Forest above ground biomass estimation methodology based on polarization coherence tomography

Abstract

Forest above ground biomass (AGB) estimation using microwave backscattering coeffi cient is normally limited to low level AGB because of the "saturation" problem in backscattering coeffi cient. In addition, forest height may be used to estimate AGB by allometric equation, but the changing conditions of the forest in terms of density, tree species composition etc. limit the accuracy and performance of the method. In order to overcome the above disadvantages and improve the estimation accuracy, a method for AGB estimation is proposed in this paper, which is based on polarization coherence tomography (PCT) technology. Using repeat pass ESAR L-band PolInSAR data collected by DLR at the Traunstein test site, the radar relative refl ectivity function of each pixel is reconstructed using PCT, from which the average relative refl ectivity profi les for the 20 validation stands are computed. Then 9 profi le characteristic parameters closely related to biomass are defi ned and extracted for each forest stand. The natural logarithms of these 9 profi le parameters are taken as independent variables for multivariate linear regression analysis with the natural logarithm of the fi eld-measured AGB as dependent variable using stepwise regression method. Forest AGB estimation model is established and evaluated, and the factors possibly affecting the performance of the AGB estimation model are also analyzed. The results show that these parameters, which are extracted from the average relative refl ectivity function inversed with PCT, are sensitive to forest AGB. The accuracy of AGB estimation can be improved if we make full use of the information contained in the relative refl ectivity function.