Forest aboveground biomass estimation using polarization coherence tomography and PolSAR segmentation

Abstract

Forest aboveground biomass (AGB) is essential for monitoring the carbon cycle budget and climate change. This study proposes a method for the estimation of forest AGB based on polarization coherence tomography (PCT) and polarimetric synthetic aperture radar (PolSAR) segmentation. The data used are the single-baseline polarimetric SAR interferometry data acquired by the German Aerospace Center’s E-SAR sensor at the L-band over the city of Traunstein and its vicinity in 2003. First, vertical structure tomographic profiles (relative reflectivity distribution in vertical direction) were produced by PCT for each pixel and then averaged within each field-surveyed forest stand to obtain the mean tomographic profile. Next, the mean vertical tomographic profiles were parameterized by 10 shape parameters. Several models for biomass estimation were designed based on the relationships between the definition parameters and the in situ measurements using backward step-wise regression and the M-fold (10-fold) cross-validation method. The best model was chosen by evaluation criteria such as R, root mean square error (RMSE), R2, etc. Forest polygons (objects) were produced by image segmentation using forest heights, (produced by coherence optimization representing double-bounce scattering mechanism), (produced by coherence optimization representing the volume scattering mechanism), and the volume scattering mechanism class (produced by Freeman–Durden decomposition and Whishart classification). Finally, the selected model was used to estimate the AGB of each forest polygon and was validated using ground-measured biomass with R2 of 0.883 and RMSE of 39.98 tons ha−1. The results show that the proposed method works well for the estimation of forest AGB. No saturation phenomena have been observed even for the forest stands with AGB larger than 500 tons ha−1.