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Scientific paper - Original scientific paper
The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments
Land, 11 (2022), 8; 1360. https://doi.org/10.3390/land11081360
Sinčić, Marko; Bernat Gazibara, Sanja; Krkač, Martin; Lukačić, Hrvoje; Mihalić Arbanas, Snježana

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Sinčić, M., Bernat Gazibara, S., Krkač, M., Lukačić, H. & Mihalić Arbanas, S. (2022). The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments. Land, 11. (8). doi: 10.3390/land11081360

Sinčić, Marko, et al. "The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments." Land, vol. 11, no. 8, 2022. https://doi.org/10.3390/land11081360

Sinčić, Marko, Sanja Bernat Gazibara, Martin Krkač, Hrvoje Lukačić and Snježana Mihalić Arbanas. "The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments." Land 11, no. 8 (2022). https://doi.org/10.3390/land11081360

Sinčić, M., et al. (2022) 'The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments', Land, 11(8). doi: 10.3390/land11081360

Sinčić M, Bernat Gazibara S, Krkač M, Lukačić H, Mihalić Arbanas S. The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments. Land [Internet]. 2022 August 21 [cited 2024 April 25];11(8). doi: 10.3390/land11081360

M. Sinčić, S. Bernat Gazibara, M. Krkač, H. Lukačić and S. Mihalić Arbanas, "The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments", Land, vol. 11, no. 8, August 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:169:849033. [Accessed: 25 April 2024]

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Metadata
Title (english)The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments
AuthorMarko Sinčić
AuthorSanja Bernat Gazibara
AuthorMartin Krkač
AuthorHrvoje Lukačić
AuthorSnježana Mihalić Arbanas
Author's institutionUniversity 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		NATURAL SCIENCES
Geology
Abstract (english)
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 km2) 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 m2. 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).
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleLand
Numberingvol. 11, no. 8, 1360
e-ISSN2073-445X
DOIhttps://doi.org/10.3390/land11081360
URN:NBNurn:nbn:hr:169:849033
Publication2022-08-21
ProjectNumber: IP-2019-04-9900
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: IP
ProjectNumber: HRZZ 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: DOK
Document URLhttps://www.mdpi.com/2073-445X/11/8/1360
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2023-02-07 09:24:32