perte de forêt primaire de l'UMD/GFW en hectares par classe de forêt du MoEF en 2022" class="wp-image-16149"/></figure>



<h2>L’amélioration des données au fil du temps a entraîné des incohérences dans les données antérieures à 2015&nbsp;&nbsp;</h2>



<p>Les ajustements des algorithmes et de meilleures données satellitaires ont <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">amélioré les données de perte de la couverture arborée au cours du temps</a>. Les changements dans l’algorithme de détection de la perte de la couverture arborée pour les années 2011 à 2014 ainsi que 2015 et au-delà, de même que l’intégration des données de Landsat 8 à partir de 2013, facilitent la détection des changements à plus faible échelle, comme les pertes dues aux incendies, la coupe sélective et des changements agricoles.&nbsp;Les variations de disponibilité des images satellites signifient également qu’il y a des incohérences dans la qualité et le nombre d’images disponibles pour la capture de données chaque année.</p>



<p>Pour corriger ce problème, nous&nbsp;:</p>



<ul><li>Concentrons nos analyses sur les tendances après 2015</li><li>Évaluer la moyenne mobile sur trois ans pour interpréter les tendances à long terme.</li><li>Ne pas tenir compte de l’augmentation des pertes après 2012 dans les régions dominées par l’agriculture à petite échelle, comme l’Afrique centrale. Cette augmentation est probablement due à l’amélioration des données qui permet de mieux détecter la perte plus tard dans la série chronologique.</li></ul>



<h2>Comment les données évolueront-elles dans le temps ?</h2>



<p>L’UMD travaille à l’amélioration des données sur l’étendue et l’évolution du couvert arboré, ce qui entraînera des modifications des données dans les années à venir. Les nouvelles données permettront de suivre l’étendue annuelle du couvert arboré et fourniront des informations sur les pertes et les gains annuels à l’aide d’un algorithme cohérent pour l’ensemble de la série chronologique. Elles permettront également de suivre plusieurs événements de gain et de perte en un seul endroit, ce qui nous permettra de mieux suivre les zones dynamiques de changement forestier, telles que les activités forestières et les incendies de forêt. La prise en compte des gains annuels et des pertes répétées permettra d’obtenir une image plus complète de l’évolution des forêts à l’échelle mondiale.</p>



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Explication des données du Global Forest Watch sur la perte du couvert arboré en 2022

<p>Novos dados sobre a perda de cobertura arbórea da Universidade de Maryland (University of Maryland, UMD) disponíveis no Global Forest Watch (GFW) mostram taxas altas de perda de florestas primárias em 2022, apesar das&nbsp;<a href="https://gfw.global/3L1xFpo">ambições políticas de conter a perda</a>. O que os dados medem, o que há de diferente este ano e como eles se comparam a outras estimativas oficiais de desmatamento? Aqui está o que você precisa saber sobre os novos dados.</p>



<h2>O que a perda de cobertura arbórea mede e como ela difere do desmatamento</h2>



<p>Os dados de perda de cobertura arbórea da UMD capturam a perda anual de todas as árvores com mais de cinco metros de altura entre os anos de 2001 e 2022. Como a coleta de dados por satélite óptico pode, muitas vezes, ser atrapalhada por nuvens nos trópicos úmidos, algumas perdas no final do ano podem ser detectadas no próximo ano após uma cobertura de nuvens persistente. Essa é uma ocorrência comum em muitos países tropicais onde a cobertura de nuvens é predominante.</p>



<p>Os dados sobre perda de cobertura arbórea incluem a perda de árvores em florestas naturais, bem como em plantações e cultivo arbóreo. A perda dessa cobertura pode ser devido a causas humanas ou naturais, permanente ou temporária. O desmatamento difere por se referir apenas a uma mudança permanente e causada pelo homem, partindo do estado de floresta para outros usos da terra. Algumas formas de perda de cobertura arbórea, como a conversão de uma floresta natural em terras agrícolas, são consideradas desmatamento, enquanto outras formas, como a extração de madeira em florestas plantadas ou distúrbios naturais, não são. <a href="https://research.wri.org/gfr/key-terms-definitions">Leia mais sobre as diferenças aqui</a>.</p>



