The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R² (CV-R²) = 0.79 for wavelet features vs. CV-R² = 0.69 for spectral bands) and all plots combined (CV R² = 0.71 for wavelet features vs. CV-R² = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R² = 0.77) than the original spectral bands (RMSE = 0.69, R² = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models.