Approximately *** million hectares of forest area were lost due to deforestation between 1990 and 2020. However, the deforestation rate has slowed over the last three decades, down from the total **** million hectares per year in 1990-2000 to **** million hectares per year in 2015-2020. The climatic domain that has shown the highest pace of deforestation is the tropical, which saw *** million hectares per year lost in 2015-2020. On the other hand, the boreal domain has the lowest deforestation rate, with a figure of **** million hectares per year in 2015-2020.

In 2023, the deforested area in the Legal Amazon in Brazil amounted to approximately 802,300 hectares. Just a year earlier, the Amazon deforested area surpassed 1.2 million hectares. What is behind the growing Amazon deforestation in Brazil? Illegal logging, expansion of agricultural areas for soybean cultivation, and an increase in wildfire outbreaks are all among the leading causes of deforestation in the Brazilian Amazon. Politics, however, has also played an important role. For example, the authorized budget for Brazil’s Ministry of the Environment has been on a mostly downward trend since 2013, when it reached a decade-long peak of nearly seven billion Brazilian reals. How big is the Brazilian deforestation issue? In 2023, Brazil registered by far the largest area of primary forest loss in the world, amounting to more than one million hectares. This was roughly the same area as the remaining top nine countries combined. As the country with the second-largest forest area worldwide, these developments are cause for concern amidst the conversation on climate change mitigation. With the global tree cover loss annually increasing, and the emission of greenhouse gases from forest areas along with it, reaching net-zero emissions targets by 2050 grows ever more challenging.

Deforestation led to an annual loss of *** million hectares of forest in Africa between 2015 and 2020. The conversion of forest to other land uses affected mostly the eastern and southern areas of the continent, at a deforestation rate of *** million hectares per year. In Western and Central Africa, around *** million hectares of forest were lost per year in the same period. Despite a small reduction observed in the period 2015-2020, the continental deforestation rate has overall increased since 1990. From that year until 2020, Africa has seen the greatest loss in forest area more than any region of the world.

Côte d'Ivoire lost half of its forest area between 1990 and 2022, making the African country one of the most affected by deforestation. Densely wooded countries in Central-South America, South East Asia, and Africa were among those seeing the greatest level of deforestation in the past three decades.