<p>Uma nova ferramenta disponível na Global Forest Review, o&nbsp;<a href="https://gfw.global/3L1xFpo">Targets Tracker</a>&nbsp;(Rastreador de metas), nos permite usar dados sobre perda de cobertura arbórea para medir o progresso em relação aos compromissos de desmatamento em nível global, usando uma&nbsp;<a href="https://research.wri.org/gfr/forest-targets-tracker#how-is-deforestation-estimated">aproximação de desmatamento</a>&nbsp;de resolução grosseira. Para isso, o Targets Tracker identifica as perdas que&nbsp;provavelmente&nbsp;representam o desmatamento dentro e fora dos trópicos, incluindo apenas a perda de cobertura arbórea de determinados fatores. Especificamente, ele inclui a expansão da agricultura de pequena escala em florestas primárias tropicais úmidas, bem como todas as perdas no desmatamento e na urbanização impulsionados por commodities. Também exclui perdas temporárias, como as decorrentes de incêndios ou atividades florestais.</p>



<h2>Por que nos concentramos nos trópicos?</h2>



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



<h2>Como a perda de cobertura arbórea se compara aos dados nacionais oficiais no Brasil e na Indonésia?</h2>



<h3>Brasil&nbsp;</h3>



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



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/PT-23-05-04-gfr-technical-blog_UMDvsProdes-PT.png" alt="Explicação das diferenças da UMD e do PRODES" class="wp-image-16134"/></figure>



<p>Separar a perda de cobertura arbórea de incêndios nos dados da UMD resultou em uma correspondência muito mais próxima entre esses dois conjuntos de dados. No entanto, eles parecem mostrar tendências diferentes na Amazônia brasileira no ano de 2022: o PRODES mostra uma redução de 11% no desmatamento, enquanto a UMD mostra um aumento de 21% na perda de floresta primária sem relação com incêndios.</p>



<h4>Dados do PRODES vs. UMD sobre a Amazônia brasileira&nbsp;</h4>



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<p>O motivo para essa discrepância pode ser a diferença nos períodos dos relatórios. O PRODES se utiliza do período entre agosto e julho (nesse caso, agosto de 2021 a julho de 2022), enquanto a UMD usa o ano-calendário (janeiro a dezembro de 2022). Os alertas do DETER do governo brasileiro, que rastreiam diariamente o desmatamento, sugerem que as taxas de desmatamento aumentaram no segundo semestre de 2022 em comparação com os anos anteriores. Os alertas do DETER detectaram uma redução de 2% no desmatamento entre agosto de 2021 e julho de 2022 em comparação com agosto de 2020 a julho de 2021, mas detectaram um aumento de 25% entre o ano-calendário de 2021 e 2022.</p>



<h4>Alertas do DETER na Amazônia brasileira</h4>



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<h3>Indonésia</h3>



<p>Neste ano, o WRI e o Ministério do Meio Ambiente e Florestas da Indonésia (MoEF) estão trabalhando juntos para analisar e compreender os dados da Universidade de Maryland e do GFW de perda de florestas primárias pela lente das definições e classificações do MoEF. Sobrepondo os dados de perda de florestas primárias de 2022 da UMD/GFW com os mapas de uso e cobertura da terra, podemos identificar que 67% da perda ocorre em classes de cobertura de florestas definidas pela Indonésia, enquanto 33% restantes se dão em áreas de outros usos, como agricultura mista, arbustos, áreas alagáveis e outros tipos de cobertura de vegetação. Aproximadamente 107 mil hectares de perda foram identificados dentro das classes oficiais do governo. Desses 107 mil hectares, cerca de 12 mil hectares estão áreas legalmente classificadas como florestas primárias na Indonésia. A análise conjunta irá melhorar nossa compreensão sobre essas questões. &nbsp;</p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/06/PT-23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-PT.png" alt="Perda de florestas primárias UMD/GFW em hectares segundo as classes do MoEF em 2022" class="wp-image-16135"/></figure>



