IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v78y2015i1p681-697.html
   My bibliography  Save this article

Landslide susceptibility modeling assisted by Persistent Scatterers Interferometry (PSI): an example from the northwestern coast of Malta

Author

Listed:
  • Daniela Piacentini
  • Stefano Devoto
  • Matteo Mantovani
  • Alessandro Pasuto
  • Mariacristina Prampolini
  • Mauro Soldati

Abstract

Persistent Scatterers Interferometry (PSI) techniques are widely employed in geosciences to detect and monitor landslides with high accuracy over large areas, but they also suffer from physical and technological constraints that restrict their field of application. These limitations prevent us from collecting information from several critical areas within the investigated region. In this paper, we present a novel approach that exploits the results of PSI analysis for the implementation of a statistical model for landslide susceptibility. The attempt is to identify active mass movements by means of PSI and to avoid, as input data, time-/cost-consuming and seldom updated landslide inventories. The study has been performed along the northwestern coast of Malta (central Mediterranean Sea), where the peculiar geological and geomorphological settings favor the occurrence of a series of extensive slow-moving landslides. Most of these consist in rock spreads, evolving into block slides, with large limestone blocks characterized by scarce vegetation and proper inclination, which represent suitable natural radar reflectors for applying PSI. Based on geomorphometric analyses and geomorphological investigations, a series of landslide predisposing factors were selected and a susceptibility map created. The result was validated by means of cross-validation technique, field surveys and global navigation satellite system in situ monitoring activities. The final outcome shows a good reliability and could represent an adequate response to the increasing demand for effective and low-cost tools for landslide susceptibility assessment. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Daniela Piacentini & Stefano Devoto & Matteo Mantovani & Alessandro Pasuto & Mariacristina Prampolini & Mauro Soldati, 2015. "Landslide susceptibility modeling assisted by Persistent Scatterers Interferometry (PSI): an example from the northwestern coast of Malta," 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. 78(1), pages 681-697, August.
  • Handle: RePEc:spr:nathaz:v:78:y:2015:i:1:p:681-697
    DOI: 10.1007/s11069-015-1740-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-015-1740-8
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11069-015-1740-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," 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. 30(3), pages 451-472, November.
    2. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    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. Yue Wang & Deliang Sun & Haijia Wen & Hong Zhang & Fengtai Zhang, 2020. "Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)," IJERPH, MDPI, vol. 17(12), pages 1-39, June.
    2. Charalampos Kontoes & Constantinos Loupasakis & Ioannis Papoutsis & Stavroula Alatza & Eleftheria Poyiadji & Athanassios Ganas & Christina Psychogyiou & Mariza Kaskara & Sylvia Antoniadi & Natalia Spa, 2021. "Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data," Land, MDPI, vol. 10(4), pages 1-25, April.
    3. Qing Yang & Zhanqiang Chang & Chou Xie & Chaoyong Shen & Bangsen Tian & Haoran Fang & Yihong Guo & Yu Zhu & Daoqin Zhou & Xin Yao & Guanwen Chen & Tao Xie, 2023. "Combining Soil Moisture and MT-InSAR Data to Evaluate Regional Landslide Susceptibility in Weining, China," Land, MDPI, vol. 12(7), pages 1-34, July.
    4. Lidia Selmi & Thais S. Canesin & Ritienne Gauci & Paulo Pereira & Paola Coratza, 2022. "Degradation Risk Assessment: Understanding the Impacts of Climate Change on Geoheritage," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    5. Mirko Francioni & Riccardo Salvini & Doug Stead & John Coggan, 2018. "Improvements in the integration of remote sensing and rock slope modelling," 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. 90(2), pages 975-1004, January.
    6. Geoff Main & John Schembri & Ritienne Gauci & Kevin Crawford & David Chester & Angus Duncan, 2018. "The hazard exposure of the Maltese Islands," 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. 92(2), pages 829-855, June.
    7. Stefano Devoto & Linley J. Hastewell & Mariacristina Prampolini & Stefano Furlani, 2021. "Dataset of Gravity-Induced Landforms and Sinkholes of the Northeast Coast of Malta (Central Mediterranean Sea)," Data, MDPI, vol. 6(8), pages 1-16, July.
    8. I. P. Kovács & T. Bugya & Sz. Czigány & M. Defilippi & D. Lóczy & P. Riccardi & L. Ronczyk & P. Pasquali, 2019. "How to avoid false interpretations of Sentinel-1A TOPSAR interferometric data in landslide mapping? A case study: recent landslides in Transdanubia, Hungary," 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(2), pages 693-712, March.

