Title:

High resolution tree canopy for Boston Metro (2016)

Author(s):

University of Vermont Spatial Analysis Laboratory

Publication Year:

2018

How to Cite:

If you use these data in a publication, presentation, or other research product please use the following citation:

University of Vermont Spatial Analysis Laboratory. 2018. High resolution tree canopy for Boston Metro (2016). Burlington, VT: University of Vermont Spatial Analysis Laboratory. https://gis.w3.uvm.edu/utc/Landcover/BostonMetro_2016TC.zip

Abstract:

Tree canopy was derived from 2016 NAIP, 2014 Orthoimagery, and 2013 LiDAR, high-resolution remotely sensed data. 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 1:2500 and all observable errors were corrected.

Keywords:

environment; imageryBaseMapsEarthCover; planningCadastre; Environment and People; Urban natural resources management; Natural Resource Management & Use; urban tree canopy; UTC; tree canopy; tree canopy assessment; Massachusetts; Boston

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