<h2>Melhorias de dados ao longo do tempo resultam em inconsistências nos dados antes de 2015&nbsp;</h2>



<p>Ajustes de algoritmo e melhores dados de satélite <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/">melhoraram o conjunto de dados de perda de cobertura arbórea ao longo do tempo</a>. Mudanças no algoritmo de detecção de perda de cobertura arbórea para os anos de 2011 a 2014 e 2015 em diante e a incorporação de dados do Landsat 8 a partir de 2013 facilitam a detecção de mudanças de menor escala, como perdas devido a incêndios, corte seletivo e agricultura itinerante. As variações na disponibilidade de imagens de satélite também significam que há inconsistências com a qualidade e o número de imagens disponíveis para capturar dados a cada ano.</p>



<p>Para abordar esses problemas, nós:</p>



<ul><li>concentramos nossa análise em tendências após 2015;</li><li>avaliamos a média móvel de três anos para interpretar as tendências de longo prazo; e</li><li>desconsideramos o aumento da perda após 2012 em locais dominados pela agricultura de pequena escala, como a África Central. Esse aumento provavelmente se deve a melhorias nos dados que detectam melhor a perda posterior na série temporal.</li></ul>



<h2>Como os dados mudarão com o passar do tempo?</h2>



<p>A UMD está trabalhando para aprimorar os dados de extensão e alteração de cobertura arbórea, o que resultará em mudanças nos dados para os próximos anos. Os novos dados rastrearão a extensão anual de&nbsp;cobertura arbórea e oferecerão informações anuais de perdas e ganhos usando um algoritmo consistente em toda a série temporal. Eles também rastrearão vários eventos de ganho e perda em um mesmo local, o que nos permitirá rastrear melhor as áreas dinâmicas de mudança florestal, como atividades de reflorestamento e incêndios florestais. A incorporação de eventos de ganho e perda anuais e repetidos fornecerá um cenário mais completo das mudanças florestais globais.</p>



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Explicação dos dados do Global Forest Watch sobre a perda de cobertura arbórea de 2022

<p>Nuevos datos sobre la pérdida de cobertura arbórea facilitados por la Universidad de Maryland (University of Maryland, UMD) y disponibles en Global Forest Watch (GFW) muestran tasas altas de pérdida de bosques primarios en 2022, a pesar de las <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">ambiciones políticas para frenar la pérdida</a>. ¿Qué miden los datos, qué es diferente este año y cómo se compara con otras estimaciones oficiales de deforestación? Esto es lo que usted debe saber sobre los nuevos datos.&nbsp;</p>



<h2>Qué mide la pérdida de cobertura arbórea y cuál es su diferencia con la deforestación</h2>



<p>Los datos sobre la pérdida de cobertura arbórea de la UMD registran la pérdida anual de todos los árboles de más de cinco metros de altura en los años naturales comprendidos entre 2001 y 2022. Debido a que la recolección de datos ópticos por satélite a menudo puede estar oscurecida por la cobertura de nubes en los trópicos húmedos, se puede registrar alguna pérdida tardía en el año siguiente después de la cobertura de nubes persistente. Este fenómeno es común en muchos países tropicales en los que predomina la cobertura de nubes.&nbsp;&nbsp;</p>



<p>Los datos sobre la pérdida de cobertura arbórea incluyen la pérdida de árboles en bosques naturales, así como en plantaciones y cultivos de árboles. La pérdida de esta cobertura arbórea puede ser de origen humano o natural y puede ser permanente o temporal. La deforestación consiste en un cambio permanente y provocado por el hombre de un bosque a otro uso del suelo. Algunas formas de pérdida de cobertura arbórea, como la conversión de un bosque natural en tierras agrícolas, se consideran deforestación, mientras que otras formas de pérdida de cobertura arbórea, como la tala de madera en plantaciones forestales o las alteraciones naturales, no. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Lea más sobre las diferencias aquí</a>.&nbsp;&nbsp;</p>



