The Tree Cover Dynamics (TCD) Conterminous United States (CONUS) dataset is a suite of 30 m wall-to-wall products, derived from USGS Landsat-4, Landsat-5 and Landsat-7 Collection 1 Analysis Ready Data (ARD), defining for each year: (i) the estimated percent tree cover (PTC), (ii) if tree cover loss is detected, the estimated percent tree cover decrease from the previous year (ΔPTC), (iii) if tree cover loss is detected, the Landsat acquisition dates bounding the tree cover loss event (i.e., the last valid observation before the loss, and the first valid observation after the loss) and (iv) a forest status thematic map (three thematic classes: stable forest, stable non-forest, forest cover loss). The products are available for every year from 1985 to 2019. The dataset is provided as georeferenced GeoTIFF images, defined in the CONUS Albers Equal-Area Conic map projection at 30m resolution.

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.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011 through 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the median of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is carried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/

Increase the number of trees.

The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme.

Fractional tree cover facilitates the depiction of forest density and its variations. However, it remains challenging to estimate fractional tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. We generated a global annual fractional tree cover dataset (GLOBMAP FTC) from 2000 to 2021 with a resolution of 250 m using the MODIS land surface reflectance products. MODIS annual observations were realigned at the pixel level to a common phenology with the midpoint date of growing season centered in the calendar year. Twelve features that can differentiate between trees and herbaceous vegetation were extracted from the realigned MODIS observations. A massive training data, consisting of 465.88 million sample points, was collected across the globe from four high-resolution forest cover products, including ESA WorldCover, GlobeLand30, PALSAR FNF, and ESRI Land Cover. A feedforward neural network model was trained to predict tree cover. Global and regional analysis demonstrated that the dataset could characterize the forest cover changes, including both abrupt forest loss and gradual forest gain. This dataset can be applied for assessment of forest cover dynamics and monitoring the progress of forest restoration projects and policies.

The dataset is provided by 296 to 323 1200 km × 1200 km tiles for each year, aligning with the MODIS standard tiling system. The data are stored in the Sinusoidal projection with Geotiff format. The files are designated with the naming structure "GLOBMAPFTC.AYYYY001.hHHvVV.V01.tif", where “YYYY” represents the year of the data, while “HH” and “VV” indicate the horizontal and vertical tile indices respectively, aligning with the MODIS standard tile. The valid range of tree cover value is 0-100, with a scale factor of 1.0 and the unit of percentage (%).

Tree cover loss in Brazil amounted to 2.81 million hectares in 2023, down by around 15 percent from the previous year. During the period in consideration, tree cover loss peaked at 5.38 million hectares in 2016. This development was also reflected in the Brazil's primary forest loss.

This EnviroAtlas dataset categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). Forest is defined as Trees & Forest and Woody Wetlands. Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This dataset is associated with the McDonald et al. paper, entitled "The urban tree cover and temperature disparity in US urbanized areas: Quantifying the effect of income across 5,723 communities". Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use NAIP imagery to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other places. We compared tree cover to summer surface temperature, as measured using Thematic Mapper imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter (surface temperature) than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks often have at least 30% less tree cover and are at least 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the association between income and tree cover is significant, as is the association between race and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, a compensatory value of $56 billion dollars ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would close the tree cover disparity for 42 million people in low-income blocks.

The dataset has been compiled from the biennial Indian State of Forests Reports of the Forest Survey of India (FSI). The trend in forest cover, forest area, and tree cover at the national level has been collated for the decade from 2011 to 2021.The data pertaining to forest cover for the year 2015 has been taken from the revised numbers of the year as presented in the 2017 report.

This statistic displays the tree cover loss in the United States from 2001 to 2017, in million hectares. In 2017, around **** million hectares of tree cover was lost in the U.S., this represents a decrease of approximately ** percent since the year 2000.

