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/

Increase the number of trees.

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 Science data 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 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 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/

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

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.

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

Summary

This repository contains spatial datasets with metadata on land cover, tree canopy change, and estimated tree points and crown polygons for New York City (NYC; New York, USA) as of 2021, made available by The Nature Conservancy, New York Cities Program and developed under contract by the University of Vermont Spatial Analysis Lab. The datasets are provided herein with high-level background and information; additional analysis, particularly on tree canopy change and distribution across NYC considering various geogrpahic units are planned for release in a forthcoming report by The Nature Conservancy. For questions about these data, contact Michael Treglia, Lead Scientist with The Nature Conservancy, New York Cities Program, at michael.treglia@tnc.org.

Datasets included here are as follows (file names in italics):

• Land cover as of 2021 (landcover_nyc_2021_6in.tif):
  • Raster dataset with six-inch (15.24 centimeter) pixel resolution, delineating land covers as: 1) tree canopy (with crowns greater than eight feet [2.44 meters] tall; 2) grass/shrub (including vegetation less than or equal to eight feet [2.44 feet] tall; 3) bare ground; 4) open water; 5) building; 6) road; 7) other impervious; and 8) railroad. This is intended to serve as an update to high-resolution land cover data for 2010 and 2017 made available by the City of New York.
• Tree canopy change during 2017-2021 (treecanopychange_nyc_2017_2021_6in.tif):
  • Raster dataset with six-inch (15.24 centimeter) pixel resolution, with pixels that were estimated tree canopy in 2017 (based on 2017 land cover data) or 2021 delineated as: 1) canopy that did not change (“no change”); 2) canopy that was gained (“gain”); 3) canopy that was lost (“loss”). This is intended to be an updated tree canopy change dataset, analogous to a canopy change dataset for 2010-2017 made available by the City of New York.
• Estimated tree points, crown polygons, and objects as of 2021 (Trees_Centroids_Crown_Objects_2021.gdb.zip):
  • The approximated locations (centroids) and approximated tree crowns as circles (shapes), and tree objects themselves based on canopy data (objects) for individual trees with crowns taller than eight feet (2.44 meters); in cases where there are trees with overlapping crowns, only the top trees are captured. These data are based on automated processing of the tree canopy class from the land cover data; additional methodological details are included in the metadata for this dataset. Given the height cutoff, that this dataset only captures the trees seen from above, and the large number of understory trees in some areas (e.g., forested natural areas), and limits in the automated processing this is not intended to be a robust census of trees in NYC, but may serve as useful for some purposes. Unlike the land cover and tree canopy change datasets, no directly comparable datasets for NYC from past years that we are aware of.

These datasets were based on object-based image analysis of a combination of 2021 Light Detection and Ranging (LiDAR; data available from the State of New York) for tree canopy and tree location/crown data in particular) along with high-resolution aerial imagery (from 2021 via the USDA National Agriculture Inventory Program and from 2022 via the New York State GIS Clearinghouse), followed by manual corrections. The general methods used to develop the land cover and tree canopy datasets are described in MacFaden et al. (2012). A per-pixel accuracy assessment of the land cover data with 1,999 points estimated an overall accuracy of 95.52% across all land cover classes, and 99.06% for tree canopy specifically (a critical focal area for this project). Iterative review of the data and subject matter expertise were contributed by from The Nature Conservancy and the NYC Department of Parks and Recreation.

While analyses of tree canopy and tree canopy change across NYC are pending, those interested can review a report that includes analyses of the most recent data (2010-2017) and a broad consideration of the NYC urban forest, The State of the Urban Forest in New York City (Treglia et al 2021).

References

MacFaden, S. W., J. P. M. O’Neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis. Journal of Applied Remote Sensing 6(1):063567.

Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., & Maxwell, E.N. (2021). The State of the Urban Forest in New York City. The Nature Conservancy. doi: 10.5281/zenodo.5532876

Terms of Use

© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 (CC BY-NC-SA 4.0) license.

