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Brazil

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forest change

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restoration

restoration | Global Forest Watch Blog

Katie Reytar

Jefferson Ferreira-Ferreira

Luciana Alves

Carlos Leandro de Oliveira Cordeiro

Miguel Calmon

<p>In 2022, Global Forest Watch (GFW) in partnership with <a href="https://landcarbonlab.org/" target="_blank" rel="noreferrer noopener">Land & Carbon Lab</a> <a href="https://www.globalforestwatch.org/blog/data-and-tools/new-gfw-tree-cover-gain-net-change-data/">released new tree cover gain data</a> from the University of Maryland that shows <a href="https://www.wri.org/insights/tracking-global-tree-cover-gain" target="_blank" rel="noreferrer noopener">where and how much land has gained tree cover globally</a> between 2000 and 2020. This data set provided much-needed information to complement data on tree cover loss, revealing a more complete picture of forest change dynamics.</p>



<p>We’ve <a href="https://www.globalforestwatch.org/blog/data-and-tools/new-gfw-tree-cover-gain-net-change-data/">now added a new dimension</a> to the gain data showing not just the cumulative change between 2000 and 2020, but also how much tree cover was gained in the intervening five-year periods — 2000-2005, 2005-2010, 2010-2015, and 2015-2020. Data from these intervals provides valuable information on the global trajectory and pace of gain, which can help us better track the extent to which tree cover is coming back. This can include gain resulting from <a href="https://www.wri.org/forest-landscape-restoration-principles" target="_blank" rel="noreferrer noopener">forest landscape restoration</a>, which typically involves a variety of strategic interventions that lead to increased tree cover across the landscape.</p>



<p>While we now have a better global picture of tree cover gain, the data captures a wide variety of forms of tree cover, from industrial scale monoculture of Eucalyptus and oil palm — which are not typically considered forms of restoration — to regenerating natural forests. This means that though the data does include gain resulting from forest landscape restoration, it is difficult to identify or measure the success of restoration investments and interventions using the tree cover gain data alone.</p>



<p>Some contextual data, such as mapped plantations from the <a href="https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2" target="_blank" rel="noreferrer noopener">Spatial Database of Planted Trees</a>, can provide additional information that can help users distinguish gain in plantations from more natural forms of tree cover gain. However, local information — such as geographic information on where and what type of restoration activities are taking place — is the most useful for interpreting what is actually occurring in different locations and whether it would count toward <a href="https://research.wri.org/gfr/forest-targets-tracker" target="_blank" rel="noreferrer noopener">achieving global targets</a>.</p>



<p>In this blog, we explore a few such examples in Brazil where we see areas of gain, but that represent very different scenarios for what is happening on the ground.</p>



<h2 class="wp-block-heading">Tree cover gain data can capture restoration successes</h2>



<h3 class="wp-block-heading"><em>Landless Rural Workers Movement supports forest regeneration in Pará State</em></h3>



<p>The Landless Workers’ Movement (MST) is a national social movement in Brazil that focuses on agrarian land reform, seeking to achieve a self-sustainable way of life for rural poor in the country. It is the largest rural network of smallholder farmers in Latin America, supporting more than 450,000 families.</p>



<p>In 2004, MST began occupying the Abril Vermelho settlement in Pará State, an area of 6,668 hectares that had previously been used for oil palm monoculture and belonged to a bankrupt palm oil company. The land was then designated for agrarian reform and became a rural settlement. Today, Abril Vermelho has more than 300 families <a href="https://www.wri.org/insights/amazon-restoration-brazil-agrarian-reform-settlements" target="_blank" rel="noreferrer noopener">dedicated to producing food with sustainable practices</a>.</p>



<p>When MST arrived, the land was degraded including an unbalanced insect population, poor soil quality and deteriorated water springs. MST first planted small crops of beans, corn and manioc. Over the years they experimented with several management practices to improve soil productivity, cope with pests and improve water quality and quantity. The settled families practiced agroecology, an approach focused on the recovery of native vegetation through the elimination of oil palm production areas, allowing the forest to regenerate.</p>



