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GM2.6

Inputs of UAS/drones to study the Geological and Geomorphological objects and their processes

We aim herein to focus and illustrate all UAS/drone classical and new techniques and processing in order to study all Geological & Geomorphological objects in terms of 3D geometry (description and localisation, characterisation, quantification, modelisation...) in order to better constrained Earth Sciences processes.
Consequently, dealing with the technical point of view, it takes into account not only classical photogrammetric data processing through aerial photographs but also new techniques such as UAS-Lidar acquisition, and/or new UAS-interferometric acquisitions.
Many resulting dataset should be provided and discussed as key examples such as Very High Resolution Digital Terrain Model and/or Digital Surface Model (VHR DTM/DSM) less than 10cm ground resolution pixels that lead to acquire much more precise geometries, to up-date geological and geomorphological mapping and up-date structural scheme of geological targets. Many case examples could be listed herein such as analogs of sandstones or limestones reservoirs, active sedimentological processes in shoreline areas, or Natural Hazards processes such as landslides, etc...
For instance, in the active tectonic areas the HR UAS DTM may be combined with classical geodetic measurements such as levelings, numerous GPS and RTK surveys... and/or spatial PSInSAR, Smallbase line interferometric analyses in order to decipher and precise the active faults and folds structures and evenmore participate to determine the seismic cycles of major active faults.
We aim in this session to share our UAS/drone experiences on the various geological and geomorphological objects outcropping wherever the place of the Earth.

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Co-organized by NH6

Convener: Kuo-Jen Chang | Co-convener: Benoit DeffontainesECSECS

Presentations

| Wed, 25 May, 13:20–14:06 (CEST)

Room 0.16

Wed, 25 May, 13:20–14:50

Chairpersons: Kuo-Jen Chang, Benoit Deffontaines

13:20–13:24

Introduction

13:24–13:31

EGU22-3443

Environmental evolution and Landslide hazard assessment based on UAS multi-sensors

Kuo-Jen Chang et al.

Taiwan, due to the high seismicity and high annual rainfall, numerous landslides triggered frequently, thus caused severe social impacts. Landslides pose long-lasting threats to humans and their property and are detrimental to the environment in general. The vigorous development of geospatial information technology has not only achieved good results in land monitoring, but has also been gradually extended to other application fields. Hazards monitoring is one of the important applications. Geospatial information can be obtained through surveying and mapping technology, and through multi-temporal geospatial data, the production, migration and migration of debris deposits can be quantitatively evaluated in a reasonable time and space in catchment scale. In recent years, the development and integration of MEMS technology has contributed to the rapid development of UAV measurement. This goal can be achieved due to the advantages of UAVs, such as efficiency, timeliness, low cost, and easy operation in severe weather conditions. The real-time, clear and comprehensive low- and middle-altitude photos of the area can be used as the most basic and important spatial information for research and analysis.

Based on the aforementioned technologies, some specific potential landslides situated in the Laonongshi Stream southern Taiwan was been assigned. In order to evaluate potential hazards and hazard monitoring, multi-temporal high precision terrain geomorphology in different periods is essential. For these purpose, we integrate several technologies, especially by unmanned aircraft system imageries and existed airphotos, to acquire and to establish the geoinfomatic datasets. The methods, including, (1) Remote-sensing images gathered by UAS and by aerial photos taken in different periods; (2) UAV LiDAR acquired in different periods; (3) field in-situ ground control points and check points installation and geomatic measurement; (4) 3D geomorphological virtual reality model construction; (5) Geologic, morphotectonic and landslide micro-geomorphologic analysis; (6) DEM of difference from multi-temporal dataset to evaluate the topographic and environment changes. We focused on the potential large-scale deep-seated landslides, acquired high-precision and high-resolution DTMs, proving as the essential geoinformatic datasets, so as able to monitoring the slope behavior and to decipher the potential landslide hazard, sediment budgets and the consequence of social impact. The results show that there are still landslide activities in different periods and regions within the study area; different sections of the river channel also have different degrees of siltation or erosion. Therefore, regular monitoring and potential assessment are necessary. The developing methods may apply for other potential large-scale landslide monitoring and assessment in Taiwan, and in world as well.

How to cite: Chang, K.-J., Tseng, C.-W., Hsieh, Y.-C., and Huang, M.-J.: Environmental evolution and Landslide hazard assessment based on UAS multi-sensors , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3443, https://doi.org/10.5194/egusphere-egu22-3443, 2022.

13:31–13:38

EGU22-7054

Highlight

On-site presentation

Landslide fissures and fractures mapping and classification from UAV imagery

Ionut Cosmin Sandric et al.

