Impact of Input Data on the Quality of the Landslide Susceptibility Large-Scale Maps: A Case Study from NW Croatia

Scientific paper - Chapter in Book

• Author: Martin Krkač
• Author: Sanja Bernat Gazibara
• Author: Marko Sinčić
• Author: Hrvoje Lukačić
• Author: Gabrijela Šarić
• Author: Snježana Mihalić Arbanas
• Editor: Irasema Alcántara-Ayala
• Editor: Željko Arbanas
• Editor: Snježana Mihalić Arbanas
• Author's institution: University of Rijeka; Faculty of Civil Engineering
• Author's institution: University of Zagreb; Faculty of Mining, Geology and Petroleum Engineering; (Department of Geology and Geological Engineering)
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mining, Petroleum and Geology Engineering
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Civil Engineering

Abstract (english)

Abstract The study presents analyses of input data impact on the quality of the landslide susceptibility large-scale maps. For comparison, two input data sets were used to produce two landslide susceptibility maps. The first input data set included free-available, small-scale data with low spatial accuracy, while the second set included high-resolution remote sensing data. The same nine types of landslide causal factors were derived and used for susceptibility analyses. Furthermore, LiDAR-based landslide inventory and bivariate statistical method, i.e. Information Value method, were used for susceptibility modelling. The resulting landslide susceptibility maps were compared with ROC curves. Success and prediction rates showed that the landslide susceptibility model based on causal factors derived from high-resolution remote sensing data is approximately 10% more accurate than the model based on causal factors derived from small-scale input data. Furthermore, based on the conducted research, it can be concluded that susceptibility modelling based on small-scale data and LiDAR-based inventories enables reliable landslide susceptibility assessments at the regional level.

• Keywords (english): landslide susceptibility; LiDAR landslide inventory; input data; causal factor maps; resolution; spatial accuracy
• Language: english
• Publication type: Scientific paper - Chapter in Book
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Book title: Progress in Landslide Research and Technology, Volume 2, Issue 1
• Numbering: pp. 135-146
• ISBN: 978-3-031-39011-1
• ISBN: 978-3-031-39012-8 (eBook)
• p-ISSN: 2731-3794
• e-ISSN: 2731-3808
• DOI: https://doi.org/10.1007/978-3-031-39012-8_4
• URN:NBN: urn:nbn:hr:169:224501
• Publication: 2023
• Project: Number: IP-2019-04-9900; Title (croatian): Razvoj metodologije procjene podložnosti na klizanje za planiranje namjene zemljišta primjenom LiDAR tehnologije; Acronym: LandSlidePlan; Leader: Snježana Mihalić Arbanas; Jurisdiction: Croatia; Funding stream: Research Projects
• Project: Number: DOK-2020-01-2432; Title (croatian): Razvoj metodologije procjene podložnosti na klizanje za planiranje namjene zemljišta primjenom LiDAR tehnologije; Title (english): Methodology development for landslide susceptibility assessment for land-use planning based on LiDAR technology; Acronym: LandSlidePlan; Leader: Snježana Mihalić Arbanas; Jurisdiction: Croatia; Funding stream: Young Researchers’ Career Development Project - Training New Doctoral Students
• Document URL: https://link.springer.com/10.1007/978-3-031-39012-8_4
• Type of resource: Text
• Publisher: Springer Science and Business Media LLC
• Publishing place: Cham
• Access conditions: Open access
• Created on: 2024-04-30 11:44:33