# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 2 Related Work  
The bathymetric system of LiDAR matured as a technology in recent years. However, the data processing software of the bathymetric system is limited to some extent, and bathymetric accuracy is affected by the processing of waveform data, which are affected by bottom reflection, suspended matter, and sea waves [8]. The bathymetric algorithm of the LiDAR waveform accordingly became a research focus of many scholars. The bathymetric inversion methods mainly include the peak detection method [9; 10], mathematical function fitting method [11; 12; 13], and convolutional filtering method [14; 15; 16]. [PERSON] [17] proposed a method to detect seabed reflection signal by removing the low-frequency part of the echo signal through high-pass filtering. [PERSON] [9] employed the peak detection method to search the echo signal from the surface and the bottom of the sea, and the method was found to be feasible. [PERSON] [18] decomposed two kinds of independent reflection signals, namely, sea surface echo signals and sea bottom echo signals, from which water depth was then estimated. [PERSON] [19] improved the inversion accuracy of water depth by using the phase ambiguity resolution technique to correct wave and tide measurements. [PERSON] [20] established a LiDAR echo waveform model based on the bi-Gaussian pulse fitting method and applied it to separate surface and bottom reflection signals. A minimum depth of 0.4 m was retrieved from their study. [PERSON] [21] utilized a Gaussian function entailing the double fitting of simulated waveform data to retrieve water depth. Their results showed that a mean absolute error (MAE) of 15.6 cm and a mean relative error (MRE) of 4.58% could be achieved at the water depth of 1-15 m using the LiDAR depth-detection model. In practice, a portion of the LiDAR effective signal beaches and the low-frequency signal is removed when using a filtering method, but the error is larger and the efficiency rate is slower when a peak detection method is used directly. The resultant fitting effect is unsatisfactory for deep waters due to the large energy gap between the echo signals from the sea surface and the sea bottom. Moreover, the bathymetric inversion error for deep waters is greater than that in shallow waters. The accuracy of depth inversion by passive remote sensing is lower than that by LiDAR. Meanwhile, bathymetric retrieval by active optical remote sensing has high precision and maneuverability. Consequently, bathymetric retrieval methods based on active and passive remote sensing were studied in recent years. [PERSON] [22] utilized the multi-band logarithmic linear model and Landsat 8 remote sensing image to invert shallow water depths (0-12 m) in areas around islands. The inversion algorithm of passive optical remote sensing was optimized using LiDAR bathymetric data. The average error of the inversion results was 0.2 m, and the median error was 0.1 m. [PERSON] [23] carried out bathymetric retrieval by active and passive remote sensing with the use of a passive optical image from Landsat-8 and airborne LiDAR depth data, and subsequently analyzed the results of different density LiDAR data for depth inversion by passive optical sensing. Their results showed that the change in LiDAR density only minimally affected the water depth inversion results, and the average relative error changed by 0.3%. Many scholars also conducted substantial research and experiments on sediment classification and topographic mapping using active and passive remote sensing data [24; 25; 26]. Depth inversion by active and passive remote sensing can not only improve the accuracy of passive optical depth inversion, but also reduce the use of active optical data, as well as reduce costs. On the basis of the above discussions, this study takes Ganquan Island as an example and uses the bathymetric values extracted from Aquarius LiDAR waveform data.