• The Nature Conservancy (TNC) oversaw development of these data and reserves all rights in the data provided.
• TNC makes no guarantee of accuracy or completeness.
• Data are for informational purposes and are not suitable for legal, engineering, or surveying purposes. Data do not represent an on-the-ground survey and represent only the approximate relative location of feature boundaries.
• TNC is not obligated to update/maintain the data to reflect changing conditions.
• Commercial use is not allowed.
• Redistribution (sublicensing) is allowed, provided all accompanying metadata as well as these Terms of Use are provided, unaltered, alongside the data.
• TNC should be credited as the data source in derivative works, following the recommended citation provided herein.
• Users are advised to pay attention to the contents of this metadata document.

Recommended Citation

If using any of these datasets, please cite the work according to the following recommended citation:

The Nature Conservancy. 2024. New York City Land Cover (2021), Tree Canopy Change (2017-2021), and Estimated Tree Location and Crown Data (2021). Developed under contract by the University of Vermont Spatial Analysis Laboratory. doi: 10.5281/zenodo.14053441.

Technical Notes about the Spatial Data

All spatial data are provided in the New York State Plan Long Island Zone (US survey foot) coordinate reference system, EPSG 2263. The land cover and tree canopy change datasets are made available as raster data in Cloud Optimized GeoTIFF format (.tif), with associated metadata files as .xml files. The vector data of estimated tree locations and crown objects and shapes are made available in a zipped Esri File Geodatabase, with metadata stored within the File Geodatabase.

Statewide (30m) and individual County (1m, 1.2m, 2m) high resolution tree canopy cover showing forest vs non-forest land. High Resolution Carbon Monitoring and Modeling: A NASA CMS Phase II Study.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/imap/rest/services/Biota/MD_CanopyCover/ImageServer

This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.

Discover the lush greenery of the City of Unley with our comprehensive Tree Canopy dataset. Explore valuable information on the distribution and coverage of trees, enabling informed decisions for sustainable urban planning. Accessible, open data for a greener future.

Georgia 2021 Tree Canopy Cover (TCC) raster subset from the national TCC dataset created by the USDA Forest Service. Click here to download the dataset.

This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (*.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.

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.

This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland.

We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.

The statistic provides the Green View Index of selected cities worldwide as of 2017. With a score of **** out of 100, Vancouver was ranked second. This score measured the canopy cover in cities that one perceives while walking down the street, it does not include parks within the city.

This data set provides a map of selected areas with defined tree canopy cover over the circumpolar taiga-tundra ecotone (TTE). Canopy cover was derived from the 500-meter MODIS Vegetation Continuous Fields (VCF) product as averaged over six years from 2000-2005 and processed as described in Ranson et al. (2011). This process identified patches of low tree canopy cover which are indicative of the transition from forest to tundra and differentiate the circumpolar taiga–tundra ecotone for the 2000–2005 period.

The TTE is the Earth's longest vegetation transition zone and stretches for more than 13,400 km around Arctic North America, Scandinavia, and Eurasia. In Eurasia, the map extends from 60 degrees N to 70 degrees N, and in North America from 50 degrees N to 70 degrees N, excluding Baffin Island in northeastern Canada and the Aleutian Peninsula in southwestern Alaska. Note that for this product, taiga is being used one and the same as boreal forest.

This circumpolar TTE area was classified according to VCF tree canopy cover.

