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GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
NAD_1983_StatePlane_Massachusetts_Mainland_FIPS_2001_Feet
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20260213
16064400
20260213
16064400
150000000
5000
Jarlath O'Neil-Dunne
University of Vermont Spatial Analysis Laboratory
Director
802-656-3324
802-656-8683
Aiken Room 205
81 Carrigan Drive
Burlington
VT
05405
US
joneildu@uvm.edu
TreeCanopy_2019_Boston
vector digital data
Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees 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. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.
Tree canopy feature class developed for land cover mapping for the City of Boston.
University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston.
Jarlath O'Neil-Dunne
University of Vermont Spatial Analysis Laboratory
Director
802-656-3324
802-656-8683
Aiken Room 205
81 Carrigan Drive
Burlington
VT
05405
US
joneildu@uvm.edu
Boston
Suffolk
Massachusetts
2018
Tree Canopy
Tree Canopy Assessment
Tree Canopy
2018
Boston
Suffolk
Massachusetts
Tree Canopy Assessment
Esri ArcGIS Pro 2.6
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42.227041
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TreeCanopy_2019_Boston
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0
OBJECTID
OBJECTID
OID
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10
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Esri
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Shape
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Esri
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Shape
Geometry
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Shape.STArea()
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Double
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Shape.STLength()
Shape.STLength()
Double
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0
0
Enterprise Geodatabase Feature Class
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dataset
EPSG
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Simple
FALSE
0
TRUE
FALSE
20260213