IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i3p681-d1097218.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/3/681/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/3/681/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Faraz S. Tehrani & Michele Calvello & Zhongqiang Liu & Limin Zhang & Suzanne Lacasse, 2022. "Machine learning and landslide studies: recent advances and applications," 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. 114(2), pages 1197-1245, November.
    2. Chong Niu & Kebo Ma & Xiaoyong Shen & Xiaoming Wang & Xiao Xie & Lin Tan & Yong Xue, 2023. "Attention-Enhanced Region Proposal Networks for Multi-Scale Landslide and Mudslide Detection from Optical Remote Sensing Images," Land, MDPI, vol. 12(2), pages 1-12, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:681-:d:1097218. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.