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An Investor Guide on Agricultural Supply Chain Risk

Investor Guide to Deforestation and Climate Change

Additional Resources

Data Tables & Figures

Methodology

Overview of methods.

Estimates of tropical commodity-driven deforestation and associated GHG emissions were calculated by a team of researchers at Stockholm Environment Institute’s Trase Initiative, Chalmers University of Technology, and the Senckenberg Biodiversity and Climate Research Centre. The methodology used to provide estimates presented in this guide were reviewed by a technical advisory committee of experts in forests and climate change.

November 2020 update : An updated and downloadable version of the underlying dataset for the Pendrill et al. paper cited below is now available for public download with the following citation:

Florence Pendrill, U. Martin Persson & Thomas Kastner (2020). 'Deforestation risk embodied in production and consumption of agricultural and forestry commodities 2005-2017'. Chalmers University of Technology, Senckenberg Society for Nature Research, SEI, and Ceres Inc. https://doi.org/10.5281/zenodo.4250532

Calculations for the estimates of GHG emissions from tropical commodity-driven deforestation used in this guide drew heavily from methods employed in two recent papers:

Pendrill, Florence, Martin U. Persson, Javier Godar and Thomas Kastner. (2019). Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environmental Research Letters 14(5). https://doi.org/10.1088/1748-9326/ab0d41

Pendrill, Florence, Martin U. Persson, Javier Godar, Thomas Kastner, Daniel Moran, Sarah Schmidt and Richard Wood. (2019). Agricultural and forestry trade drives large share of tropical deforestation emissions. Global Environmental Change 56:1-10. https://doi.org/10.1016/j.gloenvcha.2019.03.002  

The analysis linking deforestation risk to agricultural and forestry production, trade and consumption includes three main steps:

Forest loss was detected via spatial datasets on tree cover loss, and then attributed to expanding land uses (cropland, pastures and forest plantations) in proportion to their relative area of expansion. This attribution loss was implemented at the national level, except for Brazil and Indonesia, where loss was attributed at the subnational level. Subnational-level data is useful for these two countries which hold a large share of remaining tropical forests, but also account for a large share of tropical forest loss.   The model for attributing forest loss to these three land-use categories is based on two main premises: (a) where cropland expands, it first expands into pastures, and then into forests, and (b) where pastures and forest plantation areas expand, they primarily replace forest land. These premises are consistent with data from additional studies that describe, quantify and assess the predominant deforestation-related land-use transitions across the tropics.   Next, forest loss attributed to cropland expansion was allocated to individual crops in relative proportion to their expansion area. For example, if areas planted to soybeans accounted for half of the total cropland expansion in a country, half of the country’s cropland deforestation was attributed to the country’s soybean production. National-level data on cropland and pasture areas in 2000-2018 were taken from the Food and Agriculture Organization Corporate Statistical database ( FAOSTAT ). The sub-attribution of cropland deforestation to individual crops was based on harvested area data from FAOSTAT .

Carbon emissions resulting from land cover changes were quantified by estimating net carbon stock changes (changes in the amount of carbon that has been sequestered and stored in a forest) for these previously forested areas. Emissions from peatland drainage were also included. Loss of carbon stock was quantified using satellite remote sensing techniques and existing literature on carbon losses in the tropics.

An international trade model was used to link the deforestation footprint involved in the production of each commodity, tracing any deforestation involved from production to countries of consumption throughout international supply chains. This analysis used production data from FAOSTAT and bilateral trade data. The calculations aimed to track products along supply chains, including re-exports and processing, up to the point where they are physically consumed either as food as livestock feed or in industrial processes.

Uncertainty in attribution of deforestation-related GHG emissions to countries, commodities, and trade flows

The data and claims presented here are based on multiple types of scientifically rigorous evidence with large sample sizes; yet, there is always some inherent uncertainty embedded in the methods of any model. Overall, we present this data with a high level of confidence and describe remaining uncertainties below. Given these uncertainties, the values provided in this guide are relatively consistent with other credible studies that have produced similar estimates, with a few differences due to methodological choices. There are several sources of uncertainty associated with the data presented in this guide:

Data availability and use of model to attribute deforestation levels . Ideally, attribution of forest loss and associated carbon emissions to agricultural and forestry production would be based on spatially explicit (e.g., remotely-sensed) data. However, existing spatial analyses of land cover and use following forest loss in the tropics are limited both geographically and temporally. Although pan-tropical data on forest loss and land cover exist, quality and consistency of land classifications across datasets is still too poor for combining these to sufficiently assess post-forest land. Therefore, a model was used to attribute detected forest loss. This model does not describe all possible land-use transitions, but rather reflects the predominant land-use transitions related to tropical deforestation.

Agricultural statistics. The area of forest plantations in the FAO Forest Resources Assessment is only quantified every five years (e.g., 2010, 2015) and linearly interpolated between. Data for the years 2016 and 2017 are extrapolated based on the 2010 to 2015 trend, so uncertainty increases slightly for those years. FAOSTAT data vary considerably in quality between different countries and years. The subnational data for Brazil and Indonesia also vary in quality between administrative units and years. Production data are obtained from FAOSTAT, which are in turn based on statistics reported by national statistical offices. However, as the data are directly related to national-level food security for each respective country, reasonable data quality can be assumed.

Trade model. For data on bilateral trade flows, two values are typically reported for each flow from country A to country B: export data reported by country A, and import data from country B. Sometimes these two values do not match, for legitimate reasons. The presented data give priority to reported import data, but in a similar study which gave priority to reported export data, the overall findings did not change.

EVS Project On Forest Management For Class 11th And 12th

Table of Contents

Acknowledgment

I wish to extend my heartfelt gratitude to all those who have played an invaluable role in bringing this Forest Management project to fruition.

Foremost, I express my deep appreciation to my esteemed EVS teacher, Mr./Ms. [Teacher’s Name], whose guidance, mentorship, and unwavering encouragement have been instrumental in shaping the content and direction of this project.

I owe a debt of gratitude to my classmates and friends whose collaborative efforts and support in data collection and research have been pivotal in transforming this project from an idea into a reality. Your enthusiasm and teamwork have made a significant difference.

I would also like to recognize the generous contributions of various organizations, environmental experts, and forestry professionals who graciously shared their expertise and insights during interviews and discussions. Their knowledge has added depth and authenticity to this project.

To my family and parents, I extend my deepest appreciation for providing me with the necessary resources, an environment conducive to learning, and constant encouragement throughout this endeavor.

Lastly, I want to acknowledge the authors, researchers, and organizations whose published works and online resources have been indispensable in comprehending and presenting the multifaceted realm of forest management.

To each and every one of you, thank you for your unwavering support and guidance.

Introduction to Forest Management

Forests, those lush and intricate ecosystems covering approximately 31% of Earth’s land area, hold a special place in our planet’s tapestry. They are not just collections of trees; they are life’s grand orchestration. Forests provide an essential balance in our environment, sustaining life in myriad ways. Managing these intricate ecosystems effectively is the essence of the field known as Forest Management.

Forest management involves meticulous planning and strategic implementation to ensure the sustainable utilization and conservation of forest resources. It’s a multidisciplinary endeavor that draws from ecology, economics, sociology, and policy-making to strike a harmonious balance between human needs and the preservation of forest ecosystems. This discipline acknowledges that forests are more than just trees; they are complex interwoven ecosystems where plants, animals, and humans coexist in a delicate dance of interdependence.

The Importance of Forests

Environmental Balance: Our forests are the lungs of the Earth. They absorb carbon dioxide from the atmosphere and, in return, release life-sustaining oxygen. They are vital in mitigating climate change by storing carbon.

Biodiversity Conservation: Forests are home to an astounding variety of plant and animal species, many of which are unique and irreplaceable. Effective forest management is critical to safeguard this biodiversity.

Water Resources: Forests play a significant role in regulating the water cycle, maintaining clean water sources, and preventing erosion and flooding.

Economic Prosperity: Timber, non-timber forest products (NTFPs), and ecotourism generate economic value for communities living near forests.

Cultural and Recreational Benefits: Forests hold cultural significance for indigenous communities and offer recreational opportunities for people worldwide.

Scientific Discovery: Forests serve as vast natural laboratories for scientific research, offering insights into ecology, biology, and climate science.

Principles of Forest Management

Forest management adheres to a set of foundational principles to guide decisions and actions in achieving sustainable and responsible forest resource use while preserving the integrity of forest ecosystems. These principles serve as a compass for navigating the complexities of forest conservation and utilization. Here are the key principles of forest management:

Sustainability: Sustainability is the cornerstone of forest management. It entails ensuring that the use of forest resources does not deplete or degrade the forest ecosystem to a point where it cannot regenerate and provide for future generations. Sustainable forest management balances ecological, economic, and social considerations.

Ecosystem-Based Approach: Forests are complex ecosystems with interconnected components. Forest management must adopt an ecosystem-based approach that considers not just individual tree species but the entire web of life within the forest. This approach emphasizes maintaining ecological processes, biodiversity, and overall forest health.

Multifunctionality: Forests serve multiple purposes, from timber production to recreation and wildlife habitat. Forest management recognizes this multifunctionality and aims to optimize the various benefits that forests provide to society. It involves assessing trade-offs and synergies between these functions.

Conservation of Biodiversity: Preserving biodiversity is a critical component of forest management. This principle emphasizes protecting and enhancing the diversity of plant and animal species within the forest, including endangered or rare species. Strategies may include creating protected areas and restoring habitat.

Legal and Institutional Framework: Effective forest management operates within a legal and institutional framework that defines property rights, responsibilities, and regulations. This framework ensures that forest activities adhere to environmental laws, social norms, and ethical standards.

Stakeholder Involvement: Forest management recognizes the importance of involving all stakeholders, including local communities, indigenous peoples, governments, and industry, in decision-making processes. Engaging diverse perspectives and interests helps achieve more inclusive and effective management outcomes.

Adaptive Management: Forest ecosystems are dynamic and subject to change due to natural processes and external factors like climate change. Adaptive management involves regularly assessing the forest’s condition and adjusting management strategies accordingly. Flexibility and responsiveness to new information are key elements of this principle.

Responsible Forest Practices: Responsible forest practices encompass the use of techniques and technologies that minimize negative impacts on the environment and maintain ecological integrity. This includes sustainable harvesting methods, reforestation, and the use of non-destructive technologies.

Certification and Verification: Many forest management programs and organizations offer certification and verification processes to assess and confirm compliance with sustainable practices. Certification schemes like the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC) help consumers identify responsibly managed products.

