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The document discusses using UAV LiDAR data to map landslides in a densely populated and forested area of Georgia called Zindisi district. High density point cloud data was collected using a UAV equipped with a LiDAR camera. This data was used to create a high resolution DEM which enabled identifying landslide boundaries. Field investigations and aerial photos were used to validate the results. The approach provides insights for land management and a method for landslide mapping in similar environments.

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16 views1 page

EGU24 2467 Print

The document discusses using UAV LiDAR data to map landslides in a densely populated and forested area of Georgia called Zindisi district. High density point cloud data was collected using a UAV equipped with a LiDAR camera. This data was used to create a high resolution DEM which enabled identifying landslide boundaries. Field investigations and aerial photos were used to validate the results. The approach provides insights for land management and a method for landslide mapping in similar environments.

Uploaded by

helik susilo
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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EGU24-2467, updated on 20 Mar 2024

https://doi.org/10.5194/egusphere-egu24-2467
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Landslide Inventory Mapping in Densely Populated and Forested


Environments using UAV LiDAR Data: A Case Study in Zindisi, Surami
District, Georgia
David Bakhsoliani1, Archil Magalashvili1, and George Gaprindashvili2,3
1
Ilia State University, Faculty of Natural Sciences and Medicine, Earth Science, Tbilisi, Georgia (d.bakhsoliani@gmail.com)
2
Department of Geology, National Environmental Agency, Tbilisi, Georgia (gaprindashvili.george@gmail.com)
3
Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia

In the country of Georgia, the administrative territories of Surami (Khashuri municipality, Shida
Kartli region) are particularly susceptible to the development of landslide processes. Among these
areas, the Zindisi district stands out as a focal point for our research due to the occurrence of a
significant landslide process in 2007, which remains active and poses periodic threats to
residential houses and infrastructure. Zindisi district is characterized by dense forest cover and a
high population density. Conducting a detailed landslide survey in such a challenging terrain using
standard methods is difficult. Therefore, our research aims to overcome these challenges by
employing lidar technology in a similar environment.

The research initiative commenced with the acquisition of high-density point cloud data utilizing
UAV lidar surveys. A UAV (DJI- The Matrix 300 RTK) equipped with a lidar camera (DJI Zenmuse L-1),
was deployed to scan the study area. This approach allowed for the capture of detailed
topographical information crucial for understanding the landslide processes. The obtained dataset
serves as the foundation for creating a precise Digital Elevation Model (DEM) with a spatial
resolution of 1 meter. This DEM enabled the identification of landslide boundaries by leveraging
lidar-derived high-resolution topographic information. Linear structures were mapped based on
hillshade, aspect, slope, and other thematic maps, providing a comprehensive understanding of
the terrain.

To validate the accuracy of our results, both aerial photos and on-site field investigations were
utilized. The combination of lidar technology, high-resolution topographic data, and thorough
validation techniques enhances the reliability of our landslide inventory in the Zindisi district. This
research contributes valuable insights for effective land management and mitigation strategies in
landslide-prone areas. Furthermore, the approach outlined in this research provides a method for
landslide mapping in similar environments and demonstrate the potential of UAV LiDAR
technology in enhancing landslide risk management in densely populated and forested regions.

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