<p>The main practice in the settlement today is agroforestry, where food crops are introduced in the regenerating forests, or native tree species are planted among food crops.</p>



<p>The tree cover gain data captures the successful restoration efforts that resulted from forest regeneration and native tree species planting. The data shows that the landscape of Abril Vermelho gained a total of 1,981 hectares between 2000 and 2020, with the vast majority (89%) of the new tree cover gained between 2000 and 2010, corresponding with when MST began occupying and settling the area.</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/20025052?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/20025052/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



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<p><em>Blue pixels show the tree cover gain in the Abril Vermelho settlement area.</em></p>



<p>Forest restoration and assisted natural regeneration in the settlement became part of MST’s program “Plant Trees – Produce Healthy Food” launched in 2020, which aims to plant 100 million trees all over the country. Besides being a significant contribution for their agroecology practices, this initiative is a strong contributor to Brazilian restoration goals and Nationally Determined Contributions (NDCs). Future updates to the gain data combined with local information will help to visualize and measure the impact of this initiative.</p>



<h3 class="wp-block-heading"><em>Restoration of ecological corridors gives hope to black lion tamarin population in São Paulo</em></h3>



<p>In the Pontal do Paranapanema region, located in the western corner of São Paulo state between the Parana and Paranapanema Rivers, a project to save the endangered black lion tamarin evolved into a landscape-scale restoration project that has successfully restored thousands of hectares of forest cover to support the lion tamarins’ habitat. Once thought to be extinct, the population of black lion tamarins has recovered significantly, largely due to the landscape-scale restoration in and around the only forests where they are known to exist.</p>



<p><a href="https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200939" target="_blank" rel="noreferrer noopener">A combination of factors</a> and collaboration across several sectors led to the successful restoration of the region. These factors included scientists and researchers educating local farmers about conservation and agroforestry, agreements with government to include conservation area in land titles for newly settled farming communities, and coordination among local farmers and researchers to set aside certain areas to create forest corridors that allow the tamarins and other wildlife to move freely between the larger forests of Morro do Diabo State Park and the Mico Leão Preto Ecological Station. Brazil’s forest code, which obligates landowners to reforest riparian areas, created an incentive for reforestation of these corridors, and funding for the restoration activities was secured through dozens of organizations via voluntary carbon markets.</p>



<p>The tree cover gain data shows the results of these efforts for forest recovery. The Pontal do Paranapanema landscape gained a total of 2,476 hectares of tree cover between 2000 and 2020 with nearly three-quarters of that gain occurring after 2010, which reflects the dedicated efforts of the community to reforest the ecological corridors that began in 2012.</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/20025099?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/20025099/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



<p>The tree cover gain data also shows the results of restoration across the ecological corridors connecting the Morro do Diabo State Park to the Mico Leão Preto Ecological Station.</p>



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<p><em>Blue pixels show the tree cover gain in forested corridors between the forests of Morro do Diabo State Park, the Mico Leão Preto Ecological Station, other patch forest and the Paranapanema River.</em></p>



<h2 class="wp-block-heading">Not all tree cover gain is restoration</h2>



<h3 class="wp-block-heading"><em>Production forests dominate the landscape in Southern Bahia state</em></h3>



<p>Southern Bahia in eastern Brazil is known for its diverse ecosystems and agricultural practices. Though one of the main agricultural activities, growing cacao, uses agroforestry systems, the region has also been a significant hub for cultivating eucalyptus over the past few decades. Eucalyptus is a non-native species that is used in manufacturing pulp and paper as well as various other uses like energy.</p>



<p>Historically, companies were drawn to the region due to the availability of large, inexpensive tracts of land that were ideal for mechanized practices. The favorable climate and soil conditions supported the rapid growth of tree species like eucalyptus and pine. Additionally, government policies, including fiscal incentives and investments from the National Social and Economic Development Bank (BNDES), further encouraged the expansion of tree plantations. Many of these plantations were established on previously degraded lands implementing ecological zoning systems to ensure ecosystems protection, promoting ecological restoration and native species in a “mosaic approach,” and adopting sustainable forestry practices, such as reduced-impact logging and maintaining buffer zones around water bodies to prevent soil erosion and protect water quality.</p>



