# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 6 Conclusions  
On the one hand, bathymetry derived by LiDAR has the advantage of high maneuverability and efficiency, but the cost of using the system is high. On the other hand, the application of passive optical remote sensing entails poor accuracy, but the cost of using it is low. On the basis of the popularity of LiDAR bathymetry for depth measurements, bathymetric inversion with passive optical image became a research topic with high value. In this study, a multi-Gaussian function was used to fit the LiDAR waveform data, and the depth data were extracted for use as the measured depth data of bathymetric inversion. Then, the improved band ratio model was applied to estimate the bathymetry of the entire water around Ganquan Island within the QuickBird image range. The main conclusions are highlighted below.  
On the basis of the multi-Gaussian function to fit the LiDAR waveform data, the overall MAE of the whole water depth (3-17 m) was 5.6%, the overall MRE was 58.2 cm, and the \(R^{2}\) could reach 0.97. The accuracies meet the requirements of ocean surveying and bathymetric mapping.  
Figure 14: MAE and MRE of specific depth ranges of validation points.  
The bathymetric data derived from the Aquarius airborne LiDAR were divided into control points and validation points. On the basis of the improved band ratio model, a bathymetric inversion was carried out with the QuickBird image. The fitting coefficients \(R^{2}\) of validation points and control points were 0.95 and 0.96, respectively. In the range of 2-22 m, the overall MAEs were 0.80 m and 0.95 m, and the overall MREs were 7.0% and 8.9%, respectively. In specific depth ranges, the MAEs of all the depth ranges were within the range of 0.5-1.2 m. From the aspect of accuracy of both overall and specific depth ranges, the proposed method is an effective and inexpensive way to extract water depth information, given the combination of active and passive remote sensing. At the same time, costs can be reduced, indicating its applicability in coastal engineering in the future.  
Suitable depth ranges need to be determined prior extracting the depth from LiDAR waveforms and combing active and passive remote sensing. Bathymetric inversion is limited in extremely shallow waters (<2 m) and deep waters. The difference in signals from the sea surface and the bottom in extremely shallow waters is somewhat small and, thus, difficult to distinguish, and the detection accuracy is low. By contrast, in deep waters, the signal from the bottom is too weak to be detected, thus resulting in poor accuracy.  
Conceptualization, Z.Z. and Y.M.; methodology, Z.Z. and T.J.; software, J.Z.; validation, Y.M., H.T., and T.J.; investigation, Y.M.; resources, Z.Z. and H.T.; Writing--Original Draft preparation, Z.Z.; Writing--Review and Editing, J.Z.  
This research was funded by the Postgraduate Science and Technology Innovation Project of Shandong University of Science and Technology, grant number SDKDYC190204; the National Natural Science Foundation of China (NSFC), grant number 41706194; and the second remote sensing survey of East India Ocean environmental parameters, grant number GASI-02-IND-YGST2-04.  
The authors declare no conflict of interest.