Southbury, Connecticut (2016)
Southbury, Connecticut (2016)
Southbury, Connecticut (2016), SpatialCover Land Cover
Southbury, Connecticut (2016)
Southbury, Connecticut (2016)
Southbury, Connecticut (2016)
Southbury, Connecticut (2016), SpatialCover Land Cover
Southbury, Connecticut (2016)

SpatialCover Land Cover Connecticut

SpatialCover Land Cover Connecticut is a high resolution (1 meter) land cover data product that provides baseline land cover information for the state of Connecticut in multiple formats to support different land management applications. It is derived from 1 meter, 4 band color infrared imagery flown between July 3, 2016 and August 3, 2016 as part of the National Agriculture Imagery Program (NAIP). Additionally statewide LIDAR data flown in 2016 was used to aid the classification process. Over 2 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 Connecticut 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 Connecticut 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 286.2 million image-objects were created from over 5,033 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 96.5%.


Contact Us



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

Technical Specifications
Resolution1 meter
Minimum Mapping Unit0.005 acre1
Accuracy96.5%
Classes6
Area5,033 sq. miles
1. Minimum mapping unit for shrubs and water classes is 0.1 acres.