Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq This dataset shows point locations of public trees inventoried by the City of Austin as of March 13th, 2020. Data is compiled from various sources: Development Services Department's Tree Division, AISD, Parks and Recreation Department, and Public Works Department's downtown tree inventory (2013). This is not a complete comprehensive inventory of all trees. Some errors and/or duplicate data may exist. For more information on Austin's urban forest, visit the U.S. Forest Service's Urban Forest Inventory and Analysis report: https://www.fs.usda.gov/treesearch/pubs/50393 Austin Development Services Data Disclaimer: The data provided are for informational use only and may differ from official department data. Austin Development Services’ database is continuously updated, so reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different data sources may have been used. Austin Development Services does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided.

Seattle Parks and Recreation's Tree Inventory of individual trees on SPR property.This Hosted Feature Layer View is for public facing web maps and applications. The HFL and view get updated weekly from the source on Monday morning through mid-day.

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

This dataset includes Tempe’s tree inventory data and benefits of the trees as calculated by i-Tree Eco in October 2021. The dataset was put together by West Coast Arborists, Inc. (WCA) in 2021.About Tempe's Tree Inventory and i-Tree EcoThis dataset contains the point locations and attributes of trees within City of Tempe and Facilities. The point dataset was originally collected by WCA, Inc. in 2017 and is routinely updated by WCA and the City of Tempe. The attributes used included TreeID, Exact DBH, Height Range, Exact Height, Condition, Botanical Name, Common Name, Latitude, and Longitude. Updates to the Tempe's point layer was made using the results from i-Tree Eco. An i-Tree Eco Analysis was run in September 2021 using i-Tree Eco v6.0.22 and the results were joined based on unique tree ID to Tempe's Tree inventory. The results from i-Tree Eco were added as attributes to the Tempe's Tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr) , Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual.i-Tree Eco. i-Tree Software Suite v6.x. Web. Fall 2021. https://www.itreetools.orgi-Tree Eco Manual:https://www.itreetools.org/documents/275/EcoV6_UsersManual.2021.09.22.pdfTempe Tree and Shade Coverage (data hub site):https://urbanforestry.tempe.gov/Additional InformationSource: West Coast Arborists, Inc. (WCA) 2021; i-Tree Eco v6 2021Contact: Richard AdkinsContact E-Mail: richard_adkins@tempe.govData Source Type: GPS and Google map data; tables in CVS and Excel formatPreparation Method: Field observations and records of individual trees; value calculations based on i-Tree Eco v6 found at https://www.itreetools.org/support/resources-overview/i-tree-manuals-workbooksPublish Frequency: Every 5 years or as data becomes availablePublish Method: ManualData Dictionary

Vermont Urban and Community Forestry Program's municipal tree inventory approach and tools have been developed to engage community members in the care and management of their trees and forests, and help them to identify, prioritize, and take action on the management needs identified in their inventory. Tree inventory support for Vermont communities has fallen under two initiatives: municipal tree inventories and ash inventories to support community preparedness for emerald ash borer.Data is updated at 11PM daily.

Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

A tree crowns layer was derived from 2018 NAIP and 2019 LiDAR, and then each tree crown polygon was populated with the 95th percentile nDSM (height above ground) values from LiDAR collected in 2014 and in 2019. Object-based image analysis techniques (OBIA) were employed to extract potential tree crowns including the area of the crown 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 insure 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 1:2000 and all observable errors were corrected.

This database contains distribution maps of 135 eastern U.S. tree species based on Importance Values (IV) derived from Forest Inventory Analysis (FIA) data and a geographical information system (GIS) database of Elbert L. Jr. Little's published ranges. Between 1971 and 1977, Elbert L. Jr. Little, Chief Dendrologist with the U.S. Forest Service, published a series of maps of tree species ranges based on botanical lists, forest surveys, field notes ad herbarium specimens. These published maps have become the standard reference for most U.S. and Canadian tree species ranges.

The USDA Forest Service's FIA units are charged with periodically assessing the extent, timber potential, and health of the trees in the United States. The investigators have created a spatial database of individual species IV based on the number of stems and basal area of understory and overstory trees using FIA data from more than 100,000 plots in the eastern United States. The IV ranges from 0 to 100 and gives a measure of the abundance of the species. (See the investigator's Climate Change Atlas for 80 Forest Tree Species of the Eastern United States at [http://www.fs.fed.us/ne/delaware/atlas/web_atlas.html] for details). The investigators have aggregated the plot-level IV to 20km cells.

Both sets of maps (Little's ranges and IV based on FIA data) are available for download. The Little's range maps (little.shp) are vector based and are provided as shape files. These maps can span United States or United States and Canada in extent depending on the species. The Importance Value (IV) are raster maps (asciigrid) in asciigrid format. This is an ascii file with header information that can be used to import data into ArcInfo GRID or ArcView's Spatial Analyst GIS software. The spatial resolution is 20km. These raster maps span the eastern U.S. (east of the 100th meridian) in extent.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

This is a view into the official Tree feature service.While this view is not editable, any changes to the data in the official feature service will be automatically reflected.

Public Works department Parks and Open Space division

The Urban Forestry section of Parks and Open Space is responsible for the management all trees located within the public Right-of-Way. Urban Forestry staff also consult on the tree management in city-owned properties. As part of these duties, management of the city's tree inventory includes, but not limited to: Tree pruning, tree removals, grinding away stumps leftover from removed trees, the planting of new trees, the tracking of damaged or sick trees that require monitoring, the watering of new trees, and more. This public application allows the public to locate their address, see the special trees in the City of Eugene Urban Forestry Tree Inventory, and to see the vast scope of the Urban Forest.

Inventory of trees maintained by the City of Kelowna.

