High-Resolution Thermal Mapping for Urban Heat Mitigation: Random Forest Downscaling in a Rapid Urbanization Context (Case Study Malang City, Indonesia)
DOI:
10.29303/jppipa.v11i12.13393Published:
2025-12-25Downloads
Abstract
This study aims to enhance the spatial resolution of land surface temperature (LST) mapping in Malang City, Indonesia, and to analyze spatiotemporal urban thermal dynamics associated with rapid urbanization. Landsat 8 OLI/TIRS thermal data were integrated with Sentinel-2 multispectral imagery using a Random Forest (RF)–based downscaling approach to refine LST resolution from 30 m to 10 m. Spectral indices, including NDVI, NDBI, NDWI, and NDMI, were employed as predictor variables, and model performance was evaluated using R² and RMSE metrics, supported by in-situ temperature measurements for validation. The results demonstrate strong downscaling performance, with R² values of 0.8374 (2019), 0.8468 (2022), and 0.7675 (2024), while field validation yielded a correlation coefficient of 0.722 and an RMSE of 4.63°C. Spatial and temporal analyses reveal a significant increase in mean LST from 24.67°C in 2019 to 27.21°C in 2024, indicating accelerated urban warming, particularly during 2022–2024. This warming is closely associated with land-use transformation, increased impervious surfaces, and regional climatic influences. In conclusion, the RF-based downscaling approach effectively captures fine-scale urban thermal heterogeneity and provides reliable high-resolution LST information, supporting urban heat mitigation planning and climate adaptation strategies in rapidly growing tropical cities.
Keywords:
Downscaling Land surface temperature Landsat-8 Random forest Remote sensing Sentinel-2References
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