<p>This has attracted large companies to grow and harvest eucalyptus, resulting in high eucalyptus production. Due to the expansion of eucalyptus and native species, the Southern Bahia region gained 220,100 hectares of tree cover between 2000 and 2020, which translates to a more than 5% increase in tree cover.</p>



<div class="flourish-embed flourish-chart" data-src="visualisation/20023118?163031"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img decoding="async" src="https://public.flourish.studio/visualisation/20023118/thumbnail" width="100%" alt="chart visualization" /></noscript></div>



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<p><em>In Bahia state, tree cover gain is widespread, but represents a large network of eucalyptus plantations and monoculture, which is typically not considered restoration.</em></p>



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<p><em>Detailed view of eucalyptus plantations in Nova Viçosa, Bahia, Brazil.</em></p>



<p>Despite the increase in tree cover captured by the tree cover gain data, these plantations are not typically considered restoration. This is because plantations, especially those involving non-native species, do not restore the original biodiversity and ecological functions of the native ecosystems they replace. While they may contribute to carbon sequestration and other ecosystem services, they are not considered true ecological restoration.</p>



<p>In the case of Southern Bahia, the consolidation of land ownership by the companies cultivating eucalyptus has caused conflict with local organizations and communities due to impact on the landscape, among other issues. An initiative was established in 2005 that led to a national Forest Dialogue and regional forums to promote collaborative efforts to minimize the environmental impacts of eucalyptus plantations. Now, a program led by the Forest Dialogue has assigned several locations as conservation areas, and the companies are enacting restoration programs and initiatives to form ecological corridors.</p>



<p>Future data will reveal whether the region continues to gain tree cover. However, more localized information will be necessary to determine how much of this gain can be attributed to the restoration efforts led by those companies and how much is due to plantations. As a start, we know that there are 19,300 hectares of restoration reported in the Bahia state in the Brazilian Restoration and Reforestation Observatory, although the amount of reported restoration is known to be underrepresented.</p>



<h2 class="wp-block-heading">What does this mean for using the tree cover gain data?</h2>



<p>The new data on tree cover gain represents a significant step forward in understanding the dynamics of forest change from space, and as the best-available global data, significantly expands the scale at which we can view such changes. Being able to see this gain in five-year intervals, instead of just as one 20-year cumulative sum, provides important detail on the timing of when new tree cover was established and the trajectory, which are important for monitoring efforts and tracking restoration targets over time. Additionally, when combined with data on tree cover loss, it provides the missing piece on calculating net tree cover change.</p>



<p>However, there are limitations in how this data can be used. Satellites can more easily capture taller trees with at least 10% canopy cover — which means that newly sprouting growth is seldom counted. Furthermore, even though the new five-year periods provide a more complete picture of gain, there is still a lag time between when tree growth begins and when it can be detected.</p>



<p>The Bonn Challenge, UN Decade on Ecosystem Restoration and other major global initiatives have helped national governments and other communities to set priorities and channel investments toward restoration, but global data on tree cover gain or other types of land cover change alone can’t measure their success. Detailed landscape restoration plans, as well as georeferenced information on local projects and investments, are still important for understanding the scope and scale of restoration success.</p>



<p>For example, the <a href="https://www.africa.terramatch.org/" target="_blank" rel="noreferrer noopener">TerraFund for AFR100</a> initiative, which channels funding to restoration projects in Africa, has developed a <a href="https://terramatchsupport.zendesk.com/hc/en-us/articles/21178354112539-The-TerraFund-Monitoring-Reporting-and-Verification-Framework" target="_blank" rel="noreferrer noopener">Monitoring, Reporting, and Verification Framework</a> that outlines a clear set of indicators and standards for how to measure and report information on restoration progress, integrating both field-based and satellite-based sources of data.</p>



