data presentation on deforestation

2024: A decade of deforestation data

After 10 years and 1.3 million data points charting the companies and financial institutions most exposed to tropical deforestation, conversion of natural ecosystems and associated human rights abuses, ‘Forest 500: A Decade of Deforestation Data’ sets out 10 lessons for enabling and accelerating action.

Download the 10th annual Forest 500 report

Continual laggards.

Almost a quarter (23%) of the companies and financial institutions that have been in the Forest 500 for the past 10 years are still yet to publish a single deforestation commitment or policy. After a decade of being in the spotlight and numerous engagement attempts from Forest 500, it is inexcusable that this group has failed to produce a single publicly available deforestation commitment. Ignorance has long ceased to be defence. The group that has been willfully ignoring the data includes Europe’s biggest shoe manufacturer, Deichmann Group, the second largest Chinese food and beverage company, Bright Food, and one of the world’s largest investment companies, Vanguard.

The leaders

Over the past decade, front-running companies have demonstrated beyond doubt that market-driven deforestation is a solvable crisis. These companies have made strong progress on deforestation, conversion and associated human rights abuses. This includes developing policies, implementing them and reporting on progress. Nestlé has the highest average score (81%) over the 10 years it has been assessed. No financial institutions have scored above 50% for every year they have been assessed.

The next five years

To ensure a livable future, the world cannot endure another decade of limited progress. This is the critical decade for humanity. To avoid catastrophic consequences, we need to see significant progress over the next five years. With just a handful of years remaining to meet the target of halting and reversing all deforestation by 2030, COP30 in 2025 will be a pivotal moment. Taking place in the closest major city to the Amazon Rainforest, at the halfway point towards these global goals, the eyes of the world will be focused on deforestation. Those who have not yet woken up to acting on this issue should finally pay attention.

Deforestation: Accelerating climate change and threatening biodiversity

Thijs benschop.

SDG 15 aims to protect, restore, and promote the sustainable use of terrestrial ecosystems and forests, to halt and reverse land degradation, and to put a stop to loss in biodiversity. 

Forests are crucial for sustainable development. Covering almost one third (31 percent) of the Earth’s land surface, they offer a habitat to over 80 percent of all terrestrial species and are key to preserving biodiversity. Forested watersheds and wetlands supply three quarters of all accessible freshwater in the world. Worldwide an estimated 300 to 350 million people live in or close to forests and largely depend on them for their livelihoods, and over a billion people rely on forests for employment, forest products, and contributions to livelihoods and income. Many of those living in extreme poverty are highly dependent on forests for their livelihood. 

Forests also play a key role in the mitigation of climate change, removing an estimated 16 billion tonnes of carbon dioxide (CO2) from the atmosphere annually, equaling about half of the annual CO2 released from burning fossil fuels.  

As visualized in this map of the 2023 Atlas of Sustainable Development Goals , global tropical rainforests sequester more CO2 than boreal and temperate forests combined. CO2 emissions caused by loss of trees, for instance due to logging or wildfires, averaged 8.1 billion tonnes annually over the past 20 years, which is roughly half the CO2 that is removed from the atmosphere by forests. The CO2 released as a result of tree cover loss partially offsets the removal. The net effect is a removal of 7.6 billion tonnes of CO2 annually, which is about one fifth of the total global CO2 emissions from other sources. 

Deforestation is high in tropical rainforests. This causes the tropical rainforests in Southeast Asia to be a net source of CO2 emissions, with the remaining trees unable to absorb the CO2 released by forests lost in a given year. The rainforests in the Amazon and Congo river basins are still a net “sink,” meaning they absorb more CO2 than the amount of emission caused by forest loss. 

To slow climate change, critical steps include protecting forests, reforestation, and afforestation, as well as restoring degraded forests. These measures can increase the amount of CO2 sequestered by forests and reduce emissions caused by deforestation and forest loss. 

Explore the data stories and visualizations of the fifteenth chapter of the Atlas , to see how forest area is unequally distributed among regions, how deforestation and forest degradation undermine sustainable development, and how the deforestation rate has changed over time. 

In the spirit of the World Development Report 2021: Data for Better Lives , we follow an open data and open code approach: all of the data, code, and visualizations of the Atlas are available for download and reuse. 

• Environment
• The World Region
• Atlas of the Sustainable Development Goals
• Sustainable Development Goals (SDG)

Get updates from Data Blog

Thank you for choosing to be part of the Data Blog community!

Your subscription is now active. The latest blog posts and blog-related announcements will be delivered directly to your email inbox. You may unsubscribe at any time.

Statistician, Development Data Group, World Bank

Join the Conversation

• Share on mail
• comments added

Deforestation

Forests are an important and common feature of the Earth’s land cover. Human activity and other factors result in deforestation. Humans clear the natural landscape to make room for farms and pastures, to harvest timber, and to build roads and houses. Tropical forests of all varieties, in particular, are disappearing rapidly by human activity. Other causes of deforestation may include drought, forest fires, and climate change. Although deforestation meets some human needs, it also causes major problems, including social conflict, the extinction of plants and animals, and climate change. These challenges aren’t just local. Deforestation also has global impacts.

>> Read More >>

Featured Mini Lessons

Estimating Biomass Loss from a Large Fire

The fires in Greece during the summer of 2007 devastated large tracks of forest and ground cover in this Mediterranean region. Students analyze these data to determine the scale, area, and percentage of the forest impacted by of these fires.

Computing Vegetation Health

Explore using units for calculations with Normalized Difference Vegetation Index (NDVI). NDVI is a ratio of different light wavelength reflectance which can be used to map the density of green vegetation.

Computing Vegetation Cover

Explore using units in calculations with the Leaf Area Index (LAI). LAI is a ratio that describes the number of square meters of leaves per square meter of available land surface. Because of the units in the ratio, it is dimensionless.

Comparing Global Land Use Over Time

Examine the images to see the projected differences in land use between 1900 and 2100. 

Evaluating Plants as Energy Stores

Students learn how to estimate the "energy efficiency" of photosynthesis, or the amount of energy that plants absorb for any given location on Earth.

Carbon Dioxide Production and Sequestration

Carbon dioxide concentration in the atmosphere is affected by many processes including fires, deforestation, and plant respiration. Students will evaluate a Landsat image to determine the rate of carbon dioxide sequestration in a particular area.

