Tree Canopy (TC) Assessment metrics for New York City. This dataset consists of TC metrics summarized to several different sets of geographic base layers. The metrics presented in this table are based on 2010 high resolution land cover dataset.

The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC) and amount of land where is it biophysically feasible to establish tree canopy on (termed Possible TC).

Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is determined by identifying land where canopy could possibly exist. Possible TC in a GIS context is determined by overlaying high resolution land cover with cadastral and planimetric datasets to include building polygons and road polygons.

Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions.

TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the parcel in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy. This assessment was completed by the University of Vermont's Spatial Analysis Laboratory with funding from National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF) and in cooperation with the USDA Forest Service's Northern Research Station.

The TC Assessment protocols were developed by the USDA Forest Service's Northern Research Station and the University of Vermont's Spatial Analysis Laboratory in collaboration with the Maryland Department of Natural Resources. TC assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing TC goals.

Street Travel Permits allow approved business vehicles to travel on the Church Street Bus Way at Battery Place and Greenwich Street, an otherwise restricted road.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets --
City Boundary (2017, NYC DoITT)
Buildings (2017, NYC DoITT)
Hydrography (2014, NYC DoITT)
LiDAR Hydro Breaklines (2017, NYC DoITT)
Transportation Structures (2014, NYC DoITT)
Roadbed (2014, NYC DoITT)
Road Centerlines (2014, NYC DoITT)
Railroads (2014, NYC DoITT)
Green Roofs (date unknown, NYC Parks)
Parking Lots (2014, NYC DoITT)
Parks (2016, NYC Parks)
Sidewalks (2014, NYC DoITT)
Synthetic Turf (2018, NYC Parks)
Wetlands (2014, NYC Parks)
Shoreline (2014, NYC DoITT)
Plazas (2014, NYC DoITT)
Utility Poles (2014, ConEdison via NYCEM)
Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/
Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.