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Monitoring Land Use/Cover Change Using Remotely Sensed Data in Guangzhou of China

Author

Listed:
  • Liang Guo

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 440100, China)

  • Xiaohuan Xi

    (Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Weijun Yang

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 440100, China)

  • Lei Liang

    (Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

Land use/cover change (LUCC) has a crucial influence on ecosystem function, environmental change and decision support. Rapid and precise monitoring of land use/cover change information is essential for utilization and management of land resources. The objectives of this study were to monitor land use/cover change of Guangzhou of China from 1986 to 2018 using remotely sensed data, and analyze the correlation between artificial surface expansion and the gross domestic product (GDP) growth. Supervised classification was performed using Random Forest classifier, and the overall accuracy (OA) ranged from 86.42% to 96.58% and kappa coefficient (K) ranged from 0.8079 to 0.9499. The results show that the built-up area of Guangzhou of China from 1986 to 2018 continued to increase. However, the vegetation area continued to decrease during 32 years. The built-up area increased by 1315.56 km 2 (increased by 439.34%) with an average growth of 41.11 km 2 /year. The vegetation area reduced by 1290.78 km 2 (reduced by 19.99%) with an average reduction of 40.34 km 2 /year. Research has shown that the reduced vegetation area was mainly converted into built-up area. The area of water bodies and bare lands was relatively stable and had a little change. The results indicate that the GDP had a strong positive correlation with built-up area (R 2 = 0.98). However, there is a strong negative correlation between the GDP and vegetation area (R 2 = 0.97) in Guangzhou City, China. As a consequence, the increase of built-up area was at the cost of the reduction of vegetation area.

Suggested Citation

  • Liang Guo & Xiaohuan Xi & Weijun Yang & Lei Liang, 2021. "Monitoring Land Use/Cover Change Using Remotely Sensed Data in Guangzhou of China," Sustainability, MDPI, vol. 13(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2944-:d:513135
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    References listed on IDEAS

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    1. Bhanage Vinayak & Han Soo Lee & Shirishkumar Gedem, 2021. "Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    2. Adnan, Mohammed Sarfaraz Gani & Abdullah, Abu Yousuf Md & Dewan, Ashraf & Hall, Jim W., 2020. "The effects of changing land use and flood hazard on poverty in coastal Bangladesh," Land Use Policy, Elsevier, vol. 99(C).
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    4. Jinming Yang & Shimei Li & Huicui Lu, 2019. "Quantitative Influence of Land-Use Changes and Urban Expansion Intensity on Landscape Pattern in Qingdao, China: Implications for Urban Sustainability," Sustainability, MDPI, vol. 11(21), pages 1-18, November.
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    Cited by:

    1. Fengqiang Wu & Caijian Mo & Xiaojun Dai, 2022. "Analysis of the Driving Force of Land Use Change Based on Geographic Detection and Simulation of Future Land Use Scenarios," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    2. Md. Mostafizur Rahman & György Szabó, 2021. "Impact of Land Use and Land Cover Changes on Urban Ecosystem Service Value in Dhaka, Bangladesh," Land, MDPI, vol. 10(8), pages 1-27, July.

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