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

•   Facebook
•   Twitter
•   Linkedin
•   Digg
•   Reddit
•   Pinterest
•   Email

Latest Earthquakes |    Chat Share Social Media  

Satellite Data Shows Value in Monitoring Deforestation, Forest Degradation

Deforestation is a significant concern for many parts of the globe, particularly in places like the rainforests of the Amazon or the Congo. Scientists, governments, and non-governmental organizations turn to satellite data to track deforestation, as well as to set targets for improvement.

On the latest episode of Eyes on Earth, we hear from a remote sensing specialist with the U.S. Forest Service Rocky Mountain Research Station, Dr. Eric Bullock. As a post-doctoral researcher at Boston University, Dr. Bullock developed an open source algorithm for monitoring tropical deforestation using Landsat data. Dr. Bullock is now piloting this methodology for use as part of the United Nations Reducing Emissions from Deforestation and Degradation in Developing Countries Program., which offers incentives for reduction in emissions and sustainable practices.

Here are some highlights from our conversation with Dr. Bullock on Episode 46 of Eyes on Earth, edited for length and clarity.

You can find the full episode at this link or find every episode at this link . You can also subscribe through Google Podcasts at this link .

EYES ON EARTH (EOE):  First off, let’s talk a little bit about your background. How did you come to remote sensing and how did you land on this particular area of research?

ERIC BULLOCK (EB):  Like anyone, my road to where I am today has been rather long and windy. I was working as a forest restoration technician at the Santa Monica Mountains. That’s a really beautiful park right outside of L.A. I was doing a lot of field work for restoration of ecosystems after fires, and what became really clear to me was the importance of data—especially spatial data. What we really needed was real-time information on fire damage, species distribution, ecosystem change. We just needed all the information that we could get our hands on while we were out in the field.

So that really interested me, enough to want to pursue my master’s in remote sensing. I went to (Boston University) to try and learn more about how I could start to develop remotely sensed information. I just loved it. I got to be involved in a little bit of research through my master’s, but it really wasn’t until I started to pursue me PhD that I really started to get more involved in global initiatives to monitor forests.

EOE:  What’s the difference between deforestation and degradation? And why is it important to monitor those things? How does satellite data help us do that?

EB:  Often when you see on the news or in movies, somewhere in Borneo or in the Amazon being cleared for something like agriculture or pastureland, that’s really large-scale deforestation. So that’s the conversion of a forest to another land cover, such as croplands. That usually occurs over large areas, not always, but the most common of what you see in say the Amazon.

Degradation is a process where forests are disturbed, or they’re affected by people through causes such as selective logging—just removing a branch here, some timber there. But it remains a forest.

This is important because with the degradation of something like selective logging, there’s still a lot of the ecological consequences there, like carbon being emitted into the atmosphere. The forest will still be reducing its ability to perform ecological functions.

EOE:  Tell us about the Continuous Degradation Detection (CODED) algorithm. How did you develop this? What does it look for? What kinds of data go into this algorithm, and how has it been applied?

EB:  CODED comes out of what has really been a paradigm shift in the remote sensing community towards the use of time series analysis. The basic idea is that instead of just using one image or one bit of data from one year and comparing it to five or ten years later, you’re using all of the data that you have available to you. What CODED does is characterizes the normal state of a stable forest through time, and then automatically detects the subtle deviations from that trend and labels them either degradation or deforestation.

EOE:  What did you learn by applying CODED to the Amazon?

EB:  I’ve now used CODED in over 20 countries and what we’re finding has been pretty consistent everywhere. The more we look in detail for these subtle degradation events, the more change we’re finding. In the Amazon, for example, we found that there was between 44 and 60 percent more area of disturbed Amazonian forest. Previous analyses had mostly ignored these degradation events, either for practical purposes or because it was not the focus of that study. This is similar to what we’re finding elsewhere—when you account for degradation, a lot more of the forest is disturbed than we previously thought.

EOE:  It’s my understanding that you previously worked on a deforestation algorithm using NASA’s MODIS sensor. Can you tell us about that? How did, or how does, that work factor into what you did with Landsat?

EB:  CODED has primarily used historical data—between 30 and five years ago, for example, just trying to understand how much of the forest has been disturbed. Landsat data is recorded between every eight and sixteen days. With MODIS data, you get a new image of almost everywhere in the world, every single day. This is ideal for near real-time monitoring. We developed an algorithm that used daily MODIS data to create alerts of forest disturbance. If you are trying to mitigate illegal deforestation, if you had an alert of disturbance as it was occurring, you could theoretically go out and try and stop that event. We tested this in the Amazon and it worked really well.

What we’re currently trying to do is create a similar system, but instead of using this daily MODIS data, we’re trying to use a combination of different sensors to try and replicate that daily repeat cycle of MODIS, but at the spatial resolution of something like Landsat.

EOE:  What data sources or data products are on the way that you see as particularly useful for monitoring deforestation?

• Download Video

I’m really excited about Harmonized Landsat-Sentinel. Sentinel 2 is a couple sensors from the European Space Agency that records very similar data to Landsat. HLS is a product that as a user you’ll get an image that looks like a Landsat image or a Sentinel 2 image. You won’t need to know which sensor it is coming from. It’s comparable to the Landsat data we’ve already been working with, so we can input it directly into our analysis and just get much, much more data.

One sensor I’m really excited about is called GEDI. GEDI is a lidar sensor onboard the International Space Station. Lidar uses laser light to basically measure distance, so it can be used to study different attributes of a forest that you can’t get with an image like from Landsat. This will be really useful, not so much for trying to identify deforestation or change of any sort, but for learning about the different properties of that forest. GEDI is going to be used to create a pretty high resolution, high-quality forest biomass data set. Which will be really useful in trying to better understand carbon implications from cutting down the forest.

The other general type of new data I’m excited about is radar data. We’ve thus far been talking about optical imagery, which is basically like taking a photo from space and doing an analysis on that. That’s what Landsat, MODIS, and Sentinel-2 are. It’s really good for identifying change on the landscape when there are not clouds. If there is a cloud, the image you take from space is really an image of the cloud. In the tropics, it is very frequent that we see cloud cover. Right now, I’m working on a project to incorporate not just different sources of optical imagery, but to also radar data through a sensor called Sentinel-1, which is C-Band radar. A radar beam can penetrate through a cloud, reflect off of a forest or off the canopy, and return to the satellite without being horribly affected by that cloud.