This indicator provides information about the percentage of land with tree canopy coverage, weighted by population size.Trees are essential for mitigating the effects of climate change, including extreme heat waves, because they provide shade and cooling to surrounding areas. Trees also provide mental and physical health benefits to residents living in the communities. Communities in which a large proportion of trees or natural land have been replaced by pavement and buildings are especially vulnerable to the urban heat island effect , in which heat becomes trapped and leads to warmer temperatures relative to other surrounding areas that have retained trees or natural land. In Los Angeles County, low-income communities are more likely to experience the urban heat island effect and are consequently at higher risk for negative outcomes associated with excess heat, including air pollution and heat-related illnesses. Increasing tree canopy coverage in areas with low tree density is one strategy that cities and communities can implement to mitigate the urban heat island effect and promote local climate resiliency.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

NLCD 2023 Tree Canopy Cover (COG)

This dataset hosts a Cloud-Optimized GeoTIFF (COG) version of the NLCD 2023 Tree Canopy Cover dataset for the conterminous United States. The source data are from the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2023.The GeoTIFF was reprocessed and converted to a Cloud-Optimized GeoTIFF using GDAL. Source: https://www.mrlc.gov/dataTool used: GDALLicense: Public Domain (U.S. Government Work) – CC0 1.0 Attribution:

U.S.… See the full description on the dataset page: https://huggingface.co/datasets/jrdn-sentosa/nlcd-tree-canopy-coverage.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq

This dataset was created to depict approximate tree canopy cover for all land within the City of Austin's "full watershed regulation area." Intended for planning purposes and measuring citywide percent canopy.

Definition: Tree canopy is defined as the layer of leaves, branches, and stems of trees that cover the ground when viewed from above.

Methods: The 2022 tree canopy layer was derived from satellite imagery (Maxar) and aerial imagery (NAIP). Images were used to extract tree canopy into GIS vector features. First, a “visual recognition engine” generated the vector features. The engine used machine learning algorithms to detect and label image pixels as tree canopy. Then using prior knowledge of feature geometries, more modeling algorithms were used to predict and transform probability maps of labeled pixels into finished vector polygons depicting tree canopy. The resulting features were reviewed and edited through manual interpretation by GIS professionals. When appropriate, NAIP 2022 aerial imagery supplemented satellite images that had cloud cover, and a manual editing process made sure tree canopy represented 2022 conditions. Finally, an independent accuracy assessment was performed by the City of Austin and the Texas A&M Forest Service for quality assurance. GIS professionals assessed agreement between the tree canopy data and its source satellite imagery. An overall accuracy of 98% was found. Only 23 errors were found out of a total 1,000 locations reviewed. These were mostly omission errors (e.g. not including canopy in this dataset when canopy is shown in the satellite or aerial image). Best efforts were made to ensure ground-truth locations contained a tree on the ground. To ensure this, location data were used from City of Austin and Texas A&M Forest Service databases.

Analysis: The City of Austin measures tree canopy using the calculation: acres of tree canopy divided by acres of land. The area of interest for the land acres is evaluated at the City of Austin's jurisdiction including Full Purpose, Limited Purpose, and Extraterritorial jurisdictions as of May 2023. New data show, in 2022, tree canopy covered 41% of the total land area within Austin's city limits (using city limit boundaries May 2023 and included in the download as layer name "city_of_austin_2023"). 160,046.50 canopy acres (2022) / 395,037.53 land acres = 40.51% ~41%. This compares to 36% last measured in 2018, and a historical average that’s also hovered around 36%. The time period between 2018 and 2022 saw a 5 percentage point change resulting in over 19K acres of canopy gained (estimated).

Data Disclaimer: It's possible changes in percent canopy over the years is due to annexation and improved data methods (e.g. higher resolution imagery, AI, software used, etc.) in addition to actual in changes in tree canopy cover on the ground. For planning purposes only. Dataset does not account for individual trees, tree species nor any metric for tree canopy height.

Tree canopy data is provided in vector GIS format housed in a Geodatabase. Download and unzip the folder to get started. Please note, errors may exist in this dataset due to the variation in species composition and land use found across the study area. This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries.

Data Provider: Ecopia AI Tech Corporation and PlanIT Geo, Inc. Data derived from Maxar Technologies, Inc. and USDA NAIP imagery

Data and Map Disclaimer:

These products are for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. They do not represent an on-the-ground survey and represent only the approximate relative location of property boundaries. These products have been produced by the City of Austin for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness.

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 pixel's 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 USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The CONUS TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.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.