DC 2022 LiDAR was used and processed using the “Extract Trees using Cluster Analysis” script which is included as part of Esri’s 3D Basemap solution. All LiDAR-derived trees within 2 meters of a Urban Forestry Division tree were removed as being duplicates.Tree diameter (DBH, in inches) was estimated for the LiDAR-derived trees from calculated tree height (in feet) based on the equation: DBH = 0.4003*height - 1.9557. This equation was derived from a statistical analysis of a detailed park inventory tree data set and has an R^2 = 0.7418.Extreme outliers were also modified, with any DBH larger than 80 inches being converted to a DBH of 80 inches.The combined data set was processed using the USDA Forest Service i-Tree eco software, where structure and environmental benefits were estimated.

Dataset

This dataset was created by Tanmay Bankar

Contents

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

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.

Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.

This dataset contains simulated but realistic records of urban trees in a metropolitan setting. Each entry corresponds to a single tree, uniquely identified by GPS coordinates, species, and health-related features. The data captures various environmental conditions, such as air quality metrics, ambient weather, and tree-specific characteristics like canopy width, height, and disease symptoms.

The dataset is ideal for tasks including:

Predictive modeling of tree disease or decline.

Analyzing environmental stress impacts.

Spatial analysis of tree health.

Air quality correlation with biodiversity.

This is a synthetic dataset designed with reference to real-world urban forestry datasets such as those provided by NYC Open Data and San Francisco’s Open Data portal, ensuring realism while avoiding privacy or licensing issues.

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States. We matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016.

The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a code for cause of death where applicable. The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Because falling snags cause hazard to firefighting personnel and other forest users, in response to requests from the field, we provide a separate map that provides a rating of the severity of snag hazard based on the density and height of snags. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. The TreeMap 2016 dataset updates the TreeMap 2014 dataset to landscape conditions c2016. Prior to this imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB).

These data were published on 08/26/2021. On 02/01/2024, the metadata was updated to include reference to a recently published article and update URLs for Forest Service websites.

For more information about these data, see Riley et al. (2022).

US Christmas Tree Sales Data

US Christmas Tree Sales 2010-2016: Number of Trees, Prices & Revenue

By Throwback Thursday [source]

About this dataset

Throwback Thursday: US Christmas Tree Sales

This dataset provides a comprehensive record of the annual Christmas tree sales in the United States from 2010 to 2016. The dataset consists of six columns which include relevant information about each year's sales data.

The Year column indicates the specific year in which the Christmas tree sales data was recorded, allowing analysts to compare and track trends over time.

The Type of tree column specifies the various species or types of Christmas trees that were sold during each particular year, enabling researchers to analyze market preferences and consumer choices.

The Number of trees sold column represents the total quantity of Christmas trees that were purchased by customers in a given year. Identifying fluctuations in this metric can offer insights into changes in demand and market performance.

The Average Tree Price column provides important information on pricing dynamics within the industry. By calculating and tracking this average price for each year, analysts can assess variations in consumer spending behavior as well as identify potential economic factors influencing purchasing decisions.

Finally, the Sales column presents valuable data on total revenue generated from these Christmas tree sales annually. This metric offers a holistic perspective on market performance and business profitability within the holiday season.

Overall, this detailed dataset serves as a reliable resource for researchers aiming to understand historical trends and patterns within the US Christmas tree industry from 2010 to 2016. By analyzing variations across years, types of trees, number of units sold, average prices, and total sales revenue statistics, professionals can gain meaningful insights into consumer preferences while also uncovering opportunities for growth or operational improvements within this festive market segment

How to use the dataset

Introduction:

• Year: The column Year indicates the specific year in which the Christmas tree sales data was recorded. You can analyze trends over time by grouping data by year or comparing different years' performance.

• Type of tree: The Type of tree column specifies the type or species of Christmas trees sold. This information allows you to analyze which types are popular among consumers and explore any notable shifts or preferences over time.

• Number of trees sold: The Number of trees sold column represents the total count or quantity of Christmas trees sold in a given year. You can perform various analyses such as finding annual growth rates, identifying peak selling years, or comparing sales between different types of trees.

• Average Tree Price: The Average Tree Price column indicates the average price at which each Christmas tree was sold in a particular year. By analyzing this data, you can identify pricing trends across different types of trees and understand consumer behavior regarding affordability and willingness to pay.

