Abstract
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 R2 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.
Highlights
Our study demonstrates that the understory leaf area index (LAI) can be quantified through the combined use of discrete return that the understory LAI can be quantified through the combined use of discrete return and full-waveform Light detection and ranging (LiDAR) data
We proposed a new method for quantifying understory LAI in a temperate forest, which combined the advantages of both point cloud and full-waveform LiDAR
The results of this study demonstrate that the gap probability distribution derived from point cloud data is a good indicator of overstory and understory height boundaries, and it is useful in overstory and understory waveform component discrimination
Summary
Vegetation in forests often displays distinct layering [1], which can be divided into overstory and understory. The overstory refers to the uppermost layer made up of woody plants, while the understory is located beneath the overstory, typically consisting of trees stunted by lack of light, other small trees with low light requirements, saplings, vines, and undergrowth [2]. Between the overstory and understory, a gap height interval often exists (Figure 1). In view of the great financial value and critical ecological functions, the retrieval of overstory vegetation attributes has been advanced considerably with the help of remote sensing techniques [3,4,5].
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