The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the 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 raw model outputs referred to as the annual Science data; and - A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations: Science: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife NLCD: https://www.mrlc.gov/data https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC ...

LANDFIRE's 2023 Update (LF 2023) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In fire behavior models, CC supplies information to determine the probability of crown fire initiation, provides input in the spotting model, and aids in calculating wind reductions and fuel moisture conditioning. To create CC, LANDFIRE's Existing Vegetation Cover (EVC) product must be produced first. EVC is a continuous scaled product which assigns cover to all life forms in the LF data, this product is created using an image-based process (within the Conterminous United States (CONUS)) to assess canopy structure for areas disturbed in the past twenty years. CC is then derived from EVC by assigning bins of 10% for fuel production and use in fire behavior software. CC is used in the calculation of Forest Canopy Bulk Density (CBD) and Base Height (CBH). To designate disturbed areas where CC is modified, the aggregated Annual Disturbance products from 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release). When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://www.landfire.gov/data/lf2023.

LANDFIRE's (LF) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. Specifically, canopy cover describes the vertical projection of the tree canopy onto an imaginary horizontal surface representing the ground's surface. At non-disturbed locations, CC is assigned the midpoint of the Existing Vegetation Cover (EVC) forested classes. These products are provided for forested areas only.

The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. CC describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for tra ...

LANDFIRE's (LF) Remap Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand, CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In disturbed locations CC is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Cover (FVC) forested classes. In some instances, LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. LF Remap Annual Disturbance products are incorporated into CC to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. Reporting of the pre-disturbance scenario helps to calculate CC, by providing information about vegetation impacted by a disturbance. Then, vegetation adjustments are modeled in disturbance areas based on disturbance type and severity. CC is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CC supplies information to fire behavior models in order to; determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. CC also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an “effective year." For example, year 2020 fuels may be calculated for the year 2020. This new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in this example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska (SEAK), Hawaii (HI), and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include:- The raw model outputs referred to as the annual Science data; and- A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations:Science:https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlifeNLCD:https://www.mrlc.gov/datahttps://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlifeThe 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.

A 6-in resolution tree canopy change (2010 - 2017) dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset represents a "top-down" mapping perspective and all tree polygons are classed as: (1) No Change, (2) Gain, (3) Loss. No change indicates that this portion of the canopy has undergone no modifications during the time period. Gain indicates that new tree canopy has appeared during the time period. Loss indicates that this portion of the tree canopy was removed during the time period.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_TreeCanopyChange.md

LANDFIRE's (LF) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. Specifically, canopy cover describes the vertical projection of the tree canopy onto an imaginary horizontal surface representing the ground's surface. At non-disturbed locations, CC is assigned the midpoint of the Existing Vegetation Cover (EVC) forested classes. These products are provided for forested areas only.

The tree canopy layer displays the proportion of the land surface covered by trees for the years 2011 to 2021 from the National Land Cover Database. Source: https://www.mrlc.govPhenomenon Mapped: Proportion of the landscape covered by trees.Time Extent: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021Units: 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: April 1, 2023ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/Time SeriesBy default, this layer will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year changing appearance every year in the lower 48 states from 2011 to 2021. (In Alaska, Hawaii, Puerto Rico and the US Virgin Islands, the animation will only show a change between 2011 and 2016.) To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Alaska, Hawaii, Puerto Rico, and the US Virgin IslandsAt this time Alaska, Hawaii, Puerto Rico, and the US Virgin Islands do not have tree canopy cover for every year in the series like MRLC produced for the Lower 48 states. Furthermore, only a portion of coastal Southeastern Alaska from Kodiak to the Panhandle is available, but not the entire state. Alaska, Hawaii, Puerto Rico, and the US Virgin Islands have data in the series only from 2011 and 2016. Dataset SummaryThe 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.

LANDFIRE's (LF) 2022 Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. CC supplies information for fire behavior models to determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. To create this product, plot level CC values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance CC and Canopy Height (CH) are used as predictors of disturbed CC using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since disturbance. CC is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years. To designate disturbed areas where CC is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The “capable” year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php .

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska (SEAK), Hawaii (HI), and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include:- The raw model outputs referred to as the annual Science data; and- A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations:Science:https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlifeNLCD:https://www.mrlc.gov/datahttps://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlifeThe 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.

This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (*.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.

This 30-meter resolution data set represents the tree canopy layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition.jpg

The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to ...

This raster dataset shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to September 2024. It is similar to the product developed in Thonfeld et al. (2022) but was fully reprocessed and updated to reveal the most recent forest disturbance dynamics. The combination of Sentinel-2A/B and Landsat-8/9 data allows for a high temporal resolution while the pixel size of the product is 10 m. The results are clipped to the stocked area 2018 mapped by the Johann-Heinrich-von-Thünen Institute (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218). The dataset contains predominantly larger canopy openings resulting from different drivers but also larger clusters of standing deadwood. FCCL can result from abiotic (e.g. wind, fire, drought, hail) drivers, biotic (e.g. insects, funghi) drivers or a combination of both as well as from sanitary and salvage logging and planned harvest.

This dataset contains percent tree canopy cover (TCC) in 2021 for CONUS, Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands at 30m resolution. The source dataset was created by the USDA Forest Service (USFS) and modified for the National Land Cover Database (NLCD). Raw data was downloaded from the MRLC site (Multi-Resolution Land Characteristics Consortium) and the raster layers from each geographic region were stitched together by members of the NatCap team. The dataset hosted here is in a Cloud Optimized GeoTIFF format with internal overviews. More information on the dataset hosted here can be found in the accompanying metadata (YML) file. For more information on the source data, please visit: https://data-usfs.hub.arcgis.com/datasets/usfs::science-tree-canopy-cover-tcc-conus-image-service/about

This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format.

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. 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 nominal years of 2011 and 2016 are available.

The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available.

The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.

These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

LANDFIRE's (LF) 2016 Remap (Remap) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand, CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In disturbed locations CC is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Cover (FVC) forested classes. In some instances, LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. LF Remap Annual Disturbance products are incorporated into CC to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. Reporting of the pre-disturbance scenario helps to calculate CC, by providing information about vegetation impacted by a disturbance. Then, vegetation adjustments are modeled in disturbance areas based on disturbance type and severity. CC is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CC supplies information to fire behavior models in order to; determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. CC also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.

LANDFIRE's (LF) 2016 Remap (Remap) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand, CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In disturbed locations CC is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Cover (FVC) forested classes. In some instances, LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. LF Remap Annual Disturbance products are incorporated into CC to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. Reporting of the pre-disturbance scenario helps to calculate CC, by providing information about vegetation impacted by a disturbance. Then, vegetation adjustments are modeled in disturbance areas based on disturbance type and severity. CC is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CC supplies information to fire behavior models in order to; determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. CC also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.

Tropical rainforests make up almost one third of all forest area in the world, when looking at the area of canopy coverage. The world's largest rainforests are found near the equator in South America, Central Africa, and the South Pacific. In fact, the combined total of all types of tropical forest account for over 51 percent of the world's canopy coverage.

The second most common individual biome type is boreal coniferous forest, which refers to the Taiga - an almost continuous forest that stretches from Scandinavia, across Russia, and covers much of Alaska and northern Canada.