Synergy of UAV-LiDAR Data and Multispectral Remote Sensing Images for Allometric Estimation of Phragmites Australis Aboveground Biomass in Coastal Wetland

Abstract

Quantifying the vegetation aboveground biomass (AGB) is crucial for evaluating environment quality and estimating blue carbon in coastal wetlands. In this study, a UAV-LiDAR was first employed to quantify the canopy height model (CHM) of coastal Phragmites australis (common reed). Statistical correlations were explored between two multispectral remote sensing data (Sentinel-2 and JL-1) and reed biophysical parameters (CHM, density, and AGB) estimated from UAV-LiDAR data. Consequently, the reed AGB was separately estimated and mapped with UAV-LiDAR, Sentinel-2, and JL-1 data through the allometric equations (AEs). Results show that UAV-LiDAR-derived CHM at pixel size of 4 m agrees well with the observed stem height (R2 = 0.69). Reed height positively correlates with the basal diameter and negatively correlates with plant density. The optimal AGB inversion model was derived from Sentinel-2 data and JL-1 data with R2 = 0.58, RMSE = 216.86 g/m2 and R2 = 0.50, RMSE = 244.96 g/m2, respectively. This study illustrated that the synergy of UAV-LiDAR data and multispectral remote sensing images has great potential in coastal reed monitoring.