Fusing waveform LIDAR and hyperspectral data for species-level structural assessment in savanna ecosystems
ABSTRACT 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.
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ABSTRACT: Full-waveform small-footprint Light Detection and Ranging (LiDAR) is still in the early stages of development for forest structure assessment, in part due to the complex interaction between a laser pulse and the forest structure, which is not yet fully understood. In recent years, simulation studies (which claim absolute ground truth) have sought to tackle this problem. The challenge remains to determine the limit of structural fidelity, in terms of tree structural components, that is required for waveform-based simulation studies. Understanding of such interactions could lead to improved biophysical modeling from LiDAR waveform signals. We present a simulation study that evaluates the impact of tree structural components on received waveform signals across different outgoing pulse widths and scanning angles. The simulation was performed on a small red maple (Acer rubrum) and red oak (Quercus rubra) stand. It was concluded the back-scattered waveform is dominated by the leaves, while the trunks, twigs, and leaf stems had a minimal impact on the signal. Scan angle (0°, 10°, and 20°) and outgoing pulse width (4 ns, 8 ns, and 16 ns) do not have as statistically significant (95% confidence) impact on mean waveform comparison statistics. This result has implications on the level of complexity required for future simulations and for waveform LiDAR based structural algorithm development.Canadian journal of remote sensing 07/2013; · 1.09 Impact Factor