Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar
AbstractSpecies information is a key component of any forest inventory. However, when performing forest inventory from aerial scanning Lidar data, species classification can be very difficult. We investigated changes in classification accuracy while identifying five individual tree species (Douglas-fir, western redcedar, bigleaf maple, red alder, and black cottonwood) in the Pacific Northwest United States using two data sets: discrete point Lidar data alone and discrete point data in combination with waveform Lidar data. Waveform information included variables which summarize the frequency domain representation of all waveforms crossing individual trees. Discrete point data alone provided 79.2 percent overall accuracy (kappa = 0.74) for all 5 species and up to 97.8 percent (kappa = 0.96) when comparing individual pairs of these 5 species. Incorporating waveform information improved the overall accuracy to 85.4 percent (kappa = 0.817) for five species, and in several two-species comparisons. Improvements were most notable in comparing the two conifer species and in comparing two of the three hardwood species.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Vaughn, N.R.; Moskal, L.M.; Turnblom, E.C. Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar. Remote Sens. 2012, 4, 377-403.
Vaughn NR, Moskal LM, Turnblom EC. Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar. Remote Sensing. 2012; 4(2):377-403.Chicago/Turabian Style
Vaughn, Nicholas R.; Moskal, L. Monika; Turnblom, Eric C. 2012. "Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar." Remote Sens. 4, no. 2: 377-403.