The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

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 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 2011-2022 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 2022 (Version 1.10). 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 2011-2022, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, 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 1.7 should be performed with caution. Read more about the Version 1.7 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.

Source code for image processing and statistical analysis, and the patch size data files for the manuscript:Power laws and critical fragmentation in global forests. (2016) Saravia L.A. Doyle S.R. Bond-Lamberty B. bioRxiv 091751; doi: http://dx.doi.org/10.1101/091751Zip Files:Patch Sizes distribution by region, year and threshold from 2000 to 2015: these are binary files consisting on a vector of doubles, the R command used to read it is readBin(connection_file, "double", n = 10^8)Africa_PatchSizes.zipNorthamerica_PatchSizes.zipSouthamerica_PatchSizes.zipEurasia_PatchSizes.zipOceania_PatchSizes.zipSouthasia_PatchSizes.zipLargestPatchAnimations.zip Animations of the two largest patches for all the regions defined in the paper. PercolationAnimations.zip Animation demonstrating percolation in a forest/non-forest modelStudy_areas_definition.zip These files needed to be unziped in a folder and the location added to the script data_input_script.m fileR markdown files:Threshold_sensitivity.Rmd # Fitting heavy tails to modis VCF, Smax & RSmax patch analysis. Download_modis.Rmd # Download modis files MOD44B version 051Map_study_areas.Rmd # Generate maps of study areas Map_Max_patches.Rmd # Generate GIF animations of maximum patch dynamics R Filesmap_fun.r # Map raster functionspower_fun.r # Function to fit continuos heavy tail distributionsMATLAB FilesExtract patch sizes from bin, and other analysis not used in the paperdata_input_script.mGCF_spatial_analyses.mPYTHON Filespowlawfit.py : Call the Python powerlawpackage from command line DATA Tables: csv and tab separated text files with model fits for the patch size distribution models.Fitted_Patch_models.csvFitted_Patch_models.txt

From 1990 to 2022, the forest area worldwide changed at a rate of *** million hectares of land every year. Nevertheless, in the past three decades, the loss rate has been decreasing from *** million hectares per year in the '90s to *** million hectares per year in the 2010s.

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).

SpatioTemporal Asset Catalog (STAC) Item - hansen-gfc-2023-v1.11-80N-120W in glad-global-forest-change-1.11

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.

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2015. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013).

The dataset contains time-period- and country-wise global data on forest expansion, including afforestation and natural expansion, deforestation, and net change Note:

The UN defines types of forest expansion and other terms as follows:

Forest expansion: Expansion of forest on land that, until then, was under a different land use, implies a transformation of land use from non-forest to forest.

Afforestation: Establishment of forest through planting and/or deliberate seeding on land that, until then, was under a different land use, implies a transformation of land use form non-forest to forest.

Natural Expansion of Forest: Expansion of forest through natural succession on land that, until then, was under a different land use, implies a transformation of land use form non-forest to forest (e.g. forest succession on land previously used for agriculture).

Deforestation: The conversion of forest to other land use independently whether human-induced or not.

Main data set and code for our analysis of how the effects of forest loss vary depending on current forest cover. Consists of the following files:1. species_data_simple.csvData set for our main analysis containing data on mammals, amphibians, and birds. Data set consists of taxonomic information, whether or not the species is tropical, was listed as threatened under range-related issues, geographic region, IUCN Red List category/trend/change, location of geographic range centroid, and averages across ranges of the following variables: 2000 forest cover, 2000-2012 forest gain, 2000-2014 forest loss, historic forest loss, and human footprint.2. model_everything_simple_serial.RCode to fit our primary spatial models using logistic regression with spatial autocovariates. Models are fit separately for each response variable (category/trend/change) and level of forest dependency.3. plot_models_simple.RCode to plot the primary results (logistic regression standardized coefficients). The plot produced is used in Figure 2 of our manuscript.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors.

Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf).

Forests cover over four billion hectares of the Earth's landmass, around 31 percent of the total land area. As of 2022, worldwide forest area measured some 4.05 billion hectares, down from approximately 4.24 billion hectares in 1990. From 1990 to 2022, no country saw a greater percentage change in forest area than Côte d'Ivoire, which lost more than half of its forests.

El mapa representa los sitios donde se observó la pérdida o reemplazo de la cobertura forestal, durante el periodo 2000-2019.

1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.
2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon.
3. The networks could predict the location of future forest loss to a high degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).
4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas. Methods Input raster data all freely available online Original input raster data from:

Global Forest Change - https://glad.earthengine.app/view/global-forest-change ALOS JAXA - https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm

Processed with code at https://github.com/PatBall1/DeepForestcast Dataset includes:

Input shapefiles for each study site. Input geotiff files (.tif) for each study site. Input PyTorch tensors (.pt) for each study site. Model weights (.pt) for trained networks (for testing and forecasting). Output deforestation forecasts for each study site as geotiffs (.tif).

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) and Forest Cover Change (GFCC30FCC) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

The general objective of this project was to update the national forest cover map to the year 2006. The specific objectives included calculating the rates of land cover changes at national, departmental, and municipal scales and comparing the 2006 map to the 2001 map that was generated with the same methodology. The new methodology used to generate the 2006 map made it necessary to create a new 2001 map. To describe forest cover at the national scale, the use of information from remote sensing, whether satellite images or aerial photographs, represented the most accurate data source.The elaboration of national maps of forest cover was based on satellite images from Landsat 5 and 7 as well as ASTER (only for the southwestern section of the country, in the area corresponding to the Landsat image of Path 21 Row 50). The forest cover for Guatemala in 2006 was estimated as 3,866,383 hectares, equivalent to 35.5% of the national territory. The revised value for 2001 is 4,152,051 hectares corresponding to 38.1% of the national territory. These values represent an annual net loss of 48,084 hectares, equivalent to a deforestation rate of - 1.16%. The net annual loss reported is the difference between a gross loss of 101,852 hectares/year and a gain of 53,768 hectares/year.

Tree canopy is defined as area of vegetation (including leaves, stems, branches, etc.) of woody plants above 5m in height. The dataset developers derived tree canopy cover estimates from the Global Forest Cover Change (GFCC) Surface Reflectance product (GFCC30SR), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM ) images at 30 meter resolution.CANUE staff retrieved tree canopy cover data from Google Earth Engine (GEE) for the year 2010 and 2015, extracted values (percent coverage) to postal codes and calculated summary measures (average percent coverage) within buffers of 100, 250, 500, and 1000 metres.

Through the implementation of USAID's Supporting Forests and Biodiversity (USAID SFB) ensuring ecological stability and biological productivity for landscape management and conservation policy, along with other stakeholders in Cambodia, the effective biodiversity and related conservation projects over large areas in Cambodia context were required insight's reports so as to have been developed Biophysical M&E Dashboard tool by SERVIR-Mekong, monitoring the performance of landscape-scale efforts and biophysical conditions on the ground. In this tool development, the input data are opened and available data on the Google Earth Engine (GEE), including Enhanced Vegetation Index from MODIS (MOD13Q1), tree canopy cover and tree canopy height to characterize forest extent and change from the Global Land Analysis & Discovery of University of Maryland (GLAD/UMD) team for the Mekong region, forest alert from GLAD Alert product from UMD and SAR Alert System (SARAS), forest fire from the Fire Information for Resource Management System (FIRMS), and the Cambodia national Land Cover times series (2000-2022) through a collaborative effort of SERVIR-SEA using the Regional Land Cover Monitoring. This dataset section focuses on Cambodia's annual forest cover. The quantitative data about the area of forest cover is derived from the tool and compiled to be available to download from 2015 to 2023 for purposes.