Monitoring the crisis of a rock  glacier with repeated UAV surveys

Abstract. In this study, rapid topographic changes and high creeping rates caused by the destabilisation of an active rock glacier in a steep mountain flank were investigated in detail with five unmanned aerial vehicle (UAV) surveys between June 2016 and September 2017. State-of-the-art photogrammetric techniques were employed to derived high-density point clouds and high-resolution orthophoto mosaics from the studied landform. The accuracy of the co-registration of subsequent point clouds was carefully examined and adjusted based on comparing stable areas outside the rock glacier, which minimised 3-D alignment errors to a mean of 0.12 m. Elevation and volumetric changes in the destabilised rock glacier were quantified over the study period. Surface kinematics were estimated with a combination of image correlation algorithms and visual inspection of the orthophoto mosaics. Between June 2016 and September 2017, the destabilised part of the rock glacier advanced up to 60–75 m and mobilised a volume of around 27 000 m3 of material which was dumped over the lower talus slope. This study has demonstrated a robust and customisable monitoring approach that allows a detailed study of rock glacier geometric changes during a crisis phase.

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Rock glacier deformation using an Unmanned Aerial Vehicle (UAV) with RTK GNSS capability

10.5194/egusphere-egu21-14844 ◽

2021 ◽

Author(s):

Sebastián Vivero ◽

Christophe Lambiel

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicle ◽

Point Clouds ◽

Swiss Alps ◽

Image Correlation ◽

Reference Station ◽

Ground Control ◽

Rock Glacier ◽

Slope Structure ◽

Aerial Vehicle

In high alpine geomorphological research, different technologies are increasingly used to describe and monitor mass movements such as rock glaciers. Among them, the combination of Unmanned Aerial Vehicle (UAV) systems and Structure from Motion (SfM) techniques is gaining continuous interest due to rapid technological developments. In this study, we test the capability of repeated UAV surveys to accurately survey rock glacier deformation in the Lac des Vaux area, Valais Alps. The studied landform is located on a typical anthropic alpine environment in the Swiss Alps, where ski facilities and alpine tracks are a commonplace. A DJI Phantom 4 RTK UAV was flown twice in September 2019 and September 2020 to cover an area of about 0.25 km2 with nearly 1000 images each time. Differential corrections using a Virtual Reference Station (VRS) provided image geotags with centimetre-level accurate 3-D coordinates, thereby allowing dispensing with ground control. High-resolution orthomosaics and high-density point clouds are derived from the UAV-RTK surveys using a standard SfM processing workflow. The corresponding point clouds' accuracy was evaluated and adjusted based on stable terrain, reducing the 3-D alignment errors to a mean of 0.02 m. Elevation changes and surface kinematics in the rock glacier complex and its margins were quantified using point cloud operations and image correlation techniques. The results indicate that the landform has at least eight different lobes with mean velocities ranging between 0.1 and 1.3 m yr-1. The high-resolution analysis also permitted identifying moving lobes without morphological expression and small thermokarst depressions on the ski slope structure that traverses the rock glacier's active zone. Without relying on ground control, our approach achieves horizontal and vertical accuracies nearly as good as monitoring techniques using more traditional differential GNSS devices.

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Comparing Two Photo-Reconstruction Methods to Produce High Density Point Clouds and DEMs in the Corral del Veleta Rock Glacier (Sierra Nevada, Spain)

Remote Sensing ◽

10.3390/rs6065407 ◽

2014 ◽

Vol 6 (6) ◽

pp. 5407-5427 ◽

Cited By ~ 25

Author(s):

Álvaro Gómez-Gutiérrez ◽

José de Sanjosé-Blasco ◽

Javier de Matías-Bejarano ◽

Fernando Berenguer-Sempere

Keyword(s):

Sierra Nevada ◽

Point Clouds ◽

High Density ◽

Density Point ◽

Rock Glacier ◽

Reconstruction Methods

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Geomorphological mapping of an alpine rock glacier with multi-temporal UAV-based high density point cloud comparison

10.5194/egusphere-egu2020-17912 ◽

2020 ◽

Author(s):

Francesca Bearzot ◽

Roberto Garzonio ◽

Biagio Di Mauro ◽

Umberto Morra Di Cella ◽

Edoardo Cremonese ◽

...

Keyword(s):

