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.

All taken form site: https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh/about_data

Citywide street tree data from the 1995 Street Tree Census, conducted by volunteers organized by NYC Parks & Recreation. Trees were inventoried by address, and were collected from 1995-1996. Data collected includes tree species, diameter, condition.

Abstract: These are results from a network of 65 tree census plots in Panama. At each, every individual stem in a rectangular area of specified size is given a unique number and identified to species, then stem diameter measured in one or more censuses. Data from these numerous plots and inventories were collected following the same methods as, and species identity harmonized with, the 50-ha long-term tree census at Barro Colorado Island. Precise location of every site, elevation, and estimated rainfall (for many sites) are also included. These data were gathered over many years, starting in 1994 and continuing to the present, by principal investigators R. Condit, R. Perez, S. Lao, and S. Aguilar. Funding has been provided by many organizations.

Description:

marenaRecent.full.Rdata5Jan2013.zip: A zip archive holding one R Analytical Table, a version of the Marena plots' census data in R format, designed for data analysis. This and all other tables labelled 'full' have one record per individual tree found in that census. Detailed documentations of the 'full' tables is given in RoutputFull.pdf (see component 10 below); an additional column 'plot' is included because the table includes records from many different locations. Plot coordinates are given in PanamaPlot.txt (component 12 below). This one file, 'marenaRecent.full1.rdata', has data from the latest census at 60 different plots. These are the best data to use if only a single plot census is needed. marena2cns.full.Rdata5Jan2013.zip: R Analytical Tables of the style 'full' for 44 plots with two censuses: 'marena2cns.full1.rdata' for the first census and 'marena2cns.full2.rdata' for the second census. These 44 plots are a subset of the 60 found in marenaRecent.full (component 1): the 44 that have been censused two or more times. These are the best data to use if two plot censuses are needed. marena3cns.full.Rdata5Jan2013.zip. R Analytical Tables of the style 'full' for nine plots with three censuses: 'marena3cns.full1.rdata' for the first census through 'marena2cns.full3.rdata' for the third census. These nine plots are a subset of the 44 found in marena2cns.full (component 2): the nine that have been censused three or more times. These are the best data to use if three plot censuses are needed. marena4cns.full.Rdata5Jan2013.zip. R Analytical Tables of the style 'full' for six plots with four censuses: 'marena4cns.full1.rdata' for the first census through 'marena4cns.full4.rdata' for the fourth census. These six plots are a subset of the nine found in marena3cns.full (component 3): the six that have been censused four or more times. These are the best data to use if four plot censuses are needed. marenaRecent.stem.Rdata5Jan2013.zip. A zip archive holding one R Analytical Table, a version of the Marena plots' census data in R format. These are designed for data analysis. This one file, 'marenaRecent.full1.rdata', has data from the latest census at 60 different plots. The table has one record per individual stem, necessary because some individual trees have more than one stem. Detailed documentations of these tables is given in RoutputFull.pdf (see component 11 below); an additional column 'plot' is included because the table includes records from many different locations. Plot coordinates are given in PanamaPlot.txt (component 12 below). These are the best data to use if only a single plot census is needed, and individual stems are desired. marena2cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for 44 plots with two censuses: 'marena2cns.stem1.rdata' for the first census and 'marena3cns.stem2.rdata' for the second census. These 44 plots are a subset of the 60 found in marenaRecent.stem (component 1): the 44 that have been censused two or more times. These are the best data to use if two plot censuses are needed, and individual stems are desired. marena3cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for nine plots with three censuses: 'marena3cns.stem1.rdata' for the first census through 'marena3cns.stem3.rdata' for the third census. These nine plots are a subset of the 44 found in marena2cns.stem (component 6): the nine that have been censused three or more times. These are the best data to use if three plot censuses are needed, and individual stems are desired. marena4cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for six plots with four censuses: 'marena3cns.stem1.rdata' for the first census through 'marena3cns.stem3.rdata' for the third census. These six plots are a subset of the nine found in marena3cns.stem (component 7): the six that have been censused four or more times. These are the best data to use if four plot censuses are needed, and individual stems are desired. bci.spptable.rdata. A list of the 1414 species found across all tree plots and inventories i... Visit https://dataone.org/datasets/urn%3Auuid%3A07030ed9-e51f-4ffa-a4b5-921392681123 for complete metadata about this dataset.

