Dissolved oxygen estimation in aquaculture sites using remote sensing and machine learning

Chatziantoniou, Andromachi; Spondylidis, Spyros; Zachou, Orestis Stavrakidis; Papandroulakis, Nikos; Topouzelis, Konstantinos

doi:10.1016/j.rsase.2022.100865

Cited by 6 publications

(9 citation statements)

References 19 publications

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“…But sea surface salinity is only provided through satellite derived models at a course resolution of 4km and is not suitable for the purpose of this study. Further implementations could try to incorporate downscaling techniques to include such information (Chatziantoniou et al, 2022).…”

Section: Datamentioning

confidence: 99%

Can we actually monitor the spatial distribution of small pelagic fish based on Sentinel-3 data? An example from the North Aegean Sea (Eastern Mediterranean Sea)

et al. 2023

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Fish population spatial distribution data provide essential information for fleet monitoring and fishery spatial planning. Modern high resolution ocean color remote sensing sensors with daily temporal coverage can enable consistent monitoring of highly productive areas, giving insight in seasonal and yearly variations. Here is presented the methodology to monitor small pelagic fish spatial distribution by means of 500m resolution satellite data in a geographically and oceanographically complex area. Specifically, anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) acoustic biomass data are modeled against environmental proxies obtained from the Sentinel-3 satellite mission. Three modeling techniques (Logistic Regression, Generalized Additive Models, Random Forest) were applied and validated against the in-situ measurements. The accuracy of anchovy presence detection peaked at 76% and for sardine at 78%. Additionally, the spatial distribution of the models’ output highlighted known fishing grounds. For anchovy, biomass modeling highlighted the importance of bathymetry, SST, and the distance from thermal fronts, whereas for sardine, bathymetry, CHL and chlorophyll fronts. The models are applied to a sample dataset to showcase a potential outcome of the proposed methodology and its spatial characteristics. Finally, the results are discussed and compared to other habitat studies and findings in the area.

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Section: Datamentioning

confidence: 99%

Can we actually monitor the spatial distribution of small pelagic fish based on Sentinel-3 data? An example from the North Aegean Sea (Eastern Mediterranean Sea)

et al. 2023

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“…Thus, satellite remote sensing is a useful complementary method to traditional monitoring approaches [15] and has great potential for use in water quality parameter (WQP) monitoring. Recent research has indicated that satellite remote sensing technology can be effectively used for indirect monitoring of DO concentrations across various types of water bodies [16][17][18][19][20][21][22][23]. Specifically, researchers have developed numerous models based on remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat), including regional statistical models [16] and machine learning algorithms (e.g., random forest (RF) and support vector regression (SVR)) [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has indicated that satellite remote sensing technology can be effectively used for indirect monitoring of DO concentrations across various types of water bodies [16][17][18][19][20][21][22][23]. Specifically, researchers have developed numerous models based on remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat), including regional statistical models [16] and machine learning algorithms (e.g., random forest (RF) and support vector regression (SVR)) [17][18][19][20]. These models indirectly infer the DO concentrations in different water bodies globally, including the Yangtze River estuary [16], the California Current System [21], the coastal waters of Korea [22], and smaller water bodies, such as lakes [18].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

Dong,

Wang,

Song

et al. 2024

Remote Sensing

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The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

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“…For example, a back-propagation (BP) neural network was successfully leveraged to estimate chemical oxygen demand (COD), the permanganate index (COD Mn ), the total nitrogen (TN), and TP [28][29][30]. In addition, decision trees, support vector machine regression (SVR), random forest (RF), and other methods are also widely used [31][32][33]. Some studies compared the performance of empirical methods and machine learning methods on the same data, and the results often showed that machine learning had higher accuracy [5,28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Gao,

Shangguan,

Sun

et al. 2024

Remote Sensing

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Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

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Dissolved oxygen estimation in aquaculture sites using remote sensing and machine learning

Cited by 6 publications

References 19 publications

Can we actually monitor the spatial distribution of small pelagic fish based on Sentinel-3 data? An example from the North Aegean Sea (Eastern Mediterranean Sea)

Can we actually monitor the spatial distribution of small pelagic fish based on Sentinel-3 data? An example from the North Aegean Sea (Eastern Mediterranean Sea)

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

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