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Remote Sensing Letters Volume 2, 2011 - Issue 4
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Original Articles

Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

Matthew C. Hansen Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USACorrespondence[email protected]
,
Alexey Egorov Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
,
David P. Roy Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
,
Peter Potapov Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
,
Junchang Ju Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
,
Svetlana Turubanova Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
,
Indrani Kommareddy Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, 57007, USA
&
Thomas R. Loveland Earth Resources Observation and Science Center, US Geological Survey, Sioux Falls, SD, 57198, USA
 show all
Pages 279-288 | Received 20 May 2010, Accepted 23 Aug 2010, Published online: 06 Nov 2010
 
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Abstract

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

Acknowledgement

This research was funded by NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, grant number NNX08AL93A.

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