Blockface data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Blockface data includes tree counts and data collection status by block. Accompanying street tree data is available, indicating tree-level details such as tree species, size and other characteristics.

This dataset consists of data collected during the October 2021 census. A few trees were also measured in January 2022 as they could not be accessed in 2021. The data collection includes treeID, position, DBH_cm (girth in cm), observations, POM_cm (Point of measurement) status, census, date, family, genus and species. Botanical identification was done by Julien Engel (IRD). Trees were positioned using TLS scan by Olivier Martin. This tree census was funded by CNES (France).

New York City’s tree inventory includes 592,130 publicly managed street trees. This represents 584,036 live trees and 8,036 standing dead trees tallied over the course of two summer inventory periods. The inventory contains 168 tree species with London planetree (Platanus acerifolia), Norway maple (Acer platanoides), callery pear (Pyrus calleryana), honeylocust (Gleditsia triacanthos) and pin oak (Quercus palustris) as the predominant species. 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. Ref: https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh/data See also: https://www.nycgovparks.org/trees/treescount

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. 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.

These data represent Census Block Groups 2020 in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Census Block Groups 2020 within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).

[This dataset is embargoed until November 10, 2025]. The data resource consists of tree census data and shapefiles about plots established on invaded sub-tropical mountain secondary forests (Yungas) in the Horco Molle experimental reserve and Parque Sierra de San Javier (Horco Molle), Tucumán, Argentina. An experiment was conducted to investigate management control of the invasion of nonnative species Ligustrum to restore native tree diversity from June 2020 to November 2023. The data includes the census of 6 plots (240 metres by 140 metres, 3.36 hectares for each plot, the location specified in the shapefile), with arbitrary coordinates (x for the longest side and y for the shortest) to assess the location of each tree. For each tree stem, a tagged ID was assigned and the species recorded, the presence of secondary stems, its perimeter at breast height, the number of small stems, the number of death stems, stem shape and stem fusion. The work was carried out as part of NERC grant NE/S011641/1 Optimising the long-term management of invasive species affecting biodiversity and the rural economy using adaptive management Full details about this dataset can be found at https://doi.org/10.5285/c3295206-052a-4c87-911a-75d357791b5c

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.

The study was carried out in the temperate forest in Jiaohe, Jilin Province, in northeastern China. This study uses data from a 42 ha (840 × 500 m2) forest dynamics plot (hereafter referred to as the Jiaohe forest plot; 43°57.524′–43°58.042′N, 127°44.111′–127°44.667 E), established in the summer of 2010, according to the standard of the Center of Tropical Forest Science (CTFS; http://www.ctfs.si.edu/). All trees and shrubs with ≥ 1 cm diameter at breast height (dbh) in the Jiaohe forest plot were mapped, identified to species, and measured for the first time in the summer of 2010. A second census was carried out in the summer of 2015. The data set contains the tree census data, the status in 2015 (A,alive and D, dead), the initial size (dbh) in 2010, conspecific and heterospecific tree neighbor density within a given radius (5, 10, 15 or 20 m), as well as the soil properties and topography. (More details about the data set, please see our paper, doi: 10.1111/1365-2745.13335)

In 1995, hundreds of volunteers joined the New York City (NYC) Department of Parks & Recreation staff to inventory the city's street tree population. These inventory efforts resulted in an unprecedented understanding of NYC’s urban forest, and catalyzed major advances in its urban forest management.

See: https://www.nycgovparks.org/trees/treescount/past-censuses and: https://data.cityofnewyork.us/Environment/1995-Street-Tree-Census/7gmq-dbas

The 50 ha plot at Barro Colorado Island, Panama (utm: easting 625754, northing 1011569, zone 17), completed 4 censuses: 1981-83, 1985, 1990, and 1995. All trees >=10 mm dbh were tagged, measured, mapped and identified to species. The tab-delimited text files consist of a main census file with the location and diameter measurements, 4 multiple-stem files with the multiple stem measurements (including the largest measurement in the main database), the species list, and measurements of so-called "big" trees (i.e. trees with buttresses where the diameter was taken at a height higher than 1.3 m).

Zone-wise tree census data as of Nov 2024, Jan and April 2025.

This dataset shows the New York City (NYC) street tree census data for the year 2015 provided by the Department of Parks and Recreation (DPR).

