International Journal of Marine Science, 2025, Vol.15, No.1, 45-52 http://www.aquapublisher.com/index.php/ijms 49 example, if there is a problem in a sea area, it can automatically go over to view and sample. This is especially useful when studying harmful algae outbreaks or ascending flow fronts ( Zhang et al., 2019 ). AUVs are also used in the construction of underwater Internet of Things, such as helping detect pollution, observing ocean changes, arranging tasks and division of labor. It can also combine remote sensing data to make smarter decisions (Ullah et al., 2024). 6 Case Studies of Inversion Algorithm Applications 6.1 Monitoring coral reef degradation Remote sensing technology has now become an important means to observe changes in coral reefs. Researchers will use hyperspectral images and inversion algorithms to determine the health status and species composition of coral reefs. There is an algorithm called semi-analytical model that is very commonly used. It can extract underwater information from images, such as what type of seabed is, how deep is the water, and what components are there. This method can combine images with actual physical and biological characteristics to draw a fine three-dimensional seabed structure diagram. This approach is particularly practical for ecosystems that are particularly sensitive and changeable, as fast as coral reefs. It can detect even small environmental changes, whether it is due to climate change or human interference. The study found that this model can clearly distinguish different seabed types when estimating water depth, such as sand bottoms, rocky areas, or places covered by coral debris. This technology has been successfully used in Hawaii, Reunion Island and other places, and the effect is very good. It can also help us better understand the habitat composition of benthic organisms (Figure 2) (Petit et al., 2017; Goodman et al., 2020). In addition, a device called RASC-LSD is also used to improve the estimation accuracy and stability of water depth in shallow sea areas. Figure 2 Derived bathymetry for 2001 and 2017 Molokai data at 18 m spatial resolution: (a) 2001 true-color image and derived water depth; (b) 2017 true-color image and derived water depth. Areas with no data and masked areas of land, cloud, cloud shadow, and wave breaks are shown in white (Adopted from Goodman et al., 2020) 6.2 Tracking oil spills and marine pollution Remote sensing and inversion algorithms can also be used to monitor ocean pollution, especially oil spills. Through hyperspectral remote sensing images and some spectral separation algorithms, scientists can estimate the amount of oil in the water. Even at low concentrations, these methods can be detected (Lu et al., 2023). In addition, polarization remote sensing technology can also measure the refractive index of the oil film. This is helpful in eliminating sun-reflective interference, allowing us to more accurately estimate the area and thickness of oil leakage (Zhou et al., 2020). There are still some deep learning algorithms, such as multi-scale multi-dimensional residual CNN, which are also used to identify oil pollution. This type of method can process images more finely and the recognition accuracy can reach more than 95% (Seydi et al., 2021).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==