Continuous Improvement: Forest management is an evolving field. Continuous improvement involves ongoing research, monitoring, and adaptation to incorporate new knowledge and technologies into management practices. It ensures that management remains effective and up-to-date.

Forest Management Practices

Forest management practices encompass a wide range of strategies and techniques employed to achieve the sustainable use and conservation of forest resources. These practices are tailored to the specific characteristics of the forest, its ecological importance, and the goals of forest management. Below are some common forest management practices:

Selective Logging (Selection Cutting):

• Selective logging involves the careful removal of individual trees or specific groups of trees from the forest while leaving others intact.
• This practice is used to harvest mature or valuable trees while preserving the overall structure and biodiversity of the forest.
• It minimizes the ecological impact of harvesting and allows for natural regeneration.

Clear-Cutting:

• Clear-cutting involves the complete removal of all trees from a designated area of the forest.
• It is often used in industrial forestry for efficient timber production but can have significant ecological impacts if not carefully managed.
• Reforestation efforts are typically required after clear-cutting to restore the forest.

Shelterwood Cutting:

• Shelterwood cutting is a two-stage process that involves the gradual removal of trees in multiple cuts.
• Initially, some mature trees are harvested to allow more sunlight to reach the forest floor, promoting the growth of understory vegetation.
• After the new trees are established, the remaining mature trees are harvested.

Coppicing and Pollarding:

• Coppicing and pollarding are traditional practices involving the periodic cutting of trees or shrubs close to the ground to stimulate new growth.
• These practices are often used for the production of fuelwood, poles, and other small-diameter products.
• They can enhance the longevity of certain tree species and provide a sustainable source of resources.

Ecosystem-Based Management (EBM):

• Ecosystem-based management is an approach that considers the entire ecosystem when making management decisions.
• It focuses on maintaining the ecological processes, biodiversity, and overall health of the forest.
• EBM aims to balance the needs of ecological conservation with human demands.

Non-Timber Forest Products (NTFPs) Harvesting:

• NTFPs include a wide range of forest resources such as fruits, nuts, mushrooms, medicinal plants, and resin.
• Sustainable harvesting of NTFPs is essential for the economic well-being of local communities and often aligns with conservation goals.

Forest Restoration and Reforestation:

• Forest restoration involves the active rehabilitation of degraded or deforested areas to restore their ecological functions.
• Reforestation focuses on planting or naturally regenerating forests in areas where they have been lost or heavily degraded.
• These practices help combat deforestation and enhance ecosystem resilience.

Fire Management:

• Controlled or prescribed burning is used in some forest ecosystems to mimic natural fire regimes and reduce the risk of catastrophic wildfires.
• It can promote the growth of fire-adapted species and rejuvenate ecosystems.

Invasive Species Control:

• Invasive species can threaten native ecosystems. Management practices may involve controlling or eradicating invasive plants and animals to protect the forest’s native biodiversity.

Sustainable Harvesting Technologies: – Utilization of modern technologies, such as Geographic Information Systems (GIS) and remote sensing, helps monitor forests, assess their health, and plan sustainable harvests.

Challenges in Forest Management

Forest management faces numerous challenges, which can vary depending on the region, ecosystem type, and specific management goals. Here are some common challenges in forest management:

• Deforestation: Widespread deforestation, often driven by agricultural expansion, urbanization, and infrastructure development, poses a significant global challenge. It leads to the permanent loss of forest cover, biodiversity, and carbon storage capacity.
• Illegal Logging: The illegal harvest of timber and non-timber forest products is a prevalent issue. It not only contributes to deforestation but also results in lost revenue for governments and local communities.
• Climate Change: Climate change presents a significant challenge to forest management. Altered precipitation patterns, increased temperatures, and more frequent extreme weather events can affect forest health, composition, and susceptibility to pests and diseases.
• Invasive Species: Invasive species, both plant and animal, can disrupt native ecosystems and outcompete native species. Controlling and managing invasive species is an ongoing challenge.
• Fire Management: Wildfires can have devastating impacts on forests, ecosystems, and human communities. Managing fire, whether through prevention or controlled burns, is essential to reduce wildfire risks.
• Biodiversity Conservation: Striking a balance between timber production and biodiversity conservation can be challenging. Forest management must ensure that logging practices do not threaten vulnerable or endangered species and habitats.
• Land Tenure and Rights: Clarifying land tenure and ownership rights, especially in regions with indigenous or local communities, is crucial. Respecting and incorporating local knowledge and traditions into forest management is essential for sustainability.
• Economic Pressures: Economic pressures may incentivize unsustainable forest management practices. Balancing the need for economic development with long-term ecological health is an ongoing challenge.
• Infrastructure Development: Infrastructure projects, such as roads and dams, can fragment forest landscapes, disrupt wildlife corridors, and exacerbate deforestation. Proper planning and mitigation measures are necessary to reduce these impacts.
• Market Demand: Market demands for forest products can put pressure on forests to meet high production quotas. Managing these demands while maintaining sustainable practices is a persistent challenge.
• Limited Resources: Many forest management agencies and organizations operate with limited resources, which can hinder their ability to monitor and manage forests effectively.
• Climate Mitigation and Adaptation: Meeting climate mitigation and adaptation goals often requires forests to act as carbon sinks and adapt to changing conditions. Implementing these strategies effectively can be challenging.
• Conflict Resolution: Conflicts between different stakeholder groups, such as indigenous communities, logging companies, and conservationists, can arise over forest management decisions. Finding mutually agreeable solutions is challenging but necessary.
• Global Cooperation: Forest management often involves transboundary issues, such as shared ecosystems and wildlife corridors. Global cooperation and agreements are needed to address these challenges effectively.
• Human-Wildlife Conflict: As human populations encroach on forested areas, conflicts between humans and wildlife can escalate. Finding ways to mitigate these conflicts while conserving wildlife is a growing challenge.

Case Studies

1. The Amazon Rainforest – Brazil:

• The Amazon rainforest in Brazil is one of the world’s most critical and biodiverse ecosystems, facing significant deforestation pressures due to agriculture, logging, and infrastructure development.
• Brazil has implemented various initiatives, such as the Amazon Fund and Protected Areas Network, to combat deforestation and promote sustainable forest management.
• The case study could delve into the impact of these initiatives on reducing deforestation rates and conserving biodiversity.

2. Bialowieza Forest – Poland and Belarus:

• Bialowieza Forest, located on the border between Poland and Belarus, is a UNESCO World Heritage site and one of the last and largest parts of the primeval forest that once covered Europe.
• The forest faces challenges related to timber harvesting, which is a source of revenue for local communities.
• This case study could explore how the conflict between economic interests and conservation goals has been managed through sustainable forest management plans and UNESCO’s involvement.

3. Community-Based Forest Management – Nepal:

• Nepal has implemented successful community-based forest management programs where local communities are actively involved in decision-making and benefit-sharing.
• These programs have helped reduce deforestation, improve livelihoods, and conserve biodiversity.
• The case study could focus on how community involvement has contributed to sustainable forest management and the challenges faced in implementing these programs.

4. Forest Stewardship Council (FSC) Certification – Worldwide:

• The FSC is an international certification system that promotes responsible forest management practices.
• Various forested regions worldwide have adopted FSC certification to ensure that timber and non-timber forest products come from sustainably managed forests.
• The case study could examine the impact of FSC certification on forest conservation and the forest products industry.

5. Reforestation and Ecotourism – Costa Rica:

• Costa Rica has implemented successful reforestation programs while also capitalizing on ecotourism as an economic driver.
• Reforestation efforts have led to the recovery of degraded landscapes, and ecotourism has provided incentives for conservation.
• This case study could explore how Costa Rica’s approach to sustainable forest management has contributed to economic and ecological well-being.

6. Taiga Forest Management – Finland and Russia:

• The taiga, or boreal forest, is a vast ecosystem spanning Russia and Finland, facing pressures from logging and mining.
• Both countries have adopted sustainable forest management practices, including certification systems.
• The case study could assess how these practices have impacted the conservation of this unique forest ecosystem and local economies.

Benefits and Outcomes

Biodiversity Conservation: Sustainable forest management helps protect and conserve a wide range of plant and animal species by maintaining diverse forest ecosystems. This benefits both endangered species and those with economic or cultural significance.

Carbon Sequestration: Forests act as carbon sinks, absorbing and storing significant amounts of carbon dioxide from the atmosphere. Sustainable forest management contributes to climate change mitigation by preserving and enhancing this carbon sequestration capacity.

Economic Benefits: Sustainable forest management provides economic benefits by enabling the responsible harvest of timber and non-timber forest products (NTFPs). It supports livelihoods in forestry, logging, and related industries.

Ecosystem Services: Forests provide essential ecosystem services, including clean air and water, erosion control, and pollination. Sustainable management helps maintain these services for the benefit of communities and ecosystems.

Enhanced Resilience: Sustainable practices can increase the resilience of forests to environmental stressors, such as climate change, pests, and diseases, by promoting diverse and healthy forests.

Long-term Resource Availability: Sustainable forest management ensures that timber and other forest resources are available for future generations. This prevents overexploitation and depletion of forest resources.

Cultural and Recreational Benefits: Forests are culturally significant for many indigenous communities and offer recreational opportunities for people worldwide. Sustainable management helps preserve these cultural and recreational values.

Reduced Deforestation: Sustainable forest management can help reduce deforestation rates by promoting responsible land use practices and discouraging illegal logging.

Improved Water Quality: Forests play a crucial role in maintaining water quality by filtering pollutants and regulating water flow. Sustainable management helps protect water sources and reduces the risk of erosion and sedimentation.

Enhanced Global Partnerships: Sustainable forest management often involves international cooperation and partnerships, fostering global efforts to combat deforestation and protect biodiversity.

Forest Certification: Certification programs like the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC) enable consumers to make environmentally conscious choices by purchasing products sourced from sustainably managed forests.

Adaptation to Climate Change: Sustainable forest management practices can help forests adapt to changing climate conditions by promoting tree species and management strategies that are better suited to the evolving climate.

Local Community Involvement: Sustainable forest management often includes local communities in decision-making processes, leading to improved governance and equitable distribution of benefits.

Scientific Research Opportunities: Well-managed forests serve as living laboratories for scientific research, offering insights into ecology, biology, and climate science.

Reduced Conflicts: Sustainable forest management can help mitigate conflicts between different stakeholder groups, such as indigenous communities, logging companies, and conservationists, by finding mutually agreeable solutions.