EOE:  Tell us about the UN’s Reducing Emissions From Deforestation and Degradation in Developing Countries Program (REDD+). What role do you play there, and where have you taken it so far?

EB:  The general idea is that if a country could prove that they’re reducing their forest-related emissions, they will receive monetary compensation through the United Nations. In order to do that, they need to be able to monitor their emissions from deforestation and degradation, to prove that a, this is what their emissions used to be, and b, now they have taken actions to reduce those emissions, and this is what they are now. We’ve piloted CODED now in a couple of countries West Africa, the Pacific Islands. It is shown to be a pretty effective tool in getting this really necessary information on forest change, deforestation and degradation.

EOE:  So, that’s what you’re doing for this U.N. program. But you work at US Forest Service which is kind of interesting. How does that translate into something that could potentially be useful for the Forest Service or useful state-side? Where would you use this in the U.S.?

EB:  The Forest Service monitors for change in U.S. forests. The algorithms that I develop or that my colleagues develop, could then be applied in the U.S. to better monitor or change what is happening here. It kind of goes back to my reason for getting into remote sensing in the first place, right? I was a restoration technician that needed real-time information on fire damage. The algorithms that I help develop could be used back in the United States for looking at disturbance from fire. Pest damage is another one—beetle damage—or just for getting a better handle of the state of our forests, even the ones that are not changing.

EOE:  Dr. Bullock, any closing thoughts? Anything you would like to make sure that we talk about on this podcast?

EB:  I do just want to say that all of the research that I get to do wouldn’t be possible without free and open data policies from the USGS , NASA and European Space Agency, which are both creating this data and making it very accessible to scientist like me. I do think it is incredibly important to give credit where that is due, because it’s really, really, remarkable that we get all this high-quality data to work with.

Related Content

Eyes on Earth Episode 46 – Deforestation and Forest Degradation

Eyes on Earth is a podcast on remote sensing, Earth observation, land change and science, brought to you by the USGS Earth Resources Observation and Science (EROS) Center. In this episode, we hear how Landsat can aid the study of deforestation around the world.

Eyes on Earth Episode 45 – Harmonized Landsat Sentinel Data

Eyes on Earth is a podcast on remote sensing, Earth observation, land change and science, brought to you by the USGS Earth Resources Observation and Science (EROS) Center. In this episode, we hear about the value of combining satellite data sources.

Eyes on Earth

Eyes on Earth is a podcast on remote sensing, Earth observation, land change and science, brought to you by the USGS Earth Resources Observation and Science (EROS) Center. 

Eyes on Earth Episode 45 - Harmonized Landsat-Sentinel

Landsat satellites have monitoring the Earth’s surface for nearly 50 years, providing critical information for countless areas of study and real-world applications. But with observations only collected every 8-16 days, there are limits to what can be tracked.

Image of the Week: Deforestation in Peru

Landsat helps document large-scale deforestation in Peru. The widespread land change is captured over the past few decades.

Fusion of Landsat and Sentinel Data to Offer More Opportunities to Track Rapid Change Across the Globe

Landsat satellites have monitored the Earth’s surface for nearly 50 years, providing critical information for countless areas of study and real-world...

Get Our News

These items are in the RSS feed format (Really Simple Syndication) based on categories such as topics, locations, and more. You can install and RSS reader browser extension, software, or use a third-party service to receive immediate news updates depending on the feed that you have added. If you click the feed links below, they may look strange because they are simply XML code. An RSS reader can easily read this code and push out a notification to you when something new is posted to our site.

Climate News

Core Science Systems Mission Area News

Earth Resources Observation and Science (EROS) Center News

Landsat Legacy News

Landsat Missions News

United States of America News

• Society ›

Geography & Nature

Global deforestation - statistics & facts

Wildfires, a threat on the rise, efforts to stop deforestation, key insights.

Detailed statistics

Global forest size 1990-2021

Annual tree cover loss worldwide 2001-2023

Global tree cover loss 2023, by leading country

Editor’s Picks Current statistics on this topic

Largest rainforests in the world by primary forest and tree coverage 2020

Pulp Products

Global pulp for paper production 2022, by country

Global primary forest loss 2023, by leading country

Further recommended statistics

• Premium Statistic Global forest size 1990-2021
• Premium Statistic Global change in forest area 1990-2021, by decade
• Basic Statistic Ten countries with most forest area 2021
• Premium Statistic Tree species richness worldwide 2023, by country
• Premium Statistic Agricultural land by type of use worldwide 2021
• Premium Statistic Area harvested for selected food crops worldwide 1961-2022

Total forest area worldwide from 1990 to 2021 (in million hectares)

Global change in forest area 1990-2021, by decade

Area change rate in forest land worldwide from 1990 to 2021, by decade (in million hectares per year)

Ten countries with most forest area 2021

Ten countries with the largest forest area in 2021 (in million hectares)

Tree species richness worldwide 2023, by country

Number of tree species worldwide as of 2023, by country

Agricultural land by type of use worldwide 2021

Agricultural land in use worldwide in 2021, by type (in million hectares)

Area harvested for selected food crops worldwide 1961-2022

Area harvested for selected food crops worldwide from 1961 to 2022 (in million hectares)

• Basic Statistic Global deforestation level by region 1990-2021
• Basic Statistic Global deforestation level by country 2000-2021
• Premium Statistic Annual tree cover loss worldwide 2001-2023
• Basic Statistic Global tree cover loss 2023, by leading country
• Basic Statistic Global tree cover loss 2023, by dominant driver
• Premium Statistic Primary forest loss worldwide 2001-2023
• Basic Statistic Global primary forest loss 2023, by leading country
• Premium Statistic Protected areas with the most tree cover loss worldwide 2001-2023

Global deforestation level by region 1990-2021

Percentage change in forest area worldwide between 1990 and 2021, by region

Global deforestation level by country 2000-2021

Percentage change in forest area worldwide between 1990 and 2021, by select country

Global annual tree cover loss from 2001 to 2023 (in 1,000 hectares)

Leading countries in annual tree cover loss worldwide in 2023 (in 1,000 hectares)

