Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques
In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/106 |
| _version_ | 1797406468906942464 |
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| author | Megha Shrestha Chandana Mitra Mahjabin Rahman Luke Marzen |
| author_facet | Megha Shrestha Chandana Mitra Mahjabin Rahman Luke Marzen |
| author_sort | Megha Shrestha |
| collection | DOAJ |
| description | In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet’s Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events. |
| first_indexed | 2024-03-09T03:26:52Z |
| format | Article |
| id | doaj.art-e6e86a9de3a947c38229aada6fd30cc1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:26:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e6e86a9de3a947c38229aada6fd30cc12023-12-03T15:02:18ZengMDPI AGRemote Sensing2072-42922022-12-0115110610.3390/rs15010106Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning TechniquesMegha Shrestha0Chandana Mitra1Mahjabin Rahman2Luke Marzen3Department of Geosciences, Auburn University, Auburn, AL 36849, USADepartment of Geosciences, Auburn University, Auburn, AL 36849, USADepartment of Geosciences, Auburn University, Auburn, AL 36849, USADepartment of Geosciences, Auburn University, Auburn, AL 36849, USAIn the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet’s Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events.https://www.mdpi.com/2072-4292/15/1/106urbanizationLUCCfuture predictionmedium-size citiesCA Markov |
| spellingShingle | Megha Shrestha Chandana Mitra Mahjabin Rahman Luke Marzen Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques Remote Sensing urbanization LUCC future prediction medium-size cities CA Markov |
| title | Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques |
| title_full | Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques |
| title_fullStr | Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques |
| title_full_unstemmed | Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques |
| title_short | Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques |
| title_sort | mapping and predicting land cover changes of small and medium size cities in alabama using machine learning techniques |
| topic | urbanization LUCC future prediction medium-size cities CA Markov |
| url | https://www.mdpi.com/2072-4292/15/1/106 |
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