WWF developed a global analysis of the world's most important deforestation areas or deforestation fronts in 2015. This assessment was revised in 2020 as part of the WWF Deforestation Fronts Report.Emerging Hotspots analysisThe goal of this analysis was to assess the presence of deforestation fronts: areas where deforestation is significantly increasing and is threatening remaining forests. We selected the emerging hotspots analysis to assess spatio-temporal trends of deforestation in the pan-tropics.Spatial UnitWe selected hexagons as the spatial unit for the hotspots analysis for several reasons. They have a low perimeter-to-area ratio, straightforward neighbor relationships, and reduced distortion due to curvature of the earth. For the hexagon size we decided on a unit of 1,000 ha, based on the resolution of the deforestation data (250m) meant that we could aggregate several deforestation events inside units over time. Hexagons that are closer to or equal to the size of a deforestation event means there could only be one event before the forest is gone and limit statistical analysis.We processed over 13 million hexagons for this analysis and limited the emerging hotspots analysis to only hexagons with at least 15% forest cover remaining (from the all-evidence forest map). This prevented including hotspots in agricultural areas or where all forest has been converted.OutputsThis analysis uses the Getis-Ord and Mann-Kendall statistics to identify spatial clusters of deforestation which have a non-parametric significant trend across a time series. The spatial clusters are defined by the spatial unit and a temporal neighborhood parameter. We use a neighborhood parameter of 5km to include spatial neighbors in the hotspots assessment and time slices for each country described below. Deforestation events are summarized by a spatial unit (hexagons described below) and the results comprise a trends assessment which defines increasing or decreasing deforestation in the units determined at 3 different confidence intervals (90%, 95% and 99%) and the spatio-temporal analysis classifying areas into 8 hot unique or cold spot categories. Our analysis identified 7 hotspot categories:Hotspot TypeDefinitionNewA location with a statistically significant increasing hotspots only in the final time stepConsecutiveAn uninterrupted run of statistically significant hotspot in the final time-steps IntensifyingA statistically significant hotspot for >90% of the bins, including the final time stepPersistentA statistically significant hotspot for >90% of the bins with no upward or downward trend in clustering intensityDiminishingA statistically significant hotspot for >90% of the time steps, with where the clustering is decreasing, or the most recent time step is not hot.SporadicA on-again then off-again hotspot where <90% of the time-step intervals have been statistically significant hot spots and none have been statistically significant cold spots.HistoricalAt least ninety percent of the time-step intervals have been statistically significant hot spots, with the exception of the final time steps..For the evaluation of spatio-temporal trends of tropical deforestation we selected the Terra-i deforestation dataset to define the temporal deforestation patterns. Terra-i is a freely available monitoring system derived from the analysis of MODIS (NVDI) and TRMM (rainfall) data which are used to assess forest cover changes due to anthropic interventions at a 250 m resolution [ref]. It was first developed for Latin American countries in 2012, and then expanded to pan-tropical countries around the world. Terra-i has generated maps of vegetation loss every 16 days, since January 2004. This relatively high temporal resolution of twice monthly observations allows for a more detailed emerging hotspots analysis, increasing the number of time steps or bins available for assessing spatio-temporal patterns relative to annual datasets. Next, the spatial resolution of 250m is more relevant for detecting forest loss than changes in individual tree cover or canopies and is better adapted to process trends on large scales. Finally, the added value of the Terra-i algorithm is that it employs an additional neural network machine learning to identify vegetation loss that is due to anthropic causes as opposed to natural events or other causes. Our dataset comprised all Terra-i deforestation events observed between 2004 and 2017. Temporal unitThe temporal unit or time slice was selected for each country according to the distribution of data. The deforestation data comprised 16-day periods between 2004 and 2017 for a total of 312 potential observation time periods. These were aggregated to time bins to overcome any seasonality in the detection of deforestation events (due to clouds). The temporal unit is combined with the spatial parameter (i.e. 5km) to create the space-time bins for hotspot analysis. For dense time series or countries with a lot of deforestation events (i.e. Brazil) a smaller time slice was used (i.e. 3 months, n=54) with a neighborhood interval of 8 months, meaning that the previous year and next year together were combined to assess statistical trends relative to the global variables together. The rule we employed was that the time slice x neighborhood interval was equal to 24 months, or 2 years, in order to look at general trends over the entire time period and prevent the hotspots analysis from being biased to short time intervals of a few months.Deforestation FrontsFinally, using trends and hotpots we identify 24 major deforestation fronts, areas of significantly increasing deforestation and the focus of WWF's call for action to slow deforestation.

Africa has seen the greatest loss of forest area out of any region in the world. Between 1990 and 2022, over 15 percent of forest area was lost on the African continent. South America has also recorded a prominent rise in deforestation, with the region's combined forest area shrinking by 13.8 percent. Côte d'Ivoire was found to have the greatest negative forest area percentage change of any country.

Annual forest loss in the Brazilian Amazon had in 2012 declined to less than 5,000 sqkm, from over 27,000 in 2004. Mounting empirical evidence suggests that changes in Brazilian law enforcement strategy and the related governance system may account for a large share of the overall success in curbing deforestation rates. At the same time, Brazil is experimenting with alternative approaches to compensate farmers for conservation actions through economic incentives, such as payments for environmental services, at various administrative levels. We develop a spatially explicit simulation model for deforestation decisions in response to policy incentives and disincentives. The model builds on elements of optimal enforcement theory and introduces the notion of imperfect payment contract enforcement in the context of avoided deforestation. We implement the simulations using official deforestation statistics and data collected from field-based forest law enforcement operations in the Amazon region. We show that a large-scale integration of payments with the existing regulatory enforcement strategy involves a tradeoff between the cost-effectiveness of forest conservation and landholder incomes. Introducing payments as a complementary policy measure increases policy implementation cost, reduces income losses for those hit hardest by law enforcement, and can provide additional income to some land users. The magnitude of the tradeoff varies in space, depending on deforestation patterns, conservation opportunity and enforcement costs. Enforcement effectiveness becomes a key determinant of efficiency in the overall policy mix.

