NH3.8

Landslides annually cause thousands of casualties and billions of dollars in property loss. Mitigation of their hazards demands answers to three fundamental questions: where are the landslides, how are they evolving, and what damages would they cause upon a runout failure? Radar remote sensing, capable of capturing landslide deformation in near real-time, proves itself an effective and efficient tool to help address these challenges. Here, we highlight a workflow that incorporate SAR (Synthetic Aperture Radar)’s unique values to aid landslide detection, monitoring, and runout damage forecasting. By integrating field instrumentation and hydromechanical modeling, our recent studies over the U.S. West Coast substantiated SAR’s powerful capabilities: (1) Discovering approximately 600 destabilized, slow-moving landslides that were missing from the currently existing, non-systematically mapped landslide database of the United States; (2) Monitoring and characterizing spatiotemporal dynamics of landslides that destroy highways (e.g., the Hooskanaden landslide in southwestern Oregon), damage aquatic habitats (e.g., tens of irrigation-induced landslides in eastern Washington ), and endanger communities (e.g., the Cascade Locks landslide in southern Washington); (3) Constraining source volume to help predict runout hazard of landslides that threaten popular campgrounds (e.g., the Gold Basin landslide in central Washington) and urban communities (e.g., the Cape Meares landslide in northwestern Oregon). Adaptation of our methodology to assimilate SAR observations could prove useful for mitigating similar landslide hazards beyond the regional scale.

How to cite: Lu, Z., Xu, Y., Burgmann, R., and George, D.: Landslides on the radar: detection, monitoring, and runout hazard forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8647, https://doi.org/10.5194/egusphere-egu22-8647, 2022.

Multiple landslide events are one of the most critical natural hazards. Landslide occurrences have become more frequent in recent decades because of rapid urbanization and climate change, causing widespread failures throughout the world. Extreme landslide events can cause severe damages to both human lives and infrastructures. Hence, there is a growing need to intervene quickly in the impacted areas. Although a vast quantity of research have been carried out to address rapid mapping of landslides by employing optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with optical images, since they present limitations due to weather-related issues such as cloud cover.
 
To address this issue, various combinations of composites of SAR backscatter data and state-of-art Deep Learning (DL) models are evaluated by analyzing and comparing object detection and image segmentation approaches. The study area lies in the eastern Iburi sub-prefecture in Hokkaido. At 03.08 local time (JST) on September 6, 2018, the area was hit by an Mw 6.6 earthquake that triggered about 8000 co-seismic landslides. The models' predictions are compared against an accurate landslide inventory obtained by manual mapping on pre- and post-event PlanetScope imagery, by using evaluation metrics. When dealing with object detection, a tri-temporal combination of SAR backscatter data yielded the best results (88% F1-score). Similarly, for the landslide segmentation, the best result was given by the augmented ascending tri-temporal SAR composite image and slope angle (61% F1-score). Results show that the landslide location is usually predicted correctly, while the landslide boundaries are often wrongly detected or may present dimension overestimation. Our findings demonstrate that the combination of SAR data and Deep Learning algorithms may help detect landslides quickly, even during storms and under deep cloud cover. For the chosen study area, the first suitable Sentinel-2 optical image was acquired more than a month after the earthquake event of September 6, 2018, while SAR data were readily available right after and before. However, further investigations and improvements are still needed, this being the first attempt in which the combination of SAR data and DL algorithms are employed for landslide detection and mapping purposes.

How to cite: Nava, L., Bhuyan, K., Meena, S. R., Monserrat, O., and Catani, F.: Assessment of deep learning based landslide detection and mapping performances with backscatter SAR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10072, https://doi.org/10.5194/egusphere-egu22-10072, 2022.

