Satellite Imagery-Estimated Intertidal Seaweed Biomass Using UAV as an Intermediary

Abstract

The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for Ulva pertusa and Sargassum thunbergii species (Ulva pertusa, RSentinel22 = 0.74, RPlanetScope2 = 0.8; Sargassum thunbergii, RSentinel22 = 0.88, RPlanetScope2 = 0.69). The average biomasses of Ulva pertusa and Sargassum thunbergii in the intertidal zone of Gouqi Island are 456.84 g/m2 and 2606.60 g/m2, respectively, and the total resources are 3.5 × 108 g and 1.4 × 109 g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.