Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research

UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

Assessment of the Evolution of a Landslide Using Digital Photogrammetry and LiDAR Techniques in the Alpujarras Region (Granada, Southeastern Spain)

In this work a detailed analysis of the temporal evolution of the Almegíjar landslide is presented. It is a rock slide located in the Alpujarras region (Granada, Spain) that has developed over the last 30 years. Six datasets and photogrammetric flights corresponding to the years 1956, 1984, 1992, 2001, 2008, and 2010 were surveyed. The more recent flight of 2010 combined an aerial digital camera and a LiDAR sensor and was oriented by means of in-flight data and tie points. This 2010 flight allowed for the generation of a reliable and high-precision Digital Terrain Model (DTM). The other flights were oriented using second-order ground control points transferred from the 2010 flight, and the corresponding DTMs were prepared by automatic matching and subsequent editing from the stereoscopic models. After comparing the DTMs of different dates, it has been observed that the landslide was triggered after 1984 and since then has evolved in an irregular pattern with periods of variable activity. On average, the ground surface dropped more than 8 m in depleted zones and rose nearly 4 m in the accumulation zones, with a velocity catalogued as very slow (about 15–30 cm/year) over a time span corresponding to a degree VIII of diachroneity. The total volume of the mobilized mass of this large contemporary slide was about 300 × 103 m3.This research was funded by the projects P06-RNM-02125 and RNM-06862 (ISTEGEO) funded by the Andalusian Research Plan, projects CGL2008-04854 and TIN2009-09939 funded by the Ministry of Science and Innovation of Spain and Research Groups TEP-213, and RNM 221 of the Andalusian Research Plan

Unmanned Aircraft System Assessments of Landslide Safety for Transportation Corridors

An assessment of unmanned aircraft systems (UAS) concluded that current, off-the-shelf UAS aircraft and cameras can be effective for creating the digital surface models used to evaluate rock-slope stability and landslide risk along transportation corridors. The imagery collected with UAS can be processed using a photogrammetry technique called Structure-from-Motion (SfM) which generates a point cloud and surface model, similar to terrestrial laser scanning (TLS). We treated the TLS data as our control, or “truth,” because it is a mature and well-proven technology. The comparisons of the TLS surfaces and the SFM surfaces were impressive – if not comparable is many cases. Thus, the SfM surface models would be suitable for deriving slope morphology to generate rockfall activity indices (RAI) for landslide assessment provided the slopes. This research also revealed that UAS are a safer alternative to the deployment and operation of TLS operating on a road shoulder because UAS can be launched and recovered from a remote location and capable of imaging without flying directly over the road. However both the UAS and TLS approaches still require traditional survey control and photo targets to accurately geo-reference their respective DSM.List of Figures ...................................................................................................... vi List of Abbreviations ......................................................................................... vii Acknowledgments ................................................................................................ x Executive Summary ............................................................................................. xi CHAPTER 1 INTRODUCTION .......................................................................... 1 CHAPTER 2 LITERATURE REVIEW ................................................................ 4 2.1 Landslide Hazards .................................................................................... 4 2.2 Unmanned Aircraft Systems Remote Sensing.......................................... 6 2.3 Structure From Motion (SfM) .................................................................. 7 2.4 Lidar terrain mapping ............................................................................... 8 CHAPTER 3 STUDY SITE/DATA .................................................................. 11 CHAPTER 4 METHODS ................................................................................ 13 4.1 Data Collection ............................................................................................. 13 4.1.1 Survey Control ..................................................................................... 14 4.1.2 TLS Surveys ........................................................................................ 16 4.1.3 UAS Imagery ....................................................................................... 17 4.1.4 Terrestrial Imagery Acquisition ........................................................... 19 4.2 Data Processing ............................................................................................ 20 4.2.1 Survey Control ..................................................................................... 20 4.2.2 TLS Processing .................................................................................... 20 4.2.3 SfM Processing .................................................................................... 21 4.2.4 Surface Generation .............................................................................. 22 4.3 Quality Evaluation ........................................................................................ 23 4.3.1 Completeness ....................................................................................... 23 4.3.2 Data Density/Resolution ...................................................................... 23 4.3.3 Accuracy Assessment .......................................................................... 23 4.3.2 Surface Morphology Analysis ............................................................. 24 4.2.6 Data Visualization ............................................................................... 25 CHAPTER 5 RESULTS ................................................................................. 27 v 5.1 UTIC DSM evaluation.................................................................................. 27 5.1.1 Completeness evaluation ..................................................................... 28 5.1.2 Data Density Evaluation ...................................................................... 29 5.1.3 Accuracy Evaluation............................................................................ 30 5.2 Geomorphological Evaluation ...................................................................... 32 CHAPTER 6 DISCUSSION ............................................................................ 35 6.1 Evaluation of UAS efficiencies .................................................................... 35 6.2 DSM quality and completeness .................................................................... 37 6.3 Safety and operational considerations .......................................................... 37 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS ................................ 40 7.1 Technology Transfer..................................................................................... 41 7.1.1 Publications ......................................................................................... 41 7.1.2 Presentations ........................................................................................ 42 7.1.3 Multi-media outreach .......................................................................... 43 6.4 Integration of UAS and TLS data ................................................................. 44 REFERENCES .............................................................................................. 4