<p>Further advancements to the tree cover gain data will continue to improve its usefulness. For example, dedicated research to differentiate natural tree cover gain from industrial or plantation-based gain has <a href="https://www.nature.com/articles/s41893-022-00904-w" target="_blank" rel="noreferrer noopener">been successfully applied</a> to a prior version of tree cover gain data, with plans to expand this research to the 20-year gain data shortly. But in addition to improving global data, tying local information on actions to the global data on change will continue to be critical for monitoring restoration at scale, and having confidence that we are achieving our goals to restore degraded landscapes.</p>



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What can Tree Cover Gain Data Tell Us About Restoration? Perspectives from Brazil

O que os dados de ganho de cobertura arbórea podem nos dizer sobre restauração? Perspectivas do Brasil

This blog explores case studies in Brazil where we see tree cover gain, but that are not always restoration.

What can Tree Cover Gain Data Tell Us About Restoration? Brazil Case Studies

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



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<p></p>



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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

 

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

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



<iframe width="560" height="450" src="https://www.youtube.com/embed/IipacbHyMGU?si=O7qrSPWsK16t1gcT" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



<iframe width="560" height="450" src="https://www.youtube.com/embed/IipacbHyMGU?si=O7qrSPWsK16t1gcT" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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

 

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

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

Voices of GFW: Vinicius Silgueiro, Instituto Centro de Vida

overview of a river in Pantanal, Brazil

amazon

amazon | Global Forest Watch Blog

Congo Basin

Congo Basin | Global Forest Watch Blog

deforestation

deforestation | Global Forest Watch Blog

Indonesia

Indonesia | Global Forest Watch Blog

Mikaela Weisse

Liz Goldman

Sarah Carter

Tropical Forest Loss Drops Steeply in Brazil and Colombia, but High Rates Persist Overall

Despite progress in some countries, the rate of tropical primary forest loss in 2023 remained persistent, according to new data from UMD on GFW.

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

Data &amp; Tools

Data &amp; Tools | Global Forest Watch Blog

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



<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>



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



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



<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>
			<td style="width:350px;height:39px;">
			<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>



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



<p></p>



<p></p>

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

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



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



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



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



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



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



<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">mejorado el conjunto de datos de pérdida de cobertura arbórea a lo largo del tiempo</a>.&nbsp;&nbsp;</p>



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



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



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



<p>Para abordar estas inconsistencias, nosotros:&nbsp;&nbsp;</p>



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



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



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



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



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



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



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



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



<p><em>Brasil</em> &nbsp;</p>



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



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



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



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



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



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



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

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

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



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



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



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



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



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



<h2 class="wp-block-heading">L’amélioration des données au fil du temps entraîne des incohérences dans les séries de données chronologiques&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/" target="_blank" rel="noreferrer noopener">amélioré les données de perte de la couverture arborée au cours du temps</a>.&nbsp;&nbsp;</p>



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



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



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



<p>Pour gérer ces incohérences, nous :&nbsp;&nbsp;</p>



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



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



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



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



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



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



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



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



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



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



<p><em>Brésil</em> &nbsp;</p>



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



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



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



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



<p><em>Indonésie</em>&nbsp;</p>



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



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

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

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



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



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



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



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



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



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



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



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



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



<p>Untuk menangani ketidakkonsistenan ini, kami:&nbsp;&nbsp;</p>



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



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



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



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



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



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



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



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



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



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



<p><em>Brasil</em> &nbsp;</p>



<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 metodologi dan definisi yang penting untuk dipahami ketika membandingkan kedua sumber data ini. PRODES mengukur deforestasi tebang habis dan kehilangan hutan antropogenik akibat kebakaran yang lebih besar dari 6,25 hektare, sedangkan UMD mencatat kehilangan yang lebih besar dari 0,09 hektare dari semua pohon yang tingginya lebih dari 5 meter. Keduanya merupakan langkah penting yang membantu kita memahami bagaimana hutan berubah karena deforestasi, kebakaran hutan, dan gangguan kecil pada kanopi hutan, semuanya dapat berdampak pada iklim, keanekaragaman hayati, dan jasa ekosistem. &nbsp;</p>



