Impact of nature-based solutions (NBS) on urban surface temperatures and land cover changes using remote sensing and machine learning

Abstract

Urban areas are increasingly vulnerable to climate change, with rising urban heat driven by the replacement of vegetation with impervious surfaces. Nature-Based Solutions (NBS) provide promising strategies to mitigate urban heat while promoting environmental sustainability. This study analyzes the spatiotemporal dynamics of Land Cover (LC) and Land Surface Temperature (LST) in Guimarães, Portugal, from 2013 to 2023, and forecasts scenarios for 2028 using advanced machine learning techniques.

Key methodologies included supervised LC classification via Random Forest (RF), LC prediction using the MOLUSCE plugin, and LST prediction using ensemble models such as XGBoost, Bagging, and AdaBoost, with XGBoost demonstrating the highest performance (R² = 0.9543). The results highlight significant transitions from barren and built-up areas to vegetation, reflecting localized environmental recovery. NBS interventions, such as green roofs and urban gardens, achieved measurable cooling effects, reducing temperatures by up to 2.49 °C in their surroundings. Projections for 2028 indicate a slight decline in vegetation (−0.35 %), underscoring the urgent need for strengthened conservation efforts. Identified thermal hotspots, particularly in urban and industrial zones, further emphasize the importance of expanding NBS strategies.

This research advances the integration of remote sensing and machine learning for urban climate analysis, offering practical insights for urban planning and climate mitigation policies. Future studies should incorporate additional variables to refine prediction models, assess large-scale impacts of distributed NBS, and leverage high-resolution data for broader applications. These findings provide a scalable framework for sustainable urban development worldwide.