This study examines the utility of cocollected, dual-wavelength, full-waveform lidar data to characterize vegetation and landscapes through the extraction of waveform features, such as total waveform energy, canopy energy distribution, and foliage penetration metrics. Assessments are performed using data collected in May 2014 over Monterey, California, using the Chiroptera dual-laser lidar mapping system from Airborne Hydrography AB. Both full-waveform and discrete return data were collected simultaneously at green (532 nm) and near-infrared (NIR) (1064 nm) wavelengths; however, the two channels are operated independently at different pulse repetition frequencies, thus measurements are not spatially coincident. A voxelization approach is employed to generate pseudowaveforms for each wavelength along vertical columns in a regularly spaced grid, such that spectral waveform properties can be evaluated independently of spatial variations resulting from instrumentation configuration and collection scenario. The pseudowaveforms are parameterized and extracted parameters are mapped to raster layers, which are then used as inputs to a random forest classifier to predict land cover classifications across the survey area. In comparison to independent classification results for the two wavelength channels, the combination of the NIR and green response provided an improvement in overall classification accuracy of up to 6%. This effort presents the methodology associated with the voxelization approach and the exploitation of the pseudowaveform features, while illustrating a potential utility for geospatial classification using multiple wavelengths.