Fusing waveform LIDAR and hyperspectral data for species-level structural assessment in savanna ecosystems

Research groups at Rochester Institute of Technology and Carnegie Institution for Science are studying savanna ecosystems and are using data from the Carnegie Airborne Observatory (CAO), which integrates advanced imaging spectroscopy and waveform light detection and ranging (wLIDAR) data. This component of the larger ecosystem project has as a goal the fusion of imaging spectroscopy and wLIDAR data in order to improve per-species structural parameter estimation. Waveform LIDAR has proven useful for extracting high vertical resolution structural parameters, while imaging spectroscopy is a well-established tool for species classification. We evaluated data fusion at the feature level, using a stepwise discrimination analysis (SDA) approach with feature metrics from both hyperspectral imagery (HSI) and wLIDAR data. It was found that fusing data with the SDA improved classification, although not significantly. The principal component analysis (PCA) provided many useful bands for the SDA selection, both from HSI and wLIDAR. The overall classification accuracy was 68% for wLIDAR, 59% for HSI, and 72% for the fused data set. The kappa accuracy achieved with wLIDAR was 0.49, 0.36 for HSI, and 0.56 for both modalities.