High-resolution land cover dataset for the States of Delaware, Maryland, New Jersey, New York, and Pennsylvania. Twelve land cover classes were mapped: (0) Background, (1) Water, (2) Emergent Wetlands, (3) Tree Canopy, (4) Scrub/Shrub, (5) Low Vegetation, (6) Barren, (7) Structures, (8) Other Impervious Surfaces, (9) Roads, (10) Tree Canopy over Structures, (11) Tree Canopy over Other Impervious Surfaces, (12) Tree Canopy over Roads. The complete class definitions and standards can be viewed here: http://goo.gl/THacgg The primary sources used to derive this land-cover layer were: Delaware: 2014 leaf-off LiDAR data, 2012 leaf-off imagery, and 2013 leaf-on imagery; Maryland: 2013 Leaf-On NAIP, 2011-2015 Leaf Off Lidar (.46 - 1.4m resolution), and 2013-2014 Orthoimagery; New Jersey: 2013 Leaf-On NAIP (1m resolution), 2015 Leaf Off Lidar (1m resolution) and 2015 Orthoimagery (1ft resolution); New York: 2013 Leaf-On NAIP (1m resolution), 2015 Leaf Off Lidar (1m resolution) and 2015 Orthoimagery (1ft resolution); Pennsylvania: 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as road centerlines and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) 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 1:3000 and all observable errors were corrected. This dataset was developed to support land-cover mapping and modeling initiatives in the Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Delaware, Maryland, New Jersey, New York, and Pennsylvania portion of the Delaware River Basin.

High-resolution land cover dataset for the States of Delaware, Maryland, New Jersey, New York, and Pennsylvania. Twelve land cover classes were mapped: (0) Background, (1) Water, (2) Emergent Wetlands, (3) Tree Canopy, (4) Scrub/Shrub, (5) Low Vegetation, (6) Barren, (7) Structures, (8) Other Impervious Surfaces, (9) Roads, (10) Tree Canopy over Structures, (11) Tree Canopy over Other Impervious Surfaces, (12) Tree Canopy over Roads. The complete class definitions and standards can be viewed here: http://goo.gl/THacgg The primary sources used to derive this land-cover layer were: Delaware: 2014 leaf-off LiDAR data, 2012 leaf-off imagery, and 2013 leaf-on imagery; Maryland: 2013 Leaf-On NAIP, 2011-2015 Leaf Off Lidar (.46 - 1.4m resolution), and 2013-2014 Orthoimagery; New Jersey: 2013 Leaf-On NAIP (1m resolution), 2015 Leaf Off Lidar (1m resolution) and 2015 Orthoimagery (1ft resolution); New York: 2013 Leaf-On NAIP (1m resolution), 2015 Leaf Off Lidar (1m resolution) and 2015 Orthoimagery (1ft resolution); Pennsylvania: 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as road centerlines and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) 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 1:3000 and all observable errors were corrected. This dataset was developed to support land-cover mapping and modeling initiatives in the Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Delaware, Maryland, New Jersey, New York, and Pennsylvania portion of the Delaware River Basin.