Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data

Haibo Zhang,Changcheng Wang,Peng Shen,Qinghua Xie,Jianjun Zhu,Haiqiang Fu

doi:10.3390/f9040163

Abstract

Forest above ground biomass (AGB) extraction using Synthetic Aperture Radar (SAR) images has been widely used in global carbon cycle research. Classical AGB inversion methods using SAR images are mainly based on backscattering coefficients. The polarization coherence tomography (PCT) technology which can generate vertical profiles of forest relative reflectivity, has the potential to improve the accuracy of biomass inversion. The relationship between vertical profiles and forest AGB is modeled by some parameters defined based on geometric characteristics of the relative reflectivity distribution curve. But these parameters are defined without physical characteristics. Among these parameters, tomographic height (TomoH) is considered as the most important one. However, TomoH only corresponds to the highest volume relative reflectivity, which is lower than the actual forest height, affecting the accuracy of forest height and AGB inversion. In this paper, we introduce a new parameter, the canopy height (Hac), for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter (Hac) and other parameters from the tomographic profile. The P-band polarimetric SAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign acquired over Krycklan Catchment are selected for the verification experiment at two different flight directions. The results show that Hac performs better in estimating forest height and AGB than TomoH does. The inversion root mean square error (RMSE) of the proposed method is 18.325 t ha−1, and the result of using TomoH is 21.126 t ha−1.

Highlights

Forest ecosystems cover around 30% of the land surface, accounting for 75% of terrestrial gross primary production and about 80% of the global plant biomass [1,2]
The tomographic profiles in the 2500th line along the range direction are demonstrated in Figure 5, where the horizontal axis is the pixel position, the vertical axis is the forest height, and the different colors represent the intensity of relative reflectivity
We propose a method based on the polarimetric coherence tomography for forest

Summary

Introduction

Forest ecosystems cover around 30% of the land surface, accounting for 75% of terrestrial gross primary production and about 80% of the global plant biomass [1,2]. They play an important role in the global carbon balance and climate change [3]. An important parameter reflecting the forest carbon cycle change is above-ground biomass (AGB). Many different techniques have been used to estimate. Remote sensing techniques perform better in large-scale forest AGB mapping [7,8] than traditional forest inventory techniques. Over the last two decades, airborne and spaceborne sensors have been used to estimate forest

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