IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i8p1360-d893776.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/8/1360/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/8/1360/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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.

    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. Jorge A. Salinas-Jasso & Juan C. Montalvo-Arrieta & José R. Chapa-Guerrero, 2020. "A dynamic stability analysis for the Olinalá landslide, northeastern Mexico," 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. 102(3), pages 1225-1248, July.
    2. Gianluca Esposito & Cristiano Carabella & Giorgio Paglia & Enrico Miccadei, 2021. "Relationships between Morphostructural/Geological Framework and Landslide Types: Historical Landslides in the Hilly Piedmont Area of Abruzzo Region (Central Italy)," Land, MDPI, vol. 10(3), pages 1-28, March.
    3. Ali Yalcin & Fikri Bulut, 2007. "Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey)," 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. 41(1), pages 201-226, April.
    4. 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.
    5. Serwan M. J. Baban & Deborah Thomas & Francis Canisius & Kamal J. Sant, 2008. "Managing development in the hillsides of Trinidad and Tobago using geoinformatics," Sustainable Development, John Wiley & Sons, Ltd., vol. 16(5), pages 314-328.
    6. V. Che & M. Kervyn & G. Ernst & P. Trefois & S. Ayonghe & P. Jacobs & E. Ranst & C. Suh, 2011. "Systematic documentation of landslide events in Limbe area (Mt Cameroon Volcano, SW Cameroon): geometry, controlling, and triggering factors," 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. 59(1), pages 47-74, October.
    7. Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India," 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(2), pages 1259-1294, November.
    8. Zhen Du & Li Feng & Haiheng Wang & Ying Dong & Da Luo & Xu Zhang & Hao Liu & Maosheng Zhang, 2023. "Identification of Ground Deformation Patterns in Coal Mining Areas via Rapid Topographical Analysis," Land, MDPI, vol. 12(6), pages 1-18, June.
    9. Kamila Pawluszek, 2019. "Landslide features identification and morphology investigation using high-resolution DEM derivatives," 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. 96(1), pages 311-330, March.
    10. Mehrnoosh Jadda & Helmi Shafri & Shattri Mansor, 2011. "PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran," 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. 57(2), pages 395-412, May.
    11. Wang Wensheng & Li Yueqing, 2012. "Hazard degree assessment of landslide using set pair analysis method," 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. 60(2), pages 367-379, January.
    12. Paschalis D. Koutalakis & Ourania A. Tzoraki & Georgios I. Prazioutis & Georgios T. Gkiatas & George N. Zaimes, 2021. "Can Drones Map Earth Cracks? Landslide Measurements in North Greece Using UAV Photogrammetry for Nature-Based Solutions," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    13. Wei Wang & Chuanyin Zhang & Minzhang Hu & Qiang Yang & Shiming Liang & Shengjun Kang, 2019. "Monitoring and analysis of geological hazards in Three Gorges area based on load impact change," 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. 97(2), pages 611-622, June.
    14. Paul Sestraș & Ștefan Bilașco & Sanda Roșca & Sanda Naș & Mircea V. Bondrea & Raluca Gâlgău & Ioel Vereș & Tudor Sălăgean & Velibor Spalević & Sorin M. Cîmpeanu, 2019. "Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    15. Frederico F. Ávila & Regina C. Alvalá & Rodolfo M. Mendes & Diogo J. Amore, 2021. "The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based 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. 105(1), pages 1139-1161, January.
    16. Paolo Tarolli & Giulia Sofia & Giancarlo Dalla Fontana, 2012. "Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion," 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 65-83, March.
    17. Zhi-hua Yang & Heng-xing Lan & Xing Gao & Lang-ping Li & Yun-shan Meng & Yu-ming Wu, 2015. "Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China," 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. 75(3), pages 2467-2487, February.
    18. Malcolm Anderson & Liz Holcombe & Rob Flory & Jean-Philippe Renaud, 2008. "Implementing low-cost landslide risk reduction: a pilot study in unplanned housing areas of the Caribbean," 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. 47(3), pages 297-315, December.
    19. Mohsin Butt & Muhammad Umar & Raheel Qamar, 2013. "Landslide dam and subsequent dam-break flood estimation using HEC-RAS model in Northern Pakistan," 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. 65(1), pages 241-254, January.
    20. Gerardo Grelle & Antonietta Rossi & Paola Revellino & Luigi Guerriero & Francesco Maria Guadagno & Giuseppe Sappa, 2019. "Assessment of Debris-Flow Erosion and Deposit Areas by Morphometric Analysis and a GIS-Based Simplified Procedure: A Case Study of Paupisi in the Southern Apennines," Sustainability, MDPI, vol. 11(8), pages 1-20, April.

    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:11:y:2022:i:8:p:1360-:d:893776. 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.