Climate-Adaptive Urban Landscapes for Supporting Wildlife Populations Under Global Warming Scenarios

Abstract

Habitat fragmentation, biodiversity loss and the urban heat island effect are all driven by rapid urbanization and global warming and are major threats to wildlife populations. This study suggests a framework for the implementation of a climate adapted city landscape by combining Green Infrastructure, ecological corridors, climate resilient vegetation, NbS, GIS, AI and IoT based monitoring of the environment to enhance ecological resilience of urban areas. Sentinel-2 MultiSpectral Instrument (MSI) data was used for the implementation of the framework for land-use and vegetation mapping, while Landsat-9 Operational Land Imager/Thermal InfraRed Sensor (OLI/TIRS) data was used for land surface temperature and urban heat island assessment. Spatial analysis with the help of artificial intelligence was used to assess the suitability of the habitats and to improve the ecological connectivity in the future climatic scenarios. Habitat connectivity, normalized difference vegetation index (NDVI), habitat suitability index, species richness, land surface temperature (LST), carbon sequestration potential and ecosystem resilience were used for performance analysis. Throughout the implementation, the habitat connectivity increased from 0.58 to 0.71, the mean NDVI from 0.43 to 0.56, the habitat suitability index from 0.61 to 0.76, the number of species in urban habitat increased by 18.4%, the mean land surface temperature decreased by 2.7°C, the estimated carbon sequestration increased by 16.9%, and the overall ecosystem resilience index increased by 0.81 to 0.64. The results show that the use of Sentinel-2 MSI and Landsat-9 observations in conjunction with AI-driven planning has a significant impact on the protection of urban biodiversity, climate action and bolstering the creation and management of resilient, wildlife-friendly cities in a future climate.