Gaussian process regression-based forest above ground biomass retrieval from simulated L-band NISAR data

Abstract

Accurate mapping of forest above ground biomass (AGB) is essential for understanding changes in the rate of ecosystem processes such as biomass productivity, litter productivity, actual litter decomposition, and potential litter decomposition during secondary succession. They also play a vital role in evaluating forest carbon pools. This study presents a Matérn kernel-based Gaussian process regression (GPR) approach for biomass estimation integrating Synthetic Aperture Radar (SAR) backscatter intensities with LiDAR measurements. We use the backscatter intensities from five scenes of L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) simulated NISAR data collected during the 2019 AM–PM NASA airborne campaign. The biomass map derived from LVIS (Land, Vegetation, and Ice Sensor) Lidar point clouds over the Lenoir landing site, Alabama, was used with these simulated NISAR data. We have utilized the GPR model to estimate the AGB of the entire forest alongside the major forest classes in the study area, namely deciduous, evergreen, and woody wetlands. To examine the dependency of the model on acquisition conditions, we calibrated and validated our proposed model utilizing scene combinations. The experiment shows that multiple-scene retrieval delivered improved AGB estimates compared to single-scene retrieval. We evaluated the efficacy of the GPR model for three AGB ranges, i.e., (i) 8to100Mgha−1, (ii) 8to230Mgha−1, and (iii) 8to470Mgha−1 at 20m, 30m, 50m and 100m spatial resolution, respectively. The results indicate that the RMSE incurred by the GPR model for all these AGB ranges seemed to reduce as we increased spatial extent or reduced spatial resolution from 20m to 100m. Further, we demonstrate the performance of the optimized GPR model to retrieve AGB within the biomass range of 8Mgha−1 to 100Mgha−1 for all the above-mentioned forest types. Finally, this research highlights the advantages of the Matérn kernel-based GPR model over other statistical regression models towards improved AGB mapping.

Keywords

• Above ground biomass (AGB)
• Matérn Kernel
• Gaussian process regression
• NASA AM–PM
• UAVSAR
• Lidar