Mapping landslide fissures and fractures are essential in understanding the landslide dynamics and evolution across space and time. In the current study, a particular focus on detecting, mapping, and classifying the fissures and fractures located along the landslide bodies and in their vicinity has been given. The depth, direction and width of each fissure and fracture are related to the stress and strain imposed on the landslide body. Moreover, the spatial distribution of these can indicate areas where the landslide can extend, mainly if they are located in the upper part of the main landslide scarp. Even though the fissures and fractures leave a distinct pattern on the landslide body when they are fresh or when there is a high contrast between the bare soil and the surrounding vegetation, these patterns are gradually diminished by time, making their detection difficult. The problem of landslide cracks mapping in various environmental conditions and having different ages was tackled in the current study by using very high spatial resolution UAV aerial imagery and derived products in conjunction with deep learning models. Several flights using DJI Phantom 4 RTK were performed in the Romanian Subcarpathians in areas with both recent and old landslide occurrences. The sampling dataset was collected with Esri ArcGIS Pro on products obtained by the fusion of orthoimages with terrain parameters. The dataset was fed into a Mask RCNN deep learning model with a Resnet152 architecture and trained for 50 epochs. The training and validation reached accuracies of 0.77 and 0.70, estimated from the Intersect over Union metric. No significant differences were recorded between the detection on only orthoimages and the detection on products obtained from the fusion of orthoimages with other terrain parameters. A slight decrease in the validation accuracy was observed when the images were collected on older landslides compared to recent landslides. Overall, the detection of fissures and fractures using deep learning and UAV aerial imagery proved reliable if the UAV flights are flown quickly after the landslide occurrence or after recent rainfalls.

Acknowledgement

This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III, project coordinator Ionuț Șandric (https://slidemap.gmrsg.ro), and by the project PN19450103 (project coordinator Viorel Ilinca, Geological Institute of Romania).

How to cite: Sandric, I. C., Ilinca, V., Irimia, R., and Chitu, Z.: Landslide fissures and fractures mapping and classification from UAV imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7054, https://doi.org/10.5194/egusphere-egu22-7054, 2022.

13:38–13:45

EGU22-11130

Virtual presentation

A semiautomated mapping of landslide volume displacements using UAV aerial imagery

Radu Irimia et al.

The current study is focused on assessing the spatial and temporal patterns of landslide volume displacements using a semiautomated method and Unmanned Aerial Vehicle (UAV) aerial imagery. The case study is located in the Livadea village from Curvature Subcarpathians, Romania, where a landslide was triggered on May 3, 2021. Three separate flights were flown on May 6, May 25, and July 10 using DJI Phantom 4 and Phantom 4 RTK drones. Even though there is a difference in camera resolution, each flight plan was created to correspond to a 4cm/pixel spatial resolution, meaning that the constant height above ground was different between the first flight and the next two flights. For the first flight, because the UAV equipped with the RTK receiver was not available, a graded consumer UAV equipped with a Non-RTK receiver was used. A maximum overlap with the smallest errors possible between all the flights was obtained by orthorectifying the first and the third flights with GCPs collected from the second flight. The method is based on using aerial imagery collected with UAV and their derived products obtained by applying the Structure from Motion (SfM) technique. Because it is an area with dense forest, the Visible Atmospherically Resistant Index (VARI) was used to filter out all the pixels with vegetation from the digital surface models (DSM). The gaps were filled by using the Empirical Bayesian Kriging interpolation method, implemented in ArcGIS Pro. The results show volume displacement rates of 0.005 cubic meters/meter for the period between the first and second flights and 0.05 cubic meters/meter for the period between the second and third flights. The overall displaced volume was approximately 406000 cubic meters with approximately 41000 cubic meters for the period between the first and second flights and approximately 365000 cubic meters between the second and the third flight. This approach proved quick and efficient for assessing landslide volume displacement when fast response and measures are necessary to reduce landslide consequences.

Acknowledgement

This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III (project coordinator Ionuț Șandric, https://slidemap.gmrsg.ro) and by the project PN19450103 / Core Program (project coordinator Viorel Ilinca).

How to cite: Irimia, R., Sandric, I. C., Ilinca, V., Chitu, Z., and Gheuca, I.: A semiautomated mapping of landslide volume displacements using UAV aerial imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11130, https://doi.org/10.5194/egusphere-egu22-11130, 2022.

13:45–13:52

EGU22-9881

Virtual presentation

Performance of different UAS platforms, techniques (LIDAR and photogrammetry) and referencing approaches (RTK, PPK or GCP-based) to acquire 3D data in coastal cliffs

Álvaro Gómez-Gutiérrez et al.

