Results from time-series analysis of Landsat images in characterizing global forest extent and change. The 'first' and 'last' bands are reference multispectral imagery from the first and last available years for Landsat spectral bands corresponding to red, NIR, SWIR1, and SWIR2. Reference composite imagery represents median observations from a set of quality-assessed growing-season observations for each of these bands. Please see the User Notes for this update, as well as the associated journal article: Hansen, Potapov, Moore, Hancher et al. "High-resolution global maps of 21st-century forest cover change." Science 342.6160 (2013): 850-853.

OverviewThis data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2020 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 meters in height, and 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. 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. This data set has been updated five times since its creation, and now includes loss up to 2020 (Version 1.8). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2020, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.8 should be performed with caution. Read more about the Version 1.8 update here. When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.Frequency of updates: AnnualDate of content: 2001-2020Resolution: 30x30m

A dataset visualising tree cover across the Greater Mekong Subregion at 30 x 30m resolution. For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. Dataset is encoded as a percentage per output grid cell, in the range 0-100. Open Development Mekong has trimmed this data to the area of interest represented here.

OverviewThe tropical tree cover data maps tree extent at the ten-meter scale and tree cover at the half hectare scale to enable accurate monitoring of trees in urban areas, agricultural lands, and in open canopy and dry forest ecosystems. The data extends over 4.3 billion hectares of the global tropics. The data is derived from multi-temporal convolutional neural network models applied to Sentinel optical and radar imagery. The 10-meter dataset is a binary tree extent layer that is similar to a land cover map, while the tree cover data represents fractional cover at a half-hectare scale. More details on the methodology and analyses can be found on the GitHub page.Resolution: 0.5 haGeographic Coverage: 4.3 billion hectares of the tropics (-23.44 to 23.44 latitude)Frequency of Updates: Annual change detection maps starting in 2017 are planned for 2024 releaseDate of Content: 2020CautionsThis dataset uses a different definition of a tree and a different definition of tree cover than does Hansen et al. (2013). This dataset defines a tree according to both the height and crown diameter. Woody vegetation higher than 5 meters regardless of crown diameter, or between 3 and 5 meters with a minimum crown diameter of 5 meters is considered a tree. This definition is different from Hansen et al. (2013) which defines a tree as any vegetation at least 5 meters in height. The tropical tree cover dataset does not disambiguate plantation trees from non-plantation trees.Analyses or statistics derived for shapefiles smaller than 0.5ha may not be accurate

The amount of tree cover around forest remnants was calculated using 30-m-resolution global maps of forest extent and change from 2000 through 2023, version 1.11 (Hansen et al. 2013). Each pixel value represents the percentage of canopy closure for all vegetation taller than 5 m in height for the year 2000, which was classified as either forest (≥ 50% tree cover) or non-forest (Hasui et al. 2024). Therefore, such binarization is used loosely, as tree cover can comprise both natural forests and tree plantations (e.g., monocultures of oil palm, rubber, or eucalypt) (Tropek et al. 2014). Despite this, tree cover mostly represents natural forests, and tree plantations are a high-quality matrix type for forest species, given the structural similarity between the habitat (forest) and the matrix (tree plantation) (Prevedello & Vieira 2010). When bird surveys were conducted before 2002, we used the tree cover map for the year 2000. For surveys conducted from 2002 to 2017, we updated the tree cover map by subtracting the cumulative forest loss from 2001 up to the year prior to each survey’s first year (Moulatlet et al. 2021).

The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.

The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:

• id unique feature identifier
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• area area of the polygon in squared kilometres
• geom the geometry of the features in geographical coordinates WGS84

The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from Ecoregions 2017 ^{© Resolve} (Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).

This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:

• id unique feature identifier
• id_hcluster unique feature identifier
• list_of_commodities a comma-separated list of commodities
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• ecoregion ecoregion name
• biome biome name
• year the year
• area_forest_loss_XXX_YYY

The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).

