Amityville, New York (2015)
Amityville, New York (2015)
Amityville, New York (2015), SpatialCover Land Cover
Amityville, New York (2015)
Amityville, New York (2015)
Amityville, New York (2015)
Amityville, New York (2015), SpatialCover Land Cover
Amityville, New York (2015)

SpatialCover Land Cover New York

SpatialCover Land Cover New York is a high resolution (1 meter) land cover data product that provides baseline land cover information for the state of New York in multiple formats to support different land management applications. It is derived from 1 meter, 4 band color infrared imagery flown between May 6, 2015 and September 23, 2015 as part of the National Agriculture Imagery Program (NAIP). In addition LIDAR data flown between 2007 and 2016 was used to aid the classification process. Over 15 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 New York 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 New York 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 1.6 billion image-objects were created from over 34,052 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 New York Overview

Technical Specifications
Resolution1 meter
Minimum Mapping Unit0.005 acre1
Accuracy96.2%
Classes6
Area34,052 sq. miles
1. Minimum mapping unit for shrubs and water classes is 0.1 acres.