prikaz prve stranice dokumenta The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments
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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. i 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, br. 8, 2022. https://doi.org/10.3390/land11081360

Sinčić, Marko, Sanja Bernat Gazibara, Martin Krkač, Hrvoje Lukačić i Snježana Mihalić Arbanas. "The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments." Land 11, br. 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]. 21.08.2022. [pristupljeno 06.12.2024.];11(8). doi: 10.3390/land11081360

M. Sinčić, S. Bernat Gazibara, M. Krkač, H. Lukačić i 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, br. 8, Kolovoz 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:169:849033. [Citirano: 06.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:169:849033
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Podaci o radu
Naslov (engleski)The use of high-resolution remote sensing data in preparation of input data for large-scale landslide hazard assessments
AutorMarko Sinčić
AutorSanja Bernat Gazibara
AutorMartin Krkač
AutorHrvoje Lukačić
AutorSnježana Mihalić Arbanas
Autorova ustanovaSveučilište u Zagrebu
Rudarsko-geološko-naftni fakultet
(Zavod za geologiju i geološko inženjerstvo)
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Rudarstvo, nafta i geološko inženjerstvo
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Geologija
Sažetak (engleski)
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).
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaLand
Brojčani podacivol. 11, br. 8, 1360
e-ISSN2073-445X
DOIhttps://doi.org/10.3390/land11081360
URN:NBNurn:nbn:hr:169:849033
Datum objave publikacije2022-08-21
ProjektŠifra: IP-2019-04-9900
Naziv (hrvatski): Razvoj metodologije procjene podložnosti na klizanje za planiranje namjene zemljišta primjenom LiDAR tehnologije
Naziv (engleski): Methodology development for landslide susceptibility assessment for land-use planning based on LiDAR technology
Kratica: LandSlidePlan
Voditelj: Snježana Mihalić Arbanas
Pravna nadležnost: Hrvatska
Financijer: HRZZ
Linija financiranja: IP
ProjektŠifra: HRZZ DOK-2020-01-2432
Naziv (hrvatski): Razvoj metodologije procjene podložnosti na klizanje za planiranje namjene zemljišta primjenom LiDAR tehnologije
Naziv (engleski): Methodology development for landslide susceptibility assessment for land-use planning based on LiDAR technology
Kratica: LandSlidePlan
Voditelj: Snježana Mihalić Arbanas
Pravna nadležnost: Hrvatska
Financijer: HRZZ
Linija financiranja: DOK
URL dokumentahttps://www.mdpi.com/2073-445X/11/8/1360
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2023-02-07 09:24:32