BaseServices/Tree_Canopy_Height_Change_2014

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Service Description:

A tree crowns layer was derived from 2018 NAIP and 2019 LiDAR, and then each tree crown polygon was populated with the 95th percentile nDSM (height above ground) values from LiDAR collected in 2014 and in 2019. Object-based image analysis techniques (OBIA) were employed to extract potential tree crowns including the area of the crown 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.

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PUB.Tree_Canopy_Height_Change_2014_2019 (0)

Description: A tree crowns layer was derived from 2018 NAIP and 2019 LiDAR, and then each tree crown polygon was populated with the 95th percentile nDSM (height above ground) values from LiDAR collected in 2014 and in 2019. Object-based image analysis techniques (OBIA) were employed to extract potential tree crowns including the area of the crown 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.

Service Item Id: 95a33e23205043debadc1b07e6ffb6b2

Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston.

Spatial Reference: 102686 (2249)

Single Fused Map Cache: false

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Units: esriFeet

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

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Supports Dynamic Layers: true

MaxRecordCount: 2000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 0

Max Scale: 0

Supports Datum Transformation: true

Child Resources: Info Dynamic Layer

Supported Operations: Export Map Identify QueryLegends QueryDomains Find Return Updates

BaseServices/Tree_Canopy_Height_Change_2014_2019 (MapServer)