High-Resolution Land Cover Chesapeake Bay Watershed - Washington, D.C. {2016} Full Details

Full Details

Title:

High-Resolution Land Cover Chesapeake Bay Watershed - Washington, D.C. {2016}

Description:

High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads 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.

Creator:

University of Vermont Spatial Analysis Laboratory

Provider:

Pennsylvania Spatial Data Access (PASDA)

Resource Class:

Imagery

Resource Type:

Raster data

Date Issued:

2016

Access Rights:

Public

Format:

File

Language:

English

Date Added:

2023-02-22