Influence Mechanism of Land Use/Cover Change on Surface Urban Heat Islands and Urban Energy Consumption in Severely Cold Regions DOI Creative Commons
Jinjian Jiang, Jie Zhang, Peng Cui

et al.

Land, Journal Year: 2025, Volume and Issue: 14(6), P. 1162 - 1162

Published: May 28, 2025

Intensifying global warming has disrupted natural ecosystems and altered energy consumption patterns. Understanding the impact of land use cover change on surface urban heat islands (SUHIs) is critical for sustainable development. In this study, normalized difference vegetation index (NDVI), modified water (MNDWI), built-up (NDBI), SUHI data were derived using GIS remote sensing (RS) technology, quantitative analysis was performed in combination with data. The results revealed following key findings. summer, NDVI exhibited a significant negative correlation total building (r = −0.52), whereas NDBI showed positive correlations 0.72 r 0.67, respectively). Moreover, served as mediating role between use/cover electricity consumption, direct effect accounting 36% indirect 64% effect. contrast, significantly positively correlated winter 0.53). Spline regression further that every one-unit increase corresponded to an approximately 22 million kWh summer EC 1.16 billion EC.

Language: Английский

Research Overview on Urban Heat Islands Driven by Computational Intelligence DOI Creative Commons
Chao Liu, Siyu Lu, Jiawei Tian

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2176 - 2176

Published: Dec. 13, 2024

In recent years, the intensification of urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores current status surface UHI research, emphasizing role land use and cover changes (LULC) in environments. We conducted systematic review 8260 journal articles from Web Science database, employing bibliometric analysis keyword co-occurrence using CiteSpace to identify research hotspots trends. Our investigation reveals that vegetation types are two most critical factors influencing intensity. analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, artificial neural networks, used for simulating expansion predicting effects. The study also examines numerical modeling methods, Weather Research Forecasting (WRF) model, while examining application Computational Fluid Dynamics (CFD) microclimate research. Furthermore, we evaluate potential mitigation strategies, considering planning approaches, green infrastructure solutions, high-albedo materials. comprehensive not only highlights relationship between dynamics UHIs but provides direction future intelligence-driven climate studies.

Language: Английский

Citations

5
Modeling the Impacts of Land Cover Changes on Subtropical Urban Microclimate and Mitigation Strategies in the Context of Urbanization DOI

Zhihong Zhai,

Ying Zhang,

Xiaoyang Xiang

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 113064 - 113064

Published: April 1, 2025

Language: Английский

Citations

0
Influence Mechanism of Land Use/Cover Change on Surface Urban Heat Islands and Urban Energy Consumption in Severely Cold Regions DOI Creative Commons
Jinjian Jiang, Jie Zhang, Peng Cui

et al.

Land, Journal Year: 2025, Volume and Issue: 14(6), P. 1162 - 1162

Published: May 28, 2025

Intensifying global warming has disrupted natural ecosystems and altered energy consumption patterns. Understanding the impact of land use cover change on surface urban heat islands (SUHIs) is critical for sustainable development. In this study, normalized difference vegetation index (NDVI), modified water (MNDWI), built-up (NDBI), SUHI data were derived using GIS remote sensing (RS) technology, quantitative analysis was performed in combination with data. The results revealed following key findings. summer, NDVI exhibited a significant negative correlation total building (r = −0.52), whereas NDBI showed positive correlations 0.72 r 0.67, respectively). Moreover, served as mediating role between use/cover electricity consumption, direct effect accounting 36% indirect 64% effect. contrast, significantly positively correlated winter 0.53). Spline regression further that every one-unit increase corresponded to an approximately 22 million kWh summer EC 1.16 billion EC.

Language: Английский

Citations

0