R. Defries et al., SUBPIXEL FOREST COVER IN CENTRAL-AFRICA FROM MULTISENSOR, MULTITEMPORAL DATA, Remote sensing of environment, 60(3), 1997, pp. 228-246
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.