Tree Canopy (TC) Assessment metrics for New York City. This dataset consists of TC metrics summarized to several different sets of geographic base layers. The metrics presented in this table are based on 2010 high resolution land cover dataset. The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC) and amount of land where is it biophysically feasible to establish tree canopy on (termed Possible TC). Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is determined by identifying land where canopy could possibly exist. Possible TC in a GIS context is determined by overlaying high resolution land cover with cadastral and planimetric datasets to include building polygons and road polygons. Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions. TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the parcel in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy. This assessment was completed by the University of Vermont's Spatial Analysis Laboratory with funding from National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF) and in cooperation with the USDA Forest Service's Northern Research Station. The TC Assessment protocols were developed by the USDA Forest Service's Northern Research Station and the University of Vermont's Spatial Analysis Laboratory in collaboration with the Maryland Department of Natural Resources. TC assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing TC goals.

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.

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.

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.

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 (TC) Assessment metrics for Boston, MA. These datasets consist of TC metrics summarized using various geographies. The metrics presented in these tables are based on 2019 high resolution land cover and 2019 leaf-on LiDAR. The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC). Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions. TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the area in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy.

TC_E_A =Tree canopy existing area. The area of tree canopy present when viewed from above using aerial or satellite imagery, excluding water.

TC_PV_A= Possible vegetation area. Grass or shrub area that is theoretically available for the establishment of tree canopy.

TC_Land_A = Land area. Land area excluding water bodies.

TC_Pi_A= Possible impervious area. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy.

TC_P_A= Possible area. Area theoretically available for establishment of tree canopy.

TC_E_P = Existing percent. The amount of tree canopy present when viewed from above using aerial or satellite imagery, excluding water as a percentage.

TC_Pv_P = Possible vegetation percent. Grass/shrub area that is theoretically available for the establishment of tree canopy as a percentage of land area.

TC_P_P= Possible tree canopy percent. Area theoretically suitable for tree canopy as a percentage of land area.

TC_Pi_P= Possible impervious canopy percent. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy as a percentage of land area.

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 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 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.

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.

Tree Canopy (TC) Assessment metrics for Boston, MA. These datasets consist of TC metrics summarized using various geographies. The metrics presented in these tables are based on 2019 high resolution land cover and 2019 leaf-on LiDAR. The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC). Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions. TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the area in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy.

TC_E_A =Tree canopy existing area. The area of tree canopy present when viewed from above using aerial or satellite imagery, excluding water.

TC_PV_A= Possible vegetation area. Grass or shrub area that is theoretically available for the establishment of tree canopy.

TC_Land_A = Land area. Land area excluding water bodies.

TC_Pi_A= Possible impervious area. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy.

TC_P_A= Possible area. Area theoretically available for establishment of tree canopy.

TC_E_P = Existing percent. The amount of tree canopy present when viewed from above using aerial or satellite imagery, excluding water as a percentage.

TC_Pv_P = Possible vegetation percent. Grass/shrub area that is theoretically available for the establishment of tree canopy as a percentage of land area.

TC_P_P= Possible tree canopy percent. Area theoretically suitable for tree canopy as a percentage of land area.

TC_Pi_P= Possible impervious canopy percent. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy as a percentage of land area.

Losses and gains in canopy cover of the world’s tree canopies affect carbon stocks, species habitats, water cycles, and human livelihoods. Consistent and multi-decadal global data on tree-canopy cover dynamics are needed for modelling climate scenarios, tracking progress towards restoration targets, and diverse other research, management and policy applications. However, most data only map binary ‘forest’/‘non forest’ distinctions that are regionally restricted or biassed by data gaps, and those mapping tree-canopy cover are limited to the 21st century. Here, we present an annual and global time-series of tree-canopy cover between 1992 and 2018. To develop these data, we integrated complementary products, using their respective strengths to compensate for weaknesses, and exploiting path dependencies in change processes to derive predictions into the data-sparse 1990s. Our model validation indicates we can accurately map tree-canopy cover (r2=0.95 [±0.01], RMSE=6.75% [±0.08], F1-score=0.97 [±0.0]) and our time-series agree well with national forest statistics (r2=0.94 [±0.0]).

This repository contains the Global Tree-Canopy Cover Change dataset (GTCCC), which consists of a global time-series on per-pixel tree-canopy covers estimated at a 300-m resolution between 1992 and 2018. The repository contains the following:

GTCCC_canopyDensity.tar.gz - Annual GeoTiffs on per-piel tree-canopy cover estimates (EPSG:4326)

GTCCC_uncertainty.tar.gz - Annual GeotiFFs with per-pixel estimates of the 95% confidence interval of the the predictions of each RFReg decision tree.

