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Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

Author

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  • Yuan Meng

    (College of Geography and Environment, Shandong Normal University, Jinan 250300, Shandong, China)

  • Dongyang Hou

    (College of Geography and Environment, Shandong Normal University, Jinan 250300, Shandong, China)

  • Hanfa Xing

    (College of Geography and Environment, Shandong Normal University, Jinan 250300, Shandong, China)

Abstract

Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010–2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

Suggested Citation

  • Yuan Meng & Dongyang Hou & Hanfa Xing, 2017. "Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1547-:d:110345
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    References listed on IDEAS

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    1. 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.
    2. Hirsch Hadorn, Gertrude & Bradley, David & Pohl, Christian & Rist, Stephan & Wiesmann, Urs, 2006. "Implications of transdisciplinarity for sustainability research," Ecological Economics, Elsevier, vol. 60(1), pages 119-128, November.
    3. Paul Stock & Rob J.F. Burton, 2011. "Defining Terms for Integrated (Multi-Inter-Trans-Disciplinary) Sustainability Research," Sustainability, MDPI, vol. 3(8), pages 1-24, July.
    4. Chen Jun & Yifang Ban & Songnian Li, 2014. "Open access to Earth land-cover map," Nature, Nature, vol. 514(7523), pages 434-434, October.
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    Cited by:

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    2. Hongtao Ma & Yuan Meng & Hanfa Xing & Cansong Li, 2019. "Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest," Sustainability, MDPI, vol. 11(17), pages 1-15, August.

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