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.