Global land cover mapping from MODIS: algorithm design and preliminary results

The authors describe the operational algorithm being used to map global land cover on a quarterly basis using data from MODIS. This algorithm uses a supervised classification methodology and exploits a database of over 1000 training sites identified from Landsat TM data globally. Land cover classes being mapped include the 17 categories defined by the International Geosphere-Biosphere program. However, the site database has been specifically designed to map a much richer set of classes based on the needs of the user community. The classification algorithms being developed for this purpose include decision trees and artificial neural networks. Recent results from prototyping efforts using AVHRR data demonstrate that the algorithms are performing well.