Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China
The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show t...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/11/1/14 |
| _version_ | 1797492798766710784 |
|---|---|
| author | Maomao Zhang Cheng Zhang Abdulla-Al Kafy Shukui Tan |
| author_facet | Maomao Zhang Cheng Zhang Abdulla-Al Kafy Shukui Tan |
| author_sort | Maomao Zhang |
| collection | DOAJ |
| description | The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km<sup>2</sup>, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km<sup>2</sup>, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented. |
| first_indexed | 2024-03-10T01:09:51Z |
| format | Article |
| id | doaj.art-f454e01311c44ea6a183479859958b49 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T01:09:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-f454e01311c44ea6a183479859958b492023-11-23T14:21:13ZengMDPI AGLand2073-445X2021-12-011111410.3390/land11010014Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, ChinaMaomao Zhang0Cheng Zhang1Abdulla-Al Kafy2Shukui Tan3College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, ChinaDepartment of Urban & Regional Planning, Rajshahi University of Engineering & Technology, Rajshahi 6203, BangladeshCollege of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, ChinaThe changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km<sup>2</sup>, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km<sup>2</sup>, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.https://www.mdpi.com/2073-445X/11/1/14land use/land cover changesurban thermal environmentmachine learning algorithmsartificial neural networkWuhan |
| spellingShingle | Maomao Zhang Cheng Zhang Abdulla-Al Kafy Shukui Tan Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China Land land use/land cover changes urban thermal environment machine learning algorithms artificial neural network Wuhan |
| title | Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China |
| title_full | Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China |
| title_fullStr | Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China |
| title_full_unstemmed | Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China |
| title_short | Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China |
| title_sort | simulating the relationship between land use cover change and urban thermal environment using machine learning algorithms in wuhan city china |
| topic | land use/land cover changes urban thermal environment machine learning algorithms artificial neural network Wuhan |
| url | https://www.mdpi.com/2073-445X/11/1/14 |
| work_keys_str_mv | AT maomaozhang simulatingtherelationshipbetweenlandusecoverchangeandurbanthermalenvironmentusingmachinelearningalgorithmsinwuhancitychina AT chengzhang simulatingtherelationshipbetweenlandusecoverchangeandurbanthermalenvironmentusingmachinelearningalgorithmsinwuhancitychina AT abdullaalkafy simulatingtherelationshipbetweenlandusecoverchangeandurbanthermalenvironmentusingmachinelearningalgorithmsinwuhancitychina AT shukuitan simulatingtherelationshipbetweenlandusecoverchangeandurbanthermalenvironmentusingmachinelearningalgorithmsinwuhancitychina |