From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure

Tan Zhou,Sorin Popescu,Lonesome Malambo,Kaiguang Zhao,Keith Krause

doi:10.3390/rs10121949

Abstract

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Highlights

Light Detection and Ranging (LiDAR) remote sensing has demonstrated its advantages over traditional remote sensing for forest inventory and vegetation structure characterization [1,2,3]
A closer examination revealed that the mid-story of vegetation for the Hyper Point Cloud (HPC) was filled with signals
To get an overview of DR LiDAR point cloud and the HPC at individual tree level, we present them in Figure 8 with four intensity distributions of these trees in a contour line format

Summary

Introduction

Light Detection and Ranging (LiDAR) remote sensing has demonstrated its advantages over traditional remote sensing (e.g., multispectral and radar) for forest inventory and vegetation structure characterization [1,2,3]. Compared to conventional LiDAR systems, FW LiDAR can record the whole echo scattered from intercepted objects to inform their spatial arrangements [4,5,6,7]. Such an advantage can enable FW LiDAR systems to better characterize vegetation structure with fine details. Developing alternative ways to analyze FW LiDAR data in relevant applications are critical to facilitating the widespread use of FW LiDAR data, as well as improving the accuracy of characterizing the three-dimensional structure of vegetation

Objectives

Methods

Results

Conclusion