Effect of water surface area on the remotely sensed water quality parameters of Baysh Dam Lake, Saudi Arabia

2020 ◽  
Vol 194 ◽  
pp. 369-378
Author(s):  
Mohamed Elhag ◽  
Ioannis Gitas ◽  
Anas Othman ◽  
Jarbou Bahrawi
Keyword(s):  
Water Quality ◽  
Saudi Arabia ◽  
Surface Area ◽  
Water Surface ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effect Of Water ◽  
Water Surface Area

scholarly journals Effect of Water Surface Area on the Remotely Sensed Water Quality Parameters of Baysh Dam Lake, Saudi Arabia

2019 ◽  
Author(s):  
Mohamed Elhag ◽  
Ioannis Gitas ◽  
Anas Othman ◽  
Jarbou Bahrawi
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Area ◽  
Water Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Surface Area

Abstract. Water quality parameters help to decide the further use of water based on its quality. Changes in water surface area in the lake shall affect the water quality. Chlorophyll a, Nitrate concentration and water turbidity were extracted from satellite images to record each variation on these parameters caused by the water amount in the lake changes. Each water quality measures have been recorded with its surface area reading to analyses the effects. Water quality parameters were estimated from Sentinel-2 sensor based on the satellite temporal resolution for the years 2017–2018. Data were pre-processed then processed to estimate the Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI). The Normalized Difference Water Index (NDWI), was used to calculate and record the changes in the water surface area in Baysh dam lake. Results showed different correlation coefficients between the lake surface area and the water quality parameters estimated Remote Sensing data. The response of the water quality parameters to surface water changes was expressed in four different surface water categories. MCI is more sensitive to surface water changes rather than GNDVI and NDTI. Neural network Analysis showed a resemblance between GNDVI and NDTI expressed in sigmoidal function while MCI showed a different behavior expressed in exponential behavior. Therefore, monitoring of the surface water area of the lack is essential in water quality monitoring.

scholarly journals Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

2018 ◽  
Vol 378 ◽  
pp. 85-92 ◽  
Author(s):  
Ronald Muchini ◽  
Webster Gumindoga ◽  
Sydney Togarepi ◽  
Tarirai Pinias Masarira ◽  
Timothy Dube
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Point Sources ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Information ◽  
Web Portal

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

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