• XXX can take one of:
  • 000: total tree cover loss independently from the initial tree cover
  • 025: tree cover loss on pixels with initial tree cover between 0 and 25%
  • 050: tree cover loss on pixels with initial tree cover between 25 and 50%
  • 075: tree cover loss on pixels with initial tree cover between 50 and 75%
  • 100: tree cover loss on pixels with initial tree cover between 75 and 100%
• YYY can take one of:
  • la: tree cover loss within strict nature reserve
  • Ib: tree cover loss within wilderness area
  • II: tree cover loss within national park
  • III: tree cover loss within natural monument or feature
  • IV: tree cover loss within habitat/species management area
  • V: tree cover loss within protected landscape/seascape
  • VI: tree cover loss within PA with sustainable use of natural resources
  • p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported
  • none: when YYY is omitted, total tree cover loss within the polygon

For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The id can be used to link polygons to forest loss data.

To advance our understanding of forest cover changes, given the discrepancies, this work providesan original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) toestimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, forwhich data was available. This assumes that no single approach or data source can capture majortrends everywhere; therefore, an all-available data approach is needed to overcome shortcomings ofindividual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends tounderestimate the conversion in dry forests ecosystems and lacks explicit assessment ofuncertainties across the different datasets.“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing fromforest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m giventhe original resolutions of the datasets incorporated and on the understanding that forest areasshould be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.species distribution, ecology, ecosystem services), rather than depicting individual trees or groups oftrees.According to our analysis, about 20% of total forest cover loss takes place in core forest, which welabel “primary forest loss”, while the remaining 80% results from the conversion of edge andpatched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss inthe period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperateforests. A portion of the loss in temperate and boreal forests will not be permanent and might referto other types of natural forest disturbances produced by insects, fire, and severe weather, as wellas by felling of plantations or semi-natural forests as part of forest management.Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss ismainly in South America and Africa. When looking at countries by income levels, as defined by theWorld Bank, much of deforestation takes place in upper middle and lower middle-income countries.To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurswhen countries increase economic development. In the tropics, upper-middle income countriesdominate forest cover loss in South America, due to the influence of Brazil, and lower middle-income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occursmainly in Brazil and Argentina in South America, many lower-middle income countries in SouthAmerica, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest coverloss, likely not all permanent, occurs in high-income countries (Russia), and North America (UnitedStates and Canada) Unfortunately, this data does not identify changes over time or land use interactions amongcountries. Reduced forest cover loss in some mainly high-income countries, except North America, isassociated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. Interactions are informed by the “forest transition” effect. Forest transition dynamics occur whennet forest restoration replaces net forest cover loss in some specific place. The countries thatunderwent a forest transition that reduced forest loss and encouraged regrowth may have placedadditional pressure on forests outside their borders, thus displacing deforestation. The debate onforest transitions and leakage is quite controversial given its policy implications.Recent analysis, based on a land-balance model that quantifies deforestation due to global trade atcountry level in the tropics and sub-tropics, linked to a country-to-country trade model, found thatfrom 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.About 26% of total deforestation was attributed to international demand, 87% of which wasexported to countries with decreasing deforestation or increasing forest cover in Europe and Asia(i.e. China, India). Some of this displacement pressure may be reduced by land intensification. Global patterns of forest fragmentationIn this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-factorial phenomenon that includes amongst others loss of native species, appearance of invasivespecies, pollution damage, structural changes, selective timber removal and many more. Here weuse fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect offorest degradation but does not capture all aspects. The change in spatial pattern and structure byfragmentation of forest into smaller patches or “islands” damages forest ecosystem services such ascarbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. These impacts are created by changes at forest edges, which include increased exposure to differentclimate, fire, wind, mortality, and human access. The increasing isolation of forest patchescontributes to long-term changes in biodiversity, including species richness and productivity,creating fundamental changes in forest ecosystems.We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, todetermine forest cover transitions between different type of fragmentation classes (i.e. stable core,inner edges, outer edges, and patches). Changes between fragmentation classes over time aredefined as primary and secondary degradation based on their initial state, in contrast to forestswhich remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. Inthis definition, primary degradation is a result of the fragmentation of core forests into forest withmore edges, reducing the area of continuous forest extent, and resulting in greater losses of carbonand associated ecosystem services such as biodiversity present in intact forests. Secondarydegradation is the conversion of edge forests into more fragmented classes, occurring in secondaryforests which may already be degraded and are more accessible and easier to deforest