Satellite Remote Sensing Investigation of 21 July 2020 Shaziba Landslide, China

Wandi Wang1,2, Mahdi Motagh1,2, Sara Mirzaee3, Sigrid Roessner1 and Tao Li4

• Section 1.4 - Remote Sensing and Geoinformatics, GFZ German Research Center for Geosciences, Potsdam, Germany
• Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, Hannover, Germany
• Department of Marine Geology and Geophysics, University of Miami, United States
• GNSS Research Center, Wuhan University, China

We present the results of remote sensing analysis of deformation related to the 21 July 2020 Shaziba landslide in China. The landslide, which occurred following the heavy precipitation from June to August 2020, is located at the Qingjiang River, approx. 30 km from Enshi City in Hubei Province of China.   It destroyed over 60 houses, and by blocking the course of the river, formed a landslide dam, which threatened the safety of people and infrastructure downstream. Although Shaziba landslide occurred in form of reactivation of an old landslide, the landslide prone slope was not instrumented prior to this most recent failure. Therefore, high-resolution remote sensing imagery was used as a very effective source of information for a detailed investigation of the evolution of this slope failure.  We collected the satellite remote sensing data covering a time period from June 2016 to July 2021 and comprise optical and radar data. Firstly, cross-correlation analysis using satellite optical imagery from Planet and Sentinel-2 systems was used to retrieve the lateral direction and magnitude of landslide movements. Next, multi-temporal interferometry (MTI) analysis based on Sentinel-1 and TerraSAR-X SAR data was exploited to obtain pre- and post-failure deformation. Results from different MTI techniques including Persistent Scatterer (PS), Small Baseline Subsets (SBAS), and Eigendecomposition based Maximum-likelihood-estimator of Interferometric phase (EMI) were compared to evaluate the most suitable method for InSAR time-series analysis of deformation related to the evolution of Shaziba landslide. Finally, several high-resolution DEMs derived from TanDEM-X (TDX) data were analyzed using repeat-pass interferometry and stacked together to compensate for the errors related to DEM alignment in order to precisely estimate the landslide volume. The results highlight how the integration of various remote sensing sensors helps to gain a better understanding of landslide evolution process and characterization. 

How to cite: Wang, W., Motagh, M., Mirzaee, S., Roessner, S., and Li, T.: Satellite Remote Sensing Investigation of 21 July 2020 Shaziba Landslide, China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5093, https://doi.org/10.5194/egusphere-egu22-5093, 2022.

Following intense precipitation records between mid-March and the beginning of April 2019, thousands of slope failures affected the mountainous regions in northeast and south of Iran. In particular, a catastrophic landslide occurred in Hoseynabad-e Kalpush village, in Semnan province in the Northeast of Iran, where more than 300 houses were damaged, of which 163 houses had to be evacuated due to the severity of the destruction and the danger to their residents. Several questions were raised in the aftermath of the disaster as to whether the landslide was triggered by the heavy precipitation only or by other factors such as additional load due to the increase of the hydraulic gradient and seepage from a nearby artificial reservoir built in 2013 on the opposite side of the slope. This paper provides multi-scale and multi-sensor remote sensing investigation for the pre-, co-, and post-failure slope stability of the Hoseynabad-e Kalpush landslide and assesses the role of potential external factors in triggering the 2019 catastrophic failure. C-band Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) measurements and very-high-resolution Planet scope imagery cross-correlation show a clear precursory and transient deformation in the lower part of the slope that culminated in a slope failure of more than 35 m in the upper part of the landslide in April 2019. The lower and middle parts of the landslide continued to move with a maximum displacement rate of 10 cm in the first 6 months. Satellite remote sensing results are integrated with rainfall data and in-situ records of the reservoir water levels to evaluate the role of meteorological and anthropogenic conditions in promoting slope instability. The outcomes of this study highlight how the complex interaction between climate and anthropogenic factors influence unstable hillslope conditions in space and time and the need for more integration of remote sensing measurements into early warning systems at regional and national scales. 

How to cite: Vassileva, M., Motagh, M., Roessner, S., and Akbari, B.: Evolution analysis of the April 2019 Hoseynabad-e Kalpush landslide in Iran inferred from  multi-sensor satellite remote sensing and in-situ measurements , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10402, https://doi.org/10.5194/egusphere-egu22-10402, 2022.

