Title:

High resolution tree canopy change for City of Boston, MA (2014-2019)

Author(s):

University of Vermont Spatial Analysis Laboratory

Publication Year:

2020

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. 2020. High resolution tree canopy change for City of Boston, MA (2014-2019). Burlington, VT: University of Vermont Spatial Analysis Laboratory. https://data.boston.gov/dataset/canopy-change-assessment-2014-2019-tree-canopy-change

Abstract:

This layer is a high-resolution tree canopy change-detection layer for City of Boston, MA. It contains three tree-canopy classes for the period 5 years: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2014 and 2019 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2014 and 2019 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction.

Keywords:

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

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