Innovative algorithm development for small-footprint full-waveform lidar data processing extends this technology's capabilities to more complicated acquisition scenarios then previously determined, namely success of surveys over obscured areas. Waveform decomposition and the extraction of waveform metrics provide a straightforward approach to identifying vertical structure within each laser measurement. However, there are some limitations in this approach as faint returns within the waveform go undetected within the classical processing chain. These faint returns are the result of reduced energy levels due to obscurant scattering, attenuation and absorption. Lidar surveys over non-homogeneous wooded regions indicate that there are meaningful ground returns within dense tree coverage if extracted correctly from the data. By using a waveform stacking technique with appropriate waveforms in near geospatial proximity to the original, these faint returns can be augmented and detected during data processing. In comparison to the traditional approach, the waveform stacking technique provides up to a 60% increase in perceived ground returns with the faint signal extraction for the particular datasets analyzed over a broadleaf forest in Mississippi. The enhanced capability in the presence of foliage provides a decrease in operational effort associated with data density, dwell or targeting techniques, in addition to required survey expense.