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.

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 – tree’ interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have thus a strong effect on density and distribution of tree related microhabitats (TreMs) which are key features for forest taxa at the stand scale (e.g. Bouget et al. 2013, 2014). In particular, spatially explicit data to understand patterns and mechanisms of TreM formation in forest stands are rare. To train and eventually improve decision making capacities related to the integration of biodiversity aspects into forest management to date more than 100 usually 1 ha (100 m x 100m) permanent plots were established in different forest communities of Europe. Due to their demonstration character the selection of plots was non-systematic. They do, however, cover a broad range of forest types (e.g. beech-oak, beech-fir (-spruce), oak-hornbeam, pine-spruce, etc.), altitudinal gradients (from 25 m – 1850 m) and site conditions (e.g. oligotrophic Luzulo-Fagetum or Vaccinio-Pinetum to mesotrophic Galio-Fagetum or Milio-Fagetum). For each plot the following data is collected: (1) tree location as polar coordinates (stem base map), (2) tree species, (3) forest mensuration data (dbh in [cm], tree height in [m]), (4) tree related microhabitats (TreMs) and (5) tree status (living or standing dead). In addition to the spatial dendrometric data we provide information on plot establishment, forest type, plot location (state, region, country), elevation, means for annual precipitation and temperature, and the natural forest community (Kraus et al., 2018).

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods 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 transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

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.

A database of phylogenetic trees of animal genes. It aims at developing a curated resource that gives reliable information about ortholog and paralog assignments, and evolutionary history of various gene families. TreeFam defines a gene family as a group of genes that evolved after the speciation of single-metazoan animals. It also tries to include outgroup genes like yeast (S. cerevisiae and S. pombe) and plant (A. thaliana) to reveal these distant members.TreeFam is also an ortholog database. Unlike other pairwise alignment based ones, TreeFam infers orthologs by means of gene trees. It fits a gene tree into the universal species tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual curation, and infer complex ortholog and paralog relations.The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex phylogenies. TreeFam-A families are manually curated from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families. phylogenetic tree, animal, vertebrate, invertebrate, gene, ortholog, paralog, evolutionary history, gene families, single-metazoan animals, outgroup genes like yeast (S. cerevisiae and S. pombe), plant (A. thaliana), historical duplications, speciations, losses, Human, Genome, comparative genomics

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).

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

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.

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.

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.

This dataset contains information on ‘Tree Montgomery’ which is a new program to plant shade trees for free. Please note that only planted and completed trees are included. The Montgomery County Department of Environmental Protection (DEP) is looking for places to plant, especially in yards of single family homes, parking lots, and multi-family communities. We’re also targeting areas where there is a lot of development, little tree canopy, or a real need for shade. The County will install shade trees and give them some after care; all for free. When installed, the trees will be 10 to 12 feet tall and will eventually be more than 50 feet tall, providing you with decades of shade. Funding for ‘Tree Montgomery’ is provided through the Tree Canopy Law. Update Frequency - As Needed

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 ...

TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the WorldFlora R package to World Flora Online or the World Checklist of Vascular Plants for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

TreeGOER can be used in combination with the CitiesGOER database (https://doi.org/10.5281/zenodo.8175429) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. TreeGOER could also be used with the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756. This high resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.

Changes between different versions of the databases are documented in a specific sheet in the metadata file.

The development of TreeGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, 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, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. When using TreeGOER in your work, cite the publication (Kindt 2023) as well as this repository using the DOI (https://doi.org/10.5281/zenodo.7922927).

Trees cared for and managed by the City of Pittsburgh Department of Public Works Forestry Division.

Tree Benefits are calculated using the National Tree Benefit Calculator Web Service.

NOTE: The data in this dataset has not updated since 2020 because of a broken data feed. We're working to fix it.

The database deals with the trees of the treeline ecotone worldwide. The ToTE (Trees of Treeline Ecotone) includes data on 208 tree species distributed across 34 mountain regions of the world. The database on the global tree species richness of the treeline is based on systematic review of 1202 studies published from 1962-2022. The database follows the scientific nomenclature as per Plants of the World Online (2022), the mountain classification as per Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v_2.0 (2022) and the biome classification as per Koppen and provides the current conservation status of the taxa as per IUCN (2022) ver. 2

Last updated 3/9/2016. 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 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).

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 Spatial Database of Planted Trees (SDPT) was compiled by Global Forest Watch using data obtained from national governments, non-governmental organizations and independent researchers. Data were compiled for 82 countries around the world, with most country maps originating from supervised classification or manual polygon delineation of Landsat, SPOT or RapidEye satellite imagery. The category of “planted trees” in the SDPT includes forest plantations of native or introduced species, established through deliberate human planting or seeding. Sometimes called “tree farms,” these forests infuse the global economy with a constant stream of lumber for construction, pulp for paper and fuelwood for energy. The data set also includes agricultural tree crops like oil palm plantations, avocado farms, apple orchards and even Christmas tree farms. The SDPT makes it possible to identify planted forests and tree crops as being separate from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.The SDPT contains 173 million hectares of planted forest and 50 million hectares of agricultural trees, or approximately 82% of the world’s total planted forest area in 2015 (FAO 2015). The SDPT was compiled through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table. Data is available for download in all countries except China and Papua New Guinea. If you are aware of any additional plantations data, please let us know by filling out this form.