• Sales: The Sales column represents the total revenue generated from Christmas tree sales in a given year. This information allows you to assess overall market performance, compare revenue generated by different types of trees, or calculate yearly growth rates.

Example Analysis:

a) Analyzing Revenue Over Time: Plotting a line graph with years on X-axis and sales revenue on Y-axis will help visualize if there is any increasing or decreasing trend in total revenue for all years combined.

b) Comparing Average Tree Prices: Create a bar chart comparing the average prices of different tree types. This analysis can reveal insights into consumer preferences and price elasticity for specific tree species.

c) Correlation Analysis: Explore the relationship between the number of trees sold and sales revenue by calculating correlation coefficients or creating a scatter plot. This will help identify if increased sales volume directly correlates to higher revenue.

d) Seasonal Variations: Analyze seasonal patterns in the dataset by grouping data month-wise or quarter-wise. This can provide insights into peak buying periods, allowing businesses to optimize marketing strategies around these times.

Conclusion:

Research Ideas

• Analyzing the trends in Christmas tree sales over the years: By examining the number of trees sold, average tree price, and sales revenue for each year, this dataset can provide insights into consumer preferences and economic factors that ...

Data of the example datasets of the tutorial. The archive contains one two directories: one for each of the protocol shown in the tutorial.Protocol 1 contains the marker genes and the Multiple Sequence Alignments (MSA), as well as trees obtained from two reconstruction methods (IqTree and RaXML).Protocol 2 contains the OMA Standalone directory, the locally added genomes, the selected Orthologous Groups, and their MSA, as well as the trees from two reconstruction methods.The file Species_List.csv list the species used in the tutorial, as well as their corresponding species code.

An extract of the tree measurement data collected by the Forest Inventory and Analysis program ("TREE" file). Data include various tree physiology and quality measurements for a network of permanent plots measured on a rotating basis.

This dataset describes tree growth and site characteristics for 26 monitoring plots in 3 sites in Redwood National Park in California, USA. One site (A972) was established and sampled in 2007, 2008, and 2013 (Dagley and others, 2023). Two sites (Holter Ridge and Whiskey 40) were established and sampled in 2009 (van Mantgem and Das, 2014). Six existing plots in the Holter Ridge and Whiskey 40 sites were resampled for this study in summer of 2014 and winter of 2015. Tree growth data represent basal area increments at core height. Site characteristics include treatment histories and harvest year. Plot characteristics for the resampled sites are also presented, including location, stand age since harvest, stand age at thinning (if applicable), elevation, slope, plot area, stem density (trees per hectare), and basal area (summed cross-sectional area of live stems per hectare). References Cited Dagley, C.M., Fisher, J., Teraoka, J., Powell, S., and Berrill, J.P., 2023. Heavy crown thinn ...

Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

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 in Panama, in R format. The column 'sp' in this table is a code identifying the species in the full census tables (marena.full and marena.stem, components 1-4 and 5-8 above). RoutputFull.pdf: Detailed documentation of the 'full' tables in Rdata format (components 1-4 above). RoutputStem.pdf: Detailed documentation of the 'stem' tables in Rdata format (component 5-8 above). PanamaPlot.txt: Locations of all tree plots and inventories in Panama.

One characteristic of inferred phylogenetic trees is their shape. Rooted binary trees are more balanced, or symmetric, to the extent that sister groups contain similar number of leaves. Premised on the idea that macroevolutionary processes may leave a strong signature in the shape of phylogenetic trees, inferred tree shapes can be compared to expectations under probabilistic models of speciation and extinction in an attempt to make macroevolutionary inferences (e.g. Harvey and Purvis, 1991; Kirpatrick and Slatkin, 1993; Guyer and Slowinski, 1993; Mooers and Heard, 1997; Bortolussi et al., 2006).

Underlying data and scripts necessary for reproduction are included as described in the README.md file.

