Intact Forest Landscapes

To produce the Forest Landscape Integrity Index (FLII), four data sets were combined representing: (i) forest extent; (ii) ‘observed’ pressure from high impact, localized human activities for which spatial datasets exist, specifically: infrastructure, agriculture, and recent deforestation; (iii) ‘inferred’ pressure associated with edge effects, and other diffuse processes, (e.g. activities such as hunting and selective logging) modelled using proximity to observed pressures; and iv) anthropogenic changes in forest connectivity due to forest loss. These datasets were combined to produce an index score for each forest pixel (300m), with the highest scores reflecting the highest forest integrity, and applied to forest extent for the start of 2019. Globally consistent parameters were used for all elements (i.e. parameters do not vary geographically). All calculations were conducted in Google Earth Engine. Displays forest condition as a continuous index determined by degree of anthropogenic modification

Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020. Maps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the PREDICTS database of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future. Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled “Global 100m Projections of Biodiversity Intactness for the years 2017-2020.” All years are available under a Creative Commons BY-4.0 license.

- This data set, a collaboration between the Global Land Analysis & Discovery (GLAD) () lab at the University of Maryland (UMD), 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, and additional images used for 2023, 2024 and 2025. 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” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. 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 2025 (Version 1.13). 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 2011-2025, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.13 update here:  and access on GEE 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.  Identifies areas of gross tree cover loss

Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). Recommended Citation Wildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). Recommended Citation Wildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

This data is produced by the Global Land Analysis & Discovery (GLAD) lab at the University of Maryland (UMD) and measures areas of tree cover loss due to fire compared to all other drivers across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30-meter resolution. The data were generated using global Landsat-based annual change detection metrics for 2001-2025 as input data to a set of regionally calibrated classification tree ensemble models. The result of the mapping process can be viewed as a set of binary maps (tree cover loss due to fire vs. tree cover loss due to all other drivers).  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” which is clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time and is biome-dependent. Tree cover loss due to fire may be caused by natural or human-induced fire activity.  The analysis method for the base tree cover loss map on GNW that is used as input for this dataset has been modified in numerous ways to improve detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations for data covering the 2011-2025 period. Due to these changes, comparing trends across the 2000-2010 and 2011-2025 periods should be performed with caution. You can read more about updates to the tree cover loss modeling process 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 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.  This data is available for download from UMD here and also accessible through Google Earth Engine using the following image IDs (view the download page from UMD for more information):  - Fire certainty: users/sashatyu/2001-2025\_fire\_forest\_loss  - Date of loss: users/sashatyu/2001-2025\_fire\_forest\_loss\_annual  Identifies areas of tree cover loss due to fires compared to all other drivers of tree cover loss