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How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

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  • Yu Huang

    (School of Geomatics, East China University of Technology, Nanchang 330013, China
    Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China)

  • Jianqiang Zhang

    (Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China)

  • Lili Zhang

    (Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Zaiyang Ming

    (School of Geomatics, East China University of Technology, Nanchang 330013, China
    Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China)

  • Haiqing He

    (School of Geomatics, East China University of Technology, Nanchang 330013, China)

  • Rong Chen

    (Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China)

  • Yonggang Ge

    (Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China)

  • Rongkun Liu

    (School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA)

Abstract

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m 2 could not be detected from a Landsat 8 satellite image.

Suggested Citation

  • Yu Huang & Jianqiang Zhang & Lili Zhang & Zaiyang Ming & Haiqing He & Rong Chen & Yonggang Ge & Rongkun Liu, 2023. "How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China," Land, MDPI, vol. 12(3), pages 1-19, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:681-:d:1097218
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    References listed on IDEAS

    as
    1. Yu Wang & Xiaofei Wang & Junfan Jian, 2019. "Remote Sensing Landslide Recognition Based on Convolutional Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, September.
    2. Kemal Hacıefendioğlu & Gökhan Demir & Hasan Basri Başağa, 2021. "Landslide detection using visualization techniques for deep convolutional neural network models," 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. 109(1), pages 329-350, October.
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