The GlobalUsefulNativeTrees database (GlobUNT; https://worldagroforestry.org/output/globalusefulnativetrees) was developed after a first step of combining native distribution data across 242 countries and territories from GlobalTreeSearch (accessed 8th May 2022; Beech et al. 2017; BGCI 2022) with information on ten categories of human usage documented in the World Checklist of Useful Plant Species (WCUPS; Diazgranados et al. 2020). GlobUNT was described in more detail in the following publication: Kindt et al. (2023) GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1. Version v.2023.01 of the database includes 14,014 useful tree species, representing roughly a quarter of the known tree species (as listed by GlobalTreeSearch) and a third of the plant species from WCUPS. The data set included here provides the taxonomic names for all tree species included in GlobUNT together with details on the process of standardization via the WorldFlora package (Kindt 2020). This taxonomic standardization process was completed during the preparation of the third major release of the Agroforestry Species Switchboard (as a consequence, all species listed in GlobUNT are included among the 230,000+ species from the Switchboard).

The development of GlobUNT was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration and by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia. When using the GlobUNT species list in your work, please cite the publication (Kindt et al. (2023) provided above) as well as this repository using the DOI (https://zenodo.org/record/7994433).

In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

The Trees of India (ToI, Version-I) includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022.This compendium is available via Figshare and was described by Mugal et al. 2023:

Khuroo, Anzar Ahmad; Mugal, Muzamil Ahmad; Wani, Sajad Ahmad (2023). ToI, Ver.-I : Trees of India, Version-I. figshare. Dataset. https://doi.org/10.6084/m9.figshare.23226281.v1

Mugal, M.A., Wani, S.A., Dar, F.A. et al. Bridging global knowledge gaps in biodiversity databases: a comprehensive data synthesis on tree diversity of India. Biodivers Conserv 32, 3089–3107 (2023). https://doi.org/10.1007/s10531-023-02659-y

Here I provide direct and fuzzy matches for taxa listed with accepted plant names in World Flora Online (version 2023.03; Borsch et al. 2020) and the World Checklist of Vascular Plants (WCVP version 10; Govaerts et al. 2021). Matching was done in R through the WorldFlora package (Kindt 2020). The taxonomic standardization process was similar to the one completed during the preparation of the third major release of the Agroforestry Species Switchboard and when preparing the GlobalUsefulNativeTrees database (GlobUNT; https://worldagroforestry.org/output/globalusefulnativetrees).

After matching species with the WCVP, information was compiled on the native distribution documented in the WCVP for level-3 units of the World Geographical Scheme for Recording Plant Distributions that correspond to India, including India (IND), Assam (ASS), West Himalaya (WHM), East Himalaya (EHM), Laccadive Is. (LDV), Andaman Is. (AND) and Nicobar Is. (NCB). Also included after matching with the WCVP is information on the geographic area, lifeform and main biome. Similar information is available when searching for species from Plants of the World Online.

Where a matching species was found in GlobalTreeSearch (Beech et al. 2017; https://tools.bgci.org/global_tree_search.php; accessed on 28th June 2023) filtered for India, the species name in GlobalTreeSearch is shown. Note that GlobalTreeSearch documents the native country distribution of tree species.

Where a matching species was found in the GlobalUsefulNativeTrees database (GlobUNT, version 2023.11) filtered for India, the species name in the GlobUNT database is shown. GlobUNT has been described in the following publication: Kindt et al. (2023) GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1.

See the metadata for information on versions.

Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. https://doi.org/10.1002/tax.12373

Govaerts, R., Nic Lughadha, E., Black, N. et al. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci Data 8, 215 (2021). https://doi.org/10.1038/s41597-021-00997-6

E. Beech, M.Rivers, S. Oldfield & P. P. Smith (2017)GlobalTreeSearch: The first complete global database of tree species and country distributions, Journal of Sustainable Forestry, 36:5, 454-489, DOI: 10.1080/10549811.2017.1310049

Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. Applications in Plant Sciences 8(9): e11388. https://doi.org/10.1002/aps3.11388

The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration.

The Fire and Tree Mortality (FTM) database includes standardized observations of fire injury and survival or mortality for 171,177 individual tree-level observations, representing 142 tree species across the United States. Of these, 7,191 trees have burned twice. These trees were burned in 420 prescribed fires and wildfires occurring in 35 years, from 1981 to 2016. The database was developed using 41 contributed datasets from researchers, managers, and archived data products. At a minimum, datasets had to contain measurements of individual trees, size, fire injury, and post-fire survival, but some datasets include additional data such as bark beetle attack. Only trees that were alive before the fire were included in the database. We included any trees where post-fire status was measured within 10 years of the fire. If a tree re-burned in a subsequent fire, and post-fire injury and status information were available after that fire, then a new record (row) was made for that tree aft ...

