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UAV-Based Photogrammetry and Measurements for Monitoring Natural Hazards and as a Risk Reduction Measure

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 7252

Special Issue Editors


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Guest Editor
Laboratory of Engineering Geology and Hydrogeology, School of Geology, Aristotle University of Thessaloniki (AUTH), 546124 Thessaloniki, Greece
Interests: natural hazards; landslides; rockfalls; UAVs; LiDAR; rock mass classification; tunnels; weak rocks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, Catania, Italy
Interests: UAS volcanology GIS remote sensing; volcanological monitoring; field work
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have developed into powerful tools for natural phenomena illustration, mapping, and monitoring, such as volcano eruptive crises, rockfalls and landslides, floods, earthquakes, coast erosion, and fire propagation. The combination of increasingly efficient platforms and a heterogeneous range of sensors increases acquisition productivity, enlarges the field of application, and significantly reduces the operating costs and hazards for the operator. Moreover, the use of UAVs is irreplaceable for those who must furnish stakeholders with the right information to manage a natural hazard emergency. The strengths of UAVs are speed of the mission planning, repeatability of the mission to ensure homogenization of the products, low cost, and, last but not least, operator safety. The use of non-military UAVs varies from photogrammetric mapping, updating topography after ground movements such as landslides, settlements, rock block rotation or sliding, 3D jointed rock mass modeling, object-based characterization, and thermal measurements to the sampling of gas and ash. UAV application allows us to obtain unique and novel ultra-detailed datasets that can be used to better understand the natural process, support hazard assessments, and reduce risk when monitoring a natural hazard process.

This Special Issue aims to collect the contributions of researchers and technicians who would like to show how UAVs have helped and supported them in dealing with scientific data when faced with emergency and hazard assessments.


Dr. Vassilis Marinos
Dr. Emanuela de Beni
Guest Editors

Manuscript Submission Information

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Keywords

  • UAVs
  • monitoring
  • remote measurements
  • natural hazard and risk assessment
  • photogrammetry
  • SfM technique
  • landslide
  • rockfall

Published Papers (2 papers)

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Research

26 pages, 17006 KiB  
Article
Unoccupied Aircraft Systems (UASs) Reveal the Morphological Changes at Stromboli Volcano (Italy) before, between, and after the 3 July and 28 August 2019 Paroxysmal Eruptions
by Riccardo Civico, Tullio Ricci, Piergiorgio Scarlato, Daniele Andronico, Massimo Cantarero, Brett B. Carr, Emanuela De Beni, Elisabetta Del Bello, Jeffrey B. Johnson, Ulrich Kueppers, Luca Pizzimenti, Markus Schmid, Karen Strehlow and Jacopo Taddeucci
Remote Sens. 2021, 13(15), 2870; https://doi.org/10.3390/rs13152870 - 22 Jul 2021
Cited by 19 | Viewed by 3497
Abstract
In July and August 2019, two paroxysmal eruptions dramatically changed the morphology of the crater terrace that hosts the active vents of Stromboli volcano (Italy). Here, we document these morphological changes, by using 2259 UAS-derived photographs from eight surveys and Structure-from-Motion (SfM) photogrammetric [...] Read more.
In July and August 2019, two paroxysmal eruptions dramatically changed the morphology of the crater terrace that hosts the active vents of Stromboli volcano (Italy). Here, we document these morphological changes, by using 2259 UAS-derived photographs from eight surveys and Structure-from-Motion (SfM) photogrammetric techniques, resulting in 3D point clouds, orthomosaics, and digital surface models (DSMs) with resolution ranging from 8.1 to 12.4 cm/pixel. We focus on the morphological evolution of volcanic features and volume changes in the crater terrace and the upper part of the underlying slope (Sciara del Fuoco). We identify both crater terrace and lava field variations, with vents shifting up to 47 m and the accumulation of tephra deposits. The maximum elevation changes related to the two paroxysmal eruptions (in between May and September 2019) range from +41.4 to −26.4 m at the lava field and N crater area, respectively. Throughout September 2018–June 2020, the total volume change in the surveyed area was +447,335 m3. Despite Stromboli being one of the best-studied volcanoes worldwide, the UAS-based photogrammetry products of this study provide unprecedented high spatiotemporal resolution observations of its entire summit area, in a period when volcanic activity made the classic field inspections and helicopter overflights too risky. Routinely applied UAS operations represent an effective and evolving tool for volcanic hazard assessment and to support decision-makers involved in volcanic surveillance and civil protection operations. Full article
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25 pages, 24871 KiB  
Article
In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model
by Deheng Kong, Faquan Wu, Charalampos Saroglou, Peng Sha and Bo Li
Remote Sens. 2021, 13(13), 2540; https://doi.org/10.3390/rs13132540 - 29 Jun 2021
Cited by 15 | Viewed by 2907
Abstract
The importance of in-situ rock block characterization has been realized for decades in rock mechanics and engineering, yet how to reliably measure and characterize the geometrical properties of blocks in varied forms of exposures and patterns of jointing is still a challenging task. [...] Read more.
The importance of in-situ rock block characterization has been realized for decades in rock mechanics and engineering, yet how to reliably measure and characterize the geometrical properties of blocks in varied forms of exposures and patterns of jointing is still a challenging task. Using a point cloud model (PCM) of rock exposures generated from remote sensing techniques, we developed a consistent and comprehensive method for rock block characterization that is composed of two different procedures and a block indicator system. A semi-automatic procedure towards the robust extraction of in-situ rock blocks created by the deterministic discontinuity network on rock exposures (PCM-DDN) was developed. A 3D stochastic discrete fracture network (DFN) simulation (PCM-SDS) procedure was built based on the statistically valid representation of the discontinuity network geometry. A multi-dimensional block indicator system, i.e., the block size, shape, orientation, and spatial distribution pattern for systematic and objective block characterization, was then established. The developed method was applied to a synthetic model of cardboard boxes and three different rock engineering scenarios, including a road cut slope from Spain and two open-pit mining slopes from China. Compared with existing empirical methods, the proposed procedures and the block indicator system are dependable and practically feasible, which can help enhance our understanding of block geometry characteristics in related applications. Full article
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