GTCCC_change.tar.gz - Multiple outputs describing changes in tree-canopy cover between 1992 and 2018. The contents are described in a README.txt file found within.

modelling_infrastructure.tar.gz - Infrastructure to generate the GTCCC dataset, including code, and some intermediary outputs, such as reference samples and the predictive model. The contents are described in a README.txt file found within.

The predictors used to generate the GTCCC are provided separately given large volume, and can be reached by clicking here.

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 dataset maps tree canopy for the entirety of Pennsylvania at a resolution of 1m, making it 900 times more detailed than the National Land Cover Dataset (NLCD)! With our landscapes becoming increasingly fragmented and heterogeneous high-resolution datasets add precision and accuracy to any analysis.

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=3170

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

These maps represent annual canopy cover from 1973-2018 for the state of Minnesota. This is an update to the original posting in February 2019 with improved maps for 2013-2015, and extended years through 2018. The canopy cover maps were created using Landsat time series methodologies with harmonization of spectral indices from the different sensors throughout the Landsat archive. The cover model was trained using dot grid intrepretation of a sample of plots from 2008 NAIP imagery, and applied across the harmonized and trend fitted Landsat spectral indices. Users should be aware that while the maps have undergone quality assessments, they have not been validated for the year outside model creation. In addition, the map years from 1973-1983 incorporate coaser resolution and lower quality Landat MSS imagery, thus confidence in these years of cover data may be lower than the remainder of the time series. For the purpose of these maps, we consider cover in a functional sense, meaning that large woody shrubs may be grouped with true tree species in the representation of cover estimates.

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.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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 insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

This dataset is the output from the UK canopy cover webmap project, which aimed to assess the percentage tree canopy cover in every ward in the UK. Forest Research delivered the project with partners Brillianto, Trees for Cities, and Woodland Trust. Wards were classified as urban or rural based on size: wards larger than 1,000 Ha were classed as rural. Data were gathered through citizen science volunteers, who used the i-Tree Canopy tool (https://canopy.itreetools.org/) to assess percentage canopy cover within ward boundaries. A User Guide was provided to the volunteers, with step-by-step instructions and pictorial support (https://cdn.forestresearch.gov.uk/2018/11/canopy_cover_webmap_user_guide_-_updated_march_2021.pdf). i-Tree Canopy randomly distributes points within a study area (ward boundary), overlain on Google aerial imagery. Users examine the points in sequence to determine whether the point lies over a tree canopy or not, classifying each point as either “tree” or “non-tree” accordingly. Typically 350 to 600 points were assessed per ward, leading to a standard error of less than 2%. Percentage tree canopy cover was calculated as (n/N)*100 where n is the number of “tree” points and N is the total number of sample points. Results were returned to Forest Research in the form of percentage canopy cover, standard error, number of points, and the i-Tree Canopy project file. Data quality was monitored through screening of submitted results by Forest Research project staff for all new participants, and at regular intervals for experienced contributors. Most wards were assessed only once. Where a ward was assessed by more than one contributor, the results were combined where possible. Data were collected between 2018 and 2022. All urban wards were completed.

country [Country] The country within which the Ward is located as defined by the Ward code. [E050000xx = England; N08000xxx = Northern Ireland; S1300xxxx = Scotland; W050000xx = Wales]

wardcode [Ward code] The unique code associated with the Ward boundary. Ward boundaries are based on OS Boundary-Line data (OS data © Crown copyright and database right 2017). Most Ward boundaries are from the 2017 OS Boundary-Line dataset. However, a few users returned boundaries from the 2018 OS Boundary-Line dataset; the dataset was amended to reflect which boundary was used in the i-Tree Canopy assessment.

wardname [Ward name] The name of the Ward boundary. Ward boundaries are based on OS Boundary-Line data (OS data © Crown copyright and database right 2017). Most Ward boundaries are from the 2017 OS Boundary-Line dataset. However, a few users returned boundaries from the 2018 OS Boundary-Line dataset; the dataset was amended to reflect which boundary was used in the i-Tree Canopy assessment.

designated [Urban/Rural] Whether the Ward has been identified as an Urban or Rural Ward. Wards were classified as Urban or Rural based on size: wards larger than 1,000 Ha were classed as Rural.

status [Status] Current analysis status of the ward. All Wards in the dataset have been Completed.

survyear [Survey year] The year the i-Tree Canopy assessment was completed, Data collected between 2018 and 2022.

percancov [Percentage canopy cover] The average canopy cover for the ward boundary as calculated by the i-Tree Canopy model.

standerr [Standard Error] The Standard Error associated with the average canopy cover value as calculated by the i-Tree Canopy model.

numpts [Number of points] Number of points completed for the i-Tree Canopy assessment of the ward.

warea [Ward area] The area of the Ward boundary (m2)