Surface Displacement ◽

Point Clouds ◽

Horizontal Displacement ◽

Multiscale Model ◽

Climatic Conditions ◽

High Density ◽

The Body ◽

Density Point ◽

Differential Gps ◽

Rock Glacier

The acquisition of high-resolution topographic data is a widely used tool for studies related to the processes and dynamics of the Earth's surface. In this work, we present the results of the repeated acquisition of photogrammetric data by Unmanned Aerial Vehicle (UAV) in order to detect the topographic evolution of an alpine rock glaciers located in Valtournenche (AO, Italy). Field monitoring conducted in recent years has shown significant variations in the behaviour of these landforms, with an increasing trend of their dynamism, raising questions about their stability in changing climatic conditions.&#160;The photogrammetric shots were taken with a DJ Phantom 4 UAV equipped with a compact RGB digital camera. The acquisitions were performed yearly from 2012 up to 2019 with a ground sampling distance never exceeding 5 cm/px. Contemporary to the acquisitions, approximately 20 Ground Control Points were placed on the rock glacier and on the surrounding areas and their coordinates were measured with a differential GPS (dGPS) for georeferencing UAV images. Moreover, in 2014, 2015 and 2019 geophysical campaigns were carried out for the detection of ice lenses under the debris cover of the rock glacier.&#160;Structure-from-motion techniques were applied on overlapping images to create high-density point clouds, than converted in orthophotos and digital surface models of the Earth&#8217;s surface.The point clouds were analysed using the M3C2 (Multiscale Model to Model Cloud Comparison) plug-in, freely available in the CloudCompare software. Maps of surface changes between acquisition pairs in the period from 2015-2019 have been created. The comparison allowed the identification of "material supply" and "material removal" zones, slightly variable from one year to the next. The major accumulation zones are concentrated along the frontal sector of the rock glacier, more focused on the western sector (black lobe) and secondly on the right side of the rock glacier (white lobe). The removal of material is mainly concentrated on the higher altitude of the body but also in correspondence to the systems of crevasses and scarps and on the central part of the black lobe.The surface displacement analysis of the rock glacier was also performed selecting manually several clearly identifiable features on the orthomosaics collected. Blocks and ridges-and-furrows complex were marked on the 2019 orthomosaic and found them on the 2015 orthomosaic. This approach allows improving and quantifying the dynamics of the different portions of the individual apparatus.The velocity fields&#8217; patterns highlight non-homogeneous displacements between the West (black lobe) and East part (white lobe) of the whole rock glacier. Specifically, the black lobe showed an average horizontal displacement of around 1 m/y while the white lobe moved significantly slower than the previous one (approximately 0.5 m/y). Overall, the rock glacier moved downslope at an average horizontal velocity of 0.60 m/y in the frontal tongue, 0.48 m/y in the central portion and 0.30 m/y in the upper zone.

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COMPARISON AND TIME SERIES ANALYSIS OF LANDSLIDE DISPLACEMENT MAPPED BY AIRBORNE, TERRESTRIAL AND UNMANNED AERIAL VEHICLE BASED PLATFORMS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-421-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 421-428 ◽

Cited By ~ 3

Author(s):

J. Pfeiffer ◽

T. Zieher ◽

M. Rutzinger ◽

M. Bremer ◽

V. Wichmann

Keyword(s):

Time Series ◽

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Image Correlation ◽

Landslide Monitoring ◽

Displacement Vectors ◽

Aerial Vehicle ◽

Displacement Patterns

Abstract. Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of &plusmn;15&thinsp;cm&thinsp;a&minus;1 between 2008 and 2017 and an area wide acceleration of up to 20&thinsp;cm&thinsp;a&minus;1 between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.

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Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽

10.3390/s21041228 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1228

Author(s):

Ting On Chan ◽

Linyuan Xia ◽

Yimin Chen ◽

Wei Lang ◽

Tingting Chen ◽

...

Keyword(s):

Cultural Heritage ◽

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Geometric Model ◽

Digital Camera ◽

Point Clouds ◽

Symmetry Analysis ◽

3D Point Clouds ◽

Transmission Towers ◽

Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

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A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽

10.3390/met11060870 ◽

2021 ◽

Vol 11 (6) ◽

pp. 870

Author(s):

Robby Neven ◽

Toon Goedemé

Keyword(s):

Process Parameters ◽

Visual Inspection ◽

Steel Surface ◽

Surface Defect ◽

State Of The Art ◽

Sheet Steel ◽

Real Life ◽

Single Task ◽

Severity Estimation ◽

Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

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A Termination Criterion for Probabilistic Point Clouds Registration

Signals ◽

10.3390/signals2020013 ◽

2021 ◽

Vol 2 (2) ◽

pp. 159-173

Author(s):

Simone Fontana ◽

Domenico Giorgio Sorrenti

Keyword(s):

State Of The Art ◽

Point Clouds ◽

Computational Time ◽

Termination Criteria ◽

Practical Usefulness ◽

Local Point ◽

Termination Criterion ◽

Probabilistic Point ◽

Excessive Number ◽

Number Of Iterations

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

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Robust autonomous flight in cluttered environment using a depth sensor

International Journal of Micro Air Vehicles ◽

10.1177/1756829320924528 ◽

2020 ◽

Vol 12 ◽

pp. 175682932092452

Author(s):

Liang Lu ◽

Alexander Yunda ◽

Adrian Carrio ◽

Pascual Campoy

Keyword(s):

High Speed ◽

Point Clouds ◽

Depth Sensor ◽

Cluttered Environments ◽

Industrial Plants ◽

Cluttered Environment ◽

Navigation Strategy ◽

Physical Sensors ◽

Aerial Vehicle ◽

Planning Objective

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

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Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060367 ◽

2021 ◽

Vol 10 (6) ◽

pp. 367

Author(s):

Simoni Alexiou ◽

Georgios Deligiannakis ◽

Aggelos Pallikarakis ◽

Ioannis Papanikolaou ◽

Emmanouil Psomiadis ◽

...

Keyword(s):

Soil Erosion ◽

Change Detection ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Mean Value ◽

Detection Methods ◽

Mountainous Catchments ◽

Aerial Vehicle ◽

Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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Scene Wireframes Sketching for Drones

Proceedings ◽

10.3390/proceedings2181193 ◽

2018 ◽

Vol 2 (18) ◽

pp. 1193

Author(s):

Roi Santos ◽

Xose Pardo ◽

Xose Fdez-Vidal

Keyword(s):

State Of The Art ◽

Point Clouds ◽

Outdoor Environment ◽

Point Matching ◽

3D Point Clouds ◽

Complementary Approach ◽

Straight Line ◽

Scene Representation ◽

Feature Point Matching ◽

3D Scene

The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. Likewise, line clouds constructed by short and redundant segments with inaccurate directions limit the understanding of scenes as those that include environments with poor texture, or whose texture resembles a repetitive pattern. The presented approach is based on observation and representation models using the straight line segments, whose resemble the limits of an urban indoor or outdoor environment. The goal of the work is to get a full method based on the matching of lines that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV.

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