GM2.6

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.

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.

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.

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.

Taiwan island is the result of the active rapid collision of both Eurasian and Philippine Sea Plates with an annual average convergence rate close to 10 cm.y^-1. The relief of Taiwan is composed of the metamorphic slate belt of the Backbone Range (also called Central Range) and to the east the Coastal Range mainly characterized by volcanic affinity. In between those, lay the Longitudinal Valley (125km long and N020°E trending) which is the active crustal suture zone. The latter presents both inter-seismic creeping displacement (Champenois et al., 2013) and was hit by 7 major earthquakes of magnitudes larger than 5 during the last 70 years. It highliths the geohazards importance of this area for any Taiwan citizens.

In order to better constrain the seismic hazards and the earthquake cycles of the place, we settled several years ago numerous UAS surveys above the Coastal Range and the Longitudinal Valley (E. Taiwan) and acquired so many high-resolution photographs using several drones flying at 350 meters above the ground. After photogrammetric processing, we calculate both (1) a high-resolution Digital Elevation Model (UAS-HR-DEM) that takes into account buildings and vegetations, and (2) a Digital Terrain Model (UAS-HR-DTM) corresponding to the ground. Our ground validation (GCP’s) leads us to get 7cm planimetric resolution (X, Y) and below 40cm vertical accuracy. This UAS-HR-DTM combined with field work and a detailed morphostructural analysis permit us to map into much details the structures and consequently to up-date the pre-existing geological mappings (e.g. CGS geological maps, Lin et al., 2009 ; Shyu et al., 2005, 2006, 2007, 2008). Then we combined our new structural scheme with various geodetic data (levelings, GPS…) and PSInSAR results (Champenois 2011, and Champenois et al., 2013) to locate, characterize and quantify the active tectonic structures, taking into account previous works (e.g. Yu et al., 1997; Lee et al., 2008; Hsu et al., 2009; Huang et al., 2010…). We then precise structural geometries and some geological processes as well as the location of active folds and active faults during the PSInSAR monitoring time-period.

How to cite: Deffontaines, B., Chang, K.-J., Magalhaes, S., Serries, G., and Fortunato, G.: Geological mapping and Active tectonics from UAS-HR-DTM and PSInSAR: Case examples in the Longitudinal Valley and the Coastal Range (E. Taiwan), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8662, https://doi.org/10.5194/egusphere-egu22-8662, 2022.