Assessing Groundwater Contamination Risks in Agricultural Communities Using Remote Sensing

Thesis Abstract

Groundwater contamination from agricultural activities poses a significant threat to public health and environmental sustainability in rural communities worldwide, yet comprehensive spatial assessments remain limited due to resource constraints and data accessibility. This study aims to evaluate the risks of groundwater contamination in agricultural communities through the application of remote sensing technologies, integrating geographic information systems (GIS), land use analysis, and geochemical modeling. The specific objectives are to (1) identify spatial patterns of agricultural land use and chemical fertilizer/pesticide application, (2) analyze the correlation between land use practices and groundwater quality indicators, and (3) develop a predictive model to assess contamination risk levels across the study region. The research adopts a mixed-methods design, combining satellite image analysis, field sampling, and statistical modeling within a positivist paradigm grounded in the Theory of Planned Behavior, which provides insight into how farmers' practices influence environmental outcomes. The study population comprises agricultural communities within the Lower Mississippi River Basin, with a purposive sample of 150 farm plots selected based on land use intensity, cropping patterns, and proximity to groundwater abstraction points. Data collection involves satellite imagery analysis using Landsat 8 OLI/TIRS data to map land use and identify areas with intensive fertilizer and pesticide application, complemented by field sampling of groundwater sources for analysis of physicochemical parameters including nitrates, nitrites, phosphates, and pesticide residues. Groundwater samples (n=200) are analyzed via ion chromatography and gas chromatography-mass spectrometry (GC-MS). The remote sensing data are processed with supervised classification techniques in ENVI software, while statistical analyses, including multiple regression and logistic regression models, are conducted in SPSS to examine relationships between land use variables and water quality indicators. A geospatial risk map is generated through overlay analysis in ArcGIS to visualize contamination hotspots. Validation of the models incorporates cross-validation techniques, and the reliability of chemical analyses is ensured through calibration with standard reference materials. Expected findings indicate significant spatial clusters of contamination risk corresponding with areas of intensive fertilizer and pesticide use, with statistical models elucidating strong correlations (p<0.05) between land management practices and elevated nitrate and pesticide levels in groundwater. The study anticipates that remote sensing-derived land use data can reliably predict areas at high risk of contamination, enabling targeted interventions. These findings will fill existing literature gaps by providing an integrative model that combines remote sensing with groundwater chemistry analysis to assess environmental risks in agricultural settings, thereby advancing spatial risk assessment methodologies. The theoretical contribution lies in extending the application of the Theory of Planned Behavior to environmental risk mitigation, emphasizing the influence of farmers’ practices on groundwater quality. The primary conclusion underscores the critical role of land use management and farmer behavior in reducing contamination risks, recommending that policymakers enforce best agricultural practices informed by spatial risk assessment outputs. The study advocates for the integration of remote sensing-based monitoring with community-level awareness programs to promote sustainable water use. It also suggests further research into temporal changes in land use practices and their cumulative effect on groundwater quality, proposing the development of a real-time remote sensing-based early warning system for groundwater pollution. Overall, this study demonstrates the feasibility of employing remote sensing technologies for environmental risk assessment in agricultural communities, contributing valuable tools for sustainable resource management and policy formulation aimed at safeguarding groundwater resources.