Remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain

Trescott, Adam; Park, M.-H.

doi:10.2166/wst.2013.661

Cited by 11 publications

(6 citation statements)

References 26 publications

(32 reference statements)

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“…At the same time, it can eliminate or suppress the influence on water depth inversion caused by changes in satellite attitude, the angle of the sun’s altitude, the wave on the water’s surface, and the scanning angle. The blue-green band with relatively strong water penetration is usually chosen from the two bands [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Research on Bathymetric Inversion Capability of Different Multispectral Remote Sensing Images in Seaports

Shen

Wang

Chen

et al. 2023

Sensors

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In recent years, remote sensing has become an indispensable supplementary method for determining water depth in the seaports. At present, many scholars use multi-spectral satellite data to invert the water depth of the seaports, but how to select the appropriate satellite data in the seaports area is worth exploring. In this article, the differences in the retrieving ability between domestic and foreign multispectral images are compared, through building the random forest model and the band ratio model, which use different multispectral images to conduct retrieving water depth in Nanshan Port in conjunction with the WBMS multi-beam sounding system. The band ratio model and random forest model are chosen for water depth exploration, remote sensing images use GF-6, GF-2, Sentinel-2B, and Landsat 8 OLI data, which are all popular and easily accessible. The final experiment results from the constant adjustment of the model parameter show that the domestic series of GF-6 images performed the best in this experiment. The Root Mean Square Error (RMSE) and Mean Relative Error (MRE) of the random forest model are only 1.202 and 0.187, respectively. Simultaneously, it is discovered that the ‘Red Edge’ band of GF-6 is also very helpful in improving the accuracy of water depth inversion, which is rarely mentioned in previous studies. To some extent, the preceding studies demonstrate that it is possible to investigate water depth using common multispectral remote sensing images. In the case of some bathymetry inversion models or in some waters, the aforementioned study demonstrates that it is possible to examine the water depth using domestic remote sensing images that are superior to foreign multispectral images in terms of bathymetry inversion ability.

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Section: Methodsmentioning

confidence: 99%

Research on Bathymetric Inversion Capability of Different Multispectral Remote Sensing Images in Seaports

Shen

Wang

Chen

et al. 2023

Sensors

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“…For noise reduction, the resulting image is subjected to a cluster removal of N ¼ 4 pixels using the bwareaopen function, available in MATLAB ® environment. Applying the mask to band 2 Band 2 was used for extracting characteristics and classifying, as it yielded the best results in terms of classification during the training and test phases in relation to the other bands available in the Landsat 8 satellite images (Trescott & Park 2013;Wang et al 2018).…”

Section: Segmentation Methods and Masksmentioning

confidence: 99%

“…In 2013, the Landsat 8 satellite was launched with two sensors OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), and 11 satellite frequency bands with characteristics that allow the identification of different contents in the target regions. Trescott & Park (2013) use the bands 1, 2 and 3 for analyzing eutrophication and Wang et al (2018) for analyzing oxygen and dissolved permanganate concentrations. In addition to bands 2 and 3, Pisani et al (2016) include band 4 to determine the sedimentation present in two Brazilian rivers.…”

Section: Introductionmentioning

confidence: 99%

Analysis of water quality at hydrographic basin scale using satellite images, co-occurrence matrices and Bayes classifier

Silva

Costa

et al. 2021

Water Supply

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Given the increased risks of water scarcity and the presence of polluting agents in water resources, this paper aims at the development and presentation of a computational tool capable of assessing water quality based on digital processing techniques applied to satellite images. Initially, a database was created for Brazilian regions, consisting of hydrographic basins satellite images associated with the Water Quality Index (WQI), according to the criteria established by the National Water Agency (ANA). Hitherto, the database consists of 85 images, 61 are used in the training stage and 24 in the testing stage. In both stages, the images are subjected to thresholding using the Otsu's Method, binarization, linear expansion on saturation, application of a Laplacian filter, extraction of characteristics by using co-occurrence matrices and classification by the Bayes Discriminant. Such techniques were also implemented on a computational platform in MATLAB® environment, responsible for the interface between the system and users. The proposed system presented an approximate 70% success rate regarding the classification of WQIs, which can be improved as more information is made available to improve the databases.

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“…Saab et al (2019) combined smart water monitoring with a risk assessment approach to ensure early detection of water contamination (turbidity and chlorine) in Strasbourg, France. Trescott and Park (2013) developed remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain (Canada/USA). Similar technologies (smart grids) have also been used in smart water quality monitoring systems (Fang et al, 2012; Gao et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Delineating Effluent Exposure and Cumulative Ecotoxicological Risk of Metals Downstream of a Saskatchewan Uranium Mill Using Autonomous Sensors

Cupe-Flores

Mendes

Panigrahi

et al. 2022

Enviro Toxic and Chemistry

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There is increasing interest in using autonomous sensor technology to monitor aquatic ecosystems in real time and in employing such monitoring data to perform better ecological risk assessments. At seven locations in McClean Lake in northern Saskatchewan (Canada) that received diluted uranium milling effluent, we deployed sensor units to track effluent distribution and help predict potential biological effects on aquatic invertebrates. Water was also collected from each location on multiple occasions to measure major ions, dissolved metals, and routine water quality, and sediment was sampled to analyze total metals. The ecotoxicological risk to aquatic invertebrates was estimated using hazard quotients (HQs). The cumulative risk was estimated by summing the individual HQs, and the major ions risk was based on total osmolarity. The results indicated temporal and spatial variations in effluent exposure based on sensor electrical conductivity (EC) measurements in the McClean Lake East Basin. Individual HQs for water ranged from “moderate” (0.40–0.69) to “very high” (greater than 1) for silver, cadmium, arsenic, selenium, mercury, iron, and thallium. At all sites, major ions risk was less than 1. Individual HQs for sediment were “moderate” (0.40–0.69), “high” (0.7–0.99), and “very high” (greater than 1) for vanadium and cadmium. The cumulative risk in water and sediment for all metals combined was greater than 1 at some sites in Vulture Lake (which discharged into McClean Lake) and in McClean Lake itself. A more detailed estimation of the risks for aqueous selenium and arsenic (the only two metals that had good correlation with sensor EC data) indicated that their 90th percentile HQ values were less than 1 in McClean Lake, suggesting that these contaminants of concern do not represent a significant direct risk to aquatic invertebrate communities. Environ Toxicol Chem 2022;41:1765–1777. © 2022 SETAC

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Remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain

Cited by 11 publications

References 26 publications

Research on Bathymetric Inversion Capability of Different Multispectral Remote Sensing Images in Seaports

Research on Bathymetric Inversion Capability of Different Multispectral Remote Sensing Images in Seaports

Analysis of water quality at hydrographic basin scale using satellite images, co-occurrence matrices and Bayes classifier

Delineating Effluent Exposure and Cumulative Ecotoxicological Risk of Metals Downstream of a Saskatchewan Uranium Mill Using Autonomous Sensors

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