In the intricate tapestry of our planet’s ecosystems, forests stand as majestic and indispensable wonders. They are the lungs of the Earth, custodians of biodiversity, and silent sentinels against climate change. The importance of sustainable forest management, as we have explored in this project, cannot be overstated. It is the key to unlocking a harmonious coexistence between humanity’s needs and the preservation of these vital ecosystems.

Through the journey of this project, we have delved into the principles, practices, challenges, and case studies that shape the world of forest management. We have witnessed the delicate dance of selective logging, clear-cutting, and shelterwood cutting, each with its unique impact on forests. We have seen how communities, from Nepal to Costa Rica, have played a pivotal role in preserving their forests through sustainable practices. We have also encountered the complexities of balancing economic growth with conservation in regions like the Amazon and Bialowieza Forest.

The challenges faced by forest management are substantial and global in scale. Deforestation, illegal logging, climate change, and invasive species cast long shadows over these precious ecosystems. Yet, as we have seen, there are success stories where dedication, innovation, and international cooperation have paved the way for responsible forest management.

The benefits and outcomes of sustainable forest management are far-reaching. From conserving biodiversity and sequestering carbon to supporting livelihoods and providing cultural and recreational sanctuaries, forests offer a wealth of gifts to humanity. By embracing sustainability, we unlock these gifts while safeguarding the long-term health and vitality of our forests.

The legacy of sustainable forest management is a legacy of harmony, where human progress and ecological preservation walk hand in hand, and where the enduring song of the forest continues to inspire and enrich our world.

As we conclude this project, let us remember that the responsibility for sustainable forest management lies not only with policymakers and forest managers but with every individual. Our daily choices and actions can contribute to the conservation and responsible use of forests. By collectively embracing the principles of sustainability, we can ensure that forests continue to thrive as vibrant, life-sustaining ecosystems on our planet.

In the pursuit of sustainable forest management, we find hope for the future. It is a testament to the power of informed choices, responsible stewardship, and our capacity to protect the natural wonders that enrich our lives. Forests are not only the cradle of life but also the foundation of a sustainable future. It is incumbent upon us to heed their silent wisdom and safeguard these invaluable treasures for generations to come.

Certificate of Completion

This is to certify that I, [Student’s Name], a [Class/Grade Level] student, have successfully completed the project on “Forest Management For Class 11th And 12th.” The project explores the fundamental principles and key aspects of the chosen topic, providing a comprehensive understanding of its significance and implications.

In this project, I delved into in-depth research and analysis, investigating various facets and relevant theories related to the chosen topic. I demonstrated dedication, diligence, and a high level of sincerity throughout the project’s completion.

Key Achievements:

Thoroughly researched and analyzed Evs Project On Forest Management For Class 11th And 12th. Examined the historical background and evolution of the subject matter. Explored the contributions of notable figures in the field. Investigated the key theories and principles associated with the topic. Discussed practical applications and real-world implications. Considered critical viewpoints and alternative theories, fostering a well-rounded understanding. This project has significantly enhanced my knowledge and critical thinking skills in the chosen field of study. It reflects my commitment to academic excellence and the pursuit of knowledge.

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Revision of Verra’s Avoiding Unplanned Deforestation and Degradation Project Methodologies: Update

**Please note that this information is out of date; see the Update on Proposed Revisions to VCS AUDD Methodologies , published on 14 February 2023, for more information.**

Verra will phase the transition of projects to a new consolidated REDD methodology. This transition will enable proponents using existing VCS Avoiding Unplanned Deforestation and Degradation (AUDD) project methodologies to familiarize themselves with and plan for the new approach. This staggered timeline also allows Verra to develop and operationalize a new consolidated REDD methodology incorporating the allocation approach.

The goal of adopting the allocation approach is to ensure the integrity of greenhouse gas accounting for individual projects within a jurisdiction and enable the utilization of the most up-to-date science, data, and technologies. The allocation approach is aligned with Verra’s Jurisdictional and Nested REDD+ (JNR) framework.

During the transition to the new consolidated REDD methodology, Verra will:

• Strengthen elements of the current VCS AUDD methodologies (VM0006, VM0007, VM0009, VM0015, and VM0037) prior to projects’ mandatory transition to the allocation approach.
• Continue the development of the new consolidated REDD methodology that will establish robust project baselines consistent with jurisdictional activity data (AD). Verra will do this by finalizing the Avoiding Unplanned Deforestation (AUD) allocation approach modules that were open for consultation in April and May 2022. We will also develop a framework to guide the application of those modules and enable the addition of other REDD project activities over time.

During the transition period, projects may use either of the above approaches.

Phased Approach: Details

Revised versions of current audd methodologies.

Verra will release revised versions of the current VCS AUDD methodologies (VM0006, VM0007, VM0009, VM00015, and VM0037) prior to requiring projects to transition to the allocation approach. The methodologies will be updated to ensure the integrity of the associated VCUs issued. Updates may include:

• Revisions to the selection procedures for reference regions;
• Standardization of approaches for projecting the future location of deforestation for large, ecologically intact, and relatively undisturbed natural forests (frontier deforestation);
• Restriction of accounting for frontier deforestation to methodologies that include projection of future deforestation location
• Standardization of projecting deforestation rates using the historical average, except in cases with a clear downward trend, when a linear regression must be used; and
• Revisions to capturing remote sensing and field-data uncertainties and ensuring conservativeness in net emission reductions calculations.

Verra will open the proposed updates for public consultation in October 2022. After the consultation has ended, we will revise each individual methodology. Prior to their publication, the revised methodologies will be assessed by a validation/verification body (VVB) to ensure consistency with the VCS Program rules and requirements.

Consolidated REDD Methodology

Concurrent to the above revisions of existing VCS AUDD methodologies, Verra will develop a new consolidated REDD methodology to ensure projects align with jurisdictional-level carbon accounting. By April 2023, this novel methodology will include the following components:

• A framework methodology to guide REDD project proponents in the allocation approach and define other core components for any REDD activity, and
• Procedures for activity data generation, baseline development, monitoring, and assessing leakage for Avoiding Unplanned Deforestation activities. These procedures will be adapted from the AUD modules that underwent public consultation in April and May 2022 .

The consolidated REDD methodology will undergo assessment by a VVB to ensure consistency with the VCS Program rules and requirements. By 2025, Verra plans to build this methodology out by incorporating modules for additional REDD activities, including avoiding planned deforestation and avoiding unplanned degradation. Each new module added to the methodology after its initial assessment will undergo public consultation and VVB assessment.

Implications for Projects

VCS REDD projects may choose to use the consolidated REDD methodology at any time after it is published in 2023. By 31 December 2025, all VCS REDD projects must use this new methodology.

The mandatory deadlines for projects to start using the revised AUD methodologies or the consolidated REDD methodology are as set out in the table below. When reviewing the table, proponents should consider their project’s current status, noting that during the transition period (between 1 January 2025 and 31 December 2025) a project’s status (and resulting methodology options) may change.

If you have any questions about this phased approach, please see the frequently asked questions or contact Salvador Sánchez Colón, Manager, REDD+ Technical Innovation, ( [email protected] ). If your question is related to a project listed on the Verra Registry, please provide that project’s identification number in your email.

More Announcements

Verra releases revised improved forest management methodology , connect with verra at north american carbon world 2024, projects open for public comment: march 11, 2024.

Deforestation EVS Project Class 11th And 12TH PDF Download

How to Write an Effective EVS Project – A Step-by-Step Guide for Students

Environmental science, or EVS, projects provide students with an opportunity to learn about important environmental issues and solutions. Selecting an engaging topic, setting clear goals, performing thoughtful research, and presenting conclusions effectively are all key to writing a successful EVS project. This comprehensive guide breaks down the process into 7 essential steps.

Table of Contents

Introduction

As our world faces mounting environmental challenges, from climate change to pollution and biodiversity loss, understanding these issues and developing solutions have never been more vital. Environmental science projects allow students to explore topics that interest them while making a positive difference.

Choosing a compelling topic sets the foundation. Consider local problems you are curious about or global challenges you want to help address. Selecting a topic important to you will make the project more meaningful.

Provide background on why you chose the particular issue. Explain its history and current status. Convey the topic’s significance and your motivation for selecting it. This introduction grounds the reader in the subject and rationale.

The Importance of the Topic

After introducing your selected issue, elaborate on its significance. Explain why this topic merits attention and research.

Identify the scientific, social, and environmental importance. How does this topic relate to broader ecological concepts or societal concerns? What groups are most impacted? Answering these questions spotlights the topic’s relevance.

For example, exploring the impacts of plastic pollution could highlight how plastics affect marine life through ingestion and chemical contamination. You might emphasize the disproportionate burdens coastal communities face from plastic waste washing ashore. Demonstrating significance compels readers to care.

Now describe your project’s specific objectives – the particular questions or outcomes you will address through your research. Setting clear goals gives direction and clarifies the scope.

Objectives should:

• Directly relate to the topic
• Be feasible within project parameters
• Produce shareable findings or conclusions

If examining plastic pollution, objectives might include:

• Assessing local shoreline contamination
• Quantifying accumulation rates over 6 months
• Proposing mitigation strategies based on findings

Tailor objectives to produce meaningful contributions on your selected issue. They act as guideposts for project activities.

Project Work Methodology

With the topic and objectives framed, summarize the methods for conducting research and presenting findings. Explain where information will come from and how it will be analyzed.

Potential approaches include:

• Field research – Directly observe/measure topic in environment
• Surveys – Question relevant groups to collect perceptions
• Interviews – Capture insights from experts through discussions
• Literature review – Compile existing research for comparison

Outlining the intended approach provides transparency on how conclusions will be formed. Our plastic pollution student may combine shoreline contamination sampling with interviews of waterway managers and a policy analysis.

The approach section sets expectations for project components readers will see in subsequent sections.

Observations

Having established the relevance, goals, and direction of the project, zero in on key focus areas for investigation. These focal points guide research efforts to efficiently yield information most likely to achieve stated objectives.

For plastic pollution, priority focal areas might be:

• Types/sources – Determine composition of debris and links to inland origins
• Accumulation – Quantify concentration changes by location over time
• Impacts – Identify ecological effects based on scientific literature

Spotlighting focus areas clarifies for readers how research will home in on prime concerns within the broader topic.

Analysis of Data

With research completed, the next critical step is analyzing the findings to discern meaningful conclusions. Describe what analysis methods were applied to translate raw observations into insights.

Common analytical approaches include:

• Statistical analysis – Detect patterns/trends in numerical data
• Coding of qualitative data – Identify frequent themes in open-ended responses
• Comparison to existing research – Note agreements and contradictions

Analyzing results, rather than just presenting raw numbers or quotes, allows meaningful interpretation. For plastic debris, tallying types by location, tracking accumulation changes, and evaluating associated ecological harms all constitute analysis.

