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<CreaDate>20220304</CreaDate>
<CreaTime>15263800</CreaTime>
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<resTitle>Canopy_Landcover_2014</resTitle>
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<searchKeys>
<keyword>canopy</keyword>
<keyword>2014</keyword>
<keyword>landcover</keyword>
</searchKeys>
<idPurp>This layer is a high-resolution land-cover dataset for Montgomery County, Maryland. Seven land-cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads; and (7) other paved surfaces. The primary sources used to derive this land cover layer were LiDAR acquired in Spring 2014 and 4-band National Agricultural Imagery Program (NAIP) imagery acquired in Summer 2013. Ancillary data sources included GIS data provided by Montgomery County (e.g., building footprints, water, roads, bridges, railroads) or developed by the UVM Spatial Analysis Laboratory (e.g., bare soil). This land cover dataset is considered current as of 2013. Note, however, that tree canopy is current to 2014 because it was mapped primarily from the available 2014 LiDAR. Object-based image analysis (OBIA) was used to extract land-cover information 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 used to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but this land-cover map was subjected to thorough manual quality control; about 14,000 edits were incorporated into the final map.</idPurp>
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</Data>
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