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The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

Author

Listed:
  • Marko Sinčić

    (Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia)

  • Sanja Bernat Gazibara

    (Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia)

  • Martin Krkač

    (Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia)

  • Hrvoje Lukačić

    (Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia)

  • Snježana Mihalić Arbanas

    (Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia)

Abstract

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km 2 ) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m 2 . This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

Suggested Citation

  • Marko Sinčić & Sanja Bernat Gazibara & Martin Krkač & Hrvoje Lukačić & Snježana Mihalić Arbanas, 2022. "The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments," Land, MDPI, vol. 11(8), pages 1-37, August.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:8:p:1360-:d:893776
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    References listed on IDEAS

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    1. A. Carrara & F. Guzzetti & M. Cardinali & P. Reichenbach, 1999. "Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard," 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. 20(2), pages 117-135, November.
    2. Petra Jagodnik & Sanja Bernat Gazibara & Željko Arbanas & Snježana Mihalić Arbanas, 2020. "Engineering geological mapping using airborne LiDAR datasets – an example from the Vinodol Valley, Croatia," Journal of Maps, Taylor & Francis Journals, vol. 16(2), pages 855-866, December.
    3. Sanja Bernat Gazibara & Martin Krkač & Snježana Mihalić Arbanas, 2019. "Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia)," Journal of Maps, Taylor & Francis Journals, vol. 15(2), pages 773-779, July.
    4. Michel Jaboyedoff & Thierry Oppikofer & Antonio Abellán & Marc-Henri Derron & Alex Loye & Richard Metzger & Andrea Pedrazzini, 2012. "Use of LIDAR in landslide investigations: a review," 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. 61(1), pages 5-28, March.
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    Cited by:

    1. 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.
    2. Yuandong Huang & Chong Xu & Lei Li & Xiangli He & Jia Cheng & Xiwei Xu & Junlei Li & Xujiao Zhang, 2022. "Inventory and Spatial Distribution of Ancient Landslides in Hualong County, China," Land, MDPI, vol. 12(1), pages 1-17, December.

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