Rapid urbanization in tropical megacities intensifies surface warming, yet spatial heat diagnostics frequently lack alignment with administratively actionable adaptation planning. This study develops and validates a regression-informed spatial prioritization framework for vegetation-based climate adaptation using Jakarta, the capital city of Indonesia, as a case study. Harmonized pixel-level layers of Land Surface Temperature (LST), vegetation deficit, built-up intensity, and population density were derived from multisource satellite and reanalysis datasets and integrated through cross-sectional ordinary least squares regression. Contribution-based and equal redistribution scenarios, combined with threshold relaxation tests, were applied to evaluate prioritization stability. High-LST clusters persistently coincide with dense built-up corridors, and regression results indicate that built-up intensity exhibits the strongest standardized association with surface temperature, while vegetation deficit retains an independent contribution. Priority tiers remain structurally consistent across alternative weighting and threshold configurations, demonstrating robustness of administratively aggregated heat cores. The empirical correspondence between LST and Universal Thermal Climate Index supports the planning relevance of surface-based diagnostics in dense tropical environments. By linking spatial analytics to governance-relevant intervention tiers, the proposed framework bridges the gap between urban heat assessment and phased adaptation planning. The approach offers a transferable model for evidence-informed vegetation-based adaptation in rapidly urbanizing tropical cities.
Perdinan et al. (Wed,) studied this question.