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



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



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



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



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



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



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

Penjelasan Data Kehilangan Tutupan Pohon 2023 dari Global Forest Watch

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

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

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



<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://content.globalforestwatch.org/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://content.globalforestwatch.org/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded.png 1348w, https://content.globalforestwatch.org/wp-content/uploads/2023/06/23-05-04-gfr-technical-blog_Indonesia-error-bars-table-unrounded-960x1024.png 960w, https://content.globalforestwatch.org/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>



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

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



<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>



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<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>



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<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://content.globalforestwatch.org/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://content.globalforestwatch.org/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>



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<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>



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<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://content.globalforestwatch.org/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://content.globalforestwatch.org/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://content.globalforestwatch.org/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://content.globalforestwatch.org/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://content.globalforestwatch.org/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://content.globalforestwatch.org/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>



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<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://content.globalforestwatch.org/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

New data shows high rates of primary forest loss in 2022. What does the data measure and how does it compare to other official estimates of deforestation?

Global Forest Watch’s 2022 Tree Cover Loss Data Explained | GFW Blog

fires

fires | Global Forest Watch Blog

Luiz Amaral

Marcelo Matsumoto

Thailynn Munroe

<p>Fires across Brazil caught the world’s attention this August, with fears that burning — and subsequent damage to the Amazon rainforest — would only intensify when peak fire season hit in September. However, Global Forest Watch (GFW) analysis of NASA data shows that fire alerts in the Amazon decreased by 34% between August and September.</p>



<h3 class="wp-block-heading">Amazon Fires Are Down. Can We Thank Brazil’s Fire Ban?</h3>



<p>Fire season in Brazil typically peaks in September, so this reduction in fires in the Amazon is unexpected. Other forested regions in Brazil showed an increase in fire alerts in September, including the Cerrado — a tropical savanna where fire is a more natural feature of the landscape. In the wetter, Amazon region, fires are rarely natural and almost all burning is related to human activity.</p>



<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="fire counts, August to september" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Alerts-03.png" width="100%">Amazônia—the Amazon biome— was the only region of Brazil that saw a drop in fires in September, which deviates from the normal upward trend in fires across the country.</div>



<p>So what explains the 34% decrease in fire alerts in the Amazon? It likely wasn’t the weather, as September was slightly drier than normal. The other potential explanation is the fire ban enacted at the end of August. During the peak of the crisis, when both national and international media attention was focused on the Amazon, President Jair Bolsonaro issued a TV and radio announcement declaring “zero tolerance” for environmental crimes. Law enforcement was dispatched to fight the fires and the government enacted a two-month ban on the use of fire to clear land. This ban was in effect across Brazil, but stricter rules applied within the Amazon, where we have seen the greatest reduction in fire alerts.</p>



<h3 class="wp-block-heading">Forests on Privately Owned Land Disproportionately Affected</h3>



<p>Even though private properties make up only 48% of the land in the Amazon, they accounted for 62% of the fire alerts detected in the Amazon region in August and September. These fires are usually set to prepare agricultural land for production – an authorized practice before the ban.</p>



<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="land use categories in Brazil" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Concentration-02.png" width="100%"></div>



<p>While the fires typically start on farmland, they often affect nearby forests. Farmers are legally required to set aside up to 80% of their land for conservation purposes and much of this land is forested. Over one-third of the fire alerts on private lands in August and September were detected in primary forests.</p>



<p>Roughly 14% of fires over the same time period occurred on conservation units and indigenous lands in the Amazon, which comprise 24% of the region and tend to be even more forested than private lands. Fires in conservation units are never legal, and these areas may also be affected by runaway fires from nearby land-clearing activities. Fires on indigenous lands could be part of the land management practices used by the community, since the fire ban did not apply to indigenous lands. Alternatively, these fires could also represent illegal encroachment from outside actors.</p>



