A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures
In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynami...
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MDPI AG
2025-06-01
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| Series: | Urban Science |
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| author | Rasool Vahid Mohamed H. Aly |
| author_facet | Rasool Vahid Mohamed H. Aly |
| author_sort | Rasool Vahid |
| collection | DOAJ |
| description | In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature. |
| format | Article |
| id | doaj-art-ce78c868936a446dbb03726a889df974 |
| institution | DOAJ |
| issn | 2413-8851 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Urban Science |
| spelling | doaj-art-ce78c868936a446dbb03726a889df9742025-08-20T03:08:13ZengMDPI AGUrban Science2413-88512025-06-019723410.3390/urbansci9070234A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface TemperaturesRasool Vahid0Mohamed H. Aly1Environmental Dynamics Program, University of Arkansas, Fayetteville, AR 72701, USADepartment of Geosciences, University of Arkansas, Fayetteville, AR 72701, USAIn a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature.https://www.mdpi.com/2413-8851/9/7/234rapid urbanizationland use/coverland surface temperatureurban heat islandmachine learningenvironmental change |
| spellingShingle | Rasool Vahid Mohamed H. Aly A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures Urban Science rapid urbanization land use/cover land surface temperature urban heat island machine learning environmental change |
| title | A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures |
| title_full | A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures |
| title_fullStr | A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures |
| title_full_unstemmed | A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures |
| title_short | A Comprehensive Systematic Review of Machine Learning Applications in Assessing Land Use/Cover Dynamics and Their Impact on Land Surface Temperatures |
| title_sort | comprehensive systematic review of machine learning applications in assessing land use cover dynamics and their impact on land surface temperatures |
| topic | rapid urbanization land use/cover land surface temperature urban heat island machine learning environmental change |
| url | https://www.mdpi.com/2413-8851/9/7/234 |
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