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Estimation of forest structural variables using small-footprint full-waveform LiDAR in a subtropical forest, China

Quantification of forest structure is critical for making management decisions and improving our understanding of forest ecosystem functions. This research investigates the estimation of forest structural variables from small-footprint full-waveform (FW) light detection and ranging (LiDAR) data. This study was undertaken in the mixed subtropical Yushan forest dominated by Masson pine (Pinus massoniana Lamb.) and Sawtooth oak (Quercus acutissima Carruth.), in southeast China. Ground truth data were collected across 68 plots. Overall, the results indicated that FW metrics had a good performance for predicting typical forest structural indices (i.e. basal area, Lorey's height and volume). The “All metrics-included” models showed significantly improved performance for predicting forest structural variables when compared to “Height distribution-related” models, confirming that the additional waveform metrics (i.e. Outer-canopy and Pulse-attribute metrics) were important when predicting structural parameters of subtropical forest.