Name: CCV_Tree_Canopy_2018

Display Field: ACRES_TC_2018

Type: Feature Layer

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Description: This layer is a high-resolution tree canopy change-detection layer for Prince George's and Montgomery Counties, 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. No minimum mapping unit was enforced. All detectable tree canopy was retained in the dataset.

Copyright Text: The University of Vermont Spatial Analysis Laboratory created this datasets in collaboration with Sanborn.

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• ACRES_TC_2018 (type: esriFieldTypeDouble, alias: ACRES_TC_2018, SQL Type: sqlTypeOther, nullable: true, editable: true)
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Name: CCV_Tree_Canopy_2018
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Last Edit Date: 7/1/2025 4:25:25 PM

Schema Last Edit Date: 7/1/2025 4:25:25 PM

Data Last Edit Date: 7/1/2025 4:25:25 PM

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