Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia)
Landslides in the Podsljeme area of the City of Zagreb cause significant economic losses, which have been increasing over the last several decades due to the urbanisation of hilly areas and the influence of climate changes. An airborne LiDAR digital terrain model (DTM) with a spatial resolution of 3...
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2019-07-01
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Online Access: | http://dx.doi.org/10.1080/17445647.2019.1671906 |
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doaj-dbc313c292944a4a8d234f2960cb94a12020-11-25T04:09:45ZengTaylor & Francis GroupJournal of Maps1744-56472019-07-0115277377910.1080/17445647.2019.16719061671906Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia)Sanja Bernat Gazibara0Martin Krkač1Snježana Mihalić Arbanas2Faculty of Mining, Geology and Petroleum Engineering, University of ZagrebFaculty of Mining, Geology and Petroleum Engineering, University of ZagrebFaculty of Mining, Geology and Petroleum Engineering, University of ZagrebLandslides in the Podsljeme area of the City of Zagreb cause significant economic losses, which have been increasing over the last several decades due to the urbanisation of hilly areas and the influence of climate changes. An airborne LiDAR digital terrain model (DTM) with a spatial resolution of 30 × 30 cm was used to prepare a landslide inventory map of the pilot area (21 km2) with more than 700 identified landslides. The area of the smallest identified landslide is 43 m2, while 90% of the landslides are between 100 and 2,000 m2. The frequency–size distribution of all mapped landslides in the pilot area shows a very high level of landslide inventory completeness. Therefore, it is concluded that the LiDAR-based terrain model is a valuable tool for the preparation of detailed landslide inventories in heavily vegetated regions such as the hilly area of Medvednica Mt.http://dx.doi.org/10.1080/17445647.2019.1671906landslideslidarmappingpodsljeme areazagreb |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanja Bernat Gazibara Martin Krkač Snježana Mihalić Arbanas |
spellingShingle |
Sanja Bernat Gazibara Martin Krkač Snježana Mihalić Arbanas Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) Journal of Maps landslides lidar mapping podsljeme area zagreb |
author_facet |
Sanja Bernat Gazibara Martin Krkač Snježana Mihalić Arbanas |
author_sort |
Sanja Bernat Gazibara |
title |
Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) |
title_short |
Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) |
title_full |
Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) |
title_fullStr |
Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) |
title_full_unstemmed |
Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia) |
title_sort |
landslide inventory mapping using lidar data in the city of zagreb (croatia) |
publisher |
Taylor & Francis Group |
series |
Journal of Maps |
issn |
1744-5647 |
publishDate |
2019-07-01 |
description |
Landslides in the Podsljeme area of the City of Zagreb cause significant economic losses, which have been increasing over the last several decades due to the urbanisation of hilly areas and the influence of climate changes. An airborne LiDAR digital terrain model (DTM) with a spatial resolution of 30 × 30 cm was used to prepare a landslide inventory map of the pilot area (21 km2) with more than 700 identified landslides. The area of the smallest identified landslide is 43 m2, while 90% of the landslides are between 100 and 2,000 m2. The frequency–size distribution of all mapped landslides in the pilot area shows a very high level of landslide inventory completeness. Therefore, it is concluded that the LiDAR-based terrain model is a valuable tool for the preparation of detailed landslide inventories in heavily vegetated regions such as the hilly area of Medvednica Mt. |
topic |
landslides lidar mapping podsljeme area zagreb |
url |
http://dx.doi.org/10.1080/17445647.2019.1671906 |
work_keys_str_mv |
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