Abstract
In this study, we evaluated methods for reliably estimating leaf area index (LAI) and gap fraction in two different types of broad-leaved forests by the use of airborne light detection and ranging (LiDAR) data. We evaluated 13 estimation variables related to laser height, laser penetration rate, and laser point attributes that were derived from LiDAR analyses. The relationships between LiDAR-derived estimates and field-based measurements taken from the forests were evaluated with simple linear regressions. The data from the two forests were analyzed separately and as an integrated dataset. Among the laser height variables, the coefficient of variation (CV) of all laser point heights had the highest level of accuracy for estimating both LAI and gap fraction. However, we recommend that more evaluations be conducted prior to the use of CV in forests with complex structures. The simplest laser penetration variable, which represents the ratio of the number of ground points to the total number of all points (P ALL), also had a high level of accuracy for estimating LAI and gap fraction at the study sites regardless of whether the data were analyzed separately or as an integrated data set. Furthermore, P ALL values showed near 1:1 relationships with the field-based gap fraction values. Hence, the use of P ALL may be the most practical for estimating LAI and gap fraction in broad-leaved forests, even when the canopies are heavily closed.
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Acknowledgments
We wish to acknowledge the staff of the Commemorative Organization for the Japan World Exposition ‘70 and Japanese Imperial Household Agency for their support in field observation.
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Sasaki, T., Imanishi, J., Ioki, K. et al. Estimation of leaf area index and gap fraction in two broad-leaved forests by using small-footprint airborne LiDAR. Landscape Ecol Eng 12, 117–127 (2016). https://doi.org/10.1007/s11355-013-0222-y
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DOI: https://doi.org/10.1007/s11355-013-0222-y