Groundwater quality assessment using remote sensing and related datasets

Abstract

The study was carried out in Selangor and Kuala Lumpur for developing a model to assess groundwater quality using remotely sensed, borehole and ancillary datasets. Remote sensing data were useful in extracting groundwater contamination sources due to specific land usage such as agricultural activities and urbanization. They were also useful in generating digital elevation model (OEM) and extracting geological features including lineaments and faults, which influenced the movement of contamination sources to the aquifer. Borehole data providing relevant subsurface geological information such as aquifer media, vadose zone media, hydraulic conductivity and groundwater level. These information together with population census data formed the basis in formulating the suitable model to access the groundwater quality. The model was used to generate the groundwater contamination risk map. Urban and highly populated area having shallow limestone aquifer identified as having the highest risk of groundwater contamination. On the contrary, groundwater located within forested mountainous aquifer was identified as having the lowest risk of contamination. Future groundwater quality was also modelled using predicted landuse changes and population density increased for the year 20 to, 2020 and 2030.