An Improved Water-Land Discriminator Using Laser Waveform Amplitudes and Point Cloud Elevations of Airborne LIDAR
Xinglei Zhao, Xiaoyang Wang, Jianhu Zhao, Fengnian Zhou
Abstract
Zhao, X.; Wang, X.; Zhao, J., and Zhou, F., 2021. An improved water-land discriminator using laser waveform amplitudes and point cloud elevations of airborne LIDAR. Journal of Coastal Research, 37(6), 1158–1172. Coconut Creek (Florida), ISSN 0749-0208. Laser waveforms or point clouds of airborne LIDAR are used to distinguish water and land. However, false alarms often occur in coastal areas with complex environments when laser waveforms are used. Elevations of three-dimensional (3D) point clouds can be used to help discriminate ocean and land but fail to identify inland waters. An improved water-land discriminator that uses laser waveform amplitudes and point cloud elevations is proposed in this study. First, 3D point cloud elevations derived using infrared (IR) laser are used as features to conduct ocean-land discrimination with fuzzy c-means clustering. Second, amplitudes of IR laser waveforms are used to identify inland waters from land derived by ocean-land discrimination. The proposed method is applied to data collected using Optech coastal zone mapping and imaging LIDAR (CZMIL). Land boundary derived from digital orthophoto map is used as a reference to evaluate different water-land discriminators. Results showed that consistency of the water-land interface derived using the improved water-land discriminator is higher with the reference interface than that derived by traditional waveform saturation, waveform clustering, and 3D point cloud methods. Overall accuracy of water-land points discriminated via waveform saturation, waveform clustering, 3D point cloud, and improved water-land discriminator is 93.80%, 93.84%, 95.66%, and 99.27%, respectively. High accuracy of the water-land interface and points indicates the effectiveness of the improved water-land discriminator for Optech CZMIL.