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Abstract
This study investigated which predictor variables with respect to crown properties, derived from small-footprint airborne light detection and ranging (LiDAR) data, together with LiDAR-derived tree height, could be useful in regression models to predict individual stem volumes. Comparisons were also made of the sum of predicted stem volumes for LiDAR-detected trees using the best regression model with field-measured total stem volumes for all trees within stands. The study area was a 48-year-old sugi (Cryptomeria japonica D. Don) plantation in mountainous forest. The topographies of the three stands with different stand characteristics analyzed in this study were steep slope (mean slope ± SD; 37.6° ± 5.8°), gentle slope (15.6° ± 3.7°), and gentle yet rough terrain (16.8° ± 7.8°). In the regression analysis, field-measured stem volumes were regressed against each of the six LiDAR-derived predictor variables with respect to crown properties, such as crown area, volume, and form, together with LiDAR-derived tree height. The model with sunny crown mantle volume (SCV) had the smallest standard error of the estimate obtained from the regression model in each stand. The standard errors (m3) were 0.144, 0.171, and 0.181, corresponding to 23.9%, 21.0%, and 20.6% of the average field-measured stem volume for detected trees in each of these stands, respectively. Furthermore, the sum of the individual stem volumes, predicted by regression models with SCV for the detected trees, occupied 83%–91% of field-measured total stem volumes within each stand, although 69%–86% of the total number of trees were correctly detected by a segmentation procedure using LiDAR data.
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