Global tree cover loss 2023, by dominant driver

Global annual tree cover loss in 2023, by dominant driver (in million hectares)

Primary forest loss worldwide 2001-2023

Global primary forest loss from 2001 to 2023 (in 1,000 hectares)

Countries with the largest area of primary tropical forest loss in 2023 (in million hectares)

Protected areas with the most tree cover loss worldwide 2001-2023

Protected areas with the highest tree cover loss worldwide from 2001 to 2023 (in 1,000 hectares)

Emissions and carbon flux

• Premium Statistic Annual carbon flux in forest worldwide 2001-2023, by climate domain
• Premium Statistic Annual forest carbon net flux per hectare worldwide 2001-2023, by climate domain
• Premium Statistic GHG emissions from commodity deforestation worldwide 2001-2023
• Premium Statistic GHG emissions from commodity deforestation worldwide 2023, by country
• Premium Statistic Agriculture and forest land emissions worldwide 2021, by component
• Premium Statistic Global emissions from agriculture in selected countries 2021, by component
• Premium Statistic Global farm-gate agriculture emissions shares 2020, by component

Annual carbon flux in forest worldwide 2001-2023, by climate domain

Average annual carbon flux from forests worldwide from 2001 to 2023, by climate domain (in billion metric tons of CO₂e)

Annual forest carbon net flux per hectare worldwide 2001-2023, by climate domain

Average annual forest carbon net flux per hectare worldwide from 2001 to 2023, by climate domain (in metric tons of CO₂e)

GHG emissions from commodity deforestation worldwide 2001-2023

Greenhouse gas (GHG) emissions from commodity-driven deforestation worldwide from 2001 to 2023 (in billion metric tons of carbon dioxide equivalent)

GHG emissions from commodity deforestation worldwide 2023, by country

Greenhouse gas (GHG) emissions from commodity-driven deforestation worldwide in 2023, by country (in billion metric tons of CO₂e)

Agriculture and forest land emissions worldwide 2021, by component

Emissions from agriculture and forest land worldwide in 2021, by component (in million metric tons of carbon dioxide equivalent)

Global emissions from agriculture in selected countries 2021, by component

Countries with the largest agricultural emissions worldwide in 2021, by component (in million metric tons of carbon dioxide equivalent)

Global farm-gate agriculture emissions shares 2020, by component

Distribution of agriculture emissions within the farm gate worldwide in 2020, by component

Mitigation strategies

• Premium Statistic Share of terrestrial KBAs in protected areas worldwide 2000-2022
• Premium Statistic Share of forest within protected areas worldwide 2000-2020
• Premium Statistic Global certified FSC forest area by region 2023
• Premium Statistic ODA commitments for biodiversity and ecosystems worldwide 2000-2021
• Premium Statistic Global planted forest size as a share of total forest area 2020, by region

Share of terrestrial KBAs in protected areas worldwide 2000-2022

Mean proportion of terrestrial key biodiversity areas (KBAs) covered by protected areas worldwide from 2000 to 2022

Share of forest within protected areas worldwide 2000-2020

Proportion of forests within legally established protected areas worldwide from 2000 to 2020

Global certified FSC forest area by region 2023

Area of certified Forest Stewardship Council (FSC) worldwide as of December 2023, by region (in million hectares)

ODA commitments for biodiversity and ecosystems worldwide 2000-2021

Official development assistance (ODA) commitments on conservation and sustainable use of biodiversity and ecosystems (in millions U.S. dollars)

Global planted forest size as a share of total forest area 2020, by region

Area of planted forest as a proportion of total forest area worldwide in 2020, by region

Further reports

Get the best reports to understand your industry.

Mon - Fri, 9am - 6pm (EST)

Mon - Fri, 9am - 5pm (SGT)

Mon - Fri, 10:00am - 6:00pm (JST)

Mon - Fri, 9:30am - 5pm (GMT)

Interactive visualization requires JavaScript

Related research and data

• Deforestation and Forest Loss

Our World in Data is free and accessible for everyone.

Help us do this work by making a donation.

• News, Stories & Speeches
• Get Involved
• Structure and leadership
• Committee of Permanent Representatives
• UN Environment Assembly
• Funding and partnerships
• Policies and strategies
• Evaluation Office
• Secretariats and Conventions
• Asia and the Pacific
• Latin America and the Caribbean
• New York Office
• North America
• Climate action
• Nature action
• Chemicals and pollution action
• Digital Transformations
• Disasters and conflicts
• Environment under review
• Environmental law and governance
• Extractives
• Fresh Water
• Green economy
• Ocean, seas and coasts
• Resource efficiency
• Sustainable Development Goals
• Youth, education and environment
• Publications & data

Deforestation

30 per cent of emissions from industry and fossil fuels are soaked up by forests and woodlands. Yet every year the world loses 10 million hectares of forest. Deforestation and forest degradation accounts for 11 per cent of carbon emissions. The Green Gigaton Challenge catalyzes public and private funds to combat deforestation and thereby cut annual emissions by 1 gigaton by 2025.

• Climate Action
• Nature Action

© 2024 UNEP Terms of Use Privacy   Report Project Concern Report Scam Contact Us

Deforestation

Deforestation is the removal of trees from a locality. This removal may be either temporary or permanent, leading to partial or complete eradication of the tree cover. It can be a gradual or rapid process, and may occur by means of natural or human agencies, or a combination of both.

Definition source: National Geographic

You Might Also Be Interested In

GFR: Main Menu

Data and methods.

Last Updated on July 26, 2024

In this section, you'll find:

Methodology

Indicator calculations, 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 five subsections: Data Sets, Methodology, Indicator Calculations, Limitations and Change Log. 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 Indicator Calculations section builds on these two sections by summarizing the data sets and methods used for each calculation. The Limitations section summarizes key limitations for data used in GFR analyses. The Change Log section provides an account of updates to the GFR since its initial publication.

Get the Latest in Your Inbox

Want to stay up to date on the state of the world’s forests? Subscribe to our mailing list.

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.     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 2011–23 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 2011–23 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.     This data set shows the dominant driver of tree cover loss from 2001 to 2023 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 2023.

