Improvements to Shellfish Harvest Area Closure Decision Making Using GIS, Remote Sensing, and Predictive Models

Kelsey, R. Heath; Scott, Geoffrey I.; Porter, Dwayne E.; Siewicki, Thomas C.; Edwards, Donald G.

doi:10.1007/s12237-010-9264-7

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Fecal bacteria models have been attempted in limited applications [46], for example in beach recreation areas [47], storm water ponds [48] and shellfish management settings [49]. These models use different predictors, including in-situ water temperature measurements.…”

Section: Introductionmentioning

confidence: 99%

Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results

2019

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Bathing water quality has been monitored in the west coast of Tangier, Morocco due to increased urban and industrial discharge through the Boukhalef river, using in-situ bacteriological measurements which demand high economical and temporal costs. In this study, Landsat 8 Thermal Infrared Sensor (TIRS) images were used as an alternative to the classical method, for determining bathing water quality to help decision makers obtain up-to-date and cost-effective information for coastal environment protection. For this purpose, during spring and summer 2017, seven sampling points were examined in terms of bacteriological parameters: Total Coliforms (TC), Faecal Coliforms (FC), Intestinal Enterococci (IE) and Escherichia coli (E. coli). Also, a spatial-temporal analysis was performed in this temporal window to detect temperature anomalies and their spatial distribution along the coastal bathing area. In addition, a relationship between in-situ bacteriological parameter measurements and temperature from satellite images was analyzed. The results of the water temperature distribution showed the highest values next to the Boukhalef river mouth, as well as the poorest water quality according to in-situ measurements, while lower values and better water quality status were observed moving away from the Boukhalef river mouth. The relationship between water temperature and bacterial concentration showed a high correlation coefficient (R2 = 0.85). Consequently, the model development approaches used may be useful in estimating bacterial concentration in coastal bathing areas and can serve to create a monitoring system to support decision makers in the protection actions of the coast.

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Section: Introductionmentioning

confidence: 99%

Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results

2019

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“…First, remote sensing allows rainfall data to be collected and averaged over watersheds. According to Kelsey et al (2010), areally averaged rainfall values provide more predictive capability for bacteria concentrations than point estimates obtained from rain gauges. Second, remotely sensed data products can be collected, collated, and processed in automated fashion.…”

Section: Introductionmentioning

confidence: 99%

Model Results and Software Comparisons in Myrtle Beach, SC Using Virtual Beach and R Regression Toolboxes

Neet¹,

Kelsey²,

Porter³

et al. 2015

JSCWR

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Utilizing R software and a variety of data sources, daily forecasts of bacteria levels were developed and automated for beach waters in Myrtle Beach, SC. Modeled results are then shown for beach locations via a website and mobile device app. While R provides a robust set of tools for use in forecast modeling, the software has an extensive learning curve and requires skilled statistical interpretation of results. The Environmental Protection Agency (EPA) created the “Virtual Beach” software package to address these concerns. To evaluate the utility of the more user-friendly Virtual Beach modeling toolbox, predictive models were developed and model results were analyzed using the two software suites. Recommendations were made based on ease of use and several performance measures. Model results indicate the two software toolboxes yield comparable outputs. However, Virtual Beach tends to create more robust model forecasts, while R provides more options for model setup and outputs.

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“…Following rainfall events and subsequent high flow conditions, fluxes in E. coli were 50 times higher than under normal weather conditions [7]. Kelsey et al [8] developed a predictive model of bacterial contamination of shellfish using remote sensing precipitation data along with additional environmental and climatic data. The study findings indicated a substantial influence of temperature and salinity on bacteria concentrations.…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing to Identify Changes in Land Use and Sources of Fecal Bacteria to Support a Watershed Transport Model

Butler

Webster

Redden

et al. 2014

Water

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Abstract:The contamination of shellfish harvesting areas by fecal bacteria in the Annapolis Basin of Nova Scotia, Canada, is a recurring problem which has consequences for industry, government, and local communities. This study contributes to the development of an integrated water quality forecasting system to improve the efficiency and effectiveness of industry management. The proposed integrated forecasting framework is composed of a database containing contamination sources, hydrodynamics of the Annapolis Basin, Escherichia coli (E. coli) loadings and watershed hydrology scenarios, coupled with environmental conditions of the region (e.g., temperature, precipitation, evaporation, and ultraviolet light). For integration into this framework, this study presents a viable methodology for assessing the contribution of fecal bacteria originating from a watershed. The proposed methodology investigated the application of high resolution remote sensing, coupled with the commercially available product, MIKE 11, to monitor watershed land use and its impact on water quality. Remote sensing proved to be an extremely A proper evaluation of the MIKE 11 model was constrained due to limited water sampling. However, the model was very effective in predicting times of high contamination for use in the integrated forecasting framework, especially during substantial precipitation events.

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Improvements to Shellfish Harvest Area Closure Decision Making Using GIS, Remote Sensing, and Predictive Models

Cited by 12 publications

References 10 publications

Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results

Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results

Model Results and Software Comparisons in Myrtle Beach, SC Using Virtual Beach and R Regression Toolboxes

Using Remote Sensing to Identify Changes in Land Use and Sources of Fecal Bacteria to Support a Watershed Transport Model

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