Quantifying the spatial pattern of urban expansion trends in the period 1987–2022 and identifying areas at risk of flooding due to the impact of urbanization in Lao Cai city |
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| Institution: | 1. Hanoi University of Natural Resources and Environment, Phu Dien, Bac Tu Liem, Ha Noi, Viet Nam;2. VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam;3. Hanoi National University of Education, 136 Xuan Thuy, Cau Giay, Hanoi, Viet Nam;1. Politecnico di Torino, Department of Environment, Land and Infrastructure Engineering (DIATI), Corso Duca degli Abruzzi 24, 10129 Torino, Italy;2. Federal Office for the Environment, Biberfachstelle/Info Fauna – CSCF & Karch, Avenue de Bellevaux 51, CH-2000 Neuch?tel, Switzerland;1. Gazi University, Graduate School of Natural and Applied Sciences, Teknikokullar, 06500 Ankara, Turkey;2. Ba?kent University, Faculty of Commercial Sciences, Management Information Science, 06790 Ankara, Turkey;3. Gazi University, Faculty of Education, Teknikokullar, 06500 Ankara, Turkey;1. Department of Geography and Environment, Western University, 1151 Richmond St, London, Ontario N6A 3K7, Canada;2. School of Geography and Sustainable Development, Irvine Building, University of St Andrews, North Street, St Andrews, KY16 9AL Scotland, United Kingdom;3. The Alan Turing Institute, British Library, 2QR, John Dodson House, 96 Euston Rd, London NW1 2DB, United Kingdom;4. Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Norwida 25, 50-375 Wroc?aw, Poland;5. School of Earth and Environment, University of Canterbury, 20 Kirkwood Avenue, Upper Riccarton, Christchurch 8041, New Zealand;6. British Geological Survey, Research Ave South, Riccarton, Edinburgh, EH14 4AP Scotland, United Kingdom;1. College of Computer Science, Sichuan Normal University, Chengdu 610101, China;2. Chengdu Research Base of Giant Panda Breeding, Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife, Chengdu 610081, China;3. Giant Panda National Park Chengdu Administration, Chengdu 610041, China |
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| Abstract: | Analyzing urban expansion trends and its drivers is extremely important for sustainable urban development. However, in Vietnam, the urbanization process has been considered mainly in big cities, often ignoring the mountainous and border ones. The present study examined urban expansion and urbanization trends in different directions in Lao Cai city, northern Vietnam based on remote sensing and GIS data. After 35 years, the city's urban areas are mainly concentrated on the riverside and in the north-northwest direction (accounting for 27.95% of urban land area) due to the impact of the border gate economy. It is also for the reason that the intensity of urbanization (UII) in the range of 70–100% is mainly concentrated in the north-northwest region. With the urban intensity in 35 years reaching over 43%, Lao Cai is experiencing in a high rate of urbanization, but it has great potential flood risk. To determine flood risk in the study area based on natural and socio-economic factors, we used the Gauss process regression (GPR) model. However, due to the limitation of the GPR model, we combined GPR with Firefly algorithm (FA) to contribute to optimizing model performance. The results proved that the FA-GPR model is suitable for flood risk mapping in Lao Cai city. With R2 = 0.87, this work shows that the greater intensity of urbanization performs the greater flood risk. Therefore, for sustainable development, it is necessary to ensure harmony between economic goals and environmental protection goals. |
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