Tree cover gain.    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.    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.    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.     The emerging hot spots data set identifies the most significant clusters of primary humid tropical forest loss between 2002 and 2023 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 2023, 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.    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.     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.     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.    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).    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.     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)    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.     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.    This data set (version 3.0) depicts the global extent of mangrove forests for the years 1996, 2007-2010, and 2015-2020 derived by L-band Synthetic Aperture Radar (SAR) global mosaic data sets from the Japan Aerospace Exploration Agency (JAXA), thus developing a long-term timeseries of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (Global Mangrove Watch v2.5)    was updated using thresholding and a contextual mangrove change mask.

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. The habitat mask was initially developed in Global Mangrove Watch (GMW) v2.0    and has been revised in subsequent versions. 

A source of error in v3.0 included a misregistration in the SAR mosaic data sets, resulting in the omission of known change events and commissions where change was known not to have occurred. This was partially corrected for using tie points automatically generated via the method of Bunting et al. (2010).    The changes associated with misregistration errors are considered to be similar in terms of gain and loss due to the random nature of the input data, so it is recommended that the observed net change statistics are used for analysis rather than individual gain and loss statistics. Mangrove extent maps in v3.0 have an estimated overall accuracy of 87.4% (95th CI: 86.2 - 88.6%). 

Commodities

Global cocoa, coffee, soy.    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.    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.      

Global pasture.    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.    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.    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.    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.     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 February 2024 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.    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.    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.    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.    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.     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.    Key Biodiversity Areas (KBAs) are “sites contributing significantly to the global persistence of biodiversity.” The Global Standard for the Identification of Key Biodiversity Areas    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.    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.    This data set expands on the methodology presented in Baccini et al. (2012)    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;    one arc-second Shuttle Radar Topography Mission, Version 3, elevation;    GTOPO30 elevation from the U.S. Geological Survey (for latitudes greater than 60° north); and WorldClim climate data.    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.    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.    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.    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 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.    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 2001-2023 and are divided by 23 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.  

• Read more about Forest Extent

Forest Extent

The Forest Extent Indicator aims to monitor the total area of forest worldwide, including unmanaged natural forests and managed natural forests . The indicator currently measures tree cover extent in the year 2020 as a best-available proxy for forest. Tree cover extent includes unmanaged and managed natural and planted forests, as well as agricultural tree crops, which are not typically considered forests.

The Global Forest Change data offer an annual view of the world’s  forests  at locally relevant scales using globally consistent criteria. However, they also have key limitations: 

• Not all tree cover is a forest. Satellite data are effective for monitoring changes in  tree cover , but forests are typically defined as a combination of tree cover and land use. For example, agricultural tree cover, such as an oil palm  plantation , is not usually considered to be forest. As such, satellite-based monitoring systems may overestimate forest area unless combined with additional land-use data sets. No land-use data set currently exists at an adequate resolution or update frequency to enable this analysis at global scale. 
• Not all tree cover loss is deforestation. Defined as permanent conversion of forested land to other land uses,  deforestation  can only be identified at the moment trees are removed if it is known how the land will be used afterward. In the absence of a global data set on land use, it is not possible to accurately classify  tree cover loss  as permanent (i.e., deforestation) or temporary (e.g., where it is associated with wildfire, timber harvesting rotations, or shifting cultivation) at the time it occurs. However, new models analyzing spatial and temporal trends in tree cover loss are enabling better insights into the  drivers of loss .   
• Tree cover is a one-dimensional measure of a forest. Many qualities of a forest cannot be measured as a function of tree cover and are difficult, if not impossible, to detect from space using existing technologies. Forests that are vastly different in terms of form and function—such as an  intact   primary forest  and a  planted forest  managed for timber production—are nearly indistinguishable in satellite imagery based on tree cover. Detecting  forest degradation  through remote sensing is also challenging because degradation often entails small changes occurring beneath the forest canopy.
• Tree cover gain is more difficult to measure than loss. Whereas tree cover loss is distinctly visible at a specific moment in time,  tree cover gain  is a gradual process and is thus more difficult to discern from one satellite image to the next. Annual reporting of tree cover loss has not been matched by annual reporting of tree cover gain, resulting in an unbalanced view of global forest change dynamics. Ongoing improvements in detection methodologies will provide annualized global tree cover gain data in the coming years.
• Tree cover loss and gain do not equal net forest. Due to variation in research methodology and date of content, tree cover, tree cover loss, and tree cover gain data sets cannot be compared accurately against each other. Accordingly, “net” loss cannot be calculated by subtracting figures for tree cover gain from tree cover loss, and current (post-2000) tree cover cannot be determined by subtracting figures for annual tree cover loss from year 2000 tree cover.

This section provides an account of updates made to the GFR since it was first published. The GFR is a living report and therefore is updated regularly as new data becomes available.

Please email [email protected] for more information on previous versions of the GFR.

Update 8: July 2024

• Indicators of social and governance issues intro: added new text on data availability and caveats.
• New section “What role do Indigenous Peoples & local communities play in protecting forests?” with expanded citations to include literature beyond LandMark on Indigenous Peoples & local community management and its impact on forests and climate.
• Updated section “How much forestland is held by Indigenous Peoples and other forest-dependent local communities?”, formerly called “How much forestland is legally titled to and/or customarily held by Indigenous Peoples and other forest-dependent local communities?”. Updated figures on the extent of forestland held by Indigenous Peoples & local communities, including a box on LandMark coverage and limitations thereof.
• Updated LandMark figures with Jan. 2024 version of LandMark data.
• New detail on Rights and Resources Initiative (RRI) forest tenure and box on methodology. Expands on progress since RRI began tracking forest tenure in 2002 and clarifies typology.
• Section on "Limitations and future prospects": incorporate critique of Indigenous Peoples & local community territorial mapping and new literature to better explain the difference between statutory recognition of rights, titling, and tenure security.
• Deforestation and Vulnerable Populations (formerly “At-risk populations Indicator”): added additional detail and caveats to underscore the causal relationship between forests and human health.
• Box on “Correlations between forest cover and at-risk populations”: added considerations for future research, as well as new citations for recent literature.
• Section on “Limitations and future prospects”: added considerations for future research, as well as new citations for recent literature.

Update 7: April 2024

• Updated indicators and Forest Pulse with 2023 tree cover loss data.
• Added archive of past years’ Forest Pulse tree cover loss data analyses.

