Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images

Abstract

Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus making these studies suitable only for images that contain enough visible wavebands. To solve this problem, a Quasi-Analytical Algorithm (QAA) approach is proposed in this paper for estimating the water depths around Weizhou Island by developing a QAA to estimate the diffuse attenuation coefficients and simplifying the parameterization of the bathymetric model. The approach contains an initialization sub-approach and a novel global adjustment sub-approach. It is not only independent of other auxiliary data but also greatly reduces the number of unknowns. Experimental results finally demonstrated that the Root Mean Square Errors (RMSEs) were 1.01 m and 0.77 m for the ZY-3 image and the WorldView-3 (WV-3) image, respectively, so the approach is competitive to other QAA bathymetric methods. Besides, the global adjustment sub-approach was also seen to be superior to common smoothing filters: if the Signal-to-Noise Ratio (SNR) is as low as 42, i.e., ZY-3, it can smooth the water depths and improve the accuracies, otherwise can avoid the over-smoothing of water depths.

Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites

“…In NASA MODIS image analyses, a typical portion of the cloud-free satellite images over reef regions ranges from 20% to 30% [8,9]. Previous coral reef studies have been conducted using mid-spatial resolution satellite images (e.g., Landsat-8, Sentinel-2) or high-resolution images with low temporal frequency (e.g., IKONOS, Worldview) [3,4,[10][11][12][13]. Coral reef mapping could benefit from high temporal frequency satellite sensors (e.g., Planet Dove).…”

Method to design a live coral cover sensitive index for multispectral satellite images

“…The image is geo-referenced in the world geodetic system (WGS-84) with a ground resolution of 5.8 m. The geo-positioning accuracy is higher than 14.0 m without ground control after undergoing the on-orbit geometric calibration [32]. The signal-to-noise ratios (SNR) was reported to range approximately from 40-50 [33][34][35] for some typical signal radiances. More about the radiometric image quality of the ZY-3 multispectral image can be referenced to the result of Li et al [36].…”

“…The purpose is to eliminate the influence of the sunglint, atmospheric absorption and scattering of the solar radiation, i.e., make sure that the extracted subsurface remote sensing reflectance images contain only the signals of the water column and the bottom reflectance. As this preprocessing is not the focus of this study, we just simply make use of the approach developed by Huang et al [35] for extracting the subsurface remote sensing reflectance. The approach mainly consists of the following subapproaches: radiometric calibration, atmospheric correction (using both the tropical atmospheric model and maritime aerosols model in the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code), sunglint correction, and subsurface remote sensing reflectance estimation.…”

Earth observation applications for coastal sustainability: potential and challenges for implementation

“…Spatial data on coastal areas are key to the management of coastal and marine environments, covering aspects such as boundary and jurisdiction delineation; establishment of baseline, navigation, and seafloor change detection; issuing of consents and licences; and maintaining an inventory of coastal assets. Coastal bathymetry is an important source of spatial data used mainly for safe shipping and navigation (e.g., Jagalingam et al 2015), marine surveying and mapping (e.g., Huang et al 2017), but also classification of habitats and modelling of species distribution over a variety of sea floor terrains (e.g., Kostylev et al 2003;Lundblad et al 2006;Wilson et al 2007). Traditionally, active remote sensing methods, such as ship-borne multi-beam echo sounder (MBES) and airborne light detection and ranging (LiDAR), have achieved accurate bathymetry results (Calder and Mayer 2003;Van Son et al 2009;Kennedy et al 2014).…”

“…Research into the impacts of such coastal complexities on satellite-derived bathymetry and accompanying depth limitations is required to determine confidence levels and identify optimum algorithms (Halls and Costin 2016;Caballero et al 2019). Integrating high-resolution optical remote sensing with habitat classification data to ascertain the relationship between water reflectance, water depth, and seafloor habitat coverage is used to demonstrate the production of complementary bathymetry and habitat data from satellite data (Lyons et al 2011;Traganos et al 2018), with successful applications in coral reef bathymetry (Huang et al 2017) and seagrass mapping (Hossain et al 2015). Bathymetric mapping of valuable substrate, such as coral reefs, is a useful tool for monitoring vulnerable areas or areas that undergo topographic change (Vanderstraete et al 2003) and ESA's Sentinel-2 satellite captures nearly 90% of the world's coral reefs, providing vast potential for high-resolution monitoring of benthic change (Hedley et al 2018).…”