The dataset is the estimate of a tree’s structural root zone, which must not be impacted during construction. Buffer distances are calculated for trees on all land types in the District that will be used for tree preservation planning purposes.DC 2022 LiDAR was used and processed using the “Extract Trees using Cluster Analysis” script which is included as part of Esri’s 3D Basemap solution. The extracted tree data set was merged with the UFA tree inventory data, with preference given to the UFA tree inventory data. All LiDAR-derived trees within 2 meters of a UFA tree were removed as being duplicates. Tree diameter (DBH, in inches) was estimated for the LiDAR-derived trees from calculated tree height (in feet) based on the equation: DBH = 0.4003*height - 1.9557. This equation was derived from a statistical analysis of a detailed park inventory tree data set and has an R^2 = 0.7418.Extreme outliers were also modified, with any DBH larger than 80 inches being converted to a DBH of 80 inches.Critical Root Zone (CRZ) was calculated as a buffer of 1.5 feet for every inch of DBH, whereas Structural Root Zone (SRZ) was calculated as a buffer of 0.5 feet for every inch of DBH.

The dataset is a collection of images representing various conditions of bananas, specifically aimed at training machine learning models for image classification or augmentation tasks. The dataset is organized into multiple subfolders, each representing a different condition or class of bananas. These classes include:

Healthy Bananas Bananas with Fusarium Wilt Bananas with Natural Leaf Death Bananas with Rhizome Root Issues Each image in the dataset is initially stored in its respective class folder and typically contains a banana or bananas under different conditions, viewed from different angles, and possibly with varying levels of resolution or lighting.

The dataset is then processed for various machine learning tasks like classification, detection, or augmentation. Specifically, this dataset is aimed at providing a variety of augmented images to ensure a more robust training set, which is critical for improving the generalization performance of machine learning models.

Related : Shuvo, Shuvo Kumar Basak (2025), “Banana_Tree_Disease_Detection_Dataset(BTDDD)”, Mendeley Data, V2, doi: 10.17632/vp2xnb8zmb.2

I, Shuvo Kumar Basak, have created and curated the Dataset. This dataset is freely available for research, educational, and non-commercial purposes.

Free Access to the Dataset: This is available free of charge to all individuals and organizations for educational and research use. This is to support the advancement of knowledge and studies related to biodiversity, machine learning, and related fields.

Future Collaboration and Data Requests: While the dataset is provided free of charge, I encourage individuals and organizations to contact me directly if they need access to additional related data, further assistance, or if they plan on expanding their research in the future.

If you require any new data or specific related datasets, feel free to reach out to me, Shuvo Kumar Basak, for collaboration. I am happy to assist with additional data collection, cleaning, resizing, or other related services at a reasonable cost.

Paid Services - Hire for Data Collection: If you or your organization need custom data collection or wish to obtain related datasets beyond what is included in this collection, I offer a paid service to gather new data according to your specific requirements. This includes: Custom data collection for other tree species or related botanical data.

Data cleaning, resizing, and preprocessing to make the data ready for analysis.

Please contact me for a custom quote based on your specific needs. I will work with you to provide high-quality, tailored datasets to support your research, project, or business needs. Terms and Conditions: The dataset is intended for academic, research, and non-commercial purposes only. Redistribution or commercial use of the dataset without prior written consent is not permitted. Proper attribution to Shuvo Kumar Basak as the creator of the dataset should be provided when using the dataset in publications, projects, or other works.

**More Dataset:: ** 1. https://www.kaggle.com/shuvokumarbasak4004/datasets 2. https://www.kaggle.com/shuvokumarbasak2030 …………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.

This dataset was used as part of a continent-wide analysis of tree fecundity and its association with climate and tree size. This dataset consists of: plotinfo.csv, which contains basic attribute information for the field plots where the data were collected; seeddata.csv, which contains the data for seeds collected (how many, what type, etc.); trapxycoord.csv, which contains location and identification information for the seed traps used to collect the seeds; treexycoord.csv, which contains location and attribute information for the standing trees in the plot where the seeds were collected; and treedata.csv, which contains data on the size and species of standing trees in the plot where the seeds were collected.

Summary

This repository contains spatial datasets with metadata on land cover, tree canopy change, and estimated tree points and crown polygons for New York City (NYC; New York, USA) as of 2021, made available by The Nature Conservancy, New York Cities Program and developed under contract by the University of Vermont Spatial Analysis Lab. The datasets are provided herein with high-level background and information; additional analysis, particularly on tree canopy change and distribution across NYC considering various geogrpahic units are planned for release in a forthcoming report by The Nature Conservancy. For questions about these data, contact Michael Treglia, Lead Scientist with The Nature Conservancy, New York Cities Program, at michael.treglia@tnc.org.

