Evaluating the Influence of Land Use Dynamics on Groundwater Potential Zones Dindigul West Firka using Satellite-Based Mapping

Abstract

In the semiarid Dindigul West firka of Tamil Nadu, groundwater serves as a vital resource supporting both domestic consumption and agricultural activities across an area of approximately 127.56 km². Dindigul West Firka is experiencing gradual peri-urbanisation, with villages transforming into semi-urban settlements. The expansion of housing, roads, and public infrastructure has increased pressure on land and water resources. Small-scale industries, commercial establishments, and service activities have expanded along major roads and settlement clusters. Drainage is primarily seasonal, with small streams and tanks connected to the tributaries of the Kodaganar River, which flows near Dindigul town. Dindigul West Firka experiences a tropical semi-arid climate. Groundwater potential zones were delineated through the combined application of Remote Sensing (RS) and Geographic Information System (GIS) techniques, and the resulting spatial patterns were validated using Inverse Distance Weighting (IDW) interpolation. Satellite data and ancillary maps were employed to develop thematic layers of lithology, geomorphology, lineament density, slope, soil type, land use/land cover, elevation, drainage density, NDVI, NDWI, and rainfall. Relative weightages were assigned for GIS analysis to develop a composite Groundwater Potential Zone (GWPZ). The resulting groundwater potential map is classified into three zones: high (40%), moderate (35%), and low (25%). The central and southern parts of the Firka showed higher groundwater potential due to favourable lithology, dense lineament networks, and gentle slopes. In contrast, the southwestern rocky uplands exhibited low potential. The outcome is validated in groundwater prospecting and suggests sustainable water resource management in Dindigul West Firka. Multitemporal multispectral satellite imagery was used to generate LULC maps and detect land-use changes using supervised classification and post-classification comparison techniques. Higher weights were assigned to the LULC map. Future research should integrate the study of long-term groundwater level trends, utilising high-resolution satellite data, artificial intelligence, and advanced machine-learning models to enhance the accuracy and sustainability of groundwater potential assessments.