Total tree cover as measured by the fractional non-overlapping absolute tree cover, viewed vertically. Provides a first order measure of vegetation type when combined with parallel observations of shrub and herbaceous cover. Data from the National Land Cover Database (NLCD) are used for training, and NLCD definitions for cover (for example, the distinction between tree vs shrub) are expected to be similar in the CECS data sets.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data - the focus of this metadata - are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. TCC pixel values range from 0 to 100 percent. The value 254 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 255 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/

Dataset pertaining to a record of annual tree cover loss in the Solomon Islands from 2001 - 2017. The independent Global Forest Watch reported a total loss of tree cover (>30% crown cover) in the Solomon Islands of 144,000 ha between 2001-2017. The country lost 144kha of tree cover, equivalent to a 5.2% decrease since 2000, and 16.7Mt of CO₂ emissions.

This data set was created by the North Carolina Department of Agriculture and Consumer Services (NCDA&CS). This Forest (Tree) Land Cover data was derived from the North Carolina, 4 band, 2016, USDA National Agriculture Imagery Program (NAIP) imagery.It includes the entire state of NC, except Ft. Bragg. It is one (1) meter pixel resolution which makes hiding errors difficult. Some errors (incorrect classification) exists but we estimate the data is better than 90% accurate. When viewing this data, NCDA&CS highly recommends using aerials from 2016 for a base map. The original NAIP (raster) data was in TIF format (DOQQ tiles) and was natively in UTM projection.A decision rule supervised classification process was specifically designed around the tonal differences inherent in NAIP imagery. It used with spectral and textural (to separation grasslands from trees) information derived for each 4 band NAIP tile (quarter quad). A total of 3,564 tiles or 16 TBs of data were processed. The classification resulted in a 2-class classification schema. Class 1 is Forest/Trees and Class 2 Non-forest/trees. Class 2 is set to white/transparent by default. Texture processing was applied to reduce mixed pixel values between tree canopy, healthy grass and agriculture land areas. These features have similar vegetation spectral response and would otherwise result in a significant number of misclassified pixels. In many areas however, agriculture and grass land areas containing higher texture values still resulted in mixed canopy pixels. We assume this introduces around a 5% error or misclassification rate.

The tree canopy layer displays the proportion of the land surface covered by trees for the year 2016 from the National Land Cover Database. This layer was created from the analytical version of this dataset. Source: https://www.mrlc.govDataset SummaryPhenomenon Mapped: Proportion of the landscape covered by trees in 2016Units: Percent (of each pixel that is covered by tree canopy)Cell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate Systems: North America Albers Equal Area ConicMosaic Projection: WGS 1984 Web Mercator Auxiliary SphereExtent: CONUS, Southeastern Alaska, Hawaii, Puerto Rico and the US Virgin IslandsSource: Multi-Resolution Land Characteristics ConsortiumPublication Date: August 22, 2019ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation.European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788.The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel.Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe.Dataset RecordThe forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below.Code AvailabilityFor the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022). The hierarchical U-Net framework first developed a U-Net model for very high-resolution aerial images (NAIP) using training labels derived from previous work based on an interactive image segmentation tool and iterative updates with expert knowledge (Basu et al., 2015). The developed NAIP U-Net model and NAIP data produced 1-m NAIP TC across all lower 48 CONUS states. A Landsat U-Net model was developed for multi-year and large-scale TC mapping based on the very high-resolution NAIP TC made in the earlier stage. The Landsat U-Net model developed was adopted over the CONUS for testing its transferability, validation, and improvement across Mexico. This dataset provides national-scale percent tree cover estimates over Mexico and can be helpful for studies of carbon cycling, land cover and land use change, etc. The team has been working on improving temporal stability of the product and will update the product once the next version is ready to be shared.

This data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones. SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology. # Limitations 1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates. 2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates. 3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version. 4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions. Updates of this dataset will eventually be available on this metadata page. # Details on the product development and validation can be found in the following publication: Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118 # Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available: • NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer • Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies • Height (meters): vegetation height • Crown closure (%): percentage of pixel covered by the tree canopy • Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)