Spatiotemporal analysis of vegetation influence on land surface temperature and urban heat Island using a digital twin framework

This study investigates the spatiotemporal relationship between vegetation cover, land surface temperature (LST), and urban heat island (UHI) effects in Khulna City Corporation from 2010 to 2024, using the Digital Twin Framework. The NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) data from the summer and winter periods were analyzed to assess the impact of urbanization on the thermal environment of a rapidly growing coastal town. Digital Twin (DT) approach integrates satellite data, spatial modeling, and visualization to assess temporal trends, spatial diversity, and vegetation-temperature relationships. The UHI intensity increased with the summer LST from 32.31 °C in 2010 to 33.90 °C in 2020, while the winter LST remained stable but slightly declined in 2024. The NDVI showed a limited improvement, indicating a loss of vegetation due to urban sprawl. Spatial analysis confirmed that the dense vegetation in the northern part of the area had lower LST and UHI values, while the central and southern built-up areas experienced increased heat. The scatter plots showed a negative relationship between NDVI and LST and UHI, which underlined the critical role of vegetation in cooling. Digital Twin (DT) framework has demonstrated considerable potential in integrating multiple data sources for the analysis and prediction of urban heat dynamics, offering valuable decision support capabilities for climate adaptation and sustainable urban planning. The findings underscore the pressing need for policies that promote urban greening, water protection, and climate-sensitive land use strategies to mitigate heat stress in coastal cities facing rapid urbanization and the impacts of climate change.