This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.

Abstract: NetCDF files of the forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid generated at ICDC from the original HDF files (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html and https://doi.org/10.25592/uhhfdm.16655) obtained from https://lpdaac.usgs.gov/mod44bv061/ are used to compute globally gridded maps of these parameters at 0.5 degree grid resolution on an equi-rectangular climate modeling grid (CMG). The global maps contain the grid-cell mean fractions of the three mentioned parameters, their variance within the grid cells, and - for the forest cover fraction - the grid-cell mean standard deviation. In addition, the data set includes maps of the number of valid forest cover fraction values at 250 m resolution per 0.5 degree grid cell, a grid cell mean quality flag and fractions of the two most abundant quality flags (primary and secondary). Generally all valid data are used; the user is advised to check the quality flags to eventually discard data of low quality.

TableOfContents: grid cell mean forest cover fraction; grid cell mean forest cover fraction standard deviation; forest cover fraction variance within grid cell; grid cell mean vegetation cover fraction; vegetation cover fraction variance within grid cell; non-vegetated area cover fraction; non-vegetated area cover fraction variance within grid cell; number of useful vegetation cover fraction values per grid cell; grid cell mean quality flag; primary quality flag fraction; secondary quality flag fraction

Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2023-03-06; temporalExtent_endDate: 2024-03-04; temporalResolution: yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA

Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380.

Units: Units for all variables (see TableOfContents): percent; percent; 1; percent; 1; percent; 1; 1; 1; 1; 1

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land

Size: 1 file, ~5.2 Mb

Format: netCDF

DataSources:

Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2024-12-27), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2024-12-27)

Reprocessed data on sinusoidal grid tiles in netCDF format: https://doi.org/10.25592/uhhfdm.16655 (last access 2025-01-15), see also https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html (last accessed 2025-01-15)

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html

This repository includes the raster datasets which mask plantations—mainly oil palm and rubber—in the Hansen's data of forest loss during the period 2001–2015. The pixel values representing plantations are encoded as 1.The plantation mask covers the main production countries of oil palm (Malaysia, Nigeria, and Indonesia) and rubber (Indonesia, Thailand, Malaysia, and Vietnam) in 2000.

Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.

If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:

Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues

General questions and comments: https://disqus.com/home/forums/landgis/

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

lcv = theme: land cover,

bareground = variable: occurrence of bareground,

landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),

p = probability or fraction,

250m = spatial resolution / block support: 250 m,

s0..0cm = vertical reference: land surface,

2010..2010 = time reference: year 2010,

v1.0 = version number: 1.0,

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).

Forest clearing polygons were assigned to a county or land ownership type based on the location of its centroid. We converted the forest cover dataset by Hansen et al. (2013) to create a forest-non/forest image, where pixels with ≥30% tree cover were considered forest. We used this forest cover dataset to estimate the proportion of forestland.

Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. https://doi.org/10.1126/science.1244693

AbstractForest-savanna mosaics exist across all major tropical regions. Yet, the influence of environmental factors on the distribution of these mosaics is not well explored, limiting our understanding of the environmental constraints on savannas especially in Southeast Asia, where most savannas exist in mosaics. Despite clear structural and functional characteristics indicative of savannas, most SE Asian savannas continue to be classified as forest. This designation is problematic because SE Asian savannas are threatened by both fragmentation and forest-centric management practices. By studying forest-savanna mosaics across SE Asia, we aimed to parse out how landscape mosaics of forest and savanna may be constrained by fire, climate, and soil characteristics. We used remotely sensed data to characterize the distribution of tree cover and forest-savanna mosaics. Using regression models, we quantified the relative effects of precipitation, fire frequency, seasonality, and soil characteristics on average tree cover and landscape patchiness. We found that low tree cover, indicative of savannas, occurs in drier, seasonal subregions that experience frequent fire. Further, our results demonstrate that fire and precipitation strongly shape landscape patchiness. Landscapes were patchiest in subregions with low precipitation and intermediate fire frequency. These results demonstrate that the environmental factors important in delineating the distribution of savannas globally shape the distribution of tree cover and landscape patchiness across SE Asia. Fire especially drives patterns of tree cover across scales. In a region where fire suppression is a common management strategy, our results suggest that further research studying vegetation response to fire and fire suppression is needed to improve management and conservation of these mosaic landscapes. More broadly, this work demonstrates a useful approach for studying the environmental drivers that influence the distribution of forest-savanna mosaics., MethodsFire frequency was derived from the MCD64A1.006 MODIS Burned Area Monthly Global product (500 m resolution) (Giglio et al. 2018) Mean annual precipitation (MAP) was derived from both CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data from 1981-2020 (0.05° resolution; roughly 5.6 x 5.6 km) (Funk et al. 2015) and TRMM 3B43: Monthly Precipitation Estimates (0.25° resolution; roughly 28 x 28 km) (Huffman et al. 2007). Precipitation seasonality was derived as described by Schwartz et al. (2020) and Feng et al. (2013). Soil sand content was extracted from Harmonized World Soil Database (30 arc seconds resolution) (FAO/IIASA/ISRIC/ISS-CAS/JRC 2012). We characterized tree cover using the Global Forest Change (GFC) v1.7 (2000-2019) product which contains high resolution (30 m) maps of tree cover (%) from the year 2000 (Hansen et al. 2013). Aggregated to 0.05 degrees resolution. For analysis of within landscape patterns, 1,000 grid cells or landscapes were randomly subsampled, and 30 m resolution tree cover was extracted for each. Characterizing landscape mosaics: Within each landscape, raw 30 m tree cover data was classified as either forest (tree cover >65%), savanna (tree cover <65%), or anthropogenic land cover. Landscape metrics: number of patches, mean patch area, Shannon evenness, and landscape shape index.

The dataset encompasses median values for Tree cover (Hansen Global Forest Change v1.9 (2000-2021)) and Canopy height ( Global Forest Canopy Height, GEDI_V27), measured at a 30-meter resolution, across 25 protected areas in the Western Ghats of India. To provide a detailed analysis, the data is further segmented into eight distinct slope aspects characterized using Shuttle Radar Topography Mission (SRTM) data with 30 m resolution, allowing for a comprehensive understanding of how tree cover and canopy height vary across different topographical features. Additionally, the dataset includes median values computed for various slope and elevation ranges for different aspects, offering insights into how these factors influence vegetation characteristics in each protected area.

Displays the gross greenhouse gas emissions from stand-replacing forest disturbance globally from 2001 onwards. Geospatial data are in 10x10 degree geotifs. The northwest corner of each geotif is noted in the file name, e.g., 50N_030E has its northwest corner at (50 deg N, 30 deg E) and has its southeast corner at (40 deg N, 40 deg E). Use the shapefile in GFW_Hansen_tile_footprints.zip to determine which 10x10 degree geotifs cover your area(s) of interest. Description (adapted from GFW Open Data Portal, https://data.globalforestwatch.org/datasets/gfw::forest-greenhouse-gas-emissions/about): This emissions layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2023). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GFW. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Emissions are available for download in megagrams of CO2e/ha from 2001 onwards. It is appropriate for visualizing (mapping) emissions because it represents the density of emissions per hectare from 2001 onwards. Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/ for more information. 1. The source of the ratio between belowground carbon and aboveground carbon. Previously used one global constant; now uses map from Huang et al. 2021. 2. The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from Potapov et al. 2022. 3. The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from Tyukavina et al. 2022. 4. The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. 5. Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC Sixth Assessment Report. 6. Removal factors for older (>20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in Table 4.9 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. 7. Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) Version 1.0; now uses SDPT Version 2.0 and associated removal factors. Cautions: 1. Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.  2. Values are applicable to forest areas only (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis. 3. Although emissions in each pixel are associated with a specific year of disturbance, emissions over an area of...

