ESTIMATING GRASSLAND BIOMASS AND LEAF-AREA INDEX USING GROUND AND SATELLITE DATA

We compared estimates of regional biomass and LAI for a tallgrass prai rie site derived from ground data versus estimates derived from satell ite data. Linear regression models were estimated to predict LAI and b iomass from Landsat-TM data for imagery acquired on three dates spanni ng the growing season of 1987 using co-registered TM data and ground m easurements of LAI and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topograp hic position were included as indicator variables in the models to acc count for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth -Thomas greenness (from TM), LAI, biomass and the various terrain vari ables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substant ial differences were observed in June. In a number of cases, the regre ssion models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas enco mpassing heterogeneous land-cover the inclusion of categorical terrain data in calibration procedures is a useful technique.