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To evaluate canopy metrics at landscape or regional levels, remote sensing techniques have usually been employed. In particular, ratio metrics such as laser penetration index (LPI: %) calculated as the percentage of ground returns of laser pulses within the forest from light detection and ranging (LiDAR) data have proved to be closely related to canopy metrics obtained in the field. In our previous study, we successfully proposed a new methodology that automatically and easily separated the laser pulses into those of canopy and below-canopy returns. In this study, we assessed the effectiveness of LPI that was calculated using our methodology to estimate relative illuminance (RI: %) and also evaluated the optimal scale to calculate LPI for estimating RI. We found that LPI was closely related to RI (P

In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.

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.

Recently, it was shown that individual tree heights could be accurately estimated using small-footprint airborne light detection and ranging (LiDAR) remote sensing. Because most of the areas studie...