| The potential of waveform Lidar is investigated in a series of three articles. In the first, a new approach is found to capture patterns within waveforms using an old technique: the Fourier transform. The mean spectral pattern between waveforms hitting an individual tree is found to aid in discriminating species. Using the full dataset, an overall accuracy of 75 percent is achieved using a classification tree approach for 44 sample trees of 3 hardwood species native to the Pacific Northwest of the United States. Important wavelengths within the waveforms include 1.5, 0.75, and 0.35 meters.;In a second article, the ability of the above technique to classify species using datasets of lower densities is analyzed. From the original dataset with approximately 10 waveforms/pulses crossing a square meter at ground level (equivalent to a first and last return discrete point dataset of about 20 points per square meter), reduced datasets were created at 80, 60, 40 and 20 percent of the original density. The classification was then performed at each density level. Reducing the density to 80 percent actually increased the overall accuracy to 82 percent, while subsequent reductions reduced the accuracy to 61, 54, and 66 percent respectively for the 60, 40 and 20 percent reduced datasets.;A third article compares a combination of several variables obtained from a discrete point Lidar dataset before and after the addition of variables obtained from waveform Lidar data. The addition of waveform information aided in the classification of five species, as well as in the classification of several two-species subsets. Performance of small groups of similar discrete point Lidar-derived variables varied much more between different species combinations, but when grouped they performed very well in all combinations. These results provide some suggestive evidence that fine-scale waveform Lidar information is important to classification of at least some tree species. |