Urban Tree Census Data

This dataset was collected as part of the Urban Tree Observatory Project in Ibagué, Colombia.It includes georeferenced records of urban trees, taxonomic details, physical measurements, and climate and observational data.The main goal is to build and populate a structured PostgreSQL database using Django and SQLAlchemy.

  Contents

biodiversity.csv: Main biodiversity registry per observed tree. climate.csv: Historical climate data associated with… See the full description on the dataset page: https://huggingface.co/datasets/juanpac96/urban_tree_census_data.

This data publication contains tree census data from 2004–2005 and 2014–2015 for six forest plots on Barro Colorado Island, Panama. These include the 25-ha and 10-ha plots, as well as four 6-ha tower plots: AVA, Drayton, Pearson, and Zetek. Censuses recorded all trees with a diameter at breast height (DBH) of 20 cm or larger. Census methods followed standard methods, as described in detail in detail in Condit (1998). The dataset was organized and quality-checked by Stuart Joseph Wright. Files included: Vertices_HM.csv provides the geographic coordinates of the four corners for each plot, outlining their boundaries. Coordinates are provided in UTM (WGS84; EPSG:4326). For each plot, two data files are included (12 files total): a file containing data from both census periods for the largest stem of each tree (e.g. 10ha_WorkingDraft_20150304.txt) a file containing data for additional stems, when present, from the 2004-2005 census. (e.g. 10ha_MULT.csv) Data dictionaries accompanying these files provide definitions for all columns, including explanations of any categorical abbreviations. BigPlotDataDictionary_main.txt BigPlotDataDictionary_mult.txt TreeCodeDefinitions.txt Meakem et al. (2024) describe these plots and present analyses of these data. Citation for this dataset: Wright, S. Joseph. 2024. Tree census data for the 25-ha, 10-ha and tower plots on Barro Colorado Island, Panama. Smithsonian Figshare. https://doi.org/10.25573/data.24531133 References Condit, R. 1998. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama, and a Comparison with Other Plots. Springer-Verlag, Berlin, and R. G. Landes Company, Georgetown, TX, USA. Meakem, V., S. J. Wright, and H. C. Muller-Landau. 2024. Variation in Forest Structure, Dynamics, and Composition Across 108 ha of Large Forest Plots on Barro Colorado Island. In The First 100 Years of Research on Barro Colorado: Plant and Ecosystem Science, edited by H. C. Muller-Landau and S. J. Wright. Smithsonian Institution Scholarly Press, Washington, DC.

The El Yunque Chronosequence plots consist of four sites, El Verde 1 (EV1), Sabana 1 (SB1), Sabana 2 (SB2), and Sabana 3 (SB3), which are located at the edges of El Yunque National Forest at sites to the south of El Verde and Sabana Field Stations. The plots represent a range of successional stages representing areas in agriculture or recently abandoned in 1936 but reforested after 1950, and areas in agriculture or recently abandoned in 1977 and reforested since that time. They range in size from ~0.5 to 1 ha, vary in elevation from ~150m to 550m a.s.l. and span a wide range of ages and land use histories (Table 1). Plot Name Size Age Elevation EV1 10,000 m2 (1 ha) >62 yrs but < 76 yrs ~ 550m SB1 4,625 m2 (~0.5 ha) >62 yrs but not primary forest ~100-150m SB2 6,400 m2 (~0.6 ha) >35 yrs but < 62 yrs ~100-150m SB3 4,800 m2 (~0.5 ha) Primary forest ~100-150m One of these plots (EV1) is south of El Verde Field Station, on Forest Service land just over the property boundary.  This area was in agriculture in 1936 but appeared forested in a 1950 aerial photograph, and there are differences in forest structure and species composition consistent with the known differences in land use history. The other three Chronosequence sites are just south of the Sabana Field Station on Forest Service Land on the opposite side of the forest from El Verde. One plot (SB2) is located in young secondary forest in an area immediately adjacent to an old teak plantation forest. Another plot (SB1) is located in an area that was sparsely forested in 1936 and which appeared reforested in 1950. The third plot in Sabana (SB3) is located in a patch of primary “tabonuco†(named for the abundance of this tree species) forest on a steep slope on the west side of the Sabana River.

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. As of June 2016, mapping is still in progress – this is a partial release. 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.