This data set includes tree cover extent, aboveground live biomass stocks and densities, annual tree cover loss, annual forest GHG emissions, and average annual forest CO2 removals (sequestration) and annual net GHG flux at the country and first (state, province) sub-national levels. Tree cover loss and emissions are available as annual data for 2001-2020. Emissions, removals and net flux are available as annual averages for 2001-2020. Tree cover is available for 2000 and 2010. Aboveground biomass stocks and densities are available for 2000. The tree cover data was produced by the University of Maryland's GLAD laboratory in partnership with Google. Carbon densities, emissions, removals, and net flux (megagrams CO2e/yr) are from Harris et al. 2021. The emissions data quantifies the amount of carbon dioxide emissions to the atmosphere where forest disturbances have occurred, and includes CO2, CH4, and N2O and multiple carbon pools. (This replaces the emissions data previously on GFW.) Removals includes the average annual carbon captured by aboveground and belowground woody biomass in forests. Net flux is the difference between average annual emissions and average annual removals; negative values are net sinks and positive values are net sources. All values besides emissions, removals, and net flux are presented for percent canopy cover levels >=10%, 15%, 20%, 25%, 30%, 50% and 75%, while emissions, removals, and net flux are presented only for canopy >=30%, 50%, and 75% and areas with tree cover gain. We recommend that you select your desired percent canopy cover level and use it consistently throughout any analysis. The Global Forest Watch website uses a >=30% canopy cover threshold as a default for all statistics.

Citations
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. (2021). https://doi.org/10.1038/s41558-020-00976-6
Global Administrative Areas Database, version 3.6. Available at http://gadm.org/

For further questions regarding this data set, please contact Mikaela Weisse at the World Resources Institute (mikaela.weisse@wri.org).

The deforested area of the Brazilian Amazon declined by over 21 percent in 2023, when compared to the previous year. This was the second drop recorded in the last six years. The deforested area in the Brazilian Amazon stood at approximately nine thousand square kilometers in 2022.

This dataset includes the data, the R scripts used for analysis and results that are the basis of the journal article: Black, B., Anthony, B. In review. Counterfactual assessment of protected area avoided deforestation in Cambodia: Trends in effectiveness, spillover effects and the influence of establishment date. Global Ecology and Conservation. Each folder includes a specific readme file in .txt format which includes metadata and instructions for reproducing the research. Updated version following revisions

From 1990 and up until 2010, South America was the region in the world with the highest rate of forest loss, with an estimated 5.2 million hectares of net forest lost per year in the first decade of this century. Since then, the destruction of South American forests has slowed down to an average of 2.6 million hectares per year, the second largest forest loss rate in the world after Africa. The figures suggest that, despite reforestation efforts, forest areas in South America continue to be endangered by massive deforestation and wildfires.

PNG's forest cover loss 2000-2017 downloaded from www.globalforestwatch.org

From 2001 to 2017, Papua New Guinea lost 1.28Mha of tree cover, equivalent to a 3.0% decrease since 2000, and 158Mt of CO₂ of emissions.

This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.

Description and summary statistics for avoided deforestation.

Overview

This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across 179 countries and 184 commodities within the specified period, presenting an unmatched scope and granularity of data.

Documentation

The manuscript detailing the dataset is currently archived at EarthArXiV: Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions. https://doi.org/10.31223/X5T69B

The insights from this dataset can also be viewed at: https://www.deforestationfootprint.earth

Repository contents

The input and output/data generated by the model are archived here at Zenodo, and their description is available in 'README (files in the directory).txt'.

The columns of the (final) dataset 'DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx' in the folder 'Final Attribution Results' represent the following:

Continent/Country group: All countries are divided into 8 geographical regions

ISO: Three-letter country codes defined by ISO

Producer country: Country of deforestation

Year: Year of deforestation, ranges from 2001-2022

Commodity group: All commodities are divided into 11 commodity groups

Commodity: Name of commodity aligning with FAOSTAT

Deforestation attribution, unamortized (ha): Annual deforestation estimates

Deforestation risk, amortized (ha): 5-year amortised deforestation estimates

Deforestation emissions excl. peat drainage, unamortized (MtCO2): Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)

Deforestation emissions excl. peat drainage, amortized (MtCO2): 5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage

Peatland drainage emissions (MtCO2): Annual estimates of carbon emissions from peatland drainage

Deforestation emissions incl. peat drainage, amortized (MtCO2): 5-year amortised carbon emission estimates, including emissions from peatland drainage

