A new approach is presented for deriving vegetation canopy structural characteristics from hyperspectral bidirectional reflectance distribution function (BRDF) data. The methodology is based on the relationship between spectral variability of BRDF effects and canopy geometry. Tests with data acquired with the Advanced Solid-State Array Spectroradiometer (ASAS) over Canadian boreal forests during the BOREAS campaign show that vegetation structural characteristics can be derived from the spectral variability of BRDF effects. In addition, the incorporation of both BRDF effects and hyperspectral resolution data substantially improve the classification accuracy. Best classification results are obtained when hyperspectral resolution and BRDF data are combined, but the improvement is not consistent for all classes. For example, adding BRDF information to hyperspectral data increases the overall classification accuracy for a six-class fen site from 37.8% to 44.7%. The addition, however, reduces the accuracy for the jack pine class from 43.6% to 28.8%. These new findings provide evidence for improved capabilities for applications of MISR and MODIS data. The spectral resolution of MODIS is expected to be sufficient to derive canopy structural information based on the spectral variability of BRDF effects, and for MISR a significant improvement of classification accuracies can be anticipated from the combination of nadir reflectance and off-nadir data.