Morphology-based landslide monitoring with an unmanned aerial vehicle

PhD ThesisLandslides represent major natural phenomena with often disastrous consequences. Monitoring landslides with time-series surface observations can help mitigate such hazards. Unmanned aerial vehicles (UAVs) employing compact digital cameras, and in conjunction with Structure-from-Motion (SfM) and modern Multi-View Stereo (MVS) image matching approaches, have become commonplace in the geoscience research community. These methods offer a relatively low-cost and flexible solution for many geomorphological applications. The SfM-MVS pipeline has expedited the generation of digital elevation models at high spatio-temporal resolution. Conventionally ground control points (GCPs) are required for co-registration. This task is often expensive and impracticable considering hazardous terrain. This research has developed a strategy for processing UAV visible wavelength imagery that can provide multi-temporal surface morphological information for landslide monitoring, in an attempt to overcome the reliance on GCPs. This morphological-based strategy applies the attribute of curvature in combination with the scale-invariant feature transform algorithm, to generate pseudo GCPs. Openness is applied to extract relatively stable regions whereby pseudo GCPs are selected. Image cross-correlation functions integrated with openness and slope are employed to track landslide motion with subsequent elevation differences and planimetric surface displacements produced. Accuracy assessment evaluates unresolved biases with the aid of benchmark datasets. This approach was tested in the UK, in two sites, first in Sandford with artificial surface change and then in an active landslide at Hollin Hill. In Sandford, the strategy detected a ±0.120 m 3D surface change from three-epoch SfM-MVS products derived from a consumer-grade UAV. For the Hollin Hill landslide six-epoch datasets spanning an eighteen-month duration period were used, providing a ± 0.221 m minimum change. Annual displacement rates of dm-level were estimated with optimal results over winter periods. Levels of accuracy and spatial resolution comparable to previous studies demonstrated the potential of the morphology-based strategy for a time-efficient and cost-effective monitoring at inaccessible areas

Earth Observations for Geohazards: Present and Future Challenges

Earth Observations (EO) encompasses different types of sensors (e.g., Synthetic Aperture Radar, Laser Imaging Detection and Ranging, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO data have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works. This Special Issue presents a collection of scientific contributions focusing on innovative EO methods and applications for monitoring and modeling geohazards, consisting of four Sections: (1) earthquake hazards; (2) landslide hazards; (3) land subsidence hazards; and (4) new EO techniques and services.Part of this work was supported by the UK Natural Environmental Research Council (NERC) through the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, ref.: come30001) and the LICS and CEDRRIC projects (refs. NE/K010794/1 and NE/N012151/1, respectively), European Space Agency through the ESA-MOST DRAGON-4 projects (ref. 32244) and the Spanish Ministry of Economy and Competitiveness and EU FEDER funds under projects TIN2014-55413- C2-2-P and ESP2013-47780-C2-2-R

Unmanned aerial system integration for monitoring and management of landslide: A case of Dominican Republic

Conference paper presented at the 38th annual ISARC symposium, available online: https://doi.org/10.22260/ISARC2021/0087The Unmanned Aerial System (UAS), Aerial Robot or Drone has been a multi-purpose tool for professionals, especially for its unknown versatile applications and regardless of its challenges in adoption. In the built environment and the current global situation, the topic of disaster management has grabbed attention from the scientific community, raising questions of cyberspace linking the COVID-19. As a result, the case study shows how UAS is used to evaluate the landslide provoked as cascading effect of a bridge site construction and the digital data required to feed in 2D and 3D a database of urban planning development. The findings reveal that the application of UAS reduced physical inspections, allowed professionals to obtain inaccessible data, and helped to overview the site conditions identifying the cause of the phenomenon. A safety design factor of the critical building of the school in the community to mitigate the landslide hazards at Santa Maria, Dominican Republic was recommended. Future works in 360°evaluation for similar cases in construction are recommended