Mount Pleasant, South Carolina (2015)
Mount Pleasant, South Carolina (2015)
Mount Pleasant, South Carolina (2015), SpatialCover Land Cover
Mount Pleasant, South Carolina (2015)
Mount Pleasant, South Carolina (2015)
Mount Pleasant, South Carolina (2015)
Mount Pleasant, South Carolina (2015), SpatialCover Land Cover
Mount Pleasant, South Carolina (2015)

SpatialCover Land Cover South Carolina

SpatialCover Land Cover South Carolina is a high resolution (1 meter) land cover data product that provides baseline land cover information for the state of South Carolina in multiple formats to support different land management applications. It is derived from 1 meter, 4 band color infrared imagery flown between April 22, 2015 and June 14, 2015 as part of the National Agriculture Imagery Program (NAIP). For some counties the LIDAR data was acquired between 2007 and 2014. Over 8 terabytes of LIDAR and aerial imagery were processed for the state through EarthDefine's high performance Geographic Object-Based Image Analysis (GEOBIA) processing platform to create the land cover layer.


Classes

Class NameValueClassification Description
Herbaceous1all surfaces with non-woody vegetation - lawns, cropland, grasses, etc.
Bare2non-vegetated non-impervious cover - snow, sand, barren farmland, rock, etc.
Impervious3impermeable built-up surfaces - roads and transportation infrastructure, sidewalks, buildings, parking lots, etc.
Water4open water - lakes, rivers, streams, sea, ponds, etc.
Trees5trees
Shrubs6low woody vegetation - early stage or stunted trees

What can you do with SpatialCover Land Cover?

The SpatialCover Land Cover South Carolina dataset supports numerous applications for urban planning, forest inventory, stormwater management, environmental impact assessment and conservation.


Approach

EarthDefine used a Geographic Object-Based Image Analysis (GEOBIA) processing workflow to create the SpatialCover Land Cover South Carolina dataset. Multiple ancillary datasets including transportation framework vectors, TIGER demographic data, hydrological, cadastral and terrain data were used along with color infrared aerial imagery to develop an object based classification workflow. LIDAR data was used to develop the tree class and derive buildings that were added to the impervious class. Over 1.1 billion image-objects were created from over 20,369 square miles of 1 meter resolution source imagery and classified through the GEOBIA platform to create a 6-class classification with an overall accuracy of 96.2%.


Contact Us



Phone: 1.800.579.5916
Email: info@earthdefine.com
SpatialCover Land Cover South Carolina Overview

Technical Specifications
Resolution1 meter
Minimum Mapping Unit0.005 acre1
Accuracy96.2%
Classes6
Area20,369 sq. miles
1. Minimum mapping unit for shrubs and water classes is 0.1 acres.