Automatic land-sea classification in a nearshore environment using satellite-based photon-counting LiDAR data.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can measure the global surface with unprecedented resolution. Accurate classification of land and sea data is the prerequisite for generating high-quality data products. Current land-sea classification methods rely on assisted data or manual participation, and the automation degree cannot meet the needs of massive data processing. Therefore, using the land-sea difference of photon-counting LiDAR data, an index called normalized photon rate-elevation ratio (NPRER) is designed. Inspired by this, an automatic land-sea classification method is proposed, and the results are obtained through preliminary classification, reclassification, and post-processing enhancement. The results in Cook Inlet, Alaska, show that NPRER can measure the probability of sea appearance in the nearshore environment. At the same time, the automatic classification method can achieve an overall accuracy of 97.98%. The changes in the coastal type, data collection time, and classification feature sets have little influence on this method. Therefore, the method provides a reliable technical scheme for improving the automation of land-sea classification of satellite-based photon-counting LiDAR data.

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection.

Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7-1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.

A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry.

Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as "shallow water (SW)" and "deep water (DW)". An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model's parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).