4.7 Article

Time Series Analysis of Land Cover Change Using Remotely Sensed and Multisource Urban Data Based on Machine Learning: A Case Study of Shenzhen, China from 1979 to 2022

Journal

REMOTE SENSING
Volume 14, Issue 22, Pages -

Publisher

MDPI
DOI: 10.3390/rs14225706

Keywords

land cover change; remote sensing; Shenzhen; time series; LUCC retrieval via Landsat; machines learning; land-use types; complex network

Funding

  1. Basic and Applied Basic Research Funding Program of Guangdong Province of China [2019A1515110303, 2019A1515110800]
  2. Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy [GML-KF-22-02]
  3. National Natural Science Foundation of China [42201319, 62001113]
  4. China Postdoctoral Science Foundation [2021M702233]
  5. Youth Talent Fund of Guangdong Provincial Department of Education [2022KQNCX226]
  6. Guangdong Key Construction Discipline Research Ability Enhancement Project [2021ZDJS086]
  7. Dongguan Science and Technology of Social Development Program [20221800902472, 20211800904712]
  8. Research Team Project of Dongguan University of Technology [TDY-B2019009]

Ask authors/readers for more resources

Shenzhen has undergone rapid urbanization since the establishment of the Special Economic Zone in 1978. This study used Landsat images to investigate land use and land cover changes in Shenzhen, finding that urban areas expanded while vegetation, water, and bare areas decreased. The study also identified transportation and population as key drivers of urban land development.
Shenzhen has experienced rapid urbanization since the establishment of the Special Economic Zone in 1978. However, it is rare to witness high-speed urbanization in Shenzhen. It is important to study the LUCC progress in Shenzhen (regarding refusing multisource data), which can provide a reference for governments to solve the problems of land resource shortages and urban expansion spaces. In this paper, nine Landsat images were used to retrieve land cover maps in Shenzhen, China, from 1979 to 2022. The classification method is based on support vector machines with assistance from visual interpretation. The results show that the urban area increased by 756.84 km(2), the vegetation area decreased by 546.27 km(2), the water area decreased by 132.95 km(2), and the bare area decreased by 77.62 km(2) in the last 43 years of our research region. Urban sprawl starts from the Luohu district, then propagates to Futian, Nanshan, and Yantian districts, and finally expands to other outlying districts (Baoan, Longgang, Guangming, Dapeng, and Pingshan). The spatial-temporal characteristics and the impact factors of urbanization were further analyzed. The visualization of land cover changes based on a complex network approach reveals that the velocity of urban expansion is growing. The coastline distributions were retrieved from nine observation times from 1979 to 2022; the results show that the west coastline changed more dramatically than the east and most of the east coastline remained stable, except for the parts near Yantian port and Mirs Bay, which experienced some changes. The impact factors of coastline changes are further discussed. Through a correlation analysis using urban data, such as transportation and socioeconomic factors, it was found that elevation and roads have strong constraints on the spatial patterns of a city's expansion. There is exponential decay in the urban land increase against the distance to the roads, implying that traffic factors greatly determine urban land expansion. The turning point of the exponential decay is a distance of around 150 m. Time and population are highly correlated with land use development, indicating that urban land grows linearly with time and the population, which are important driving forces of urban land development. Compared with secondary and tertiary industries, the primary industry is less related to urban land use in Shenzhen.

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