A Comparison of Two Open Source LiDAR Surface Classification Algorithms

Wade T Tinkham,Andrew T Hudak,Rupesh Shrestha,Timothy E Link,Alistair M S Smith,Danny G Marks,Hongyu Huang,Michael J Falkowski,Nancy F Glenn

doi:10.3390/rs3030638

Abstract

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

Highlights

Developing accurate Digital Terrain Models (DTM) has been a long stated goal of both researchers and resource managers interested in quantifying land surface elevations
The ANOVA indicated that there was no significant difference between Boise Center Aerospace Laboratory Light Detection and Ranging (LiDAR) (BCAL) and MCC, but that each outperformed the Combo algorithm
This study assessed the potential of the BCAL and MCC algorithms to perform in a mixed cover type landscape with a variety of terrain features

Summary

Introduction

Developing accurate Digital Terrain Models (DTM) has been a long stated goal of both researchers and resource managers interested in quantifying land surface elevations. The potential applications of a reliable DTM include habitat assessment, forest succession, snowmelt simulation, hydrologic modeling, carbon sequestration, glacial monitoring, and floodplain assessments [1,2,3,4,5,6]. In the presence of steep slopes or high biomass, traditional DTM generation methods are difficult to implement, often leading to reduced levels of accuracy [7,8,9]. Research has demonstrated that LiDAR DTM generation is more efficient and accurate as compared to traditional methods [10]

Objectives

Methods

Findings

Conclusion