Population, Tourism and Climate in Relation to Land Use/Land Cover Change: A 39-Year (1985–2024) Geospatial Analysis of the Nilgiris District, Tamil Nadu, India

Vollständiger Abstract

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Aims: The study aims to analyse the spatio-temporal pattern of land use/land cover (LULC) change in the Nilgiris district, Tamil Nadu, between 1985 and 2024, and to examine its relationship with population growth, tourism and climate. Study Design: Retrospective, multi-temporal geospatial analysis combining satellite-derived LULC classification with correlation and regression analysis of climatic and socio-economic variables. Place and Duration of Study: Nilgiris district, Tamil Nadu, India; secondary data spanning 1985–2024 (climate data 1996–2024; tourism data 2015–2025). Methodology: LULC maps for 1985, 2005 and 2024 were derived from Landsat and Sentinel-2 imagery accessed through the NRSC Bhuvan portal and Google Earth Engine, classified into five broad classes, and processed in QGIS. Change-detection statistics were correlated with Census population data, Tamil Nadu Tourism Development Corporation tourist-arrival records, and India Meteorological Department temperature and rainfall data using Pearson correlation and ordinary least-squares regression; because LULC data were available for only three reference years, this temporal mismatch is treated as a limitation of the analysis. Results: Forest cover declined from 77.03% (1964.89 km2) in 1985 to 55.66% (1420.34 km2) in 2024, at an annual rate of −28.94 km2 yr−1 during 2005–2024, while agriculture rose from 20.89% to 41.11%. Built-up area nearly doubled (0.75% to 1.33%), expanding fastest (0.74 km2 yr−1) after 2005, despite the district recording negative population growth (−3.53%, 2001–2011) even as annual tourist arrivals exceeded 20 lakh. Temperature was strongly correlated with built-up area (r = 0.937) and was a significant predictor of forest loss (β = −502.63, P = .008), agricultural gain (β = 452.91, P = .009) and settlement growth (β = 24.04, P < .001; R2 = 0.89). Rainfall was a weaker, though significant, predictor of forest area only (β = 0.791, P = .04). Conclusion: Agricultural expansion, rather than urban or tourism-related construction, was the principal proximate driver of forest loss based on the land-use transformation matrix. Built-up expansion, though comparatively small in area, occurred fastest after 2005 despite negative resident population growth, coinciding with sustained tourist inflow and rising temperature; tourism and climatic warming therefore appear more closely associated with settlement expansion than with resident population growth. As the analysis is correlational, these associations should not be interpreted as proven causal relationships. Integrated land-use planning that regulates tourism-related construction, promotes sustainable agricultural intensification, and monitors climate–land-use feedbacks is needed to reconcile development with conservation in this ecologically sensitive biosphere reserve.