Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory

Yi-Chun Lin,Raja Manish,Ayman Habib,Sang-Yeop Shin,Zainab Saka,Jinyuan Shao,Songlin Fei,Mina Joseph

doi:10.3390/rs14030649

Abstract

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.

Highlights

Global forest ecosystems, covering around 30% of the land surface, can provide various critical ecosystem services such as maintaining global carbon balance, mitigating climate change, and promoting economic and social development [1,2]
Sentinel-2 imagery are adopted to estimate nation-wide canopy height [5]; and very highresolution satellite, aerial, and unmanned aerial vehicle (UAV) imagery are acquired for tree counting and localization [6,7]
The six dadatasets acquired from different LiDAR systems, hereafter denoted as the United States Geological Survey (USGS)-3-D Elevation Program (3DEP), tasets acquired from different LiDAR systems, hereafter denoted as the USGS-3DEP, VeriDaaS, UAV leaf-on, UAV leaf-off, Backpack leaf-on, and Backpack leaf-off datasets, were used for the experiments

Summary

Introduction

Global forest ecosystems, covering around 30% of the land surface, can provide various critical ecosystem services such as maintaining global carbon balance, mitigating climate change, and promoting economic and social development [1,2]. Accurate inventory is essential for better understanding and for the management of forest ecosystems from local to global scales. Along with the development of platforms, sensors, and processing technologies, remote sensing has been widely used for forest mapping and inventory. Landsat scenes are used for forest mapping at a regional scale [3,4]; multispectral. Sentinel-2 imagery are adopted to estimate nation-wide canopy height [5]; and very highresolution satellite, aerial, and unmanned aerial vehicle (UAV) imagery are acquired for tree counting and localization [6,7]. Because satellite and aerial imagery only provides the top view perspective, it is more challenging to use such data to investigate forest vertical structure for the derivation of inventory metrics such as tree height and crown depth.

Methods

Results

Discussion

Conclusion