Description: This layer is a high-resolution tree canopy change-detection layer for Prince George's County, Maryland. It contains three tree-canopy classes for the period 2018-2020: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2018 and 2020 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 2018 and 2020 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.
Service Item Id: 87a1df48c01842929a57dc701d5add68
Copyright Text: University of Vermont Spatial Analysis Laboratory and Sanborn Map Company in collaboration with Prince George's Tree Canopy Change Assessment.
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Supported Query Formats: JSON, geoJSON, PBF
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Spatial Reference: 102685
(2248)
LatestVCSWkid(0)
Description: This layer is a high-resolution tree canopy change-detection layer for Prince George's County, Maryland. It contains three tree-canopy classes for the period 2014-2018: (1) No Change; (2) Gain; and (3) Loss. It was created by mapping the change from the source LiDAR and imagery for the two time periods. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed, felled in storms, or canopy to disease were assigned to the Loss class. New tree canopy, either the result of natural growth or new plantings was assigned to the Gain class . Change was mapped using object-based image analysis (OBIA) techniques and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs) for the two time periods. 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 ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to a detailed manual review and correction. A minimum mapping unit of 40 square feet was enforced on isolate areas. Data was retained for areas smaller than 40 square feet where a border was shared with another area, making the combined area greater than the minimum mapping unit of 40 square feet.
Service Item Id: 87a1df48c01842929a57dc701d5add68
Copyright Text: The University of Vermont Spatial Analysis Laboratory created this datasets in collaboration with Sanborn.
Description: This layer is a high-resolution tree canopy change-detection layer for Prince Georges County, Maryland. It contains three tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain; and (3) Loss. It was created by first mapping tree canopy in 2014 using LiDAR and multispectral data and then comparing the new map directly to an existing tree-canopy map for the year 2009. 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 2009 and 2014 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). No accuracy assessment was conducted, but the dataset was subjected to comprehensive manual review and correction.
Service Item Id: 87a1df48c01842929a57dc701d5add68
Copyright Text: The University of Vermont Spatial Analysis Laboratory created this map with funding from the Maryland-National Capital Park and Planning Commission.
Description: This dataset contains woodlands greater than 5000 square feet or one tenth of an acre within Prince George's County. The dripline of wooded areas was captured so it may run through other features. This data was captured for use in general mapping at a scale of 1:1200.
Description: This dataset contains woodlands greater than 5000 square feet or one tenth of an acre within Prince George's County. The dripline of wooded areas was captured so it may run through other features. This data was captured for use in general mapping at a scale of 1:1200.
Description: This dataset contains woodlands greater than 5000 square feet or one tenth of an acre within Prince George's County. The dripline of wooded areas was captured so it may run through other features. This data was captured for use in general mapping at a scale of 1:1200.
Description: This dataset contains woodlands greater than 5000 square feet or one tenth of an acre within Prince George's County. The dripline of wooded areas was captured so it may run through other features. This data was captured for use in general mapping at a scale of 1:1200.
Service Item Id: 87a1df48c01842929a57dc701d5add68
Copyright Text: Creative Commons Attribution CC BY https://creativecommons.org/licenses/by/4.0/. You can copy, modify, distribute, and perform analysis on the data, even for commercial purposes, all without asking permission, but please provide attribution to the Prince George's County Planning Department. The Planning Department makes no warranties about the data, and disclaims liability for all uses of the data, to the fullest extent permitted by applicable law.
Description: Wooded areas greater than 10,000 square feet in aerial coverage were captured from aerial photography conducted Spring, 2000. These features were captured as polygons and divided into 2 subclasses, Tree Cover and Dense Tree Cover. Wooded areas dominated by coniferous vegetation were captured as Dense Tree Cover, while wooded areas dominated by deciduous, broadleaved trees were captured as Tree Cover. Tree Cover areas were labeled with "TREE COVER" annotation, while Dense Tree Cover areas were labeled with "DTREE COVER" annotation. Trees in wooded areas captured averaged at least eight feet in height. The perimeter of a wooded area was delineated by a generalized line along the outside edge of most tree trunks. All wooded areas split by roads, where the woodland canopy is noticeably split, were shown as being split into two separate polygons by an edge-of-road polygon. Single trees having a crown spread less than 10,000 square feet were not captured. Land use types (i.e., pasture land, agricultural fields, open fields, etc.) outside of the wooded area were not separately identified or graphically labeled. This layer was created based on the the orginal year 1993 base by Buchart Horn, Inc.