<p>Una nueva herramienta disponible en Global Forest Review, el <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">Rastreador de Objetivos</a>, nos permite utilizar los datos de pérdida de cobertura arbórea para medir el progreso hacia el cumplimiento de los compromisos relacionados con la deforestación a nivel mundial gracias a un <a href="https://research.wri.org/gfr/forest-targets-tracker#how-is-deforestation-estimated" target="_blank" rel="noreferrer noopener">proxy de deforestación</a> de resolución gruesa. Para ello, el Rastreador de Objetivos identifica las pérdidas que podrían representar deforestación dentro y fuera de los trópicos incluyendo únicamente la pérdida de cobertura arbórea causada por determinados factores. Más específicamente, incluye la expansión de la agricultura a pequeña escala en los bosques primarios tropicales húmedos, así como toda pérdida en la deforestación provocada por productos básicos de consumo y urbanización. Quedan excluidas las pérdidas temporales, como las provocadas por incendios o actividades forestales.&nbsp;&nbsp;</p>



<h2>¿Por qué nos centramos en los trópicos?&nbsp;</h2>



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



<h2>¿Cómo se compara la pérdida de cobertura arbórea con los datos oficiales de Brasil e Indonesia?</h2>



<h3>Brasil&nbsp;</h3>



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



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/07/SP-23-05-04-gfr-technical-blog_UMDvsProdes-ES.png" alt="" class="wp-image-16173"/></figure>



<p>Al separar la pérdida de cobertura arbórea de los incendios en los datos de la UMD se obtuvo una correspondencia mucho más estrecha entre estos dos conjuntos de datos. Sin embargo, ambos parecen mostrar tendencias distintas para 2022 en la Amazonia brasileña: PRODES muestra un descenso del 11 % en la deforestación, mientras que UMD muestra un aumento del 21 % en la pérdida de bosques primarios no afectados por incendios.&nbsp;</p>



<h4>Datos de PRODES vs. Universidad de Maryland (UMD) en la Amazonia brasileña&nbsp;</h4>



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<p>El motivo de esta discrepancia puede ser la diferencia en los periodos reportados. PRODES utiliza el periodo que comprende entre agosto y julio (en este caso, de agosto de 2021 a julio de 2022), mientras que la UMD utiliza el año calendario (de enero a diciembre de 2022). Las alertas DETER del gobierno brasileño, que realizan un seguimiento diario de la deforestación, sugieren que las tasas de deforestación aumentaron en el segundo semestre de 2022 en comparación con años anteriores. Las alertas DETER detectaron una disminución del 2 % de la deforestación entre agosto de 2021 y julio de 2022 en comparación con agosto de 2020 y julio de 2021, pero detectaron un aumento del 25 % entre el año calendario 2021 y 2022.&nbsp;</p>



<h4>Alertas DETER en la Amazonia brasileña&nbsp;</h4>



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<h3>Indonesia&nbsp;&nbsp;</h3>



<p>Este año, WRI y el Ministerio de Medio Ambiente y Silvicultura (MoEF) de Indonesia están trabajando juntos en un análisis conjunto para comprender mejor los hallazgos de la pérdida de bosque primario según UMD/GFW a través de los datos y definiciones del MoEF. Al superponer los datos de pérdida de bosque primario de UMD/GFW para 2022 con el mapa de cobertura de tierras del MoEF, se observa que el 67% ocurre dentro de las clases oficiales de cobertura forestal de Indonesia, mientras que el 33% restante tiene lugar en áreas de agricultura mixta de tierras secas, arbustos, pantanos y otros tipos de cobertura del suelo. Aproximadamente, se encontró una pérdida de 107 mil hectáreas que se ubicaba tanto dentro de las clases oficiales de cobertura forestal de Indonesia con un área mayor a dos hectáreas. De estas 107 mil hectáreas, alrededor de 12 mil hectáreas se encontraban dentro de áreas legalmente clasificadas como bosque primario en Indonesia. El próximo análisis conjunto mejorará nuestra comprensión de estos problemas.&nbsp;</p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/07/SP-23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-ES.png" alt="" class="wp-image-16174"/></figure>



