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/

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.

Record of self-reported stewardship activity on DPR trees performed by members of the public. This dataset can be joined to the Forestry Tree Points dataset (https://data.cityofnewyork.us/Environment/Forestry-Tree-Points/hn5i-inap/data) by joining the TreeId from this dataset to OBJECTID from Forestry Tree Points.

Public map of the City of Bethlehem tree inventory data collected by ArborPro, Inc. in 2020

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.

The England species map was funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme. The map was created using satellite remote sensing data (Sentinel-2) and machine learning to classify common tree species in England. The model was trained to distinguish 35 common tree species, with minority species grouped into “Other broadleaf” or “Other conifer” classes for better classification performance. The final product comprises a species classification and confidence raster output.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes. Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

The distribution and local abundance of tree species constitute basic information about our forest ecosystems that is relevant to understanding their ecology, diversity, and relationship to people. The US Forest Service conducts a forest inventory across all forest lands in the United States. We developed geospatial models of forest attributes using this sample-based inventory which make this information available for an even wider variety of applications. From these modeled datasets, we created a series of maps for 24 US states in an effort to connect more people to trees, the datasets, and the scientific research behind them. Presenting these maps in an attractive way invites engagement. The sidebar text is presented in accessible scientific language that clearly defines terms, guides readers in interpreting the maps and histograms, and provides source details and links. The resulting maps are inviting, informative, and accessible to a broad range of people of different ages and backgrounds.

The Young Trees map was funded by DEFRA through the Natural Capital and Ecosystem Assessment (NCEA) programme. The young trees mapping project developed a machine learning methodology using remote sensing to identify restocked stands where saplings persist in healthy numbers. The approach uses an eight-year timeframe since planting, crucial for verifying government grant compliance. Automating this methodology ensures easy replication and model transferability across years by training on multi-year data, making it resilient to climatic variations. Validation has confirmed the model’s accuracy, recommending high-confidence thresholds for restock classification. In the future, integration with the National Forest Inventory will enhance woodland mapping, accelerating updates and improving national indicators for forest extent and connectivity. The aim of the young trees mapping project was to develop a machine learning methodology using remote sensing data, to identify stands where trees have been planted and saplings persist in healthy numbers. This was conducted within restock contexts across a specific timeframe, currently eight years since planting. This timeframe is significant because funding provided by government grants for planting can be reclaimed if it can be demonstrated that the funding has not been utilised by the landowner. Furthermore, the restock status of clearfell polygons has the potential to improve the accuracy of extent and connectivity environmental indicators developed as part of the Tree Health Resilience Strategy (THRS). The aim of this part of the project was to automate the methodology in such a way that it can be easily replicated, and to make the model transferable across years. Specifically, to train the model using multiple years of data, which makes the model agnostic to variable annual climactic conditions. The model is both robust and accurate, as demonstrated by the validation. It is recommended that only polygons with over 95% and under 5% confidence are treated as restocked or not restocked with any certainty. Outside of these limits confidence scores are only indicative of the restock status. In the future, the model is likely to be implemented as part of the National Forest Inventory (NFI) woodland map creation procedure. This will result in accelerated turnover of polygon labels from clearfell to young trees, where appropriate and will provide an important improvement to a national indicator for woodland extent and connectivity. Attribution statement: © Forestry Commission copyright and/or database right 2024. All rights reserved.

Current number of times a given tree has been marked as a favorite by registered users of the NYC Street Tree Map (nyc.gov/parks/treemap).

This dataset can be joined to the Forestry Tree Points dataset (https://data.cityofnewyork.us/Environment/Forestry-Tree-Points/k5ta-2trh) by joining the TreeId to OBJECTID from Forestry Tree Points.

Live data feed: https://www.nycgovparks.org/tree-map-feeds/favorite-trees.json

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.

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

Contains locations of and information about street trees within regional road right-of-ways that the Regions owns and/or maintains.The data includes all Regional owned street trees in urban areas and partial data available for Regional owned street trees in rural areas.

Ecological benefits from street trees. Indicates the physical impact and monetary value of that impact for each tree. These values were calculated using i-Tree https://www.itreetools.org/

Abstract

This dataset provides an early access version of the European tree genus map at 10 m resolution for the year 2020, derived from Sentinel-1 and Sentinel-2 satellite data. The map distinguishes eight classes (Larix, Picea, Pinus, Fagus, Quercus, other needleleaf, other broadleaf, and no trees) and is distributed as Cloud Optimized GeoTIFFs (COGs) over a 100 km grid in EPSG:3035 (ETRS89 / LAEA Europe).

The map was generated using a CatBoost model trained on forest plot inventories, citizen science observations, orthophoto interpretation, and LUCAS data, with additional features from DEM and climate datasets. Labels were filtered and aggregated to genus level to reduce noise.

Early access notice

This release is provided as an early access version. The map is still undergoing validation and fine-tuning, and a formal publication is planned. Updates and improvements may therefore be made in future releases.

We welcome feedback and contributions of additional training data to further improve the map.

Dataset description

• Resolution: 10m
• Format: Cloud Optimized GeoTIFFs (COGs)
• Tiling: 100km grid
• Coordinate reference system: EPSG: 3035 (ETRS89 / LAEA Europe)

Legend

0 – Larix
1 – Picea
2 – Pinus
3 – Fagus
4 – Quercus
5 – Other needleleaf
6 – Other broadleaf
7 – No trees

Methodology summary

The classification was performed using a CatBoost model trained on diverse reference sources [1-10]:
- National and regional plot inventories
- Citizen science observations
- Orthophoto interpretation
- LUCAS data

Training labels were filtered to reduce noise and aggregated to genus level. Predictor variables include annual statistics from Sentinel-1 and Sentinel-2, combined with auxiliary datasets on altitude (DEM) and climate.