By transforming project inputs into discernible takeaways, thoughtful analysis enhances credibility.

Results & Conclusions

With focus areas explored and data analyzed, summarize key results and conclusions.

What were the most significant findings or main takeaways? How do they align with original objectives? What potential positive impacts could this project have for the focal issue?

Revisit background from the introduction to contextualize conclusions – why do these outcomes matter? Can results inform future research or local policies? Conclusions synthesize main learnings from effort invested into advancing understanding.

By following this 7-step process – introducing an important topic, clarifying its relevance through stated objectives and focused research, analyzing inputs generated by thoughtful methodology, and deriving meaningful conclusions – EVS students can produce impactful projects on pressing environmental issues. Using this roadmap can lead to output that educates, inspires, and catalyzes change.

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• Environmental Chemistry
• Deforestation

Deforestation Process

What is deforestation.

Deforestation can be defined as the large-scale removal of trees from forests (or other lands) for the facilitation of human activities. It is a serious environmental concern since it can result in the loss of biodiversity, damage to natural habitats, disturbances in the water cycle , and soil erosion. Deforestation is also a contributor to climate change and global warming.

Table of Content

Why are forests important, the data behind deforestation, causes of deforestation, how does deforestation affect the environment, other effects of deforestation, how can deforestation be controlled, recommended video.

• Forests combat climate change by absorbing greenhouse gases (such as carbon dioxide) and acting as a carbon storehouse.
• They are a source of oxygen, food, clean water, and medicine.
• They play a vital role in the water cycle – they work to add water to the atmosphere via the process of transpiration.
• Forests help mitigate the disastrous effects of floods by acting as a floodwater sink. Therefore, deforestation also increases the vulnerability of the landmass to certain natural calamities.
• The large mass of trees in forest areas combats soil erosion by providing mechanical support to the soil.
• Forests are home to over 50% of all known species on the planet. They account for over 80% of the land-based biodiversity. Globally, forests are home to approximately 30,00,00,000 human beings.
• They are also a source of raw material for many commercially important products such as paper, wood, and fabric.
• Approximately 1.6 billion jobs are forest-dependent. Forests also account for approximately 1% of the world’s GDP (gross domestic product).
• Forests cover approximately 31% of the total land surface of the Earth.
• Tropical forests harbour over half of all land-based animal and plant species in the world.
• Between the years 2000 and 2012, over 568 million acres of forest have been claimed by deforestation.
• Approximately 9 million acres of virgin tropical forest were cut down in the year 2018.
• The Amazon rainforest, which is the source of 20% of the world’s oxygen supply, loses approximately 1.32 acres of its area every minute due to deforestation.

What are the Human Activities that Cause Deforestation?

The primary anthropogenic activities (human activities) that contribute to deforestation include:

• Agriculture – small-scale and large-scale farming
• Logging – cutting of trees for use as raw material
• Mining and urban expansion – clearing of forest area for the construction of infrastructure.

According to the secretariat of the UNFCCC (United Nations Framework Convention on Climate Change), agriculture is the root cause of 80% of deforestation. Logging accounts for another 14% and the cutting of trees for use as wood fuel account for 5%. A pie-chart detailing the driving cause of the deforestation of tropical forests between the years 2000 and 2005 is provided below.

Slash-and-burn agriculture is one of the most destructive forms of agriculture that results in large-scale deforestation. It involves the burning of a large area of forest land and the subsequent plantation of crops in the same soil (which is now fertilized by the ashes of the burnt trees). Despite the practice being abandoned by several developed countries, it is still followed in some Southeast Asian countries.

What are the Secondary Factors that Contribute to Deforestation?

Illegal logging, which accounts for approximately 80% of all logging activities, involves the harvesting and sale of timber in violation of the law. Corrupt government officials may accept bribes from illegal loggers and offer access to protected forest areas in return. Therefore, corruption can be viewed as an indirect cause of deforestation.

Overpopulation and population growth increase the requirement for several resources such as food and infrastructure. These requirements can, directly or indirectly, result in deforestation. For example, a huge explosion in the population of a city can result in the deforestation of the surrounding area for:

• The construction of homes and other buildings.
• Agriculture (to meet the increased demand for food).
• The construction of roads, dams, and other infrastructure.

Military conflicts among humans can also result in deforestation. For example, the U.S. military made extensive use of Agent Orange (a defoliant that causes the leaves of trees to wither and fall off) during the Vietnam War (1955 – 1975).

Can Deforestation Occur due to Natural Causes?

In some relatively rare cases, the deforestation of forest areas can be traced to natural causes. For example, volcanic eruptions can burn away the forest lands surrounding the volcano. Other examples of natural deforestation include:

• Destruction of forests due to hurricanes, floods, and other natural calamities.
• Invasion of the forest ecosystem by parasites that destroy trees.
• Forest fires are sparked by lightning and other natural phenomena.

It is important to note that natural factors have a very small stake in the overall deforestation of the Earth’s land surface (anthropogenic factors account for almost all of it).

Increased Carbon Dioxide Levels in the Atmosphere

Forests serve as a carbon sink by absorbing atmospheric carbon dioxide during photosynthesis . Since carbon dioxide is a greenhouse gas, deforestation is a direct contributor to the greenhouse effect and (consequently) global warming .

Apart from being responsible for allowing gaseous carbon dioxide to linger in the atmosphere, deforestation also contributes to increased carbon dioxide emissions. The CO 2 emissions caused by deforestation account for approximately 12% of all anthropogenic carbon dioxide emissions.

Deforestation and the Water Cycle

• Since trees play a vital role in the water cycle, deforestation can contribute to significant disturbances in it.
• Trees and plants regulate the moisture content in the atmosphere via the process of transpiration (they absorb groundwater through their roots and release it into the atmosphere from their leaves and flowers).
• Also, their roots burrow into the soil and create macropores in it. These macropores allow water to penetrate deeper into the soil, thereby increasing the water-holding capacity of the soil.
• Dead plant material (such as leaves and twigs) that fall to the surface of the ground impart several properties to the soil, such as increased water-holding capacity.
• Approximately 30% of the world’s freshwater supply can be sourced from tropical rainforests.
• Deforestation is accompanied by reduced humidity, owing to the absence of transpiring trees. The water content in the soil and the groundwater levels also decline in the cleared land.
• It is not uncommon for deforested land to experience extremely arid climates. In fact, deforestation has been linked to desertification and droughts.

Soil Erosion as a Consequence of Deforestation

Trees tend to bind their roots to the soil bedrock, thereby reinforcing the soil. Additionally, the plant litter generated by trees offers protection to the surface of the soil. In the absence of trees (as a consequence of deforestation), the soil becomes vulnerable to erosion.

Deforestation of sloped lands is often accompanied by landslides, which can be explained by the loss of soil adhesion due to the absence of trees. The extent of erosion is amplified by certain natural calamities such as floods (note that the plant litter found on forest surfaces helps reduce the amount of soil washed away).

Since soil erosion is a direct contributor to eutrophication , deforestation can be viewed as a contributor to other environmental concerns.

Effects of Deforestation on Biodiversity

Forests play host to a wide spectrum of wildlife. In fact, tropical rainforests are believed to be the most diverse ecosystems on the planet. Deforestation poses a grave threat to this biodiversity. On a local scale, the clearing of forest land can cause a decline in the population of certain species. On a global scale, however, deforestation can result in the extinction of several desirable species.

Approximately 50,000 species (consisting of plants, animals, and insects) are lost every year as a consequence of deforestation. Studies suggest that over 40% of all plant and animal species in the Southeast Asian region will undergo extinction over the course of the 21 st century.

The implications of large-scale biodiversity loss are difficult to predict, but it is highly probable that it would have an adverse effect on the food web . Also, the extinction of one species may play a leading role in the extinction of another via the phenomenon of co-extinction.

How Does Deforestation Affect the Economy?

• Deforestation facilitates the generation of raw materials for a wide range of industries. Examples include the agriculture industry, the wood industry, and the construction industry.
• However, the overexploitation of wood and timber can have a negative impact on the economy. The short-term economic gains made from deforestation are accompanied by reduced long-term productivity.
• For example, overenthusiastic timber harvesting from a forest area may increase the overall output temporarily, but the declining forest area will eventually cause the harvest to decline. The overall forest output is greatly reduced by such practices.
• According to some reports, the global GDP may see a 7% decline by the year 2050 due to deforestation and other factors.
• Therefore, a sustainable approach to the usage of forest resources is ideal for the economy.

Deforestation and Human Health

Deforestation can, directly or indirectly, provide a channel for the propagation of many infectious diseases . Since deforestation is often accompanied by the loss of indigenous species, it is not uncommon for new species to flourish in deforested lands.

In Malaysia, the geographic shift of the fruit bat population (as a consequence of deforestation) facilitated the transmission of the Nipah virus. Fruit bats, which are known to be vectors of the disease, lost their natural habitat due to deforestation and started feeding in the orchards surrounding habited areas. Through proximity, the Nipah virus spread from fruit bats to pigs, and then to humans.

Increased soil erosion (due to deforestation) can result in the formation of pools of stagnant water. These pools serve as breeding grounds for mosquitoes, which are vectors of several deadly diseases such as malaria and yellow fever. Some theories also suggest that deforestation has contributed to the propagation of the human immunodeficiency virus (HIV) .

Role of Governments and other Administrative Authorities

The following strategies can be implemented by governments to combat deforestation:

• Implementation of security measures and strict laws to prevent illegal logging.
• Increasing the count and range of forests under government protection.
• Carefully planning the construction of infrastructure (roads, dams, etc.) in order to minimize the loss of forest area.
• Investing in new technologies in the agricultural industry (such as hydroponics) and helping farmers implement eco-friendly agricultural practices (such as cyclic agriculture).
• Optimizing the management of forests by banning inefficient agricultural practices (such as slash-and-burn agriculture).
• Facilitating the production and use of wood alternatives to reduce the demand for timber. For example, bamboo can serve as an alternative to wood fuel.
• Launching new reforestation campaigns to restore deforested lands.
• Investing in forest plantations – forests planted with high yielding trees can offer 5 – 10 times the output (per hectare) of a natural forest.

Role of Individuals

Every human on the planet shares the responsibility of preserving its resources (for other humans, other species, and for future generations). An individual can contribute to the prevention of deforestation by implementing the 3R (Reduce, Reuse, and Recycle) principle in their daily lives.

