Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests

作者:Chen, YM (Chen, Yiming)[ 1 ] ; Zhang, WM (Zhang, Wuming)[ 1 ] ; Hu, RH (Hu, Ronghai)[ 1 ] ; Qi, JB (Qi, Jianbo)[ 1 ] ; Shao, J (Shao, Jie)[ 1 ] ; Li, D (Li, Dan)[ 1 ] ; Wan, P (Wan, Peng)[ 1 ] ; Qiao, C (Qiao, Chen)[ 1 ] ; Shen, AJ (Shen, Aojie)[ 1 ] ; Yan, GJ (Yan, Guangjian)[ 1 ]

AGRICULTURAL AND FOREST METEOROLOGY

卷: 263  页: 323-333

DOI: 10.1016/j.agrformet.2018.09.006

出版年: DEC 15 2018

摘要

Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

通讯作者地址: Zhang, WM (通讯作者)

Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, State Key Lab Remote Sensing Sci,Inst Remote Sens, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, State Key Lab Remote Sensing Sci,Inst Remote Sens, Beijing 100875, Peoples R China