Decadal Land Use and Land Cover Change in the Southernmost District of India: A Remote Sensing and GIS-Based Study

S. Chrisben Sam; Gurugnanam Balasubramaniyan; Bagyaraj Murugesan; Bairavi Swaminathan; Shankar Karupannan; Suresh Mani; V. Sudhakar

doi:10.34293/sijash.v13i3.9682

S. Chrisben Sam Centre for Applied Geology, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India https://orcid.org/0000-0003-4721-8202
Gurugnanam Balasubramaniyan Centre for Applied Geology, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India https://orcid.org/0000-0002-8775-7123
Bagyaraj Murugesan Centre for Applied Geology, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India https://orcid.org/0000-0002-3076-8884
Bairavi Swaminathan Centre for Applied Geology, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India https://orcid.org/0000-0002-3577-5282
Shankar Karupannan Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-5014-7885
Suresh Mani Department of Civil Engineering, Jayalakshmi Institute of Technology, Thoppur, Dharmapuri District, Tamil Nadu, India
V. Sudhakar Centre for Applied Geology, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India https://orcid.org/0000-0002-1365-3276

DOI: https://doi.org/10.34293/sijash.v13i3.9682

Keywords: Land Use/ Land Cover, Landsat, Maximum Likelihood Classification, Kanyakumari, Environmental Management, Remote Sensing

Abstract

The dynamic nature of Land Use and Land Cover (LULC) patterns is a critical indicator of environmental and socioeconomic transformations. This study investigated LULC changes in the Kanyakumari district, Tamil Nadu, over two decades, from 2004 to 2024, using satellite remote sensing data and GIS techniques. Landsat 5, 8, and 9 data were used to analyze and identify shifts in vegetation, settlement, barren land, saltpans, water bodies, and beach sands. The features were selected using supervised classification with the Maximum Likelihood procedure and the accuracy was measured using metrics such as overall accuracy and the kappa coefficient, which ensured the reliability of the results. The accuracy rates were consistently over 94%. The findings show that the settlement areas have increased from 313.94 km² in 2004 to 357.84 km² in 2024, while the areas of vegetation and saltpan have been steadily decreasing. This trend highlights how factors such as population growth, urban development, and climate change affect land resources. Using geospatial tools to monitor land use and land cover changes has provided crucial insights into sustainable land management and planning. This study emphasises the importance of taking proactive policy steps to tackle land degradation and support balanced growth in the area.