A MULTI-INDEX APPROACH IN KURUNEGALA TO ASSESS URBAN GROWTH AND ENVIRONMENTAL TRANSFORMATIONS (1995-2024)

Abstract

Urban expansion has become one of the most significant drivers of land use and environmental transformation in rapidly developing urban areas. This study develops a Landsat time series–based multi-index environmental assessment framework to examine the spatiotemporal dynamics of urban growth and associated environmental impacts in the Kurunegala Municipal Council area, Sri Lanka, from 1995 to 2024. Multi-temporal Landsat 5, 7, and 8 satellite imagery, along with census data, were analyzed using GIS and RS techniques. Land Use/Land Cover changes were first classified to quantify urban expansion, vegetation loss, and agricultural land transformation. Subsequently, vegetation dynamics were assessed using the NDVI, while urban growth patterns were evaluated using the NDBI. Thermal environmental changes were examined through LST and UHI analysis. An integrated ECI was developed by combining NDVI, NDBI, and LST to identify spatial environmental risk zones. The results indicate a substantial increase in built-up land by 31.9%, accompanied by a decline in dense vegetation cover and a reduction in agricultural land from 55% to 26.71%. LST increased significantly, reaching up to 34.1°C, with pronounced thermal hotspots concentrated in urban core areas. Correlation and regression analyses revealed a statistically significant negative relationship between NDVI and LST (r < 0, p < 0.05) and a positive relationship between NDBI and LST (r > 0, p < 0.05), confirming strong land–atmosphere interactions driven by urban expansion. The integrated ECI map identified approximately 27.4% of the study area as high environmental critical zones, highlighting areas vulnerable to thermal stress and ecological degradation. The findings demonstrate that rapid urban expansion, declining vegetation cover, and increasing surface temperatures are strongly interconnected processes shaping the urban environment of Kurunegala. This study provides a replicable GIS–RS-based analytical framework for monitoring urban environmental change and supporting sustainable urban planning and policy decision-making.