Application of continuous wavelet analysis to hyperspectral data for the characterization of vegetation

Abstract

This thesis explores the application of continuous wavelet analysis (CWA) to hyperspectral data for the characterization of vegetation at the leaf level. The first study dealt with the spectral detection of green attack damage (pre-visual stress) due to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation that occurs on lodgepole pines at an early stage, in contrast to considerable research on the remote detection of red attack damage. A new methodology was developed to separate healthy pine trees from beetle infested trees, based on the CWA of hyperspectral measurements for pine needles. This pilot study showed that a decline in water content occurred for the pine trees at the green attack stage and the spectral response to that physiological change could be detected using a few features in the wavelet domain. The second topic addressed the application of CWA to the determination of leaf water content from remotely sensed reflectance. Unlike most previous studies involving a limited number of species, this study examined a wide range of tropical forest species with the aim to determine reliable and effective wavelet features (coefficients) sensitive to changes in leaf gravimetric water content (GWC). Of those significant wavelet features extracted, some related to the absorption of leaf water while more related to the absorption of dry matter. An evaluation of the wavelet features as compared with published water indices indicated their great potential for the estimation of leaf GWC. Lastly, the third study tested the wavelet-based methodology developed in the second study using a leaf spectral database generated by the PROSPECT radiative transfer model. The ability of PROSPECT to simulate leaf reflectance measured for the tropical data set was first assessed. Then the performance of the aforementioned methodology was evaluated in terms of the consistency of wavelet features extracted across data sets. This work demonstrated the effectiveness of the wavelet-based methodology and the robustness and reliability of recurrent wavelet features for the estimation of leaf GWC across a wide range of species.