Urban Heat Mitigation through Intelligent Green Design: A Case Study of Terminalia mantaly using AI and Environmental Sensing

The increasing rate of global warming due to urbanization, greenhouse gas emissions, and deforestation has amplified the urban heat island (UHI) effect, particularly in tropical and semi-arid areas. Strategic tree planting has become an essential nature-based solution for climate mitigation. In this study, the cooling capacity of Terminalia mantaly, a quick-growing, evergreen tree with broad canopy, is assessed using advanced AI tools, remote sensing methodologies, and in-field sensor observations. A multi-modal research methodology was used to investigate the influence of the tree on its microenvironment. Thermography and deep learning-based algorithms (YOLOv8, Mask R-CNN) indicated that Terminalia mantaly lowered surface temperatures under its canopy by a maximum of 4.8°C, the largest effects occurring around midday. Satellite-based NDVI and LST analysis showed that tree-covered zones had NDVI values of 0.74 and LST reductions of 3.5°C compared to adjacent unvegetated areas. AI models, particularly Random Forest and LSTM networks, achieved over 88% accuracy in predicting thermal changes and temporal cooling patterns. Ground-based environmental sensors confirmed a 3.0°C drop in ambient temperature, 7% increase in relative humidity, and 100% increase in soil moisture beneath the canopy. Simulations with Unity 3D and finite element modeling demonstrated a radial cooling effect up to 5 meters, and a 45% reduction in radiative heat absorption because of leaf structural scattering. Significantly, Terminalia mantaly had foliage year-round, providing consistent shade and cooling without the loss of seasonal canopy. The research concludes that Terminalia mantaly is a viable species for urban climatic resilience. Its high growth rate, dense canopy, minimal leaf shedding, and constant coverage make it the best for heat stress mitigation, energy savings, and ecological sustainability in urban areas. This research recommends the use of AI-supported afforestation as well as precision ecological planning in fighting global warming