Micro-Topography Mapping through Terrestrial LiDAR in Densely Vegetated Coastal Environments

Xukai Zhang,Xuelian Meng,Tao Wu,Cliff Mugnier,Chunyan Li,Yaping Xu,Nan Shang,Jiaze Wang

doi:10.3390/ijgi10100665

Abstract

Terrestrial Light Detection And Ranging (LiDAR), also referred to as terrestrial laser scanning (TLS), has gained increasing popularity in terms of providing highly detailed micro-topography with millimetric measurement precision and accuracy. However, accurately depicting terrain under dense vegetation remains a challenge due to the blocking of signal and the lack of nearby ground. Without dependence on historical data, this research proposes a novel and rapid solution to map densely vegetated coastal environments by integrating terrestrial LiDAR with GPS surveys. To verify and improve the application of terrestrial LiDAR in coastal dense-vegetation areas, we set up eleven scans of terrestrial LiDAR in October 2015 along a sand berm with vegetation planted in Plaquemines Parish of Louisiana. At the same time, 2634 GPS points were collected for the accuracy assessment of terrain mapping and terrain correction. Object-oriented classification was applied to classify the whole berm into tall vegetation, low vegetation and bare ground, with an overall accuracy of 92.7% and a kappa value of 0.89. Based on the classification results, terrain correction was conducted for the tall-vegetation and low-vegetation areas, respectively. An adaptive correction factor was applied to the tall-vegetation area, and the 95th percentile error was calculated as the correction factor from the surface model instead of the terrain model for the low-vegetation area. The terrain correction method successfully reduced the mean error from 0.407 m to −0.068 m (RMSE errors from 0.425 m to 0.146 m) in low vegetation and from 0.993 m to −0.098 m (RMSE from 1.070 m to 0.144 m) in tall vegetation.

Highlights

Accurate topographic datasets are increasingly needed to detect rapid coastal morphological changes [1], which are critical for performing a reliable simulation of coastal erosion [2] and predicting areas at risk of storm-surge flooding [3]
The 95th percentile of errors correction factor is assigned to the digital surface model (DSM) in the low-vegetation area, and the regression-based adjusted correction factor is assigned to the DEM in the tall-vegetation area
A set of global position system (GPS) recordings was separately selected for bare ground, tall vegetation, and low vegetation along evenly distributed transects

Summary

Introduction

Accurate topographic datasets are increasingly needed to detect rapid coastal morphological changes [1], which are critical for performing a reliable simulation of coastal erosion [2] and predicting areas at risk of storm-surge flooding [3]. Topographic mapping for the assessment of changes in coastal morphology following disturbance (e.g., floods, storm surges and hurricanes) helps understand the sustainability of coastal communities, structures and ecosystems [4]. Elevation profiles, measured by total station, leveling instrument, and global position system (GPS) surveys in representative locations, are commonly used for coastal topographic mapping and analysis [5,6,7]. Considering the spatial heterogeneity of coastal lands and hydrodynamics, analysis based on a limited number of profiles may be insufficient for an accurate survey of morphological changes over the large area [8]. High-accuracy topographic mapping requires a large array of sensors or more measurements [9], which can be challenging and costly in field environments using these discrete measurement methods

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Conclusion