<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="Distribution of fires alert" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/PieChart.png" width="100%"></div>



<h3 class="wp-block-heading">Dry Forests of the Cerrado Are Still Burning</h3>



<p>Although fires have gone down significantly within the Amazon, they are still burning in the Cerrado. The dry forest of the Cerrado is a unique ecosystem that plays a critical role in storing carbon and maintaining watersheds. Fires can occur naturally here, and fire alerts detected from satellite cannot differentiate natural fires from fires set by humans. So far this year, fire counts for the Cerrado are in line with historical averages; however, the drying effects of climate change and increased agricultural activity in the region could result in increased fire threat to these forests in the future.</p>



<div class="imagecenter"><img decoding="async" class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="fire concentration August to september" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Concentration-01-1.png" width="100%">The highest concentration of fires moved from the Amazon biome—where the majority of Brazil’s primary forest is located— to the Cerrado and other parts of central Brazil in September.</div>



<h3 class="wp-block-heading">Future Fires Seasons</h3>



<p>The fire ban is scheduled to lift today, October 28, although Brazil’s fire season can continue into November. The reduction in Amazon fires seen in September suggests that recent attention given to this region – including the fire ban, public discourse and environmental enforcement policies – may have had a positive impact on curbing fires. Other critical forest biomes in Brazil have not received the same attention nor experienced the same reduction in fires.</p>



<p>Continued public and government attention to this issue will be critical for the remainder of this year’s fire season and beyond. The immediate aftermath of this year’s Amazon fire crisis demonstrates just how important this spotlight is.</p>



<hr> 

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

BANNER PHOTO: Satellite image of Brazil fires, August 22, 2019. Photo by NASA Goddard Space Flight Center.

 <p></p>

Originally appeared on <a href="https://www.wri.org/blog/2019/10/brazil-s-fire-ban-correlates-reduction-amazon-wildfires-ban-lifts-today" target="_blank" rel="noopener noreferrer">WRI Insights</a>. 
<p></p>
Download the raw data for these analyses <a href="http://gfw-files.s3.amazonaws.com/fires/final_fires_database/FINAL%20DATABASE%20-%20FIRES%20-%20BR%20-%20MODIS%20-%20AUG-SEP%20-%202019%20-%20Simplfied.xlsx" target="_blank" rel="noopener noreferrer">here</a>. Data for this analysis was sourced from NASA, Imaflora and ESALQ. 
</div> 

 

<hr>



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Brazil’s Fire Ban Correlates a Reduction in Amazon Wildfires. The Ban Lifts Today.

<p>Fires across Brazil caught the world’s attention this August, with fears that burning — and subsequent damage to the Amazon rainforest — would only intensify when peak fire season hit in September. However, Global Forest Watch (GFW) analysis of NASA data shows that fire alerts in the Amazon decreased by 34% between August and September.</p>



<h3 class="wp-block-heading">Amazon Fires Are Down. Can We Thank Brazil’s Fire Ban?</h3>



<p>Fire season in Brazil typically peaks in September, so this reduction in fires in the Amazon is unexpected. Other forested regions in Brazil showed an increase in fire alerts in September, including the Cerrado — a tropical savanna where fire is a more natural feature of the landscape. In the wetter, Amazon region, fires are rarely natural and almost all burning is related to human activity.</p>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="fire counts, August to september" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Alerts-03.png" width="100%">Amazônia—the Amazon biome— was the only region of Brazil that saw a drop in fires in September, which deviates from the normal upward trend in fires across the country.</div>



<p>So what explains the 34% decrease in fire alerts in the Amazon? It likely wasn’t the weather, as September was slightly drier than normal. The other potential explanation is the fire ban enacted at the end of August. During the peak of the crisis, when both national and international media attention was focused on the Amazon, President Jair Bolsonaro issued a TV and radio announcement declaring “zero tolerance” for environmental crimes. Law enforcement was dispatched to fight the fires and the government enacted a two-month ban on the use of fire to clear land. This ban was in effect across Brazil, but stricter rules applied within the Amazon, where we have seen the greatest reduction in fire alerts.</p>



