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Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

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  • Peixian Li

    (College of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

  • Peng Chen

    (College of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Jiaqi Shen

    (College of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Weinan Deng

    (State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China
    CCTEG Coal Mining Research Institute, Beijing 100013, China)

  • Xinliang Kang

    (Shanxi Coking Coal Group Co., Ltd., Taiyuan 030053, China)

  • Guorui Wang

    (Institute of Land and Resources Investigation and Monitoring of Ningxia Hui Autonomous Region, Yinchuan 750002, China)

  • Shoubao Zhou

    (College of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

Suggested Citation

  • Peixian Li & Peng Chen & Jiaqi Shen & Weinan Deng & Xinliang Kang & Guorui Wang & Shoubao Zhou, 2022. "Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning," Sustainability, MDPI, vol. 14(12), pages 1-35, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7470-:d:842321
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    References listed on IDEAS

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    1. Sayed Fakhreddin Afzali & Ali Khanamani & Ehsan Kamali Maskooni & Ronny Berndtsson, 2021. "Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
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    Cited by:

    1. Shishir K. Sarker & Junfeng Zhu & Alan E. Fryar & Ghulam Jeelani, 2023. "Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change," Sustainability, MDPI, vol. 15(11), pages 1-14, May.

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