The potential of UAV-acquired photogrammetric and LiDAR-point clouds for obtaining rock dimensions as input parameters for modeling rockfall runout zones

Žabota, Barbara (author), Berger, Frédéric (author), Kobal, Milan (author)

Rockfalls present a significant hazard to human activities; therefore, their identification and knowledge about potential spatial impacts are important in planning protection measures to reduce rockfall risk. Remote sensing with unmanned aerial vehicles (UAVs) has allowed for the accurate observation of slopes that are susceptible to rockfall activity via various methods and sensors with which it is possible to digitally collect information about the rockfall activity and spatial distributions. In this work, a three-dimensional (3D) reconstruction of rock deposits (width, length, and height) and their volumes are addressed, and the results are used in a rockfall trajectory simulation. Due to the availability of different sensors on the UAV, the aim was also to observe the possible differences in the dimension estimations between photogrammetric and LiDAR (light detection and ranging) point clouds, besides the most traditional method where rock deposit dimensions are measured on the field using a measuring tape. The motivation for reconstructing rock dimensions and volumes was solely for obtaining input parameters into a rockfall model. In order to study the differences between rock-measuring methods, rock dimensions were used as input parameters in a rockfall model, and additionally, modeling results such as propagation probability, maximum kinetic energies, and maximum passing heights were compared. The results show that there are no statistically significant differences between the measurement method with respect to rock dimensions and volumes and when modeling the propagation probability and maximum passing heights. On the other hand, large differences are present with maximum kinetic energies where LiDAR point cloud measurements achieved statistically significantly different results from the other two measurements. With this approach, an automated collection and measurement process of rock deposits is possible without the need for exposure to a risk of rockfall during fieldwork.

• Language: English
• Keywords: rockfall, UAV, LiDAR, photogrammetry, modeling, input parameters
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.01.2023
• Year: 2023
• Number of pages: 32 str.
• Numbering: Vol. 7, iss. 2 [article no. 104]
• PID: 20.500.12556/RUL-144243
• UDC: 630*38+630*58
• ISSN on article: 2504-446X
• DOI: 10.3390/drones7020104
• COBISS.SI-ID: 140835587
• Publication date in RUL: 06.02.2023

ŽABOTA, Barbara, BERGER, Frédéric and KOBAL, Milan, 2023, The potential of UAV-acquired photogrammetric and LiDAR-point clouds for obtaining rock dimensions as input parameters for modeling rockfall runout zones. Drones [online]. 2023. Vol. 7, no. 2  104. [Accessed 17 May 2025]. DOI 10.3390/drones7020104. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=144243

Record is a part of a journal

• Title: Drones
• Shortened title: Drones
• Publisher: MDPI AG
• ISSN: 2504-446X
• COBISS.SI-ID: 529755417

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: skalni podori, UAV, LiDAR, fotogrametrija, modeliranje, vhodni parametri

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: J1-3024-2021
• Name: Dešifriranje občutljivosti skalnih sten na podnebne spremembe in cikle zmrzovanja in odtaljevanja na območjih brez permafrosta

• Funder: ARRS - Slovenian Research Agency
• Project number: P4-0059-2020
• Name: Gozd, gozdarstvo in obnovljivi gozdni viri