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The tree canopy is the layer of leaves, stems, and branches of trees that cover the ground when viewed from above. An analysis of Cambridge's tree canopy based on land cover data derived from a November 2009 LiDAR capture and high resolution aerial imagery taken in 2010, found that 1,222 acres representing 30% of all land in the City were covered by tree canopy. Cambridge's urban tree canopy is a vital city asset that reduces storm water run-off, improves air quality, reduces the city's carbon footprint, enhances quality of life, contributes to savings on energy bills, mitigates heat effects, and serves as habitat for wildlife. The Spatial Analysis Laboratory at the University of Vermont's Rubenstein School of the Environment and Natural Resources carried out the assessment in collaboration with the City of Cambridge Community Development Department and the USDA Forest Service's Northern Research Station.
This layer is a high-resolution tree canopy change-detection layer for Cambridge, MA. It contains two tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2014 and 2010 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 2010 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 at a scale of 1:2500.
This layer is a high-resolution tree canopy change-detection layer for Cambridge, Massachusetts. It contains three tree-canopy classes for the period 2014-2018: (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 2018 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 2018 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.
The tree canopy is the layer of leaves, stems, and branches of trees that cover the ground when viewed from above. An analysis of Cambridge's tree canopy based on land cover data derived from a November 2009 LiDAR capture and high resolution aerial imagery taken in 2010, found that 1,222 acres representing 30% of all land in the City were covered by tree canopy. Cambridge's urban tree canopy is a vital city asset that reduces storm water run-off, improves air quality, reduces the city's carbon footprint, enhances quality of life, contributes to savings on energy bills, mitigates heat effects, and serves as habitat for wildlife. The Spatial Analysis Laboratory at the University of Vermont's Rubenstein School of the Environment and Natural Resources carried out the assessment in collaboration with the City of Cambridge Community Development Department and the USDA Forest Service's Northern Research Station.