Datasets included here are as follows (file names in italics):

Land cover as of 2021 (landcover_nyc_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, delineating land covers as: 1) tree canopy (with crowns greater than eight feet [2.44 meters] tall; 2) grass/shrub (including vegetation less than or equal to eight feet [2.44 feet] tall; 3) bare ground; 4) open water; 5) building; 6) road; 7) other impervious; and 8) railroad. This is intended to serve as an update to high-resolution land cover data for 2010 and 2017 made available by the City of New York.

Tree canopy change during 2017-2021 (treecanopychange_nyc_2017_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, with pixels that were estimated tree canopy in 2017 (based on 2017 land cover data) or 2021 delineated as: 1) canopy that did not change (“no change”); 2) canopy that was gained (“gain”); 3) canopy that was lost (“loss”). This is intended to be an updated tree canopy change dataset, analogous to a canopy change dataset for 2010-2017 made available by the City of New York.

Estimated tree points, crown polygons, and objects as of 2021 (Trees_Centroids_Crown_Objects_2021.gdb.zip):

The approximated locations (centroids) and approximated tree crowns as circles (shapes), and tree objects themselves based on canopy data (objects) for individual trees with crowns taller than eight feet (2.44 meters); in cases where there are trees with overlapping crowns, only the top trees are captured. These data are based on automated processing of the tree canopy class from the land cover data; additional methodological details are included in the metadata for this dataset. Given the height cutoff, that this dataset only captures the trees seen from above, and the large number of understory trees in some areas (e.g., forested natural areas), and limits in the automated processing this is not intended to be a robust census of trees in NYC, but may serve as useful for some purposes. Unlike the land cover and tree canopy change datasets, no directly comparable datasets for NYC from past years that we are aware of.

These datasets were based on object-based image analysis of a combination of 2021 Light Detection and Ranging (LiDAR; data available from the State of New York) for tree canopy and tree location/crown data in particular) along with high-resolution aerial imagery (from 2021 via the USDA National Agriculture Inventory Program and from 2022 via the New York State GIS Clearinghouse), followed by manual corrections. The general methods used to develop the land cover and tree canopy datasets are described in MacFaden et al. (2012). A per-pixel accuracy assessment of the land cover data with 1,999 points estimated an overall accuracy of 95.52% across all land cover classes, and 99.06% for tree canopy specifically (a critical focal area for this project). Iterative review of the data and subject matter expertise were contributed by from The Nature Conservancy and the NYC Department of Parks and Recreation.

While analyses of tree canopy and tree canopy change across NYC are pending, those interested can review a report that includes analyses of the most recent data (2010-2017) and a broad consideration of the NYC urban forest, The State of the Urban Forest in New York City (Treglia et al 2021).

References

MacFaden, S. W., J. P. M. O’Neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis. Journal of Applied Remote Sensing 6(1):063567.

Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., & Maxwell, E.N. (2021). The State of the Urban Forest in New York City. The Nature Conservancy. doi: 10.5281/zenodo.5532876

Terms of Use

© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 (CC BY-NC-SA 4.0) license.

The Nature Conservancy (TNC) oversaw development of these data and reserves all rights in the data provided.

TNC makes no guarantee of accuracy or completeness.

Data are for informational purposes and are not suitable for legal, engineering, or surveying purposes. Data do not represent an on-the-ground survey and represent only the approximate relative location of feature boundaries.

TNC is not obligated to update/maintain the data to reflect changing conditions.

Commercial use is not allowed.

Redistribution (sublicensing) is allowed, provided all accompanying metadata as well as these Terms of Use are provided, unaltered, alongside the data.

TNC should be credited as the data source in derivative works, following the recommended citation provided herein.

Users are advised to pay attention to the contents of this metadata document.

Recommended Citation

If using any of these datasets, please cite the work according to the following recommended citation:

The Nature Conservancy. 2024. New York City Land Cover (2021), Tree Canopy Change (2017-2021), and Estimated Tree Location and Crown Data (2021). Developed under contract by the University of Vermont Spatial Analysis Laboratory. doi: 10.5281/zenodo.14053441.

Technical Notes about the Spatial Data

All spatial data are provided in the New York State Plan Long Island Zone (US survey foot) coordinate reference system, EPSG 2263. The land cover and tree canopy change datasets are made available as raster data in Cloud Optimized GeoTIFF format (.tif), with associated metadata files as .xml files. The vector data of estimated tree locations and crown objects and shapes are made available in a zipped Esri File Geodatabase, with metadata stored within the File Geodatabase.