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GREAT LAKES BASIN LAND-COVER DATA: ISSUES AND OPPORTUNITIES
Citation:
IIAMES, J. S. AND R. S. LUNETTA. GREAT LAKES BASIN LAND-COVER DATA: ISSUES AND OPPORTUNITIES. Presented at ASPRS Fall Conference, Ottawa, ON, CANADA, October 28 - November 01, 2007.
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
The US Environmental Protection Agency (EPA) is developing a consistent land-cover (LC) data set for the entire 480,000 km2 Great Lakes Basin (GLB). The acquisition of consistent LC data has proven difficult both within the US and across GLB political boundaries due to disparate mapping efforts to date (i.e., regional, national, and global) as well as multi-year data gaps. To address these issues, the EPA will use NASA's 16-day composite normalized difference vegetation index (NDVI) 250-m data product (MOD13) developed using the Moderate Resolution Imaging Spectroradiometer (MODIS). The NDVI data will be preprocessed to eliminate low quality data and missing data will be estimated using a Fourier transformation to provide high quality temporal profile data. Temporal profiles will be processed for 19 separate ecoregions across the GLB (US= 12 and Canada=7) using a phenology-based analytical approach. This paper examines multiple LC products including the Moderate Resolution Imaging Spectroradiometer (MODIS) Global 1-km IGBP, the National Land Cover Dataset 2001 (NLCD 2001), the Coastal Change Analysis Program 2000 (CCAP 2000), and the Ontario Ministry of Natural Resources LC data set. An initial issue was the development of an appropriate agriculture mask for the GLB. Within one Omernik ecoregion in southern Michigan and Northern Indiana, 25 counties were analyzed for agricultural area only (includes orchards) using both the NLCD 2001 and the CCAP 2000 data compared to an assumed ground validation dataset provided by the USDA National Agricultural Statistics Service (NASS) 5-year census. A county-by-county assessment indicated a consistent overestimation bias of agriculture by both the NLCD 2001 and CCAP 2000 products. The NLCD 2001 product had the best correlation compared to the USDA county level statistics data. The percentage of agricultural land across all 25 counties yielded the following: USDA NASS (24.7%), NLCD 2001 (31.7%), and CCAP 2000 (44.2%).