Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data |
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| Institution: | 1. Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland;2. School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;1. Department of Earth Sciences, University of New Hampshire, Durham, NH 03824, United States;2. Provincetown Center for Coastal Studies, Provincetown, MA 02657, United States;3. School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, United States;4. Center for Coastal and Ocean Mapping/Joint Hydrographic Center, School of Marine Science and Ocean Engineering, University of New Hampshire, Durham, NH 03824, United States;5. Department of Natural Resources, University of New Hampshire, Durham, NH 03824, United States;6. Jackson Estuarine Laboratory, School of Marine Science and Ocean Engineering, University of New Hampshire, Durham, NH 03824, United States |
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| Abstract: | The accurate classification of tree species is critical for the management of forest ecosystems, particularly subtropical forests, which are highly diverse and complex ecosystems. While airborne Light Detection and Ranging (LiDAR) technology offers significant potential to estimate forest structural attributes, the capacity of this new tool to classify species is less well known. In this research, full-waveform metrics were extracted by a voxel-based composite waveform approach and examined with a Random Forests classifier to discriminate six subtropical tree species (i.e., Masson pine (Pinus massoniana Lamb.)), Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Slash pines (Pinus elliottii Engelm.), Sawtooth oak (Quercus acutissima Carruth.) and Chinese holly (Ilex chinensis Sims.) at three levels of discrimination. As part of the analysis, the optimal voxel size for modelling the composite waveforms was investigated, the most important predictor metrics for species classification assessed and the effect of scan angle on species discrimination examined. Results demonstrate that all tree species were classified with relatively high accuracy (68.6% for six classes, 75.8% for four main species and 86.2% for conifers and broadleaved trees). Full-waveform metrics (based on height of median energy, waveform distance and number of waveform peaks) demonstrated high classification importance and were stable among various voxel sizes. The results also suggest that the voxel based approach can alleviate some of the issues associated with large scan angles. In summary, the results indicate that full-waveform LIDAR data have significant potential for tree species classification in the subtropical forests. |
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| Keywords: | Species classification Full-waveform LiDAR Subtropical forests Segmentation of trees Metrics selection Random Forests Voxel |
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