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<resTitle>LANDCOVER_2009</resTitle>
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<searchKeys>
<keyword>canopy</keyword>
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<idPurp>High resolution land cover dataset for Montgomery County. 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 minimum mapping unit for the delineation of features was set at 3 square . The primary sources used to derive this land cover layer were 2009 NAIP and 2008 LiDAR. Ancillary data sources included GIS data (building footprints, road polygons, hydrography polygons) provided by 2008 planimetric data. This land cover dataset is considered current as of 2009. Object-based image analysis techniques (OBIA) were employed 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 employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 49,114 corrections were made to the classification.</idPurp>
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</Data>
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