A large, deep-seated ancient landslide body was partially reactivated close to the Aniangzhai village in the southwest of China on 17 June 2020. The catastrophic event occurred as a result of a  complex cascading event, started by a debris flow triggered by the heavy rainfall in the summer. The debris flows, coming from the northern Meilonggou Gully, created a dammed lake just under the ancient landslide body and blocked the Xiaojinchuan river, leading to an increase in the water level. Thereafter, the overflow of the barrier dam, influenced by the discharge of the surplus water from the nearby hydropower station to reduce the flood pressure, undercut the toe of the landslide, resulting in partial reactivation of this ancient landslide body.

This paper provided a comprehensive analysis of the evolution of this hazard chain using both radar and optical remote sensing techniques. 

Firstly, a horizontal displacement map is produced by cross-correlation technique using Planet data to retrieve co-failure motion. Results show that the horizontal displacement peaks at 14.7 m, and most of the large displacement, ranging from 12.5 m to 15.0 m, were found on the lower part of the slide compared to the middle and head parts in the large failure zone.

Next,  pre-failure slope stability analysis is performed using a stack of Sentinel-1 SAR data from 2014 to 2020.  InSAR time-series results show that the landslide has long been active before the failure. However, the rate of creep on this slow-moving landslide was not constant, rather it changed over time.  The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018-2020, in comparison to the rate observed for 2014-2017. An accelerated creep was observed on the head part of the failure body since spring 2020 before the large failure.

Finally, X-band TerrASAR-X data, C-band Sentinel-1 data, and newly designed artificial corner reflectors are used to investigate the post-failure deformation rate. Corner reflectors are helpful auxiliaries for SAR and InSAR target analysis since they are identified as stable objects during radar acquisitions, especially in vegetated or agricultural landscapes, where the widespread loss of coherence between consecutive image acquisitions could happen. We evaluated the performance of newly designed miniature artificial cornel reflectors that are constructed for retrieving displacement signals from both ascending and descending TerraSAR-X satellites. The results indicate that the lower part of the ancient landslide body is still creeping. However, the average displacement rate of the active part has decreased since the catastrophic failure, although it is  still higher than the rate recorded in the precursory analysis prior to the failure between 2014 and 2020. Given the lack of in-situ monitoring data at Aniangzhai and other large landslides in high mountain areas all over the world, the uses of high resolution remote sensing data offer a unique opportunity to assess the state of landslide activities and their relation with different triggering factors.

How to cite: Xia, Z., Motagh, M., Li, T., and Roessner, S.: Pre-, co- and post-failure analysis of the Aniangzhai landslide on 17 June 2020 with satellite remote sensing and corner reflector InSAR (CR-InSAR), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4905, https://doi.org/10.5194/egusphere-egu22-4905, 2022.

Landslides are considered one of the major hazards causing economic and human losses worldwide. Slope instability processes are affecting buildings and infrastructures in the towns of the eastern slope of the Mt. Amiata volcanic complex (Tuscany, Italy). These processes are relevant as they expose the inhabitants to risk, moreover their analysis provide hints about the mechanisms and roles of land sliding in the progressive disruption of extinct volcanic edifices.

In this study we present the first results of some monitoring and multi-temporal systems which are integrated to investigate the spatial-temporal ground displacement field in the eastern slope of the Mt. Amiata volcanic complex. In detail, we combine InSAR, GNSS, robotic total stations (TS) and levelling techniques to obtain a framework in terms of planimetric and vertical displacements. We apply the Multi-Temporal InSAR approach from 2014 to 2021 using the ESA Copernicus Sentinel-1 data. To perform the interferometry analysis, we implement the single master Stanford Method for Persistent Scatterers (StaMPS) approach for both ascending and descending geometries, and by combining both Line of Sight (LOS) results, we reveal the vertical and E-W components of the displacement. In addition, we perform multi-temporal survey-style GNSS measurements for some tens stations from 2019 to present day. About one hundred reflectors are continuously monitored by TS. Additionally, multi-temporal geometric levelling is performed to assess the vertical movements of selected relevant benchmarks. Finally, results from different monitoring systems are combined to model the ground displacements.