<h2>Las mejoras en los datos a lo largo del tiempo generan incoherencias en los datos anteriores a 2015 </h2>



<p>Los ajustes del algoritmo y la mejora de los datos satelitales han <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">perfeccionado el conjunto de datos de pérdida de cobertura arbórea a lo largo del tiempo</a>. Los cambios en el algoritmo de detección de la pérdida de cobertura arbórea para los años 2011-2014 y 2015 en adelante y la incorporación de los datos de Landsat 8 a partir de 2013 facilitan la detección de cambios a menor escala, como la pérdida debida a los incendios, la explotación selectiva y la agricultura migratoria. Las variaciones en la disponibilidad de imágenes satelitales también significa que hay inconsistencias con la calidad y el número de imágenes disponibles para registrar datos cada año.&nbsp;&nbsp;</p>



<p>Para abordar estos problemas, realizamos lo siguiente:&nbsp;&nbsp;</p>



<ul><li>Nos centramos en nuestros análisis de tendencias después del 2015.&nbsp;&nbsp;</li><li>Se evalúaa el promedio móvil de tres años para interpretar las tendencias a más largo plazo.&nbsp;</li><li>Se ignora el aumento de las pérdidas después de 2012 en lugares donde predomina la agricultura a pequeña escala, como África Central. Es probable que este aumento se deba a la mejora de los datos, que detectan mejor las pérdidas más adelante en la serie de tiempo.&nbsp;&nbsp;</li></ul>



<h2>¿Cómo evolucionarán los datos con el tiempo?&nbsp;</h2>



<p>La UMD está trabajando para mejorar los datos de cambios y extensión de la cobertura arbórea, lo que resultará en cambios en los datos en los próximos años. Los nuevos datos permitirán hacer un seguimiento de la extensión anual de la cobertura arbórea y ofrecerán información sobre las pérdidas y ganancias anuales utilizando un algoritmo consistente para toda la serie de tiempo. También hará un seguimiento de varios episodios de ganancia y pérdida en una única ubicación, lo que nos permitirá detectar mejor las áreas dinámicas de cambio en los bosques, como actividades forestales e incendios forestales. La incorporación de episodios de ganancia anual y pérdida recurrente proporcionará una imagen más completa del cambio en los bosques a escala mundial.&nbsp;&nbsp;</p>



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Explicación de los datos de Global Forest Watch sobre la pérdida de cobertura arbórea en 2022

<p>Data kehilangan tutupan pohon yang baru dari University of Maryland (UMD) dan tersedia di Global Forest Watch (GFW) menunjukkan tingginya laju kehilangan hutan primer pada tahun 2022 kendati adanya <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">ambisi politik untuk mengatasi kehilangan hutan</a>. Apa yang diukur oleh data tersebut, apa yang berbeda tahun ini, dan bagaimana perbandingannya dengan perkiraan resmi deforestasi lainnya? Inilah yang harus Anda ketahui tentang data baru.&nbsp;</p>



<h2>Apa yang diukur oleh kehilangan tutupan pohon dan apa perbedaannya dengan deforestasi&nbsp;</h2>



<p>Data kehilangan tutupan pohon dari UMD mencatat kehilangan semua pohon yang memiliki tinggi lebih dari 5 meter setiap tahunnya selama tahun kalender antara 2001 sampai 2022. Karena pengumpulan data satelit optik kerap terkendala tutupan awan di daerah tropis yang lembap, data kehilangan tutupan pohon yang terjadi pada akhir tahun dapat terdeteksi di tahun berikutnya setelah tutupan awan pekat berkurang. Hal ini umum terjadi di banyak negara tropis yang lazim mengalami tutupan awan.&nbsp;&nbsp;</p>



