SUBPIXEL FOREST COVER IN CENTRAL-AFRICA FROM MULTISENSOR, MULTITEMPORAL DATA

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa wer e coregistered with 8 km resolution data from the 1987 AVHRR Pathfinde r Land data set. Percent forest cover in each 8 km grid cell was deriv ed from the classified MSS scenes. Linear relationships between percen t forest cover and 30 multitemporal metrics derived from all AVHRR opt ical and thermal channels were determined Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for t hose metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest co ver in various locations in the study area using multiple linear regre ssion and regression trees. Overall, the multiple linear regression pr ovided more accurate results. Predicted percent forest cover estimates were within 20% of the ''actual'' percent forest cover (derived front the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest c over were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions. (C) Elsevier Science Inc., 1997.