Understanding the dynamics of coastal areas is crucial to mitigate the effects of global change though monitoring these places could be challenging, difficult and dangerous, especially in the presence of (unstable) cliffs. The recent development of Unmanned Aerial Systems (UAS) with accurate direct georeferencing systems facilitates this task. The objective of this work is to test the performance of different 3D data acquisition strategies in coastal cliffs, specifically RGB and LIDAR sensors on board UAS platforms equipped with direct georeferencing instruments based on Global Navigation Satellite Systems (GNSS: Real Time Kinematic-RTK and Post-Processing Kinematic-PPK approaches). Two UAS were used to capture data and produce point clouds of a coastal cliff in the Cantabrian Coast (Gerra beach, North Spain): a DJI Phantom 4 RTK (P4RTK) and a MD4-1000 LIDAR. The P4RTK may receive corrections to estimate accurate positions of the UAS during the acquisition of images (P4RTK processing approach), but also may record the trajectory of the UAS to carry out a PPK approach later to correct and estimate the location of the camera at every shot (P4RTK-PPK processing approach). Two GNSS receivers (Leica 1200 working as base and rover) were used to survey 31 points distributed in the study area. The surveyed points were used in different number (from 0 to 10) as Ground Control Points (GCPs: to support the production of the point clouds) or Check Control Points (CCPs: to independently test the geometrical accuracy of the point clouds) in the photogrammetric processing (using two parallel pipelines with Agisoft Metashape and Pix4Dmapper Pro software packages). The MD4-1000 LIDAR is a quadcopter UAS equipped with the following instruments: a LIDAR sensor SICK LD-MRS4 (to capture the point cloud), a Ladybug RGB camera (to acquire images and colour the point cloud), and a GNSS antenna (Trimble APX-15v3) with an integrated Inertial Measurement Unit. The trajectory of the UAS recorded by the GNSS may be corrected using observations registered by a GNSS base station to obtain the accurate pose of the UAS using a PPK approach.

Additionally, a benchmark point cloud was acquired by a Terrestrial Laser Scanner (Leica ScanStation C10) placed at 5 locations. The resulting point cloud showed 23,4 million points with a registration error of 7 mm. Three parameters were used to test the quality of the resulting point clouds: point cloud density and coverage, distance to the benchmark point cloud and RMSE of CCPs. The results showed that any of the strategies produced very accurate point clouds with a geometrical accuracy <10 cm. The P4RTK (RTK, PPK or using GCPs) produced more accurate and denser point clouds than the MD4-1000 LIDAR system (only PPK approach). The use of GCPs did not improved substantially the point clouds produced by photogrammetry (and RTK or PPK approaches) if an oblique pass is included in the flight plan to improve the camera focal estimation and corrections are available.

How to cite: Gómez-Gutiérrez, Á., Sánchez-Fernández, M., and de Sanjosé-Blasco, J. J.: Performance of different UAS platforms, techniques (LIDAR and photogrammetry) and referencing approaches (RTK, PPK or GCP-based) to acquire 3D data in coastal cliffs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9881, https://doi.org/10.5194/egusphere-egu22-9881, 2022.

13:52–13:59

EGU22-3860

Highlight

On-site presentation

Unmanned Areal Vehicles for permafrost monitoring in high alpine regions within the new EU framework

Inga Beck et al.

The deployment of Unmanned Aerial Vehicles (UAV) for scientific purposes gained a lot of importance during the last years. The new EU regulations for the use of civil drones, in effect since January 2021, set out a new framework for their safe operation in the European skies. With a risk-based approach the purpose of the drone (leisure or civil) is no longer relevant, but only it’s weight, specifications and operations is considered. Also for scientific use these new rules mean a more elaborate project preparation and require the compilation of a so-called Specific Operational Risk Assessment (SORA) for each individual case.

Here we report on a three years project, in which drones will be flown at altitudes around and above 3000m asl from the Environmental Research Station Schneefernerhaus (UFS), located on the Zugspitze (Northern Limestone Alps, Germany). It is a collaborative initiative of the UFS as lead and coordinator, the TUM Chair of Landslide Research as scientific partner as well as the Georesearch mbH as technical partner. The project is financed by the Bavarian State Ministry of the Environment and Consumer Protection and started in June 2021. It stands out as an innovative pilot project, pursuing two different goals:

Expertise should be collected in writing a SORA for the use of drones in high alpine areas, crossing national borders (D/A) and operating beyond the visible line of site. Thereby a broad know-how will be gained that will facilitate future scientific drone missions with the Schneefernerhaus as starting point.
Scientific data will be collected by means of an IR camera and will record the temperature of the ground, delivering information about the current status of the permafrost-affected steep rockwalls. This will extend the present permafrost monitoring conducted on the Zugspitze (Scandroglio et al., 2021) to wider and unexplored areas. Furthermore the influence of infrastructures and their influence on the bedrock thermal behavior will be identified and monitored.
An inventory of potential rockfall areas will be recorded by means of optical sensors.

In fall 2021 areas of interest, flight routes and starting positions have been defined. After the installation of targets and rock surface temperature loggers, the first flight has been conducted with a drone of the open category, allowing the collection of the first thermal and RGB datasets. Currently a user-defined UAS gets manufactured and the SORA process – supervised by bavAIRia e. V. – is in process. The next steps will be the use of the new drone at least twice this year (2022) in order to collect more data.

How to cite: Beck, I., Delleske, R., Scandroglio, R., Rehm, T., Keuschnig, M., and Krautblatter, M.: Unmanned Areal Vehicles for permafrost monitoring in high alpine regions within the new EU framework , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3860, https://doi.org/10.5194/egusphere-egu22-3860, 2022.