<h3 class="wp-block-heading">Forests on Privately Owned Land Disproportionately Affected</h3>



<p>Even though private properties make up only 48% of the land in the Amazon, they accounted for 62% of the fire alerts detected in the Amazon region in August and September. These fires are usually set to prepare agricultural land for production – an authorized practice before the ban.</p>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="land use categories in Brazil" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Concentration-02.png" width="100%"></div>



<p>While the fires typically start on farmland, they often affect nearby forests. Farmers are legally required to set aside up to 80% of their land for conservation purposes and much of this land is forested. Over one-third of the fire alerts on private lands in August and September were detected in primary forests.</p>



<p>Roughly 14% of fires over the same time period occurred on conservation units and indigenous lands in the Amazon, which comprise 24% of the region and tend to be even more forested than private lands. Fires in conservation units are never legal, and these areas may also be affected by runaway fires from nearby land-clearing activities. Fires on indigenous lands could be part of the land management practices used by the community, since the fire ban did not apply to indigenous lands. Alternatively, these fires could also represent illegal encroachment from outside actors.</p>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="Distribution of fires alert" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/PieChart.png" width="100%"></div>



<h3 class="wp-block-heading">Dry Forests of the Cerrado Are Still Burning</h3>



<p>Although fires have gone down significantly within the Amazon, they are still burning in the Cerrado. The dry forest of the Cerrado is a unique ecosystem that plays a critical role in storing carbon and maintaining watersheds. Fires can occur naturally here, and fire alerts detected from satellite cannot differentiate natural fires from fires set by humans. So far this year, fire counts for the Cerrado are in line with historical averages; however, the drying effects of climate change and increased agricultural activity in the region could result in increased fire threat to these forests in the future.</p>



<div class="imagecenter"><img class=" wp-image-3569 alignright" style="padding-bottom: 10px;" alt="fire concentration August to september" src="https://content.globalforestwatch.org/wp-content/uploads/2019/10/Fire-Concentration-01-1.png" width="100%">The highest concentration of fires moved from the Amazon biome—where the majority of Brazil’s primary forest is located— to the Cerrado and other parts of central Brazil in September.</div>



<h3 class="wp-block-heading">Future Fires Seasons</h3>



<p>The fire ban is scheduled to lift today, October 28, although Brazil’s fire season can continue into November. The reduction in Amazon fires seen in September suggests that recent attention given to this region – including the fire ban, public discourse and environmental enforcement policies – may have had a positive impact on curbing fires. Other critical forest biomes in Brazil have not received the same attention nor experienced the same reduction in fires.</p>



<p>Continued public and government attention to this issue will be critical for the remainder of this year’s fire season and beyond. The immediate aftermath of this year’s Amazon fire crisis demonstrates just how important this spotlight is.</p>



<hr> 

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

BANNER PHOTO: Satellite image of Brazil fires, August 22, 2019. Photo by NASA Goddard Space Flight Center.

 <p></p>

Originally appeared on <a href="https://www.wri.org/blog/2019/10/brazil-s-fire-ban-correlates-reduction-amazon-wildfires-ban-lifts-today" target="_blank" rel="noopener noreferrer">WRI Insights</a>. 
<p></p>
Download the raw data for these analyses <a href="http://gfw-files.s3.amazonaws.com/fires/final_fires_database/FINAL%20DATABASE%20-%20FIRES%20-%20BR%20-%20MODIS%20-%20AUG-SEP%20-%202019%20-%20Simplfied.xlsx" target="_blank" rel="noopener noreferrer">here</a>. Data for this analysis was sourced from NASA, Imaflora and ESALQ. 
</div> 

 

<hr>

GFW analysis of NASA data shows that fire alerts in the Amazon decreased by 34% between August and September. Was brazil's temporary fire ban effective?

Brazil’s Fire Ban Correlates a Reduction in Amazon Wildfires. The Ban Lifts Today. | Forest Insights | Global Forest Watch Blog