Estimating grassland biomass and Leaf Area Index using ground and satellite data

TitleEstimating grassland biomass and Leaf Area Index using ground and satellite data
Publication TypeJournal Article
Year of Publication1994
AuthorsFriedl, MA, Michaelsen, J, Davis, FW, Walker, H, Schimel, DS
JournalInternational Journal of Remote Sensing
Keywords675 COMMONWEALTH AVE, BOSTON, CTR REMOTE SENSING, MA 02215., Remotely sensed data. Tallgrass prairie. Canopy reflectance. Noaa-avhrr. Vegetation. Photosynthesis. Transpiration. Images. Fife. Earth sciences. Reprint available from: Friedl MA. BOSTON UNIV

We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAI and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount 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 variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous land-cover the inclusion of categorical terrain data in calibration procedures is a useful technique. [References: 46] 46