• Reduce – Reducing the amount of paper consumed by using alternatives wherever possible.
• Reuse – Avoid use-and-throw products to prevent wastage.
• Recycle – Diligently recycle all used wood and paper products.

Individuals can also combat deforestation by spreading awareness about its negative consequences and participating in tree-planting campaigns.

To learn more about deforestation and other important environmental concerns (such as soil pollution ), register with BYJU’S and download the mobile application on your smartphone.

Frequently Asked Questions – FAQs

What are the key causes of deforestation, what are the environmental effects of deforestation, how can deforestation affect the water cycle, how can i as an individual help reduce deforestation.

The following practices can be incorporated to reduce the demand for forest products:

• Going paperless and using digital media wherever possible (using digital receipts, preferring the use of E-mails instead of letters).
• Purchasing only recyclable products and recycling them once used.
• Purchasing only certified wood products and supporting the organizations that are fighting deforestation.
• Educating other individuals about deforestation and its negative impact on the environment.

How does deforestation affect biodiversity?

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Data and Methods

In this section, you'll find:

Methodology

Indicators overview, limitations.

Every year, the Global Forest Review (GFR) provides an independent assessment of the state of the world’s forests based on the best available geospatial data and analysis. A key distinguishing element of the GFR is its focus on insights derived from analysis of geospatial data and maps. In 2014, breakthroughs in global forest monitoring using satellite data, computer algorithms, and cloud computing resulted in the first global map of forest change at 30-meter resolution , depicting tree cover loss annually since 2001 and tree cover gain cumulatively over the same time period. Analysis of these data, combined with hundreds of other spatial data sets, allows for granular, timely, and consistent monitoring of global forest trends over time and space.

The Global Forest Change data set, with its annual updates on tree cover loss and gain, provides a critical input to the report. The GFR also draws on spatial data and analysis techniques that are rapidly improving with the evolution of forest monitoring technologies and scientific methods. The report and the Data and Methods section will be updated annually to reflect the latest advances in data and data science.

Data and Methods is organized into three subsections: Data Sets, Methodology, and Indicators Overview. The Data Sets section describes the spatial data used in the GFR. The Methodology section describes techniques underpinning any calculations and analysis of the data conducted by World Resources Institute to derive and report results. The Indicators Overview section builds on these two sections by summarizing the data sets and methods used for each calculation.

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The Global Forest Review (GFR) uses the best-available global spatial data on forests. Over 20 different global data sets come together to help us understand why our forests are changing and the impacts these changes have on people, climate, and biodiversity. Unless otherwise specified, the data descriptions below summarize definitions and methods outlined in published papers. Additional manipulation or processing of the data sets was not done for GFR analyses. The data sets are divided between the following types:

Forest Change

Tree cover loss. Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 . This data set measures areas of tree cover loss across all global land at 30-meter (m) resolution. The data were generated using multispectral satellite imagery from the Landsat 5 Thematic Mapper, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and the Landsat 8 Operational Land Imager sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000–12 interval and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011–22 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per-pixel tree cover loss.

In this data set, tree cover is defined as all vegetation greater than 5 m in height and greater than 30 percent tree canopy density, and it may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale (i.e., tree cover from more than 30 percent to about 0 percent). Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation  (the conversion of natural forest to other land uses) as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss and can be either natural or human induced. Forest degradation  —for example, selective removals from within forests that do not lead to a nonforest state—are not included in the loss data. Therefore, partial reductions in canopy cover (e.g., from 70 percent to 40 percent) are not included in the loss data.

Data for 2011–22 were produced as annual updates, while 2001-2012 were produced as a block as part of the original publication. Recent years of data are also more sensitive to changes due to the incorporation of data from Landsat 8 (2013) and improvements to the method (most notably in 2015). Comparisons between older and more recent data should be performed with caution.

At the global scale, for the original 2001–12 product, the overall prevalence of false positives (detected as tree cover loss but, in reality, is not, also known as commission errors) in this data is 13 percent, and the prevalence of false negatives (not detected as tree cover loss but, in reality, is lost, also known as omission errors) is 12 percent, though the accuracy varies by biome and thus may be higher or lower in any particular location. The model often misses disturbances in smallholder landscapes, resulting in lower accuracy of the data in sub-Saharan Africa, where this type of disturbance is more common. There is 75 percent confidence that the loss occurred within the stated year, and 97 percent confidence that it occurred within a year before or after. The data also does not detect sparse and scattered trees in the agricultural landscape. Additional accuracy assessments for the updated algorithm and additional years of loss data beyond 2012 are not available.

Tree cover loss by dominant driver. Curtis et al. 2018, https://doi.org/10.1126/science.aau3445 .   This data set shows the dominant driver of tree cover loss from 2001 to 2022 using the following five categories:

• Commodity-driven deforestation: Long-term, permanent conversion of forest and shrubland to a nonforest land use such as agriculture (including oil palm), mining, or energy infrastructure.
• Shifting agriculture: Small- to medium-scale forest and shrubland conversion for agriculture that is later abandoned and followed by subsequent forest regrowth.
• Forestry: Large-scale forestry operations occurring within managed forests and tree plantations.
• Wildfire: Large-scale forest loss resulting from the burning of forest vegetation with no visible human conversion or agricultural activity afterward.
• Urbanization: Forest and shrubland conversion for the expansion and intensification of existing urban centers. 

For the purposes of statistics generated in the Global Forest Review, commodity-driven deforestation, urbanization, and shifting agriculture with primary forest are considered to represent permanent deforestation, whereas tree cover usually regrows in the other categories (forestry, wildfire, and shifting agriculture outside of primary forests ).

The data were generated using decision tree models to separate each 10-kilometer (km) grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells and use metrics derived from the following data sets: Hansen et al. (2013) tree cover, tree cover gain, and tree cover loss ; National Aeronautics and Space Administration fires ; global land cover ; and population count . Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map that is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world.

Regional models were created, and training samples allowed for the interpretation of local land uses or management styles. A cell was categorized as commodity-driven deforestation if it contained clearings that showed signs of existing agriculture, pasture, or mining in the most recent imagery (after the tree cover loss occurred) as well as zero or minimal regrowth in subsequent years. Cells were categorized as shifting agriculture if the cell contained clearings that showed signs of existing agriculture or pasture in most recent imagery (after the tree cover loss occurred) as well as past clearings that contained visible forest or shrubland regrowth (gain) in historical imagery spanning 2001–15. Tree crops typically considered as agricultural commodities, such as oil palm, were classified accordingly as commodity-driven deforestation . The forestry class reflects a combination of wood fiber plantations and other forestry activity, including clear-cutting and selective cuts. Cells were categorized as wildfire when large swaths of fire scarring were visible in cleared areas, indicating that the loss event was driven by wildfire. The wildfire class excludes fire used to clear land for agriculture. Cells were categorized as urbanization if the loss of tree cover coincided with visible urban expansion or intensification.

This data set is intended for use at the global or regional scale, not for individual pixels. Individual grid cells may have more than one driver of tree cover loss , with variation over space and time.

Aside from commodity-driven deforestation, urbanization, and shifting agriculture with primary forest, which are assumed to represent permanent conversion from a forest to nonforest state, this data set does not indicate the stability or changing condition of the forest land use after the tree cover loss occurs. The data set does not distinguish between natural or anthropogenic wildfires, but it does distinguish fires for conversion or agricultural activity, which are not included in the wildfire class. Only direct drivers of forest disturbance are considered, and not indirect drivers such a demographic pressures or economic markets.

The accuracy of the data was assessed using a validation sample of 1,565 randomly selected grid cells. At the global scale, overall accuracy of the model was 89 percent, with individual class accuracies ranging from 55 percent (urbanization) to 94 percent (commodity-driven deforestation). The data has been updated since the original publication to include tree cover loss data from 2016 to 2022.

Tree cover gain. Potapov et al. 2022, https://doi.org/10.3389/frsen.2022.856903 This data set measures areas of tree cover gain across all land globally based on data at 30-meter resolution, displayed as the total area with tree cover in 2020 that did not have tree cover in 2000. The data was developed by Potapov et al. (2022) through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Tree cover gain is defined as land cover with tree canopy height of at least five meters tall in 2020 but not in 2000 at the Landsat pixel scale. Tree cover gain may indicate a number of potential activities, including natural forest growth or the rotation cycle of tree plantations.

Lower Mekong height and canopy. Potapov et al. 2019, https://doi.org/10.1016/j.rse.2019.111278 . This data set measures the annual tree canopy extent and height for the lower Mekong region (Cambodia, Laos, Myanmar, Thailand, and Vietnam) at 30 m resolution for the years 2000–17. The data were generated from the University of Maryland’s Landsat Analysis Ready Data, a time-series data set of 16-day normalized surface reflectance composites, to produce regional woody vegetation structure mapping and change detection. A semiautomatic algorithm was used to map woody vegetation canopy cover and height. It used automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Any changes in vegetation cover were detected separately and then integrated into the structure time series.

Tree cover change. Potapov et al. 2022, https://doi.org/10.3389/frsen.2022.856903 This data set measures the net areas of tree cover change (loss or gain) across all land globally, and by country, between 2000 and 2020 based on data at 30-meter resolution. The data was developed by Potapov et al. (2022) through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Net tree cover change is defined as the difference between tree cover gain and loss (that is, the amount of tree cover gain minus the amount of tree cover loss) between 2000 and 2020. Tree cover gain is defined as land cover with tree canopy height of at least five meters tall in 2020 but not in 2000 at the Landsat pixel scale (30 meters). Conversely, tree cover loss is defined as an area with tree canopy height greater than five meters in 2000 and less than five meters in 2020. Tree cover disturbance, in which tree cover is lost and regrown repeatedly during the 20-year time period, is tracked separately and not considered in the net tree cover change total.

Hot spots of primary forest loss. Harris et al. 2017, https://doi.org/10.1088/1748-9326/aa5a2f .   The emerging hot spots data set identifies the most significant clusters of primary humid tropical forest loss between 2002 and 2022 within each country. The term hot spot is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. 

The emerging hot spots analysis uses the annual Hansen et al. (2013) tree cover loss data set between the years 2002 and 2022, the Turubanova et al. (2018) primary forest extent data set for the year 2001, and the Esri ArcGIS Emerging Hot Spot Analysis geoprocessing tool. The tool uses a combination of two statistical measures: the Getis-Ord Gi* statistic to identify the location and degree of spatial clustering of forest loss and the Mann-Kendall trend test to evaluate the temporal trend over time. The analysis was run for individual countries, and its results are relative to the patterns and amount of loss in each country. It has been updated since the original publication to include the latest tree cover loss data. 

