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

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection ranging (LiDAR) can provide understory information form either point cloud or full-waveform data. Point data have a remarkable ability to represent three-dimensional structures vegetation, while contain more detailed on interactions between laser pulses vegetation; both types been widely used estimate various canopy structural parameters, including leaf area index (LAI). Here, we present new method for quantifying LAI temperate by combining advantages LiDAR To achieve this, first estimated vertical distribution gap probability using automatically determine height boundary overstory at plot level. We then deconvolved remove blurring effect caused system pulse restore resolution system. Subsequently, decomposed integrated plot-level differentiate waveform components returned from overstory, understory, soil layers. Finally, modified basic equations introducing spectral quantify LAI. Our results, which were validated against ground-based measurements, show that produced good estimation with R2 0.54 root-mean-square error (RMSE) 0.21. study demonstrates be successfully quantified through combined use