Estimation and Error Analysis of Woody Canopy Leaf Area Density Profiles Using 3-D Airborne and Ground-Based Scanning Lidar Remote-Sensing Techniques

Abstract

Vertical profiles of the leaf area density (LAD) of a Japanese zelkova canopy were estimated by combining airborne and portable ground-based light detection and ranging (lidar) data and using a voxel-based canopy profiling method. The profiles obtained by the two types of lidars complemented each other, eliminating blind regions and yielding more accurate LAD profiles than could be obtained by using each type of lidar alone. In the combined results, the mean absolute errors (MAEs) of LAD ranged from 0.20 to 0.42 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , and the mean absolute percentage errors (MAPEs) of the leaf area index (LAI) ranged from 22.3% to 27.2%, for ground areas from 4 to 32 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. A laser beam coverage index ¿ incorporating the lidar's beam settings and a beam attenuation factor was proposed. This index showed general applicability to explain the LAD estimation error for LAD measurements using different types of lidars and with different beam settings. Parts of the LAD profiles that were underestimated even when data from both lidars were combined were interpolated by using a Gaussian function. The interpolation yielded improved results for ground areas of 16 and 32 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ; the respective MAEs of LAD were 0.17 and 0.11 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , and the respective MAPEs of LAI were 8.0% and 9.4%. The proposed method improves lidar-derived LAD estimation and is adapted to broadleaved canopies. The index ¿ was tested against an actual canopy scenario and could be used to determine appropriate lidar measurement settings when data from different sources of lidar data are combined to estimate LAD profiles.