Update 6: November 2023

• Trees Outside Forest Indicator: complete rewrite with tropical tree cover data from Brandt et al. (2022). Previous text primarily discussed data limitations.

Update 5: June 2023

• Updated indicators and Forest Pulse with 2022 tree cover loss data.

Update 4: October 2022

• Section on “Is global tree cover increasing or decreasing?”: replaced Song et al. (2018) with Potapov et al. (2022) to derive net forest change statistics.
• Removed box titled “A prototype for improved measurements of tree cover gain” which discussed Potapov et al. (2019) as a potential model for future gain updates.
• Added key terms and definitions to Data and Methods section.

Update 3: April 2022

• Updated indicators and Forest Pulse with 2021 tree cover loss data.

Update 2: October 2021

• Carbon Flux indicator: complete rewrite with carbon flux data from Harris et al. (2021). Previous text primarily discussed data limitations.
• Added citation page to “About” section.

Update 1: March 2021

• Updated indicators and Forest Pulse with 2020 tree cover loss data.

Original publication: January 2021

• ENVIRONMENT

Why deforestation matters—and what we can do to stop it

Large scale destruction of trees—deforestation—affects ecosystems, climate, and even increases risk for zoonotic diseases spreading to humans.

As the world seeks to slow the pace of climate change , preserve wildlife, and support more than eight billion people , trees inevitably hold a major part of the answer. Yet the mass destruction of trees—deforestation—continues, sacrificing the long-term benefits of standing trees for short-term gain of fuel, and materials for manufacturing and construction.

We need trees for a variety of reasons, not least of which is that they absorb the carbon dioxide we exhale and the heat-trapping greenhouse gases that human activities emit. As those gases enter the atmosphere, global warming increases, a trend scientists now prefer to call climate change.

There is also the imminent danger of disease caused by deforestation. An estimated 60 percent of emerging infectious diseases come from animals, and a major cause of viruses’ jump from wildlife to humans is habitat loss, often through deforestation.

But we can still save our forests. Aggressive efforts to rewild and reforest are already showing success. Tropical tree cover alone can provide 23 percent of the climate mitigation needed to meet goals set in the Paris Agreement in 2015, according to one estimate .

Causes of deforestation

Forests still cover about 30 percent of the world’s land area, but they are disappearing at an alarming rate. Since 1990, the world has lost more than 420 million hectares or about a billion acres of forest, according to the Food and Agriculture Organization of the United Nations —mainly in Africa and South America. About 17 percent of the Amazonian rainforest has been destroyed over the past 50 years, and losses recently have been on the rise . The organization Amazon Conservation reports that destruction rose by 21 percent in 2020 , a loss the size of Israel.

Farming, grazing of livestock, mining, and drilling combined account for more than half of all deforestation . Forestry practices, wildfires and, in small part, urbanization account for the rest. In Malaysia and Indonesia, forests are cut down to make way for producing palm oil , which can be found in everything from shampoo to saltine crackers. In the Amazon, cattle ranching and farms—particularly soy plantations—are key culprits .

Logging operations, which provide the world’s wood and paper products, also fell countless trees each year. Loggers, some of them acting illegally , also build roads to access more and more remote forests—which leads to further deforestation. Forests are also cut as a result of growing urban sprawl as land is developed for homes.

Not all deforestation is intentional. Some is caused by a combination of human and natural factors like wildfires and overgrazing, which may prevent the growth of young trees.

Why it matters

There are some 250 million people who live in forest and savannah areas and depend on them for subsistence and income—many of them among the world’s rural poor.

Eighty percent of Earth’s land animals and plants live in forests , and deforestation threatens species including the orangutan , Sumatran tiger , and many species of birds. Removing trees deprives the forest of portions of its canopy, which blocks the sun’s rays during the day and retains heat at night. That disruption leads to more extreme temperature swings that can be harmful to plants and animals.

With wild habitats destroyed and human life ever expanding, the line between animal and human areas blurs, opening the door to zoonotic diseases . In 2014, for example, the Ebola virus killed over 11,000 people in West Africa after fruit bats transmitted the disease to a toddler who was playing near trees where bats were roosting.

( How deforestation is leading to more infectious diseases in humans .)

You May Also Like

What is the ozone layer, and why does it matter?

Will the COP26 global deforestation pledge save forests?

‘This is Cofán land’: the fight to save Amazonia from intruders

Some scientists believe there could be as many as 1.7 million currently “undiscovered” viruses in mammals and birds, of which up to 827,000 could have the ability to infect people, according to a 2018 study .

Deforestation’s effects reach far beyond the people and animals where trees are cut. The South American rainforest, for example, influences regional and perhaps even global water cycles, and it's key to the water supply in Brazilian cities and neighboring countries. The Amazon actually helps furnish water to some of the soy farmers and beef ranchers who are clearing the forest. The loss of clean water and biodiversity from all forests could have many other effects we can’t foresee, touching even your morning cup of coffee .

In terms of climate change, cutting trees both adds carbon dioxide to the air and removes the ability to absorb existing carbon dioxide. If tropical deforestation were a country, according to the World Resources Institute , it would rank third in carbon dioxide-equivalent emissions, behind China and the U.S.

What can be done

The numbers are grim, but many conservationists see reasons for hope . A movement is under way to preserve existing forest ecosystems and restore lost tree cover by first reforesting (replanting trees) and ultimately rewilding (a more comprehensive mission to restore entire ecosystems).

( Which nation could be the first to be rewilded ?)

Organizations and activists are working to fight illegal mining and logging—National Geographic Explorer Topher White, for example, has come up with a way to use recycled cell phones to monitor for chainsaws . In Tanzania, the residents of Kokota have planted more than 2 million trees on their small island over a decade, aiming to repair previous damage. And in Brazil, conservationists are rallying in the face of ominous signals that the government may roll back forest protections.

( Which tree planting projects should you support ?)

Stopping deforestation before it reaches a critical point will play a key role in avoiding the next zoonotic pandemic. A November 2022 study showed that when bats struggle to find suitable habitat, they travel closer to human communities where diseases are more likely to spillover. Inversely, when bats’ native habitats were left intact, they stayed away from humans. This research is the first to show how we can predict and avoid spillovers through monitoring and maintaining wildlife habitats.

