Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems

The mapping of tree species, in general and specifically in diverse savanna environments, is of great interest to ecologists and natural resource managers. This study focused on the fusion of imaging spectroscopy and small-footprint waveform light detection and ranging (wLiDAR) data to improve per species structural parameter estimation towards classification and herbaceous biomass modeling. The species classification approach was based on stepwise discriminant analysis (SDA) and used feature metrics from hyperspectral imagery (HSI) combined with wLiDAR data. It was found that fusing data with the SDA did not improve classification significantly, especially compared with the HSI classification results. As for herbaceous biomass modeling, the statistical approach used for the fusion of wLiDAR and HSI was forward selection regression modeling, which selects significant independent metrics and models those to measured biomass. The results indicated that fine scale wLiDAR may not be able to provide accurate measurement of herbaceous biomass, although other factors could have contributed to the relatively poor results, such as the senescent state of grass, the narrow biomass range that was measured, and the low biomass values, i.e., the limited laser-target interactions. We concluded that although fusion did not result in significant improvements over single modality approaches in these two use cases, there is a need for further investigation during the peak growing season.