Journal
REMOTE SENSING LETTERS
Volume 2, Issue 4, Pages 347-356Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2010.523021
Keywords
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Funding
- University of Washington Precision Forestry Cooperative
- University of Washington Remote Sensing and Geospatial Analysis Laboratory
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In precision forestry, tree species identification is one of the critical variables of forest inventory. Lidar, specifically full-waveform Lidar, holds high promise in the ability to identify dominant hardwood tree species in forests. Raw waveform Lidar data contain more information than can be represented by a limited series of fitted peaks. Here we attempt to use this information with a simple transformation of the raw waveform data into the frequency domain using a fast Fourier transform. Some relationships are found among the influences of component frequencies across a given species. These relationships are exploited using a classification tree approach to separate three hardwood tree species native to the Pacific Northwest of the United States. We are able to correctly classify 75% of the trees ((kappa) over cap 0: 615) in our training data set. Each tree's species was predicted using a classification tree built from all the other training trees. Two of the species grow in proximity and grow to a similar form, making differentiation difficult. Across all the classification trees built during the analysis, a small group of frequencies is predominantly used as predictors to separate the species.
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