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Analysis of the Driving Force of Land Use Change Based on Geographic Detection and Simulation of Future Land Use Scenarios

Author

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  • Fengqiang Wu

    (School of Environment and Resource, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang 621010, China)

  • Caijian Mo

    (Mianyang S&T City Division, National Remote Sensing Center of China, 125 Biyun Road, Mianyang 621002, China)

  • Xiaojun Dai

    (School of Civil Engineering and Geomatics, Southwest Petroleum University, 8 Xindu Road, Chengdu 610500, China)

Abstract

Land use and land cover changes (LULCC) are the result of the combined action of many influencing factors such as nature, society, economy and politics. Taking Chongqing as an example, the driving factors of urban land expansion in Chongqing from 1999 to 2019 are analyzed using a geographic detection (GD) method. Based on this analysis, a land use scenario of Chongqing in 2029 is simulated by an Artificial Neural Network-Cellular Automata model. The results of the analysis of factors affecting land use change show that five factors have a significance >0.05: population, distance from central city, school density, GDP and the distance from railway, showing that these factors have a high impact on LULCC in Chongqing. In addition, the results of risk detection analysis show that areas with a population >50/km 2 ; the areas with a distance <200 km from the city center; areas with a school density >5/km 2 ; areas with a high GDP; and areas with a distance <25 km from the railway have a greater impact on urban land use change than other areas. The land use scenario in 2029 also is simulated based on the land use situation in 2019. The predicted results clearly reflect a land use change trend of increasing urban land and decreasing agricultural land in the region. These land use changes are especially related to the expansion of the population, economy, roads, and schools in the process of urbanization. This analysis also shows that the GD-ANN-CA model developed in this paper is well suited to urban land use simulation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5254-:d:803049
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    References listed on IDEAS

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