Further details on the methodology will be made available in the product publication, which will follow this early access release.

Usage Notes

• CRS: EPSG:3035 (ETRS89 / LAEA Europe). Reprojection may be required for use with other datasets.
• Tiling scheme: Provided as 100 km × 100 km COG tiles. Users may mosaic tiles if needed.
• Classes: See legend above. Class 7 (“No trees”) includes cropland, grassland, built-up, and other non-tree areas.
• Early access status: Not yet fully validated. Regional inconsistencies and misclassifications may be present.

Feedback & contributions: We invite users to share validation results and contribute additional reference data to improve future releases.

How to cite

If you use this dataset, please cite as:

De Keersmaecker, W., Zanaga, D., Senf, C., Viana-Soto, A., Klapper, J., Blickensdörfer, L., Govaere, L., Lerink, B., Leyman, A., Schelhaas, M.-J., Teeuwen, S., Verkerk, P. J., & Van De Kerchove, R. (2025). European Tree Genus Map 2020 (Early Access Release) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13341104

BibTeX

@dataset{dekeersmaecker2025_treegenus,
author = {De Keersmaecker, Wanda and Zanaga, Daniele and Senf, Cornelius
and Viana-Soto, Alba and Klapper, Johanna and Blickensdörfer, Lukas
and Govaere, Leen and Lerink, Bas and Leyman, Anja
and Schelhaas, Mart-Jan and Teeuwen, Sander and Verkerk, Pieter Johannes and Van De Kerchove, Ruben},
title = {European Tree Genus Map 2020 (Early Access Release)},
year = {2025},
publisher = {Zenodo},
version = {early-access},
doi = {10.5281/zenodo.13341104},
url = {https://doi.org/10.5281/zenodo.13341104}
}

References

[1] Alberdi, I., Bombín, R. V., González, J. G. Á., Ruiz, S. C., Ferreiro, E. G., García, S. G., Mateo, L. H., Jáuregui, M. M., Pita, F. M., & de Oliveira Rodríguez, N. (2017). The multi-objective Spanish national forest inventory. Forest systems, 26(2), 14.

[2] Álvarez-González, J. G., Canellas, I., Alberdi, I., Gadow, K. V., & Ruiz-González, A. (2014). National Forest Inventory and forest observational studies in Spain: Applications to forest modeling. Forest Ecology and Management, 316, 54-64.

[3] Finnish Forest Centre (Metsäkeskus). (2025). Forest resource lattice data (Hila-aineisto) [2019–2021]. Retrieved from https://www.metsakeskus.fi.

[4] Fridman, J., Holm, S., Nilsson, M., Nilsson, P., Ringvall, A. H., & Ståhl, G. (2014). Adapting National Forest Inventories to changing requirements–the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica, 48(3).

[5] Govaere L. & Leyman A. (2023). Vlaamse bosinventarisatie Agentschap Natuur en Bos (VBI1: 1997-1999; VBI2: 2009-2018; VBI3: 2019-2021, v2023-03-17).

[6] Heisig, J., & Hengl, T. (2020). Harmonized Tree Species Occurrence Points for Europe (0.2). https://doi.org/https://doi.org/10.5281/zenodo.5524611

[7] IGN. (2016). BD Forêt Version 2.0. January 2016

[8] Riedel T., Hennig P., Kroiher F., Polley H., Schmitz F., Schwitzgebel F. (2017): Die dritte
Bundeswaldinventur (BWI 2012). Inventur- und Auswertemethoden, 124 S.

[9] Schelhaas MJ, Teeuwen S, Oldenburger J, Beerkens G, Velema G, Kremers J, Lerink B, Paulo MJ, Schoonderwoerd H, Daamen W, Dolstra F, Lusink M, van Tongeren K, Scholten T, Pruijsten L, Voncken F, Clerkx APPM (2022). Zevende Nederlandse Bosinventarisatie; Methoden en resultaten. Wettelijke Onderzoekstaken Natuur & Milieu, WOt-rapport 142. https://edepot.wur.nl/571720

[10] Villaescusa, R. & Díaz, R. (1998) Segundo inventario forestal nacional (1986–1996). Ministerio de Medio Ambiente, ICONA, Madrid.

Acknowledgements

We are very grateful for access to the forest plot inventories. We thank the Ministerio para la Transición Ecológica y Reto Demográfico (MITECO) for open access of the Spanish Forest Inventory (https://www.miteco.gob.es/). Finally, we would like to acknowledge the ForestPaths project (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation), that receives funding from the European Union's Horizon Europe Research and Innovation Programme (ID No 101056755), as well as from the United Kingdom Research and Innovation Council (UKRI).

Directory of suggested edits to information in NYC Street Tree Map. Users can suggest a different species, diameter, or other notes about the tree. Edits are reviewed by a NYC Street Tree Map administrator before they are incorporated into the Map. This directory tracks the content and status of each suggested edit.

The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."

Estimated tree species richness per hectare.
This map can be downloaded in two formats. One is a geoTIFF file (S_mean_raster.tif) containing the fully geo-referenced map of tree species richness worldwide at a 0.025°×0.025° resolution. The other is a comma-separated file (S_mean_grid.csv) with the following attributes: S is local average tree species richness per hectare x, y are centroid coordinates of all 0.025°×0.025° pixels;

DDOT's Urban Forestry Division (UFD) is the primary steward of Washington DC's ~175,000 public trees and has a mission of keeping this resource healthy, safe, & growing. Trees in the city are critical to our well-being. Visit trees.dc.gov for more information.

This feature layer includes two point datasets representing City of Boise Parks and Recreation managed park and street tree locations. This dataset was created and is maintained by Parks and Recreation staff. It is updated as needed and is current to the date it was published.