None available--Additional Information: Category: Parks Purpose: Update Frequency: As needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=54554

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 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

For decades governments have mapped and monitored their infrastructure to support effective management of cities. That mapping has primarily focused on gray infrastructure, features such as roads and buildings. The Tree Canopy Assessment protocols were developed by the USDA Forest Service to help communities develop a better understanding of their green infrastructure through tree canopy mapping and analytics. Tree canopy is defined as the layers of leaves, branches and stems that provide tree coverage of the ground when viewed from above. When integrated with other data, such as land use or demographic variables, a Tree Canopy Assessment can provide vital information to help governments and residents chart a green future. Tree Canopy Assessments have been carried out for over 80 communities in North America. This study assessed tree canopy for the City of Providence over the 2011 – 2018 time period.

Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. 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 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.

Download UrbanTreeCanopy_2019.zip. The following information was produced from the 2019 Urban Tree Canopy Assessment for Jefferson County, KY sponsored by Trees Louisville. It is based on 2019 LOJIC Base Map data. It includes shapefiles and rasters. The study was performed by the University of Vermont Spatial Analysis Lab.

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.

Download UrbanTreeCanopy_2015.zip. The following information was produced from the 2015 Urban Tree Canopy Assessment for the City of Louisville, KY sponsored by the Office of Sustainability. It is based on 2012 LOJIC Base Map data. It includes shapefiles and raster feature classes in a file geodatabase. The study was performed by Davey Resource Group.

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

Locations of all street trees in the City of San Jose. Street trees are trees along city right-of-way and sidewalk, but do not include trees on private property or large lots like parks. It is the responsibility of the adjacent property owner to properly care for the street tree and comply with City laws and best practices. Permits must be obtained for most work on street trees to ensure it is done accordining to the requirements of the City code. Some street trees in City medians and road backups are maintained entirely by the City.Data is published on Mondays on a weekly basis.

Overview

This dataset on trees covers Dublin City, Ireland. It indicates the locations of over 300,000 trees which were acquired using a point-and-click methodology using a high-quality aerial image (resolution of 12.5 cm) taken in June 2018. Each x,y location represents the tree canopy centre. The database also includes the estimated height of these trees based on a Digital Elevation Model (horizontal resolution of 1m, vertical resolution *m). This information is complemented with detailed information on individual trees, where available. This includes, for example, data on 2440 street trees (species and dimensions) gathered in 2008/2009. The dataset will be updated at regular intervals as information on individual trees is acquired.

• to be updated

Purpose

Information on trees (location, species, age, and health) in urban areas is needed to assess the green infrastructure and the ecosystem, environmental and social services that they provide. These data were acquired to support greening strategies and actions in the Dublin City Council area.

Additional comments

The creation of the Dublin tree database was supported in part by a research grant from Ireland’s Environmental Protection Agency (EPA) and using databases acquired by the School of Geography at UCD. The tree database is part of the Mapping Green Dublin project and is being used by the Curio app, which allows citizens to add data on individual trees.

The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

Tree Inventories from 2008 and 2010. Some trees in Amherst Center were inventoried both times. The Street Trees 2009 layer is 99%+ composed of tree points collected as part of the Spring 2009 1"=40' Basemap project. These trees were collected within and in close proximity to street right of ways. The inventory layers can be joined to the Street Trees 2009 layer via their respective IDs.This data is also available for download in shapefile format.

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.

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.

City Maintained Trees. Trees or tree site locations maintained by the Urban Forestry section of the Department of Public Works. Sacramento’s Urban Forestry section is specifically focused on planting, protecting and maintaining trees located in the city right-of-way, parks and public spaces. These trees provide us with multiple benefits and are a unique green infrastructure that continually gives back when properly maintained. In fact, City trees are the only public assets that can increase in value as they age.Contact GIS at: sacgis@cityofsacramento.org

An Assessment of Existing and Potential Tree Canopy in Loudoun County, Virginia.

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

Dataset estimates location and size of trees in the District of Columbia that are not managed by the Urban Forestry Division (https://opendata.dc.gov/datasets/urban-forestry-street-trees/explore). Trees are modeled using an automated feature extraction process applied to 2022 LiDAR data. All data is an estimate, and intended for general representation purposes. DC 2022 LiDAR was used and processed using the “Extract Trees using Cluster Analysis” script which is included as part of Esri’s 3D Basemap solution. All LiDAR-derived trees within 2 meters of a Urban Forestry Division tree were removed as being duplicates. Tree diameter (DBH, in inches) was estimated for the LiDAR-derived trees from calculated tree height (in feet) based on the equation: DBH = 0.4003*height - 1.9557. This equation was derived from a statistical analysis of a detailed park inventory tree data set and has an R^2 = 0.7418. Extreme outliers were also modified, with any DBH larger than 80 inches being converted to a DBH of 80 inches.