Large-scale aboveground biomass (AGB) monitoring based on remote sensing is crucial for herbaceous communities. This research fills the research gap of fusing LiDAR and multispectral fusion sensors for AGB upscaling, which addresses the scale mismatch between ground AGB measurements with satellite monitoring. The main contribution of this research is a new insight into fusing LiDAR and multispectral images for UAV AGB monitoring. The results show that average vegetation height (HMean) generated from LiDAR is the most important UAV-based feature for AGB modeling, followed by three other UAV multispectral features (kNDVI, NDVIₑdge, and GNDVI). The HMean derived from LiDAR suffers from inevitable information loss. Comparing regression results using LiDAR-derived height only and using LiDAR and multispectral features against ground-measured vegetation height reveals that multispectral features can fill the height information loss. Based on the UAV-derived AGB, this research achieves AGB upscaling at the satellite images with a coefficient of determination equal to 0. 73 and RMSE equal to 7. 60 g/m2. This research can serve as a guideline for UAV-based AGB upscaling for herbaceous community.
Liu et al. (Thu,) studied this question.
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