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This dataset was developed as part of the Cuyahoga County 2017 Tree Canopy Assessment. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. |
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This dataset was developed as part of the Cuyahoga County 2017 Tree Canopy Assessment. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. |
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University of Vermont Spatial Analysis Laboratory in collaboration with Cuyahoga County 2017 Tree Canopy Assessment. |
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5000 |
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[] |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>High resolution land cover dataset for Cuyahoga County, Ohio. Ten land cover classes were mapped: (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads,(7) other paved surfaces, (8) tree canopy over vegetation or soil, (9) tree canopy over building, (10) tree canopy over road/railroad, and (11) tree canopy over other paved surfaces. The primary sources used to derive this land cover layer were 2017 LiDAR data, 2017 NAIP imagery and 2017 ortho imagery. Ancillary data sources included GIS data provided by Cuyahoga County, Ohio or created by the UVM Spatial Analysis Laboratory. 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. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:4000 and all observable errors were corrected.</SPAN></P></DIV></DIV></DIV> |
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landcover_10class_2017_cuyahoga |
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["Tree Canopy","Urban","UTC","Land Cover","Cuyahoga","2017","Ohio"] |
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en-US |
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50000 |
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