    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. Alejandro Gonzalez-Ollauri & Slobodan B. Mickovski, 2021. "A Simple GIS-Based Tool for the Detection of Landslide-Prone Zones on a Coastal Slope in Scotland," Land, MDPI, vol. 10(7), pages 1-15, June.
    2. Paulo Rodolpho Pereira Hader & Fábio Augusto Gomes Vieira Reis & Anna Silvia Palcheco Peixoto, 2022. "Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil," 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. 110(2), pages 1273-1304, January.
    3. L. Lombardo & M. Cama & C. Conoscenti & M. Märker & E. Rotigliano, 2015. "Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messi," 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. 79(3), pages 1621-1648, December.
    4. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    5. L. Lombardo & M. Cama & M. Maerker & E. Rotigliano, 2014. "A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster," 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. 74(3), pages 1951-1989, December.
    6. D. Costanzo & C. Cappadonia & C. Conoscenti & E. Rotigliano, 2012. "Exporting a Google Earth ™ aided earth-flow susceptibility model: a test in central Sicily," 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 103-114, March.
    7. E. Rotigliano & C. Cappadonia & C. Conoscenti & D. Costanzo & V. Agnesi, 2012. "Slope units-based flow susceptibility model: using validation tests to select controlling 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. 61(1), pages 143-153, March.
    8. Kourosh Shirani & Mehrdad Pasandi & Alireza Arabameri, 2018. "Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern 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. 93(3), pages 1379-1418, September.
    9. Mahnaz Naemitabar & Mohammadali Zanganeh Asadi, 2021. "Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques," 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. 108(3), pages 2423-2453, September.
    10. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
    11. Netra Bhandary & Ranjan Dahal & Manita Timilsina & Ryuichi Yatabe, 2013. "Rainfall event-based landslide susceptibility zonation mapping," 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. 69(1), pages 365-388, October.
    12. Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)," 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. 56(3), pages 881-898, March.
    13. Nikolaos Tavoularis & George Papathanassiou & Athanassios Ganas & Panagiotis Argyrakis, 2021. "Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System," Land, MDPI, vol. 10(2), pages 1-31, February.
    14. Raquel Melo & José Luís Zêzere, 2017. "Modeling debris flow initiation and run-out in recently burned areas using data-driven methods," 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. 88(3), pages 1373-1407, September.
    15. Javeria Saleem & Sheikh Saeed Ahmad & Amna Butt, 2020. "Hazard risk assessment of landslide-prone sub-Himalayan region by employing geospatial modeling approach," 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 1497-1514, July.
    16. Massimo Conforti & Gaetano Robustelli & Francesco Muto & Salvatore Critelli, 2012. "Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy)," 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 127-141, March.
    17. Jörg Grunert & Sigrid Hess, 2010. "The Upper Middle Rhine Valley as a risk area," 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. 55(3), pages 577-597, December.
    18. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), 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. 108(2), pages 1515-1543, September.
    19. 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.
    20. I. P. Kovács & T. Bugya & Sz. Czigány & M. Defilippi & D. Lóczy & P. Riccardi & L. Ronczyk & P. Pasquali, 2019. "How to avoid false interpretations of Sentinel-1A TOPSAR interferometric data in landslide mapping? A case study: recent landslides in Transdanubia, Hungary," 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(2), pages 693-712, March.

    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:spr:nathaz:v:78:y:2015:i:1:p:681-697. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.