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NALC/MEXICO LAND-COVER MAPPING RESULTS: IMPLICATIONS FOR ASSESSING LANDSCAPE CONDITION
Citation:
Lunetta, R S., J G. Lyon, L D. Worthy, AND R. Alvarez. NALC/MEXICO LAND-COVER MAPPING RESULTS: IMPLICATIONS FOR ASSESSING LANDSCAPE CONDITION. Presented at Monitoring Science & Technology Symposium, Denver, CO, September 20-24, 2004.
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:
An inventory of land-cover conditions throughout Mexico was performed using North American Landscape Characterization (NLAC) Landsat Mult-Spectral Scann (MSS) 'triplicate' images, corresponding to the 1970s, 1980s and 1990s epoch periods. The equivalents of 300 image scenes were analyzed using an unspupervissed classification approach by a consortium of 13 universities and institutes across Mexico. Accuracy assessments were the conducted to validate the 1970s and 1990s results using independent land-cover classifications (reference data) developed from the interpretation of 1:100,000 scale aerial photography collected in 1973, and Landstat Thematic Mapper (TM) imagery collected between 1990 - 1993. The 1980s epoch classifications were compared to both reference datasets, collectively. The relative accuracy of the classifications results was 60% for both the 1970s and 1990s epoch and 67% for the 1980s epoch. The significantly (p = 0.05) higher accuracy for 1980s epoch (67%) was thought to be an aberration resulting from the combined application of two reference datasets, resulting in a random compensation of reference data error.