Preliminary examination of influence of Chlorophyll, Total Suspended Material, and Turbidity on Satellite Derived-Bathymetry estimation in coastal turbid water

Ashphaq, Mohammad; Srivastava, Pankaj Kumar; Mitra, Debashis

doi:10.1016/j.rsma.2023.102920

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…In the high sea benthic zones, machine learning methods using linear regression, Random Forest (RF) and Support Vector Regression (SVR) are used to estimate water turbidity, and chlorophyll (mirco and macro algae blooms) [72]. In addition, deep learning models and dimensionality reduction methods are used to precisely and effectively monitor marine mammals-cetaceans especially in Canada [78].…”

Section: Relevance In Deep Sea Sheries Managementmentioning

confidence: 99%

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Isaac,

Baker,

Bhavani

et al. 2023

Preprint

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The value of fish resources to socioeconomic development is immense. However, there is growing evidence that continuous fishing-both at sea and in freshwater sources is threatening the availability of fish resources evidenced by the dwindling fisheries resources. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable fishing practices. Owing to this we use a systematic literature review to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems. The review documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change and implement new policies and regulations thus promoting sustainable fisheries management.

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Section: Relevance In Deep Sea Sheries Managementmentioning

confidence: 99%

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Isaac,

Baker,

Bhavani

et al. 2023

Preprint

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“…The optimal hyperparameters of the KNN model are presented in Table 1. The SVM model, with the radial basis function (RBF) kernel, can effectively address minor sample problems and establish a reliable relationship between satellite imagery and water depth [16,58,59]. Furthermore, the nonlinear kernel function of SVM, which transforms the training set into a high-dimensional feature space, enhances the generalization capacity.…”

Section: Modelsmentioning

confidence: 99%

“…The SDB methods offer a bathymetric view on a global scale; however, they inevitably possess the following limitations. The accuracy of the water depth estimation can be influenced by various factors, including data quality, water conditions and bottom features [59]. The SDB methods are mainly applicable to shallow water regions, where the availability and accuracy of bathymetry information could seriously reduce once water depths exceed 20 m. In addition, due to cloud coverage restricting the quantity of photons hitting the ground, ICESat-2 may provide a poor record of bathymetric data, even in near islands with high transparency [69].…”

Section: Potential Limitations Analysis Of Sdb Modelsmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

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“…In the high sea benthic zones, machine learning methods e.g. Random Forest (RF), and Support Vector Regression (SVR) are used to estimate water turbidity, and chlorophyll (micro and macro algae blooms) (Ashphaq et al, 2023), and to precisely and effectively monitor marine mammals-cetaceans, especially in Canada (Boulent et al, 2023). This is done mainly by using a robust analysis of massive datasets of photographs of cetaceans combined deep learning models and dimensionality reduction methods, for instance, via the creation of a binary land cover map, thus creating a more effective and precise method of monitoring cetaceans thanks to the application of data analysis/analytics techniques.…”

Section: Relevance In Deep-sea Fisheries Managementmentioning

confidence: 99%

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

Lukambagire,

Matovu,

Rao R.

et al. 2023

ASP.FC

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Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

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Preliminary examination of influence of Chlorophyll, Total Suspended Material, and Turbidity on Satellite Derived-Bathymetry estimation in coastal turbid water

Cited by 5 publications

References 25 publications

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

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