A LiDAR-Aided Multibaseline PolInSAR Method for Forest Height Estimation: With Emphasis on Dual-Baseline Selection

Abstract

Polarimetric synthetic aperture radar interferometry (PolInSAR) and light detection and ranging (LiDAR) have their own respective advantages and disadvantages in extracting large-scale forest height. In this letter, we present an advanced approach to obtain forest canopy height by combining these two strategies. More specifically, the novelty of the proposed method focuses on a dual-baseline selection from multibaseline PolInSAR data, which ensures the robust performance of the forest height inversion by effectively improving the estimation of volume-only coherence. The dual-baseline selection can be regarded as a supervised classification problem. We consider support vector machine (SVM) as an appropriate classifier, and a small amount of sparse LiDAR samples within the coverage of the PolInSAR data (less than 1%) are chosen to assist with the training of the dual-baseline combination classification, which can be met by the current spaceborne LiDAR missions. Finally, we validate the proposed approach by airborne P-band synthetic aperture radar (SAR) data acquired by the F-SAR system and LiDAR data acquired by the National Aeronautics and Space Administration (NASA) Land, Vegetation, and Ice Sensor (LVIS) during the 2016 AfriSAR campaign. The estimation accuracy of the proposed method [$R^{2} = 0.73$ , root-mean-square error (RMSE) = 3.17 m] is 25.24% higher than that of the existing SVM fusion approach devoted to single-baseline selection ($R^{2} = 0.59$ , RMSE = 4.24 m).