Quality Index: Flagging deforestation estimates

Contact

If you have any questions, you can contact us at:

 Chandrakant Singh and U. Martin Persson     Email: chandrakant.singh@chalmers.se and martin.persson@chalmers.se     Physical Resource Theory, Department of Space, Earth & Environment,     Chalmers University of Technology, Gothenburg, Sweden

Note: Statistics show observed (real) and counterfactual values estimated as described in section 5 on the blacklisted districts between the years 2008 to 2012. Paired values are adopted form the corresponding paired matched controls district of each blacklisted districts. Estimated values are based on estimations of mechanisms on the covariates.Statistics on counterfactual mechanism values.

Forest degradation, generally defined as a reduction in the delivery of forest ecosystem services, can have long-term impacts on biodiversity, climate, and local livelihoods. The quantification of forest degradation, its dynamics and proximate causes can help prompt early action to mitigate carbon emissions and inform relevant land use policies. The Democratic Republic of the Congo is largely forested with a relatively low deforestation rate, but anthropogenic degradation has been increasing in recent years. We assess the impact of eight independent variables related to land cover, land use, infrastructure, armed conflicts, and accessibility on forest degradation, measured by the Forest Condition (FC) index, a measure of forest degradation based on biomass history and fragmentation that ranges from 0 (completely deforested) to 100 (intact). We employ spatial panel models with fixed effects using regular 25 × 25 km units over five 3-year intervals from 2002 to 2016. The regression results suggest that the presence of swamp ecosystems, low access (defined by high travel time), and forest concessions are associated with lower forest degradation, while built up area, fire frequency, armed conflicts result in greater forest degradation. The impact of neighboring units on FC shows that all variables within the 50 km spatial neighborhood have a greater effect on FC than the on-site spatial determinants, indicating the greater influence of drivers beyond the 25 km2 unit. In the case of protected areas, we unexpectedly find that protection in neighboring locations leads to higher forest degradation, suggesting a potential leakage effect, while protected areas in the local vicinity have a positive influence on FC. The Mann-Kendall trend statistic of occurrences of fires and conflicts over the time period and until 2020 show that significant increases in conflicts and fires are spatially divergent. Overall, our results highlight how assessing the proximate causes of forest degradation with spatiotemporal analysis can support targeted interventions and policies to reduce forest degradation but spillover effects of proximal drivers in neighboring areas need to be considered.

Natural land resources in Brazil have been subject to strong pressure from agricultural expansion over the past two decades. This map identifies and classifies deforestation hotspots in the Southern American country. Moreover, it hints to land use change dynamics such as leakage effects in tropical areas. The map represents the period between 2005-2012, and classifies deforestation hotspots in three categories: a) reduced, b) increased, and c) new. Quality/Lineage: Land cover information from Global Forest Watch (https://data.globalforestwatch.org/) was used to identify deforested pixels per year. ArcGIS 10 was used to create spatial statistics of yearly information. R and RStudio were used to classify each grid cell as a hotspot and its type, and to convert the resulting cover information into a shapefile.

Ranges of parameter values used in simulations.

A Landsat-based machine learning algorithm (Reygadas et al. 2021, Environmental Research Communications) adapted from Wang et al. (2019, Remote Sensing of Environment) to the Southwestern Amazon was used to map intact forest, degradation, and deforestation in this region on a yearly basis during the 2003-2021 period. Degradation is defined as a long-term process in which forest is negatively affected but it is not converted into another land-cover. In contrast, deforestation is defined as the permanent, or long-term, conversion of forest into non-forest. The algorithm classifies forest covers by training a random forest model with sixty-six metrics derived from six time series variables (i.e., the Normalized Difference Vegetation Index, two shortwave infrared bands, two Normalized Difference Water Indices, and the Soil-Adjusted Vegetation Index) from which eleven descriptive statistics are calculated. As the algorithm uses statistical characteristics of time series to determine the forest conditions in the end of the study period, time series composed of the last 20 years prior to the target year were used in each annual run. A forest mask, composed of all areas covered by forest at least three consecutive years and never covered by water during the 2000-2018 period, was applied to all maps. A data key is included in the description of each file. Note: Although the same algorithm is used in Reygadas et al. (2021), these data differ from those of the manuscript as they are annual and cover a larger area.