This dataset of tree points in urban areas of Los Angeles County was created using imagery from 2022 by a team of researchers at the Urban Forest Ecosystems Institute at Cal Poly (UFEI). This map is based on NAIP imagery from 2022, processed by a convolutional neural network (CNN) which learned to detect trees from a collection of hand-annotated samples. The CNN takes NAIP imagery as input and outputs a confidence map indicating the locations of trees. The individual tree locations are found by local peak finding.Find more information about how these points were populated here: https://www.sciencedirect.com/science/article/pii/S1569843224002024Find more information about UFEI here: https://ufei.calpoly.edu/This project was funded by CAL FIRE (award number: 8GB18415) the US Forest Service (award number: 21-CS-11052021-201)J. Ventura, C. Pawlak, M. Honsberger, C. Gonsalves, J. Rice, N.L.R. Love, S. Han, V. Nguyen, K. Sugano, J. Doremus, G.A. Fricker, J. Yost, and M. Ritter. "Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery." International Journal of Applied Earth Observation and Geoinformation 2024.

The National Trees Outside Woodland (TOW) V1 map is a vector product funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme produced under Forest Research’s Earth Observation for Trees and Woodlands (EOTW) project.

The TOW map identifies canopy cover over 3m tall and 5m2 area which exists outside the National Forest Inventory (National Forest Inventory - Forest Research). Canopy cover is categorised into the following woodland types - lone trees, groups of trees and small woodlands.

The data set was derived from the Vegetation Object Model (VOM) (Environment Agency, EA), the National Lidar Survey (EA), and Sentinel-2 (European Space Agency) imagery using spatial algorithms. The method is fully automated with no manual manipulation or editing. The map and its production method has been quality assured by DEFRA science assurance protocols and assessed for accuracy using ground truth data.

Because the process classifies objects based on proximity to features within OS mapping, there could be some misclassifications of those objects not included in the OS (specifically: static caravans, shipping containers, large tents, marquees, coastal cliffs and solar farms).

This is a first release of this dataset, the quality of the production methods will be reviewed over the next year, and improvements will be made where possible.

The TOW map is available under open government licence and free to download from the Forestry Commission open data download website (Forestry Commission) and view online on the NCEA ArcGIS Online web portal (Trees Outside Woodland). A full report containing details on methodology, accuracy and user guide is available.

TOW map web portal link : ncea.maps.arcgis.com/apps/instant/sidebar/index.html?appid=cf571f455b444e588aa94bbd22021cd3

FR TOW map web page : https://www.forestresearch.gov.uk/tools-and-resources/fthr/trees-outside-woodland-map/

City of Waterloo managed street tree locations. This dataset contains the location and attributes of street trees, and represents an inventory of street trees maintained by the City of Waterloo.

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.

Dataset SummaryAbout this data:The Forestry Division of the Department of Environmental Services manages the care and maintenance of approximately 70,000 public trees located along City streets and in City parks and cemeteries. This includes tree pest management, pruning, planting, removal, inspection and responding to public requests.On Arbor Day, 2005, the City of Rochester released a forestry master plan entitled: "City in a Forest: An Urban Forest Master Plan for the City of Rochester."Since then, the Forestry staff in the Department of Environmental Services have worked to meet the goals outlined in the plan and develop new recommendations. In 2012, the "Urban Forest Master Plan: City in a Forest, Third Edition" was released. Download the full master plan document to read about Forestry's achievements, ongoing efforts and plans for the future.Staff members manage the care and maintenance of approximately 70,000 public trees located along City streets and in City parks and cemeteries. This includes tree pest management, pruning, planting, removal, inspection and responding to public requests. Visit the Forestry Services page to find out more.Data Dictionary: Park: The park or rec center the tree is located in (if applicable). Address: The address where the tree is located (if not a park or rec center). Street: The name of the street where the tree is located. Tree #: Indicates the tree identification. Lot Side: Indicates where the tree is relative to the address. If the tree is not in a park or a rec center, it will have one if the following identifiers: F – front S – side of the house R – rear B – behind the sidewalk M – median For an example usage, combining the lot side and the tree # will indicate which tree it is on the address (so a 2F would indicate the second tree in the front of a house). Diameter: The measurement of the tree’s trunk’s width. Genus: The genus of the tree. Species: The species of the tree. Common Name: The common name of the tree. NSC Area: The NSC Area the tree is located in. This would be either NE, NW, SE, or SW. THEME_VAL: How a tree is differentiated. This can be one of these three values: P – park trees S – street trees V – vacant lot trees MAINT_VAL: The type of maintenance or work that needs to be done to the tree (prune, remove, pull stakes), or indicate the current state of the tree or the plans for it (stump, plant, no prune) AREA_VAL: The pruning area it is in. Area values can be A1-A6, B1-B5, C1-C5, D1-D5, E1-E6, F1-F7, and CB for downtown. INV_BY: Inventoried by. The initials of who last checked the tree. INV_DATE: The date of when the tree was last checked. ASSETID: The unique number given to each tree in order to track the work history of it. DCODE_VAL: An additional identifier for a tree. Used to separate contract and in house removals or for projects which need to be queried. HISTORIC: Used to separate trees with historic significance. ROUTING_SECTION: What is used for ash trees. Ash trees are injected every three years, so the routing sections are used to create driving routes to split up the work. Source: This data is maintained by the Forestry Division of the City of Rochester Division of Environmental Services.

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.

This layer is a high-resolution tree canopy change-detection layer for Cambridge, MA. It contains two tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2014 and 2010 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 2014 and 2010 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 at a scale of 1:2500.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription Class_name type: Stringwidth: 254precision: 0 Classification name. Contains two tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain