Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques

Autor: Megha Shrestha, Chandana Mitra, Mahjabin Rahman, Luke Marzen
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Remote Sensing, Vol 15, Iss 1, p 106 (2022)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs15010106
Popis: 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.
Databáze: Directory of Open Access Journals
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