Surface water quality assessment using a remote sensing, GIS, and mathematical modelling framework
University of New Brunswick
The presence of various pollutants in water bodies can lead to the deterioration of both surface water quality and aquatic life. Surface water quality researchers are confronted with significant challenges to properly assess surface water quality in order to provide an appropriate treatment to water bodies in a cost-effective manner. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labour intensive, costly, and time consuming. Moreover, these methods can only provide individual concentrations of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. In contrast, remote sensing has shown significant benefits over conventional methods because of its low cost, spatial continuity, and temporal consistency. Thus, exploring the potential of using remotely sensed data for surface water quality assessment is important for improving the efficiency of surface water quality evaluation and water body treatment. In order to properly assess surface water quality from satellite imagery, the relationship between satellite multi-spectral data and concentrations of SWQPs should be modelled. Moreover, to make the process accessible to decision-makers, it is important to extract the accurate surface water quality levels from surface water quality raw data. Additionally, to improve the cost effectiveness of surface water body treatment, identifying the major pollution sources (i.e., SWQPs) that negatively influence water bodies is essential. Therefore, this PhD dissertation focuses on the development of new techniques for (1) estimating the concentrations of both optical and non-optical SWQPs from a recently launched earth observation satellite (i.e., Landsat 8), which is freely available and has the potential to support coastal studies, (2) mapping the complex relationship between satellite multi-spectral signatures and concentrations of SWQPs, (3) simplifying the expression of surface water quality and delineating the accurate levels of surface water quality in water bodies, and (4) classifying the most significant SWQPs that contribute to both spatial and temporal variations of surface water quality. The outcome of this PhD dissertation proved the feasibility of developing models to retrieve the concentrations of both optical and non-optical SWQPs from satellite imagery with highly accurate estimations. It exhibited the potential of using remote sensing to achieve routine water quality monitoring. Moreover, this research demonstrated the possibility of improving the accuracy of surface water quality level extraction with inexpensive implementation cost. Finally, this research showed the capability of using satellite data to provide continuously updated information about surface water quality, which can support the process of water body treatment and lead to effective savings and proper utilization of surface water resources.