<p>Data kehilangan tutupan pohon ini tidak hanya mencakup kehilangan pohon di hutan alami, melainkan juga di perkebunan dan tanaman pohon. Kehilangan tutupan pohon ini dapat diakibatkan oleh penyebab manusia atau alam serta dapat bersifat permanen atau sementara. Deforestasi berbeda karena deforestasi hanya mengacu pada perubahan dari hutan menjadi penggunaan lahan lainnya yang bersifat permanen dan disebabkan oleh manusia. Beberapa bentuk kehilangan tutupan pohon, seperti konversi hutan alami menjadi lahan pertanian, dianggap sebagai deforestasi, sedangkan bentuk kehilangan tutupan pohon lainnya, seperti pemanenan kayu di hutan perkebunan atau gangguan alami, tidak termasuk deforestasi. <a href="https://research.wri.org/gfr/key-terms-definitions" target="_blank" rel="noreferrer noopener">Baca selengkapnya mengenai perbedaannya di sini</a>.&nbsp;&nbsp;</p>



<p>Tersedia alat bantu baru di Global Forest Review, <a href="https://gfw.global/3L1xFpo" target="_blank" rel="noreferrer noopener">Pelacak Target</a>, yang memungkinkan kami menggunakan data kehilangan tutupan pohon untuk mengukur kemajuan komitmen deforestasi pada tingkat global dengan menggunakan resolusi kasar <a href="https://research.wri.org/gfr/forest-targets-tracker%22%20/l%20%22how-is-deforestation-estimated" target="_blank" rel="noreferrer noopener">proksi deforestasi</a>. Untuk melakukan hal ini, Pelacak Target mengidentifikasi kehilangan yang kemungkinan mewakili deforestasi di dalam dan di luar daerah tropis hanya dengan mencakup kehilangan tutupan pohon oleh penyebab tertentu. Secara khusus, alat bantu ini mencakup perluasan pertanian berskala kecil menuju hutan primer tropis lembap, serta kehilangan dalam urbanisasi dan deforestasi yang didorong oleh komoditas. Alat bantu ini mengecualikan kehilangan sementara, seperti karena kebakaran atau aktivitas kehutanan.&nbsp;&nbsp;</p>



<h2>Mengapa kami berfokus pada daerah tropis&nbsp;</h2>



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



<h2>Bagaimana perbandingan kehilangan tutupan pohon dengan data negara resmi di Brasil dan Indonesia?&nbsp;&nbsp;</h2>



<h3>Brasil&nbsp;</h3>



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



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/08/23-05-04-gfr-technical-blog_UMDvsProdes-IND.png" alt="" class="wp-image-16218"/></figure>



<p>Memisahkan kehilangan tutupan pohon karena kebakaran dalam data UMD memberikan perbandingan yang lebih mirip antara kedua kumpulan data ini. Namun demikian, keduanya tampak menunjukkan tren yang berbeda untuk tahun 2022 di Amazon Brasil: PRODES menunjukkan penurunan deforestasi sebesar 11%, sedangkan UMD menunjukkan kenaikan kehilangan hutan primer nonkebakaran sebesar 21%.&nbsp;</p>



<h4>PRODES vs. Data UMD di Amazon Brasil&nbsp;</h4>



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<p>Alasan dari ketidaksesuaian ini mungkin dikarenakan oleh perbedaan dalam periode pelaporan. PRODES menggunakan periode antara Agustus sampai Juli (dalam hal ini, Agustus 2021 sampai Juli 2022), sedangkan UMD menggunakan tahun kalender (Januari sampai Desember 2022). Peringatan DETER dari pemerintah Brasil — yang melacak deforestasi setiap harinya — menandakan tingkat deforestasi naik pada paruh kedua tahun 2022 dibandingkan dengan tahun sebelumnya. Peringatan DETER mendeteksi penurunan deforestasi sebesar 2% antara Agustus 2021 sampai Juli 2022 dibandingkan dengan periode Agustus 2020 sampai Juli 2021, namun mendeteksi peningkatan sebesar 25% antara tahun kalender 2021 dan 2022.&nbsp;</p>



<h4>Peringatan DETER di Amazon Brasil</h4>



<div class="flourish-embed" data-src="story/1958956?163031"><script src="https://public.flourish.studio/resources/embed.js"></script></div>



