Estimation of forest aboveground biomass using the integration of spectral and textural features from GF-1 satellite image

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 estimates obtained in this way. In this paper, a synergistic estimation model through the integration of spectral and textural features from GF-1 satellite image for AGB estimation in the arid region of Ejin, China was put forward. The synergistic model was verified by comparing with the ground measurements and the results of the best spectrum-alone and texture-alone models. The results indicated that the proposed synergistic estimation model was more effective than the spectrum-alone or texture-alone model, and showed considerable potential in forest aboveground biomass estimation by combining spectral and textural information.