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Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

Author

Listed:
  • Xinyang Yu

    (College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
    Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences, Beijing 100101, China)

  • Younggu Her

    (Tropical Research and Education Center, Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Homestead, FL 33031, USA)

  • Xicun Zhu

    (College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China)

  • Changhe Lu

    (Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China)

  • Xuefei Li

    (Linyi Natural Resources Development Service Center, Linyi 276000, China)

Abstract

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km 2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

Suggested Citation

  • Xinyang Yu & Younggu Her & Xicun Zhu & Changhe Lu & Xuefei Li, 2021. "Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index," Sustainability, MDPI, vol. 13(9), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5274-:d:550841
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    References listed on IDEAS

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    1. Fatima Zahra Benkhattab & Mounir Hakkou & Ingrida Bagdanavičiūtė & Abdelmounim El Mrini & Hafid Zagaoui & Hassan Rhinane & Mehdi Maanan, 2020. "Spatial–temporal analysis of the shoreline change rate using automatic computation and geospatial tools along the Tetouan coast in Morocco," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 519-536, October.
    2. Yaowen Xie & Qiang Bie & Hui Lu & Lei He, 2018. "Spatio-Temporal Changes of Oases in the Hexi Corridor over the Past 30 Years," Sustainability, MDPI, vol. 10(12), pages 1-18, November.
    3. Chia-An Ku, 2020. "Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
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