This data publication contains urban tree growth data collected over a period of 14 years (1998-2012) in 17 cities from 13 states across the United States: Arizona, California, Colorado, Florida, Hawaii, Idaho, Indiana, Minnesota, New Mexico, New York, North Carolina, Oregon, and South Carolina.

Measurements were taken on over 14,000 urban street and park trees. Key information collected for each tree species includes bole and crown size, location, and age. Based on these measurements, 365 sets of allometric equations were developed for tree species from around the U.S. Each “set” consists of eight equations for each of the approximately 20 most abundant species in each of 16 climate regions. Tree age is used to predict a species diameter at breast height (dbh), and dbh is used to predict tree height, crown diameter, crown height, and leaf area. Dbh is also used to predict age. For applications with remote sensing, average crown diameter is used to predict dbh. There are 171 distinct species represented within this database. Some species grow in more than one region. The Urban Tree Database (UTD) contains foliar biomass data (raw data and summarized results from the foliar sampling for each species and region) that are fundamental to calculating leaf area, as well as tree biomass equations (compiled from literature) for carbon storage estimates. An expanded list of dry weight biomass density factors for common urban species is made available to assist users in using volumetric equations.Information on urban tree growth underpins models used to calculate effects of trees on the environment and human well-being. Maximum tree size and other growth data are used by urban forest managers, landscape architects and planners to select trees most suitable to the amount of growing space, thereby reducing costly future conflicts between trees and infrastructure. Growth data are used to develop correlations between growth and influencing factors such as site conditions and stewardship practices. Despite the importance of tree growth data to the science and practice of urban forestry, our knowledge is scant. Over a period of 14 years scientists with the U.S. Forest Service recorded data from a consistent set of measurements on over 14,000 trees in 17 U.S. cities.These data were originally published on 03/02/2016. The metadata was updated on 10/06/2016 to include reference to a new publication. Minor metadata updates were made on 12/15/2016. On 01/07/2020 this data publication was updated to correct a few species' names and systematic errors in the data that were found. A complete list of these changes is included (\Supplements\Errata_Jan2020_RDS-2016-0005.pdf). In addition, we have included a list of changes for the General Technical Report associated with these data (\Supplements\Errata_Jan2020_PNW-GTR-253.pdf).

Tree Species information from the Parks Department. Data are updated by city staff as needed, and copied to VicMap and the Open Data Portal on a weekly basis. Parks Department tree species data are collected by GPS location. For surveyed trees maintained by the Engineering Department, please see the Surveyed Trees layer.Diameter at Breast Height (DBH) is in centimetres. Tree Canopy Height and Width are in metres.The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through weekly scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

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 database deals with the trees of India. The ToI, Ver.-I (Trees of India, Version-I) includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022. The database follows the scientific nomenclature as per Plants of the World Online (2022). The database also includes 609 species endemic to India, and 347 species currently threatened as per IUCN (2022).

Dataset

This dataset was created by Muhammad Usman

Released under CC0: Public Domain

Contents

The Tallo database (v1.0.0) is a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species.

For a full description of the database, see: Jucker et al. (2022) Tallo – a global tree allometry and crown architecture database. Global Change Biology, https://doi.org/10.1111/gcb.16302. If using the Tallo database in your work please cite the original publication listed above, as well as this repository using the corresponding DOI (10.5281/zenodo.6637599).

This data publication contains urban tree inventory data for 929,823 street trees that were collected from 2006 to 2013 in 49 California cities. Fifty six urban tree inventories were obtained from various sources for California cities across five climate zones. The five climate zones were based largely on aggregation of Sunset National Garden Book's 45 climate zones. Forty-nine of the inventories fit the required criteria of (1) included all publicly managed trees, (2) contained data for each tree on species and diameter at breast height (dbh) and (3) was conducted after 2005. Tree data were prepared for entry into i-Tree Streets by deleting unnecessary data, matching species to those in the i-Tree database, and establishing dbh size classes. Data included in this publication include tree location (city, street name and number), diameter at breast height, species name and/or species code, and tree type.These data were used to calculate street tree stocking levels, species abundance, size diversity, function and value, which can be used to determine trends in tree number and density, identify priority investments and create baseline data against which the efficacy of future practices can be evaluated.

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction.

The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management: https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93&Data-Collection_Data-Collection=Forestry+Management+System+%28ForMS%29

To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transform...

