A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

• Main Authors: Jinling Song, Xiao Zhu, Jianbo Qi, Yong Pang, Lei Yang, Lihong Yu
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Remote Sensing
• Subjects: LAI; understory; overstory; LiDAR; full-waveform; point cloud
• Online Access: https://www.mdpi.com/2072-4292/13/15/3036

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an <i>R</i>² of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.