URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures

 

Abstract

Mitigating Urban Heat Island (UHI) effects has become a challenge to improve urban sustainability. The simulation tool URSUS_LST has been developed to allow urban planners to estimate how the addition of different green infrastructure elements would affect temperature. To achieve this, a new methodology was defined based on data mining, geospatial image processing and the knowledge of experts in the domain that predicts the Land Surface Temperature (LST) of any location within a city. It consists of a first data mining phase in which the real LST and the different urban elements of the nearby environment are considered: buildings, vegetation and water bodies. In a second phase, different regression models are induced to predict LST. Additionally, considering the most accurate models, the relevant attributes and their relationships are identified. A real application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness.

Funding

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Citation

Please, cite this work as:

[Rod+25] F. Rodríguez-Gómez, J. del Campo-Ávila, L. Pérez-Urrestarazu, et al. “URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures”. In: Environmental Modelling & Software 186 (2025), p. 106364. ISSN: 1364-8152. DOI: https://doi.org/10.1016/j.envsoft.2025.106364. URL: https://www.sciencedirect.com/science/article/pii/S1364815225000489.

NoteBibTeX

@Article{RODRIGUEZGOMEZ2025106364,
     title = {URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures},
     journal = {Environmental Modelling & Software},
     volume = {186},
     pages = {106364},
     year = {2025},
     issn = {1364-8152},
     doi = {https://doi.org/10.1016/j.envsoft.2025.106364},
     url = {https://www.sciencedirect.com/science/article/pii/S1364815225000489},
     author = {Francisco Rodr{‘}guez-G{’o}mez and Jos{’e} {del Campo-{’A}vila} and Luis P{’e}rez-Urrestarazu and Domingo L{’o}pez-Rodr{’}guez},
     keywords = {Expert system, Urban greening, Urban heat island, Regression models, Open-source},
     abstract = {Mitigating Urban Heat Island (UHI) effects has become a challenge to improve urban sustainability. The simulation tool URSUS_LST has been developed to allow urban planners to estimate how the addition of different green infrastructure elements would affect temperature. To achieve this, a new methodology was defined based on data mining, geospatial image processing and the knowledge of experts in the domain that predicts the Land Surface Temperature (LST) of any location within a city. It consists of a first data mining phase in which the real LST and the different urban elements of the nearby environment are considered: buildings, vegetation and water bodies. In a second phase, different regression models are induced to predict LST. Additionally, considering the most accurate models, the relevant attributes and their relationships are identified. A real application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness.},
}

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URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

1. Jun Yang, Hong Li, Jiaxing Xin, et al. (2025). Investigating the effect of urban form on land surface temperature at block and grid scales based on XGBoost-SHAP. Environmental Modelling & Software DOI
2. Zeeshan Zafar, Shiqiang Zhang, Yuanyuan Zha, et al. (2025). Evaluating land surface temperature trends and environmental interactions through machine learning and wavelet analysis. Science China Earth Sciences DOI
3. Hening Tyas Subekti, Gunwon Lee (2025). Urban Development and Land Surface Temperature : Spatiotemporal Analysis in Java’s Coastal Cities, Indonesia, 2000 to 2023. KIEAE Journal DOI
4. Zeeshan Zafar, 世强 张, 元源 查, et al. (2025). 基于机器学习与小波分析评估地表温度趋势及其与环境交互作用. SCIENTIA SINICA Terrae DOI