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 Please cite this dataset as: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple),

OverviewThis data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions.CautionsThis analysis is limited by various data and attribution issues and methodological assumptions, including the following:Commodity data sets have limited coverage and quality. Only oil palm has recent, detailed maps of extent at a global level. The analysis also uses detailed data on South American soy. Outside of these regions and commodities, the analysis relies on global 10-kilometer resolution data on crop and pasture extent. These data are from 2010 (2000 for pasture), so the amount of forest replaced by a specific commodity is assumed to be proportional to its area during that year and may be misrepresented if significant expansion or contraction of that commodity has occurred since then. While Goldman et al. (2020) presents results using detailed pasture data for Brazil, this data set includes pasture results for the coarse method only.The data cannot capture complex land-use change transitions. The analysis does not consider other possible land uses between the deforestation event and the establishment of the commodity. The analysis also does not consider any forms of indirect land-use change (e.g., the target commodity displacing other activities that may, in turn, expand into forested areas).The data measure tree cover loss rather than deforestation directly. All tree cover loss in an area later used for one of the target commodities is assumed to be deforestation because forest replaced with a crop or pasture represents a permanent land-use change. Historical data from Indonesia and Malaysia were used to filter out older oil palm plantations from the analysis to avoid counting old, unproductive oil palm trees being felled as tree cover loss.The data may miss some forms of tree cover loss. The Hansen et al. (2013) tree cover loss data may not detect all changes related to commodity production. Much of the production of cocoa and coffee occurs on very small farms (less than one hectare) that may not be captured by the tree cover loss data. The analysis may also underestimate the conversion of dry forest and woody savanna areas, which are not well represented in the tree cover loss data. For the detailed soy analysis, we define tree cover as any woody vegetation with a minimum of 10 percent canopy cover (analyses for other commodities use 30 percent) to minimize underestimations in South American biomes such as the Cerrado and the Chaco.Further discussion about the methods, assumptions, and limitations of this analysis is available in Goldman et al. (2020).CitationGoldman, E., M.J. Weisse, N. Harris, and M. Schneider. 2020. “Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber.” Technical Note. Washington, DC: World Resources Institute. Available online at: wri.org/publication/estimating-the-role-of-sevencommodities-in- agriculture-linked-deforestationLicenseCreative Commons Attribution 4.0 International License (CC-BY 4.0)

Corridors between tropical Intact Forest Landscapes (Potapov et al. 2017) were derived from the UMD Global Land Analysis and Discovery lab's Global Forest Change Database (Hansen et al. 2013).Tree cover from the year 2000 and forest loss and gain between 2000 and 2012 were used as inputs to a corridor algorithm which maps a large number of corridors between source and target patches for the years 2000 and again for 2012. The algorithm preferentially maps corridors through high tree cover areas.Pixel values are weighted by the amount of tree cover in source and target patches and the importance of each pixel for connecting patches. The higher the value, the more important a pixel is for connecting patches with a large amount of tree cover. Pixels with a value of zero are not within any corridor. Corridors were not mapped for patches > 130 km apart. The algorithm assumes a linear relationship between forest cover and the ability of animals to move through the landscape. Corridors were clipped to the boundary of Peru and resampled to a resolution of 540 m.