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Appl. Sci. 2018, 8(3), 349; https://doi.org/10.3390/app8030349

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
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Received: 13 January 2018 / Revised: 23 February 2018 / Accepted: 24 February 2018 / Published: 28 February 2018
(This article belongs to the Special Issue Laser Scanning)
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Abstract

Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevation- and geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength. View Full-Text
Keywords: multispectral LiDAR; discrimination; region growing; point density; height variation multispectral LiDAR; discrimination; region growing; point density; height variation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Morsy, S.; Shaker, A.; El-Rabbany, A. Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada. Appl. Sci. 2018, 8, 349.

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