Tree cover loss due to fire. Tyukavina et al. 2022. https://doi.org/10.3389/frsen.2022.825190  This data set measures areas of tree cover loss due to fires across all global land (except Antarctica and other Arctic islands) at approximately 30-meter resolution. Tree cover loss is defined, following Hansen et al. 2013, as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Stand replacement forest fires are defined as natural or human-ignited fires resulting in direct loss of tree canopy cover exceeding 5 meters in height. This can include wildfires, intentionally set fires, or escaped fires from human activities, such as hunting or agriculture. It does not include burning of felled trees, since the direct cause of loss in these cases is mechanical removal. Therefore, trees that are cut down and later burned to clear land for agriculture would not be classified as tree cover loss due to fire in this dataset. It does not include low-intensity and understory forest fires that do not result in substantial tree canopy loss at the scale of a 30-meter pixel.

The data were generated using global Landsat-based annual change detection metrics as input data to a set of regionally calibrated classification tree ensemble models. Tree cover loss due to fire was mapped only within the extent of the global 30-m resolution tree cover loss data set (Hansen et al., 2013). The result of the mapping process can be viewed as a set of binary maps (tree cover loss due to fire vs. tree cover loss due to all other drivers).

Forest Cover

Tree cover extent. Potapov et al. 2022, https://doi.org/10.3389/frsen.2022.856903   Tree cover is defined as all woody vegetation greater than five meters in height, and can include tree plantations as well as unmanaged natural forests , managed natural forests and urban forests. The tree cover extent data set (Potapov et al. 2022) covers all global land for the year 2020 at 30-meter resolution.

Tree cover extent for the year 2000 serves as the baseline for most of the tree cover and forest loss calculations in the Global Forest Review. Data set values represent 0–100 percent tree canopy cover, with percent tree cover defined as the density of tree canopy coverage of the land surface. This data set was generated using multispectral satellite imagery from the Landsat 7 Enhanced Thematic Mapper Plus sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for Earth observation and data analysis, to determine per-pixel tree cover using a supervised learning algorithm. For the Global Forest Review, greater than 30 percent tree canopy density threshold was used to define tree cover extent baseline, unless otherwise noted.

Tree cover extent for the year 2020 is used to report on the most recent global extent data available. The data was developed through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling, with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Primary forest. Turubanova et al. 2018, https://doi.org/10.1088/1748-9326/aacd1c . Primary forests are among the most biodiverse forests , providing a multitude of ecosystem services, making them crucial for monitoring national land-use planning and carbon accounting. This data set defines primary forest as "mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history" (approximately 30 years before the year 2001, when primary forests were mapped as part of this data). Researchers classified Landsat images into primary forest data, using a separate algorithm for each region.

Tropical Tree Cover. Brandt et al. 2023, https://www.sciencedirect.com/science/article/pii/S0034425723001256 The Tropical Tree Cover (TTC) data set maps tree cover and tree extent in the tropics for the year 2020. For the Global Forest Review, it is used exclusively for the Trees Outside Forests Indicator to quantify tree cover on human-managed urban and agricultural land, and a 10 percent threshold is applied to include trees in open canopy systems. The 10-meter (m) data set is derived from Sentinel-1 and Sentinel-2 satellites using a convolutional neural network to perform image segmentation on monthly composite images. A full description of the methodology can be found in Brandt et al. (2023). Brandt et al. 2023, https://www.sciencedirect.com/science/article/pii/S0034425723001256 TTC defines a tree as woody vegetation that is either >5 m in height regardless of canopy diameter, or is between 3 and 5 m in height with a crown of at least a 5-m diameter. Tall herbaceous vegetation such as sugarcane, bananas and cacti, and short woody crops such as tea and coffee are excluded. Trees on non-forested land such as agroforestry, rotational and non-rotational tree plantations and trees in urban areas are included as trees. TTC covers 4.35 billion hectares of land in the tropics (-23.44° to 23.44° latitude) and is currently available for the year 2020.

Intact forest landscapes. Potapov et al. 2017, https://doi.org/10.1126/sciadv.1600821 . The intact forest landscapes (IFLs) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. They can include temporary treeless areas after natural disturbances, water bodies, or treeless intact ecosystems where climate, soil, or hydrological conditions prevent forest growth. 

To map IFL areas, the extent of forest areas was identified using greater than 20 percent tree canopy density in the Hansen et al. (2013) Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 .  data set. Then a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. For the most part, once an area is disturbed , it is no longer considered intact and any regrowth of IFLs is not measured in the data. These were then mapped from Landsat satellite imagery for the year 2000.

Changes in the extent of IFLs were identified within the year 2000 IFL boundary using the global wall-to-wall Landsat image composite for 2016 and the global forest cover loss data set (Hansen et al. 2013). Areas identified as “reduction in extent” met the IFL criteria in 2000, but they no longer met the criteria in 2020. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.

The world IFL map was created through visual interpretation of Landsat images by experts. The map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the methodology assumes that fires near roads or other infrastructure may have been caused by humans and therefore constitute a form of anthropogenic disturbance. This assumption could result in an underestimation of IFL extent in the boreal biome.

Tree plantations. Harris et al. 2019, https://files.wri.org/s3fs-public/spatial-database-planted-trees.pdf .   The Spatial Database of Planted Trees (SDPT) was compiled by World Resources Institute using data obtained from national governments, nongovernmental organizations, and independent researchers. Data were compiled for 82 countries around the world, through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table. Most country maps originated from supervised classification or manual polygon delineation of Landsat, SPOT, or RapidEye satellite imagery. The data is nominally representative of the year 2015, although years for individual countries vary. 

The planted trees category in the SDPT includes forest plantations of native or introduced species, established through deliberate human planting or seeding. Sometimes called tree farms, these forests infuse the global economy with a constant stream of lumber for construction, pulp for paper, and fuelwood for energy. The data set also includes agricultural tree crops such as oil palm plantations, avocado farms, apple orchards, and even Christmas tree farms. The SDPT makes it possible to identify planted forests and tree crops as separate from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.

Mangrove forests. Bunting et al. 2018, https://doi.org/10.3390/rs10101669 . This data set (version 2.0) depicts the global extent of mangrove forests for the year 2010, derived by random forest classification of a combination of L-band radar (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar, or ALOS PALSAR) and optical (Landsat 5, 7) satellite data. All satellite data and software used to derive the Global Mangrove Watch mangrove maps are available in the public domain.

Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (one-by-one degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and subtropical coastlines in the Americas, Africa, Asia, and Oceania.

The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation, and distance from ocean water. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling 38 million points using the mangrove masks (for the year 2000) of Giri et al. (2011) Giri et al. 2011, https://doi.org/10.1111/j.1466-8238.2010.00584.x . and Spalding et al. (2010) Spalding et al. 2010, https://www.routledge.com/World-Atlas-of-Mangroves/Spalding-Kainuma-Collins/p/book/9781844076574 . and the water occurrence layer defined by Pekel et al. (2016). Pekel et al. 2016, https://doi.org/10.1038/nature20584 .  

The Landsat 7 scan-line error affects the classification in certain areas, resulting in striping artifacts in the data. Classification accuracy was assessed with over 53,800 randomly sampled points across 20 randomly selected regions. Overall accuracy was 95.25 percent, and the user’s and producer’s accuracies for the mangrove class were estimated at 97.5 percent and 94.0 percent, respectively. Factors such as satellite data availability (due to clouds, cloud shadows, and Landsat 7 scan-line error), mangrove species composition, and level of degradation can all lead to local variations in accuracy. The mangrove seaward border is generally also more accurately defined than the landward side, where distinction between mangrove and certain terrestrial vegetation species can be unclear.

Areas known to be missing in this version (2.0) of the data set include Bermuda (United Kingdom); Europa Island and the Wallis and Futuna Islands (France); Fiji, east of longitude 180° east; Guam and Saipan (United States); Kiribati; Maldives; and Peru, south of latitude 4° south.

Commodities

Global cocoa, coffee, soy. Wood-Sichra et al. 2016, http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/133503/filename/133714 .  For the Global Forest Review (GFR), we use cocoa, arabica and robusta coffee, and soy maps from MapSPAM to assess which crops have replaced forests ; the exception is for soy in South America, where higher-resolution and more recent data are available. The MapSPAM data maps crop area for 42 crops in the year 2010 at a spatial resolution of 10 kilometers (km). Physical crop area was used in all analyses, as opposed to harvested area, to account for all land occupied by a specific crop. The data combines country and subnational reported production statistics, an agriculture land cover map, and crop-specific suitability information based on climate, landscape, and soil conditions into a spatial model. Suitable areas for each crop are identified in the MapSPAM data by using existing land resources and biophysical limitations to provide suitable crops areas. IIASA and FAO 2012, http://www.fao.org/fileadmin/user_upload/gaez/docs/GAEZ_Model_Documentation.pdf .  Each 10 km grid cell contains the estimated area of each of the 42 crops, further broken into physical and harvested area of irrigated high input, rain-fed high input, rain-fed low input, and rain-fed subsistence. Whereas high input includes the use of high-yield crop varieties, optimal application of fertilizer, chemical pest disease and weed controls, and might be fully mechanized, low input uses traditional varieties of crops with manual labor and minimal or no applications of fertilizers or pest control measures. Subsistence refers to crop production by small-scale farmers largely for their own consumption under rain-fed and low-input conditions, regardless of the suitability of land. It is assumed to happen more intensively in areas with large rural populations, so rural population density from the Global Rural-Urban Mapping Project (Version 1) helps to further identify subsistence farming. Balk et al. 2006, https://doi.org/10.1016/S0065-308X(05)62004-0 .    

Global pasture. Ramankutty et al. 2008, https://doi.org/10.1029/2007GB002952 .  This data set maps global pastureland at a 10 km resolution for the year 2000. In the GFR, we use EarthStat pasture data to assess where pasture has replaced forests; the exception is for Brazil, where there is higher-resolution and more recent data available. EarthStat uses the definition of permanent pasture used by the Food and Agriculture Organization of the United Nations (FAO): “land used permanently (5 years or more) for herbaceous forage crops, either cultivated or growing wild.” Agricultural inventory data from a variety of sources, including country and FAOSTAT data, were modeled onto land-use and land cover maps of agriculture and pasture derived from Moderate Resolution Imaging Spectroradiometer and SPOT imagery. The definition of pasture causes some known inconsistencies because some countries distinguish between grassland pasture and grazed land, but most do not in their reporting. 

