High-resolution lidar data, acquired over a deciduous forest, were used to investigate the potential utility and limitations of current virtual reality (VR) software for remote sensing analysis and evaluation. Although a standard remote sensing software package provided a good overview of interpolated, smoothed lidar data, functionality was lower for gridded data that had not been interpolated. However, it was possible to drape orthophotographs and other images over the gridded lidar data, providing a useful method for investigating relationships between the lidar and other data sets.
Using a commercial VR package, it was possible to view the original lidar point data; consequently, we were able to visualize the multiple returns from within the canopy of each tree. These point data were preferable for identifying surfaces within the data cloud, especially the ground surface, because the original point data allow an analyst to see, and work with, the full spatial complexity of the data. Being able to visualize the original data clouds also helps in delineating individual trees, the structure of returns within single trees and, potentially, to identify objects hidden within and beneath the trees. For a fully integrated remote-sensing VR package, additional functionality is needed to link point and interpolated coverages, and to enhance the interactive selection of data for further statistical analysis.