We employed the discrete wavelet transform to reflectance spectra obtained from hyperspectral data to improve estimation of LAI in temperate forests. We estimated LAI for 32 plots across a range of forest types in Wisconsin using hemispherical photography. Plot spectra were extracted from AVIRIS data and transformed into wavelet features using the Haar wavelet. Separately, subsets of spectral bands and the Haar features selected by a genetic algorithm were used as independent variables in linear regressions. Models using wavelet coefficients explained the most variance for both broadleaf plots (R2 = 0.90 for wavelet features versus R2 = 0.80 for spectral bands) and all plots independent of forest type (R2 = 0.79 for wavelet features vs. R2 = 0.58 for spectral bands). The forest-type specific models were better than the models using all plots combined. Overall, wavelet features appear superior to band reflectances alone for estimating temperate forest LAI using hyperspectral data.