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Open AccessArticle

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

1
Colaberry Inc., 200 Portland St, Boston, MA 02114, USA
2
LiDAR Applications for the Study of Ecosystems with Remote Sensing (LASERS) Laboratory, Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77450, USA
3
School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA
4
National Ecological Observatory Network, 1685 38th St., Suite 100, Boulder, CO 80301, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1949; https://doi.org/10.3390/rs10121949
Received: 7 November 2018 / Revised: 30 November 2018 / Accepted: 30 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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. View Full-Text
Keywords: hyper point cloud (HPC); HPC-based intensity surface; percentile height; gridding; full waveform LiDAR; tree segmentation; vegetation structure hyper point cloud (HPC); HPC-based intensity surface; percentile height; gridding; full waveform LiDAR; tree segmentation; vegetation structure
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MDPI and ACS Style

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.

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