Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China

Abstract

The West Songnen Plain is an ecologically fragile area. The grasslands on the plain have been seriously degraded over the past five decades and this process is continuing. The reliable estimation of grassland aboveground biomass (AGB) provides scientific data for determining the livestock stocking rate on rangeland. AGB is also of considerable significance for biodiversity and environmental protection in this region. Remote sensing is the most effective way to estimate grassland AGB on a regional scale. Multitemporal, remotely sensed data were used for grassland AGB estimation with statistical models and an artificial neural network (ANN), and the accuracy of estimation for these methods was compared. The results demonstrate that the use of multi-temporal remotely sensed data has advantages for grassland AGB estimation, whether with statistical models or ANN methods, compared with single-temporal remotely sensed data, although the ANN had a higher accuracy of estimation for grassland AGB. Finally, the grassland AGB on the Songnen Plain was estimated with the ANN using multitemporal MODIS data. The spatial distribution pattern of grassland AGB showed large variations, and grassland productivity was generally low. The maximum green weight of the grassland AGB was 927.22 g/m 2 and was mainly distributed on the northeast of the West Songnen Plain. The minimum green weight of the grassland AGB was 194.82 g/m 2 and was mainly distributed on the central and southwestern West Songnen Plain. Most of the areas had medium- and low-yielding grasses. The significant increases of population and livestock number were the primary and direct reasons for the decrease in grassland quality. This study will contribute to policy making for the control of grazing and for biodiversity and environmental protection.