Brazil pasture. See LAPIG, https://www.lapig.iesa.ufg.br/lapig/ .  This data set maps annual pasture extent in Brazil at a 30 m resolution. In the GFR, this data is used preferentially over the global pasture map to assess where pasture replaced forests in Brazil. The data were derived from Landsat imagery using automatic, random forest classification. Historical maps from 1985 to 2018 are available from the Image Processing and Geoprocessing Laboratory (Laboratório de Processamento de Imagens e Geoprocessamento; LAPIG) as part of the MapBiomas initiative, but only the 2018 extent was used in the GFR calculations.  

South America soy. Peng et al., forthcoming.  This data set maps annual soy extent from 2001 to 2018. We use this data set for all calculations to assess where soy replaced forests in South America. The data were derived from Landsat imagery to map the harvest season of soy annually from 2001 to 2018. All years were combined to estimate forest loss on land that was eventually used for soy production. 

Oil palm, rubber, wood fiber. Harris et al. 2019, https://files.wri.org/s3fs-public/spatial-database-planted-trees.pdf .  Oil palm, rubber, and wood fiber plantations from the Spatial Database of Planted Trees (SDPT) were used for all calculations to assess where oil palm, rubber, and wood fiber replaced forests. Oil palm is thought to be a comprehensive data set for the year 2015, whereas rubber and wood fiber plantation data is only available for specific countries (for rubber, Brazil, Cambodia, Cameroon, the Democratic Republic of the Congo, India, Indonesia, and Malaysia; and for wood fiber, Argentina, Brazil, Cambodia, China, India, Indonesia, Malaysia, Rwanda, South Africa, and Vietnam). See above for more information about the SDPT. 

Protected areas. See the World Database on Protected Areas, www.protectedplanet.net .    The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via  Protected Planet , the online interface for the WDPA, and are updated monthly (the January 2021 data update was used in the Global Forest Review). The WDPA is a joint initiative of the International Union for Conservation of Nature (IUCN) and the United Nations Environment Programme World Conservation Monitoring Centre to compile spatially referenced information about protected areas. All IUCN categories were used as part of any Global Forest Review analysis unless otherwise specified. 

Logging concessions. Managed forests refers to areas allocated by a government for harvesting timber and other wood products in a public forest . Managed forests are distinct from wood fiber concessions , where tree plantations are established for the exclusive production of pulp and paper products. Concession is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes forest permits, tenures, licenses , and other terms. 

This data set is assembled by aggregating data for multiple countries. Source and date information can be found in the table below. 

Logging concession data sources and dates 

Biodiversity

Biodiversity intactness. Hill et al. 2019, https://doi.org/10.3389/ffgc.2019.00070 . This data set quantifies the impact humans have had on the intactness of species communities. Anthropogenic pressures such as land-use conversion have caused dramatic changes to the composition of species communities, and this layer illustrates these changes by focusing on the impact of forest change on biodiversity intactness. The maximum value indicates no human impact, whereas lower values indicate that intactness has been reduced. 

The Projecting Responses of Ecological Diversity in Changing Terrestrial Systems (PREDICTS) database comprises over 3 million records of geographically and taxonomically representative data of land-use impacts to local biodiversity. Hudson et al. 2017, https://doi.org/10.1002/ece3.2579 . A subset of the PREDICTS database, including data pertaining to forested biomes only, was employed to model the impacts of land-use change and human population density on the intactness of local species communities. 

First, a relevant land-use map was produced by selecting all forested biomes and each 30-by-30-meter (m) pixel within the biomes was assigned a land-use category based upon inputs from the Global Forest Watch forest change database and a downscaled land-use map. Hoskins et al. 2016, https://doi.org/10.1002/ece3.2104 . The modeled results of biodiversity intactness derived from the PREDICTS database are projected onto the land-use and human population density maps, and the final product is aggregated to match the resolution of the downscaled land-use map. Hoskins et al. 2016, https://doi.org/10.1002/ece3.2104 . The final output models the impacts of forest change on local biodiversity intactness within forested biomes.

The metric assumes that the biodiversity found in a perfectly intact site is equivalent to the biodiversity that would be present without human interference. Human impacts on biodiversity intactness are quantified through models that extrapolate results from site-specific studies across large areas, and there is always a degree of uncertainty in such extrapolations.

Biodiversity significance. Hill et al. 2019, https://doi.org/10.3389/ffgc.2019.00070 . This data set shows the significance of each forest location for biodiversity in terms of the relative contribution of each pixel to the global distributions of all forest-dependent mammals, birds, amphibians, and conifers worldwide. To calculate it, species that are coded in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species as forest dependent are selected and their distribution maps are clipped by their known altitudinal ranges (note, the altitudinal range for amphibians has not been assessed) using a digital elevation model data set, and overlapped with the layer of forest cover. For each species, the relative “significance” of each forest pixel in their range is calculated as one divided by the total number of pixels of forest in their range. These values are summed for all species occurring within the pixel to give an overall value to the pixel. This metric is also sometimes termed range rarity .

This data set includes several caveats. There are many ways to define biodiversity significance, and this layer is based on one approach. Only forest-dependent bird, mammal, amphibian, and conifer species were included in the analysis. The individual species range maps upon which this layer is based show distributional boundaries, not occupancy, and so contain commission errors. However, when more than 15,000 species ranges are combined into this single layer, such errors become largely irrelevant. Historical ranges were excluded. Hence, the value of each pixel is related to the global loss of species richness if the pixel is deforested . Locations of high species richness do not necessarily have high scores if most of the species in the location have large global distributions. All species are treated equally, so the evolutionary distinctiveness of different taxa is not considered. When overlaid with maps of forest loss, forest gain is ignored. It is assumed that tree cover gain over the analysis period is unlikely to translate into significant gain in forest-dependent species given the natural time lags in regeneration of forest ecosystems. Finally, the data set provides a broad picture of variation in biodiversity significance of different forests globally. It is not intended to be used in isolation for priority setting or decision-making, for which additional information is typically needed.

Key Biodiversity Areas. See BirdLife International, http://www.keybiodiversityareas.org . Key Biodiversity Areas (KBAs) are “sites contributing significantly to the global persistence of biodiversity.” The Global Standard for the Identification of Key Biodiversity Areas IUCN 2016, https://portals.iucn.org/library/sites/library/files/documents/2016-048.pdf .  sets out globally agreed-upon criteria for the identification of KBAs worldwide. Sites qualify as global KBAs if they meet one or more of 11 criteria, clustered into five categories: threatened biodiversity, geographically restricted biodiversity, ecological integrity, biological processes, and irreplaceability. The KBA criteria can be applied to species and ecosystems in terrestrial, inland water, and marine environments. Although not all KBA criteria may be relevant to all elements of biodiversity, the thresholds associated with each of the criteria may be applied across all taxonomic groups (other than microorganisms) and ecosystems.

The KBA identification process is a highly inclusive, consultative, and bottom-up exercise. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, consultation with stakeholders at the national level (both nongovernmental and governmental organizations) is required during the proposal process.

Over 15,000 KBAs have been identified to date, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and KBAs identified through hot spot ecosystem profiles supported by the Critical Ecosystem Partnership Fund.

Alliance for Zero Extinction. See the Alliance for Zero Extinction, https://zeroextinction.org/ . A subset of KBAs, this data set shows 587 sites for 920 species of mammals, birds, amphibians, reptiles, conifers, and reef-building corals. The species found within these sites have extremely small global ranges and populations; any change to habitat within a site may lead to the extinction of a species in the wild. To meet Alliance for Zero Extinction site status, a site must

• contain at least one “Endangered” or “Critically Endangered” species;
• be the sole area where an Endangered or Critically Endangered species occurs;
• contain greater than 95 percent of either the known resident population of the species or 95 percent of the known population of one life history segment (e.g., breeding or wintering) of the species; and
• have a definable boundary (e.g., species range, extent of contiguous habitat, etc.).

IUCN Red List of Threatened Species. This data set contains distribution information on species assessed for the IUCN Red List of Threatened Species . The maps are developed as part of a comprehensive assessment of global biodiversity to highlight taxa threatened with extinction and thereby promote their conservation. The IUCN Red List contains global assessments for 105,732 species, with more than 75 percent of these having spatial data. The Global Forest Review (GFR) uses the Asian elephant, Bornean orangutan, Sumatran orangutan and tiger ranges to assess tree cover loss in their habitat ranges, which were mapped in 2020, 2016, 2017 and 2022 respectively. These three species represent endangered, iconic animals of Southeast Asia. Future editions of the GFR will likely include additional iconic species from South America and Africa. 

Aboveground biomass density. Harris et al. 2021. https://doi.org/10.1038/s41558-020-00976-6 .  This data set expands on the methodology presented in Baccini et al. (2012) Baccini et al. 2012, https://doi.org/10.1038/nclimate1354 .  to generate a global map of aboveground live woody biomass density at 30-meter resolution for the year 2000. Aboveground biomass (AGB) was estimated for more than 700,000 quality-filtered Geoscience Laser Altimeter System (GLAS) lidar observations using allometric equations that estimate AGB based on lidar-derived canopy metrics. The global set of GLAS AGB estimates was used to train random forest models that predict AGB based on spatially continuous data. The predictor data sets include Landsat 7 Enhanced Thematic Mapper Plus top-of-atmosphere reflectance and tree canopy cover from the Global Forest Change data set, Version 1.2; Hansen et al. 2013, https://doi.org/10.1126/science.1244693 .  one arc-second Shuttle Radar Topography Mission, Version 3, elevation; Farr et al. 2007, https://doi.org/10.1029/2005RG000183 . GTOPO30 elevation from the U.S. Geological Survey (for latitudes greater than 60° north); and WorldClim climate data. Hijmans et al. 2005, https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.1276 .  The predictor pixel values were extracted and aggregated for each GLAS footprint to link the GLAS AGB estimates with the predictor data. A random forest model was trained for each of six continental-scale regions: the Afrotropic, Australia, Nearctic, Neotropic, Palearctic, and Tropical Asia regions.  

Gross emissions, gross removals, and net forest greenhouse gas (GHG) flux. Harris et al. 2021, https://doi.org/10.1038/s41558-020-00976-6 .  This data set includes estimates for gross GHG emissions, gross carbon removals, and net GHG flux at 30-meter resolution and is derived from a model that combined ground measurements and satellite observations with national GHG inventory methods from the Intergovernmental Panel on Climate Change (IPCC).

