Improved model for estimating the biomass ofPopulus euphraticaforest using the integration of spectral and textural features from the Chinese high-resolution remote sensing satellite GaoFen-1

Abstract

Accurate estimation of forest aboveground biomass (AGB) is crucial for monitoring ecosystem responses to environmental change. Optical remote sensing is the most widely used method for obtaining AGB information. However, there is a need for improving the accuracy of AGB estimation obtained in this way. A synergistic estimation model through the integration of spectral and textural features from Chinese high spatial resolution satellite data GaoFen-1 (GF-1) for AGB estimation in the arid region of Ejin Banner, Inner Mongolia Autonomous Region, China, was put forward. The proposed model combined the spectrum-alone model and texture-alone model, which were developed to describe the relationship between image parameters (spectral vegetation indices or texture parameters) obtained from GF-1 data and field measurements, and determined the contributions of spectral and textural sensitive indices to biomass estimation under different biomass conditions. The synergistic model was verified by comparison with the ground measurements and the results of the spectrum-alone and texture-alone models. The results indicate that the proposed synergistic estimation model is more effective than the spectrum-alone or texture-alone model, and shows considerable potential in forest AGB estimation by combining spectral and textural information.