Fourier transform of waveform Lidar for species recognition - data requirements

Abstract

Waveform Lidar information is typically analyzed only after decomposing waveforms into a sum of Gaussian peaks. Under the assumption that some important information may be lost in the decomposition, an attempt was made to transform the waveform into the spectral domain using a fast Fourier transform. This approach was successful at distinguishing three deciduous species with 75 % accuracy (kappa=0.62), using a classification tree approach. The data set density used in this work was about 10 light pulses per square metre (lppm) near nadir at ground level. This allows for an analysis of data density effects on the ability of the classification method to correctly identify a given species. The data were reduced, by removing waveforms at uniform intervals, into subsets containing approximately 80, 60, 40, and 20 % of the original density. This resulted in densities of approximately 8, 6, 4 and 2 lppm. Surprisingly, not all reductions of data were found to decrease the ability of this method to correctly identify tree species. In fact the 80 % density showed marginal improvement over the full density. The 60, 40 and 20 % densities decreased classification accuracy by 10 to 20 %. The results indicate that pulse density has only slight, yet sometimes unpredictable effect on the classification accuracy outcome.