Emissions include all carbon pools and multiple greenhouse gases (CO 2 , CH 4 , N 2 O). The CO 2 e emitted from each pixel is based on maps of carbon densities in 2000 (with adjustment for carbon accumulated between 2000 and the year of disturbance), drivers of tree cover loss , forest type, and burned areas. All emissions are assumed to occur in the year of disturbance (committed emissions). Removals in standing and regrowing forests include the accumulation of carbon in both aboveground and belowground live tree biomass, while ignoring accumulation in dead wood, litter and soil organic carbon due to lack of data. Carbon removed by trees in each pixel is based on maps of forest type, ecozone, forest age, and number of years of forest growth. Net forest GHG flux represents the difference between GHG emissions and carbon removals. Forest is defined as woody vegetation with a height of at least 5 meters and a canopy density of at least 30 percent at 30-meter resolution.

Erosion risk. Qin et al. 2016, https://www.wri.org/publication/gfw-water-metadata .  This data set maps the risk of erosion around the world. Erosion and sedimentation by water involves the process of detachment, transport, and deposition of soil particles, driven by forces from raindrops and water flowing over the land surface. The Revised Universal Soil Loss Equation (RUSLE), which predicts annual soil loss from rainfall and runoff, is the most common model used at large spatial extents due to its relatively simple structure and empirical basis. The model takes into account rainfall erosivity, topography, soil erodibility, land cover and management, and conservation practices. Because the RUSLE model was developed based on agricultural plot scale and parameterized for environmental conditions in the United States, modifications of the methods and data inputs were necessary to make the equation applicable to the globe. Conservation practices and topography information were not included in this model to calculate global erosion potential due to data limitations and their relatively minor contribution to the variation in soil erosion at the continental to global scale compared to other factors. The result of the global model was categorized into five quantiles, corresponding to low to high erosion risks. 

Urban watersheds. McDonald and Shemie 2014, http://water.nature.org/waterblueprint/#/intro=true .  Urban watershed boundaries for 530 cities, mapped as part of the Urban Water Blueprint project, including 33 megacities with more than 10 million people. According to United Nations population data for 2018, there were 33 cities with a population greater than 10 million people in 2018. Due to the availability of watershed boundary data, 32 of these cities are included in the Global Forest Review

LandMark. LandMark 2019, http://www.landmarkmap.org/data/ .  This data set depicts collectively held and used lands worldwide. It consolidates the numerous ongoing local, national, and regional efforts to map and document indigenous and community lands within a single global data set. The data set distinguishes indigenous lands from other community lands in part because various international human rights instruments specifically grant Indigenous Peoples a range of rights, including rights to their land and natural resources. LandMark uses the best-quality data available from reputable organizations and recognized experts, but it does not endorse or verify the accuracy of any data set. 

Population. European Commission and Columbia University 2015, http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a .  The Global Human Settlement Layer (GHSL) Population Grid depicts the distribution and density of population, expressed as the number of people per cell, for 2015. Whereas the Global Forest Review only uses 2015 data, the GHSL is a multitemporal population data set that employs new spatial data mining technologies. These methods enable the automatic processing and extraction of analytics and knowledge from different data sets: global, fine-scale satellite image data streams; census data; and crowd sources or volunteered geographic information sources. 

To produce this population density and distribution data set, researchers mapped global built-up areas, which are defined as all aboveground constructions intended for human or animal sheltering or to produce economic goods. The locations of these built-up areas were established using Landsat imagery analysis. An additional source used to compile this data set was the Gridded Population of the World (GPW) data set assembled by Columbia University’s Center for International Earth Science Information Network. The GPW data set consists of census population data and bolstered the built-up areas data by enabling researchers to estimate residential population. To present this data as grid cells, GPW data was disaggregated from census or administrative units. 

Overall, the GHSL data set is an accurate and high-resolution estimate of global population. Known issues with this data include the insufficient availability of global test sets with the right scale, time period, and reliability to validate and improve the GHSL. Another known challenge is the lack of remote sensing studies that compare the use of different sensors to detect human settlements.  

Conflict. Global Witness 2019a, https://www.globalwitness.org/en/campaigns/environmental-activists/enemies-state/ .  Global Witness compiles location data documenting the killing and enforced disappearances of land and environmental defenders. This global data set uses credible, published, and current online media reports to identify and report the location of killings. If the exact location is unknown, the location of the media report is used instead, which is typically the closest urban area. Some regions of the world, particularly rural areas, may have underreported numbers due to limited media reports.

UN Subregions. See UNSD, https://unstats.un.org/unsd/methodology/m49/ Composition of geographic regions used by the UN Statistics Division. For the Global Forest Review, it is used as a set of boundaries in which trees outside forests are quantified.

Ecozones. FAO 2012, http://www.fao.org/3/ap861e/ap861e00.pdf .  This data set depicts major ecozones, including boreal, temperate, tropical, and subtropical regions. 

Peatlands. For more information about peatlands, see Global Forest Watch, https://gfw.global/37Pfnpw .   This data set shows peatlands in Indonesia greater than five meters in depth. 

Indonesian forest moratorium. For more information about the moratorium, see Global Forest Watch, https://gfw.global/3oxnjBJ .  Data set indicating the area of Indonesia’s moratorium against new forest concessions , designed to protect Indonesia’s peatlands and primary natural forests from future development. In May 2011, the Ministry of Environment and Forestry put into effect a two-year moratorium on the designation of new forest concessions in primary natural forests and peatlands. This moratorium is designed to allow time for the government to develop improved processes for land-use planning, strengthen information systems, and build institutions to achieve Indonesia’s low-emission development goals. The moratorium, made permanent in 2019, is part of Indonesia’s pledge to curtail forest clearing in a US$1 billion deal with the Norwegian government. 

Rural complex. Molinario et al. 2015, https://doi.org/10.1088/1748-9326/10/9/094009 .   This Democratic Republic of the Congo (DRC) land-use and land cover data set depicts core forest , forest fragmentation , and the rural complex, a land-use mosaic of roads, villages, active and fallow fields, and secondary forest that we use as a proxy for shifting cultivation in the DRC. This is separate from shifting cultivation identified in the drivers of deforestation data set and is only used in the DRC-specific analysis from the Forest Extent Indicator .  

The data set was created by characterizing forest clearing using spatial models in a geographical information system, applying morphological image processing to the Central African Forests Remotely Assessed (Forets d'Afrique Central Evaluee par Teledetection; FACET) product. This process allowed for the creation of maps for 2000, 2005, 2010, and 2015, classifying the rural complex and previously homogenous primary forest into separate patch, edge, perforated, fragmented, and core forest subtypes. 

Countries. See GADM, https://gadm.org/ .  This data set shows political boundaries, including country, provincial, and jurisdictional administrative units. 

ESA CCI Land Cover. See ESA CCI, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf This data set maps annual global land cover at 300-meter resolution for the year 2020. The annual maps are derived from a baseline land cover map based on the Medium Resolution Imaging Spectroradiometer (MERIS) Full Resolution and Reduced Resolution archive from 2003 to 2012. Land cover changes are detected based on the Very High Resolution Radiometer (VHRR) time series from 1992 to 1999, SPOT-Vegetation time series between 1999 and 2013 and PROBA-Vegetation for years 2013, 2014 and 2015. The baseline land cover map is then backdated and updated to produce annual maps. For the Global Forest Review, it is used to delineate urban, grassland and agricultural land, on which trees outside forests are quantified.

SBTN Natural Lands. Mazur et al. 2023, https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf This data maps natural and non-natural lands for the year 2020 at 30-meter resolution based on Accountability Framework Initiative (AFi) definitions and operational guidance. This data was created by WRI in collaboration with WWF and Systemiq as part of the Science Based Target Network‘s guidance for setting land science-based targets . The Natural Lands map was created by combining the best available global spatial data on land cover and land use into a single, harmonized map using a series of overlays and decision rules. Both global and regional/local data was used, and where available, regional/local data was given priority. The global binary map was independently validated by the International Institute for Applied Systems Analysis (IIASA) using a random sample of 4,730 points and has an overall accuracy of 91.6 percent. Individual class accuracies show that the map misclassifies 6 percent of the natural points as non-natural, and 18 percent of the non-natural points as natural. Limitations include definitional, temporal and resolution inconsistencies due to the combination of data from various sources. Due to a lack of global remote sensing data on pasturelands, gridded livestock densities from the UN Food and Agriculture Organization (FAO) were used as a proxy for non-natural short vegetation.

All analyses in the Global Forest Review draw heavily on per-pixel geodesic area calculations for accurate global area estimations of forests. This means that the precise geodesic area of each 30-meter (m) pixel across the globe is calculated and then summed for each year of loss and unique area of interest, such as countries or protected areas. Due to distortions from projecting the three-dimensional surface of the earth onto a flat surface, the area of a 30 m pixel can vary from roughly 900 m2 at the equator to roughly 200 m2 at the poles. These area differences are accounted for when using geodesic area calculations. Unless otherwise specified, all calculations are run at a 30 percent tree canopy density threshold as of the year 2000.

Calculations

• Area calculation: Sum the geodesic area of all pixels within an area of interest.
• Extent calculation: Sum the geodesic area of all pixels within the tree cover extent raster data set. 
• Tree cover loss calculation: Sum the geodesic area of all tree cover loss pixels within an area of interest (AOI; e.g., country boundaries or protected areas).
• Rate of loss calculation: Loss area in current year minus loss area in past year divided by loss area in past year. Only countries with at least 100,000 hectares of tree cover in the year 2000 were included.
• Percent of loss calculation: Divide loss of current year by earlier forest extent area.
• Carbon storage calculation: The aboveground biomass density data set is formatted as biomass per hectare. To convert values to carbon per pixel, each biomass pixel is multiplied by the geodesic area (in hectares) of that pixel to get biomass per pixel, and then divided by 0.47 to convert biomass to carbon. Finally, sum the aboveground biomass pixel values that overlap with the tree cover extent raster data set.
• Gross emissions, gross removals, and net forest greenhouse gas (GHG) flux calculation:  Gross emissions are estimated annually, while removals and net flux reflect the total over the period of 2001-2022 and are divided by 22 to calculate the average annual gross removals and average annual net flux. To calculate gross emissions or gross removals over specific areas, we convert emissions/removals per hectare to emissions/removals per pixel by multiplying emissions/removals (in CO2e) by the geodesic area of each pixel (in hectares), and then summing within the area of interest. Net flux is calculated by subtracting average annual gross removals from average annual gross emissions in each modeled pixel.

Using the above data sets and methodologies, the Global Forest Review (GFR) assesses the state of the world’s forests and provides insight into how they are changing year to year based on 17 indicators. The next section outlines each statistic produced by GFR authors, along with the data set and method summary used to generate each calculation.