Preprint Article Version 1 This version is not peer-reviewed

From LiDAR waveforms to Hyper Point Clouds: a novel data product to characterize vegetation structure

Version 1 : Received: 7 November 2018 / Approved: 8 November 2018 / Online: 8 November 2018 (14:56:35 CET)

A peer-reviewed article of this Preprint also exists.

Zhou, T.; Popescu, S.; Malambo, L.; Zhao, K.; Krause, K. From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure. Remote Sens. 2018, 10, 1949. Zhou, T.; Popescu, S.; Malambo, L.; Zhao, K.; Krause, K. From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure. Remote Sens. 2018, 10, 1949.

Journal reference: Remote Sens. 2018, 10, 1949
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 lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle 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 larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to 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 to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Subject Areas

hyper point cloud (HPC); HPC-based intensity surface; percentile height; gridding; full waveform LiDAR; tree segmentation; vegetation structure

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