<h3>Indonesia&nbsp;&nbsp;</h3>



<p>Tahun ini, WRI dan Kementerian Lingkungan Hidup dan Kehutanan (KLHK) bekerja sama dalam analisis gabungan untuk memahami lebih lanjut temuan kehilangan hutan primer UMD/GFW menggunakan data dan definisi dari KLHK. Membandingkan data kehilangan hutan primer UMD/GFW 2022 dengan peta tutupan lahan KLHK memperlihatkan bahwa 67% kehilangan terjadi dalam beberapa kelas tutupan hutan resmi Indonesia, sementara 33% lainnya mencakup lahan pertanian kering campuran, belukar, rawa, dan tipe tutupan lahan lainnya.&nbsp;</p>



<p>Sekitar 107 ribu hektare hutan primer yang hilang ditemukan pada data kelas tutupan hutan resmi Indonesia dan dengan potongan yang lebih besar dari 2 hektare. Dari 107 ribu hektare tersebut, sekitar 12 ribu hektare mencakup Kawasan yang secara legal diklasifikasikan sebagai hutan primer di Indonesia. Analisis gabungan yang akan diterbitkan segera akan memperdalam pemahaman kita akan hal ini. </p>



<figure class="wp-block-image size-large"><img src="https://dev-global-forest-watch-blog.pantheonsite.io/wp-content/uploads/2023/08/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-IND.png" alt="" class="wp-image-16219"/></figure>



<h2>Perbaikan data dari waktu ke waktu menghasilkan inkonsistensi dalam data sebelum 2015&nbsp;&nbsp;</h2>



<p>Penyesuaian algoritme dan data satelit yang lebih baik telah <a href="https://www.globalforestwatch.org/blog/data-and-research/tree-cover-loss-satellite-data-trend-analysis/" target="_blank" rel="noreferrer noopener">meningkatkan kumpulan data kehilangan tutupan pohon dari waktu ke waktu</a>. Perubahan pada algoritme pendeteksian kehilangan tutupan pohon untuk tahun 2011-2014 dan 2015 dan seterusnya dan penggabungan data Landsat 8 mulai tahun 2013 memudahkan untuk mendeteksi perubahan skala yang lebih kecil, seperti kehilangan karena kebakaran, tebang pilih, dan pertanian berpindah. Variasi ketersediaan citra satelit juga berarti adanya inkonsistensi dengan kualitas dan jumlah citra yang tersedia untuk diambil data setiap tahunnya.&nbsp;&nbsp;</p>



<p>Untuk mengatasi masalah ini, kami:&nbsp;&nbsp;</p>



<ul><li>Memfokuskan analisis kami pada tren pasca-2015.&nbsp;&nbsp;</li><li>Menilai rata-rata pergerakan selama tiga tahun untuk menginterpretasikan tren dalam jangka waktu yang lebih lama.&nbsp;</li><li>Mengabaikan peningkatan kehilangan pasca-2012 di tempat yang didominasi oleh pertanian skala kecil, seperti Afrika Tengah. Peningkatan ini kemungkinan diakibatkan oleh perbaikan data yang mendeteksi kehilangan tersebut secara lebih baik di kemudian waktu.&nbsp;&nbsp;</li></ul>



<h2>Bagaimana data akan berubah dari waktu ke waktu?&nbsp;</h2>



<p>UMD tengah mengupayakan perbaikan terhadap sebaran tutupan pohon dan data perubahan, yang akan menghasilkan perubahan terhadap data di tahun-tahun mendatang. Data yang baru akan melacak sebaran tutupan pohon tahunan serta menawarkan informasi kehilangan dan perolehan tahunan menggunakan algoritme yang konsisten di seluruh rangkaian waktu. Data ini juga akan melacak beberapa peristiwa perolehan dan kehilangan di satu lokasi, yang memungkinkan kami melacak dengan lebih baik luas dinamis perubahan hutan, seperti kegiatan kehutanan dan kebakaran hutan. Menggabungkan peristiwa perolehan dan kehilangan berulang tahunan akan memberikan gambaran perubahan hutan global yang lebih lengkap.&nbsp;&nbsp;</p>



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Penjelasan Data Kehilangan Tutupan Pohon 2022 dari Global Forest Watch