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Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population

Author

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  • Jun Li

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
    Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing 100871, China)

  • Yuan Zhang

    (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Qiming Qin

    (Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing 100871, China)

  • Yueguan Yan

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
    State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing 100083, China)

Abstract

Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China’s Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.

Suggested Citation

  • Jun Li & Yuan Zhang & Qiming Qin & Yueguan Yan, 2017. "Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:383-:d:92225
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    References listed on IDEAS

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    1. Chaogui Kang & Yu Liu & Xiujun Ma & Lun Wu, 2012. "Towards Estimating Urban Population Distributions from Mobile Call Data," Journal of Urban Technology, Taylor & Francis Journals, vol. 19(4), pages 3-21, October.
    2. Shen, Yue & Chai, Yanwei & Kwan, Mei-Po, 2015. "Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs," Journal of Transport Geography, Elsevier, vol. 47(C), pages 90-99.
    3. Christian Thiemann & Fabian Theis & Daniel Grady & Rafael Brune & Dirk Brockmann, 2010. "The Structure of Borders in a Small World," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-7, November.
    4. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
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    2. Na Liao & Xinchen Gu & Yuejian Wang & Hailiang Xu & Zili Fan, 2020. "Analyzing Macro-Level Ecological Change and Micro-Level Farmer Behavior in Manas River Basin, China," Land, MDPI, vol. 9(8), pages 1-17, July.
    3. Dantong Zhu & Xiangju Cheng & Wuhua Li & Fujun Niu & Jianhui Wen, 2022. "Temporal and Spatial Variation Characteristics of Water Quality in the Middle and Lower Reaches of the Lijiang River, China and Their Responses to Environmental Factors," IJERPH, MDPI, vol. 19(13), pages 1-20, July.
    4. Rui Xiao & Xiaoyu Yu & Zhonghao Zhang & Xue Wang, 2021. "Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1804-1825, September.
    5. Ge Shi & Nan Jiang & Yang Li & Bin He, 2018. "Analysis of the Dynamic Urban Expansion Based on Multi-Sourced Data from 1998 to 2013: A Case Study of Jiangsu Province," Sustainability, MDPI, vol. 10(10), pages 1-18, September.

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