Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan
Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land c...
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MDPI AG
2025-07-01
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| author | Zabih Ullah Muhammad Sajid Mehmood Shiyan Zhai Yaochen Qin |
| author_facet | Zabih Ullah Muhammad Sajid Mehmood Shiyan Zhai Yaochen Qin |
| author_sort | Zabih Ullah |
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| description | Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. |
| format | Article |
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| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Remote Sensing |
| spelling | doaj-art-ad706a69eddd4aec9b259eed827719082025-08-20T02:47:14ZengMDPI AGRemote Sensing2072-42922025-07-011714247410.3390/rs17142474Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, PakistanZabih Ullah0Muhammad Sajid Mehmood1Shiyan Zhai2Yaochen Qin3Faculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaRapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts.https://www.mdpi.com/2072-4292/17/14/2474environmental sustainabilityurban thermal environmenturbanizationvegetationurban sprawl |
| spellingShingle | Zabih Ullah Muhammad Sajid Mehmood Shiyan Zhai Yaochen Qin Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan Remote Sensing environmental sustainability urban thermal environment urbanization vegetation urban sprawl |
| title | Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan |
| title_full | Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan |
| title_fullStr | Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan |
| title_full_unstemmed | Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan |
| title_short | Analysis of LULC and Urban Thermal Variations in Industrial Cities Using Earth Observation Indices and Machine Learning: A Case Study of Gujranwala, Pakistan |
| title_sort | analysis of lulc and urban thermal variations in industrial cities using earth observation indices and machine learning a case study of gujranwala pakistan |
| topic | environmental sustainability urban thermal environment urbanization vegetation urban sprawl |
| url | https://www.mdpi.com/2072-4292/17/14/2474 |
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