For consumers, it makes sense to examine the products and meats you buy, looking for sustainably produced sources when you can. Nonprofit groups such as the Forest Stewardship Council and the Rainforest Alliance certify products they consider sustainable, while the World Wildlife Fund has a palm oil scorecard for consumer brands.

Related Topics

• DEFORESTATION
• ENVIRONMENT AND CONSERVATION
• RAINFORESTS

This is the story of the first Earth Day—and why it mattered

Don't cut them down: Letting dead trees rot can help make new life

Climbing into the secret world of an ancient Bornean rainforest

The man who died searching for the Lost City of Z

Leopards are the ultimate survivors. Can they endure these growing challenges?

• Environment
• Paid Content

History & Culture

• History & Culture
• Terms of Use
• Privacy Policy
• Your US State Privacy Rights
• Children's Online Privacy Policy
• Interest-Based Ads
• About Nielsen Measurement
• Do Not Sell or Share My Personal Information
• Nat Geo Home
• Attend a Live Event
• Book a Trip
• Inspire Your Kids
• Shop Nat Geo
• Visit the D.C. Museum
• Learn About Our Impact
• Support Our Mission
• Advertise With Us
• Customer Service
• Renew Subscription
• Manage Your Subscription
• Work at Nat Geo
• Sign Up for Our Newsletters
• Contribute to Protect the Planet

Copyright © 1996-2015 National Geographic Society Copyright © 2015-2024 National Geographic Partners, LLC. All rights reserved

ENCYCLOPEDIC ENTRY

Deforestation.

Deforestation is the intentional clearing of forested land.

Biology, Ecology, Conservation

Trees are cut down for timber, waiting to be transported and sold.

Photograph by Esemelwe

Deforestation is the purposeful clearing of forested land. Throughout history and into modern times, forests have been razed to make space for agriculture and animal grazing, and to obtain wood for fuel, manufacturing, and construction.

Deforestation has greatly altered landscapes around the world. About 2,000 years ago, 80 percent of Western Europe was forested; today the figure is 34 percent. In North America, about half of the forests in the eastern part of the continent were cut down from the 1600s to the 1870s for timber and agriculture. China has lost great expanses of its forests over the past 4,000 years and now just over 20 percent of it is forested. Much of Earth’s farmland was once forests.

Today, the greatest amount of deforestation is occurring in tropical rainforests, aided by extensive road construction into regions that were once almost inaccessible. Building or upgrading roads into forests makes them more accessible for exploitation. Slash-and-burn agriculture is a big contributor to deforestation in the tropics. With this agricultural method, farmers burn large swaths of forest, allowing the ash to fertilize the land for crops. The land is only fertile for a few years, however, after which the farmers move on to repeat the process elsewhere. Tropical forests are also cleared to make way for logging, cattle ranching, and oil palm and rubber tree plantations.

Deforestation can result in more carbon dioxide being released into the atmosphere. That is because trees take in carbon dioxide from the air for photosynthesis , and carbon is locked chemically in their wood. When trees are burned, this carbon returns to the atmosphere as carbon dioxide . With fewer trees around to take in the carbon dioxide , this greenhouse gas accumulates in the atmosphere and accelerates global warming.

Deforestation also threatens the world’s biodiversity . Tropical forests are home to great numbers of animal and plant species. When forests are logged or burned, it can drive many of those species into extinction. Some scientists say we are already in the midst of a mass-extinction episode.

More immediately, the loss of trees from a forest can leave soil more prone to erosion . This causes the remaining plants to become more vulnerable to fire as the forest shifts from being a closed, moist environment to an open, dry one.

While deforestation can be permanent, this is not always the case. In North America, for example, forests in many areas are returning thanks to conservation efforts.

Media Credits

The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. The Rights Holder for media is the person or group credited.

Production Managers

Program specialists, last updated.

August 20, 2024

User Permissions

For information on user permissions, please read our Terms of Service. If you have questions about how to cite anything on our website in your project or classroom presentation, please contact your teacher. They will best know the preferred format. When you reach out to them, you will need the page title, URL, and the date you accessed the resource.

If a media asset is downloadable, a download button appears in the corner of the media viewer. If no button appears, you cannot download or save the media.

Text on this page is printable and can be used according to our Terms of Service .

Interactives

Any interactives on this page can only be played while you are visiting our website. You cannot download interactives.

Related Resources

Newly Launched - AI Presentation Maker

AI PPT Maker

Powerpoint Templates

Icon Bundle

Kpi Dashboard

Professional

Business Plans

Swot Analysis

Gantt Chart

Business Proposal

Marketing Plan

Project Management

Business Case

Business Model

Cyber Security

Business PPT

Digital Marketing

Digital Transformation

Human Resources

Product Management

Artificial Intelligence

Company Profile

Acknowledgement PPT

PPT Presentation

Reports Brochures

One Page Pitch

Interview PPT

All Categories

Top 10 Deforestation PowerPoint Presentation Templates in 2024

Deforestation is a PowerPoint presentation template designed to educate and raise awareness about the critical issue of deforestation. With fully editable slides, this template allows users to customize content, graphics, and data to suit their specific needs. The template includes visually engaging slides that showcase the causes and effects of deforestation, statistics and data visualization, as well as solutions and ways to combat this pressing environmental issue. Use cases for the Deforestation template include presentations for environmental organizations, educational institutions, and businesses looking to highlight their sustainability efforts. The template can also be used for workshops, seminars, and conferences focusing on environmental conservation and the importance of preserving our forests. With its versatile design and customizable features, the Deforestation template is an effective tool for creating impactful presentations that advocate for the protection of our planet's valuable forests.

Deforestation Colored Icon In Powerpoint Pptx Png And Editable Eps Format

This coloured powerpoint icon is a visual representation of deforestation and its effects on the environment. It features a tree with its leaves falling off, symbolising the destruction of forests and the loss of biodiversity. It is an effective way to communicate the seriousness of this global issue.

Use this Deforestation colored icon in powerpoint pptx png and editable eps format and create amazing PowerPoint presentations or graphics with ease. This downloaded file is available in all the editable formats such as EPS, PNG and Powerpoint pptx.