Tree Trait Task Force (3TF) - Tree Functional Trait Database

Corresponding author : belluaumichael@gmail.com paquette.alain@uqam.ca messier.christian@uqam.ca

Our dataset was originally created to offer scientists and the public a “ready-to-use” functional trait database for trees. This dataset is the first step within a larger project to create a standardized tree functional trait database for applied projects. We aim to offer a clean and uniform database of traits with clear selection criteria for its retained values : that the trait value is original and can be traced back to a single publication (duplicates are removed); that the trait was measured on trees growing in their natural environment (not experimental); that measurement units are correct; that all remaining possible outliers are verified manually by going back to the original publication for confirmation.

Full dataset description will follow soon

TreeAI - Advancing Tree Species Identification from Aerial Images with Deep Learning

• Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html
• Request the dataset: Download the competition record after registration by requesting it. Enter your full name, purpose e.g. accept the TreeAI4Species data license, affiliation, and the country of affiliation in the request. This allows us to check whether you are already registered.
• Test dataset: Only the participants registered for the competition will receive the test dataset.
• Submit your DL models for evaluation by June 2025.
• Award: The best models win a prize.
• Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.

License

The street tree cadastre includes the comprehensive representation of the trees on public street corridors as the basis of the tree checks for traffic safety and planning. Key data fields are: Location, genus/species, year of planting, crown circumference, stem diameter, biological parameters and damage characteristics. The fast graphical visualization of tree locations and background maps provides an integrated GIS component. The data is stored in a SQL Server database. It is recorded as part of ongoing surveys and updates. The updates also take place within the framework of the prescribed tree inspections for road safety.

The - online street tree register - includes the comprehensive representation of the trees on public street corridors with essential trunk attributes as well as the data of the Hamburg Port Authority, which are stored in a separate database. The following data fields are assigned to each tree: Location, genus/species, year of planting, crown diameter and trunk circumference. This data, which is collected as part of regular tree checks, is updated annually for the Internet as of 1 January. They are available for download as WMS and WFS services as well as in GML format.

TreeHub is a taxonomical database for trees. TreeHub contains extracted phylogenetic data and integrates relevant species information for each identifier.

The public tree dataset includes a listing of public trees on boulevards and public trees in parks, in the City of Vancouver and provides data on tree coordinates, species and other related characteristics. Private trees are not included in the inventory.Tree records that do not have coordinates data will not show up in the list. Data currencyThe dataset refreshes daily on weekdays. Tree attributes are updated on a regular basis but it may be several years between updates for some attributes. Priorities and resources determine how fast a change in reality is reflected in the data. The coordinates were originally provided by the 2016 Geospatial Data for City of Vancouver Street Trees project. Data accuracyTree attributes are updated on a regular basis but it may be several years for some attributes. Note: 0 value in latitude and longitude fields mean there is no related information available Websites for further informationStreet Tree BylawCity trees

This database provides air-dry wood density data for over 900 indigenous and exotic tree species found in Africa. Wood density is mass of wood per unit volume. It is an important trait for estimating stored biomass and carbon content per unit volume of tree stem (Chave et al. 2009 ). Air-dry density is expressed as a ratio of mass to volume (g/cm3) for wood at about 12% moisture content. This is a different measure to basic density, which is calculated as the ratio of oven-dry mass to green volume. Wood density reported in the scientific literature is commonly reported as basic density and this must be kept in mind when examining the density values in this database and comparing them with other data. Empirical relationships between basic density and air dry density for wood of many tree species have been developed: air-dry density at 12% moisture content is calculated by multiplying basic density by a factor of 1.22. Conversely, basic density can be calculated by multiplying air-dry density by a factor of 0.82. However, these conversion ratios cannot be assumed to hold precisely for all tree species reported here. The database is intended to serve as a guide to support tree and vegetation carbon monitoring work. Typically, a range of densities, obtained from measurements on wood samples from several mature trees, is given. It should be noted that wood density is affected by tree age and varies both radially and longitudinally within the tree, so is affected by sampling position. The environment also typically influences wood density. Furthermore, significant genetic variation in wood density within a species is commonly observed. The density values provided in this database should be used with t hese caveats in mind. Tree species names are provided as both botanical and common or local names. Nomenclature for some of the tree species is out of date. Species are listed by country where their distribution is known. The African Wood Density database focuses mainly on woody trees grown in Africa. This database complements the South East Asia Database The database was developed in parallel to the development of the Global Wood Density Database Users are further encouraged to familiarize themselves with a manual on Measuring Carbon Stocks Across Land Use Systems

Sidewalk Management System is used to track and organize inspections, violations and the status of New York City sidewalks. Tracks properties where all sidewalk defects on the tax lot are due to trees/tree roots (All Tree Defects - ATD) For more information please visit NYC DOT website: www.nyc.gov/sidewalks