• reforestation
• conservation
• Climate Change
• Biodiversity

One page executive summary for forestry conservation plans presentation report infographic ppt pdf document

Here we present One Page Executive Summary For Forestry Conservation Plans Presentation Report Infographic PPT PDF Document one pager PowerPoint template. This forestry plan one pager PowerPoint template outlines the management plan for forests and to improve its condition. This forestry plan PowerPoint one pager will assist you in keeping a track activities at forest. Add you companys name and logo and present an overview of the plan to prevent illegal activities of cutting tree. The forestry plan one pager PowerPoint template covers the problem like rapid deforestation. Talk about the impact on environment and how it can be resolved. Add image related to deforestation in this one pager to make it more engaging. One can also describe the steps of forest management in this one pager and guide your audience about the same. Present the workplan in a series of step and explain the it using the diagram shown in the one pager forestry plan PowerPoint template. Elucidate the names of the team member and their respective roles in the text placeholders shown in the one pager. Also, present the assumptions and risks associated with the plan formulated and create a sustainable forest environment. Grab this One Page Executive Summary For Forestry Conservation Plans Presentation Report Infographic PPT PDF Document one pager template now.

This document titled One Page Executive Summary For Forestry Conservation Plans Presentation Report Infographic PPT PDF Document is an A4 size template designed in Powerpoint and is 100 percent editable. It displays the details in a crisp, clear and digestible format while also being visually appealing. With this document, you will be able to provide a comprehensive view to your audience with minimal effort. One Page Executive Summary For Forestry Conservation Plans Presentation Report Infographic PPT PDF Document will save you precious time and help you communicate your message with your viewers.

Agriculture and forestry kpi dashboard showing deforestation restoration of degraded lands

Stunning agriculture and forestry KPI dashboard showing deforestation restoration of degraded lands PowerPoint template. Choice to display PowerPoint template in standard and widescreen view. Presentation designs are well-suited with Google slides or MS Office PowerPoint programs. Can be transformed into JPG and PDF format. Trouble-free replacement of your company’s logo. Access to open on a big screen display. Perfect for business professionals, managers and industry leaders. Customize the fonts, colors, layout, etc.as per your requirements and business needs.

Dashboards are perfect to represent your information in an easily understandable manner. On this concept, we have come up with this beautiful agriculture and forestry KPI dashboard showing deforestation restoration of degraded lands PowerPoint design. This dashboard slide enables you to communicate the information related to restoration of degraded lands and deforestation by using editable charts and graphs. Our PowerPoint slideshow is commonly used to represent data that shows changes over time, which helps people visualize the amount of deforestation per year, impact of weather on plants, economic benefits derived, etc. Our data driven dashboard Presentation visual slide is easy to understand and fully modifiable. Moreover, people working in the agricultural and forest sector such as leaders and managers can use this PPT design to communicate any important aspect of their work in the interesting manner. Download it now, add your content and then share it with your audience. Your best will emerge with our Agriculture And Forestry Kpi Dashboard Showing Deforestation Restoration Of Degraded Lands. Your excellent attributes will come to the fore.

• Agriculture And Forestry
• cultivation

Stop Cut Trees Colored Icon In Powerpoint Pptx Png And Editable Eps Format

his coloured powerpoint icon depicts a tree with a red line across it, symbolizing the importance of stopping deforestation. It is a great visual aid for presentations on environmental conservation and sustainability.

Use this Stop cut trees colored icon in powerpoint pptx png and editable eps format and create amazing PowerPoint presentations or graphics with ease. This downloaded file is available in all the editable formats such as EPS, PNG and Powerpoint pptx.

• Deforestation
• Tree Planting

Stop Deforestation Monotone Icon In Powerpoint Pptx Png And Editable Eps Format

This Monotone PowerPoint Icon is a great way to show your support for saving the environment. It features a silhouette of a tree with a red circle and line through it, perfect for presentations about deforestation and the importance of preserving nature.

This Stop deforestation monotone icon in powerpoint pptx png and editable eps format is a 100 percent editable icon. The downloaded file will have this icon in EPS, PNG and Powerpoint pptx format and is perfect for your next project. It has a simple yet stylish design.

World Deforestation Colored Icon In Powerpoint Pptx Png And Editable Eps Format

Use this World deforestation colored icon in powerpoint pptx png and editable eps format and create amazing PowerPoint presentations or graphics with ease. This downloaded file is available in all the editable formats such as EPS, PNG and Powerpoint pptx.

Trees Trunks Cutting Colored Icon In Powerpoint Pptx Png And Editable Eps Format

Use this Trees trunks cutting colored icon in powerpoint pptx png and editable eps format and create amazing PowerPoint presentations or graphics with ease. This downloaded file is available in all the editable formats such as EPS, PNG and Powerpoint pptx.

Cut Wood With Axe Colored Icon In Powerpoint Pptx Png And Editable Eps Format

This coloured powerpoint icon depicts a person cutting down a tree with an axe. It is a great visual resource for presentations on deforestation, conservation, sustainability, and environmental protection.

Use this Cut wood with axe colored icon in powerpoint pptx png and editable eps format and create amazing PowerPoint presentations or graphics with ease. This downloaded file is available in all the editable formats such as EPS, PNG and Powerpoint pptx.

Man Cutting Tree Monotone Icon In Powerpoint Pptx Png And Editable Eps Format

This monotone powerpoint icon depicts a person cutting down a tree with an axe. It is perfect for presentations related to deforestation, conservation, and environmental protection.

This Man cutting tree monotone icon in powerpoint pptx png and editable eps format is a 100 percent editable icon. The downloaded file will have this icon in EPS, PNG and Powerpoint pptx format and is perfect for your next project. It has a simple yet stylish design.

Strategies Reduce Deforestation In Powerpoint And Google Slides Cpb

Presenting Strategies Reduce Deforestation In Powerpoint And Google Slides Cpb slide which is completely adaptable. The graphics in this PowerPoint slide showcase five stages that will help you succinctly convey the information. In addition, you can alternate the color, font size, font type, and shapes of this PPT layout according to your content. This PPT presentation can be accessed with Google Slides and is available in both standard screen and widescreen aspect ratios. It is also a useful set to elucidate topics like Strategies Reduce Deforestation. This well structured design can be downloaded in different formats like PDF, JPG, and PNG. So, without any delay, click on the download button now.

Our Strategies Reduce Deforestation In Powerpoint And Google Slides Cpb are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro.

• Strategies Reduce Deforestation