The InSAR results reveal mean velocity vectors with standard deviation less than 1 mm/y. The GNSS results have higher signal to noise ratio in the horizontal components with residuals lower than 10 mm. Accuracies of the geometrical levelling and TS results are ca. 1 mm and ca. 5 mm respectively. By combining the results, the magnitude of displacement field is ranging up to ca. 30 cm/y. The different systems provide results each other reasonably coherent in terms of magnitude and direction of the displacement vector. Integration of systems allows us to get solutions where one or more systems fail to provide data (i.e., when few or no PS are obtained by InSAR). Finally, we compare the results with seasonal data like rainfall. Velocities tend to reduce during summer low precipitation periods, while they increase during winter. Long term quantitative monitoring activities will allow us to better understand the spatial-temporal evolution of the landslide processes in the perspective of developing an early warning system.

How to cite: Pattela, T. V., Disperati, L., Marzini, L., Amaddii, M., Lombardi, G., and Rappuoli, D.: Monitoring slope instability integrating InSAR, GNSS, Total Station and Levelling: a case study in the Eastern slope of the Mt. Amiata volcanic complex, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12120, https://doi.org/10.5194/egusphere-egu22-12120, 2022.

The increase in freely available optical satellite data with 10-15 m spatial resolution offers new opportunities to monitor slow-moving landslides and study their past movements through image cross-correlation in difficult-to-access regions around the world. Here, we explore this potential using Landsat-8 and Sentinel-2 optical satellite imagery to detect and quantify slope movements in the northwestern Argentine Andes over the past eight years. Our study takes advantage of the large spatial and temporal availability of optical satellite imagery, but we also show the caveats associated with cross-correlation for slow-moving targets. The northwestern Argentine Andes, particularly the mountain ranges that border the Central Andean Plateau (Altiplano-Puna Plateau), are predisposed to slope movements because of their steep hillslopes, weakened lithologies, sparse vegetation cover, and frequent rainfall events. Previous studies based on radar interferometry have identified several landslides moving at ~1 m/yr throughout our study area. We use these areas of known offset to identify optimal processing routines, evaluate their accuracy, and define the limitations of monitoring the movement of slow-moving landslides with optical imagery. We present approaches to pre- and post-correlation filtering to reduce noise and increase signal strength and further validate our results with high spatial resolution imagery (1-3 m). In this way, we aim to better constrain the distribution of slow-moving landslides throughout our study area and understand the driving factors of past and present slope movements at the regional scale.

How to cite: Mueting, A. and Bookhagen, B.: Cross-correlation of optical satellite data for the detection and monitoring of slow-moving landslides in northwestern Argentina, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4825, https://doi.org/10.5194/egusphere-egu22-4825, 2022.

Roads can both increase and decrease the likelihood of landslides occurring in a given region. This might be due to (i) mapping biases when compiling landslide inventories, (ii) the influence of the road on the landslide susceptibility. Here, we present a spatial statistical analysis of landslide proximity to roads across a range of geographic settings and landslide inventory types. We examine the proximity of landslide centroids to roads at regional to national scales using 12 diverse landslide inventories with variations in inventory type (6 triggered event, 6 multi-temporal), mapping method (1 field-based, 6 remote sensing, 5 a combination of mapping methods), and countries of origin distinguished by their human development index (HDI) (6 high and 6 low HDI). Each inventory contains 270 < n_Landslides < 81,000 landslides with inventory regional extents ranging from 80 km² < A_inventory < 385,000 km². We have developed a PyQGIS tool that calculates the distance between each landslide centroid and the closest road vector within the same watershed. From these distance values, we create a density distribution of landslides as a function of distance from roads for that inventory. We then compare each inventory’s density distribution of landslide-to-road distance to a set of randomly generated points and their distances to roads. For the 12 inventories, we find that the landslide density near roads compared to random points is greater in 3 inventories, equal in 3 inventories, and lower in 6 inventories. We find that a comparison between landslides and random points describes each inventory well in terms of road density. We divide the 12 inventories into 4 typologies with different potential explanations for each group. We believe there is evidence of mapping bias towards roads for the typology with 3 inventories that have greater landslide density (compared to random points), which suggests that a more nuanced use of road proximity within landslide susceptibility models should be adopted. Further research should be done to understand the interactions between landslides and proximity to roads at the regional to national scale.

How to cite: Malamud, B. D., Heijenk, R. A., Taylor, F. E., and Wood, J. L.: Road influences on landslide inventories , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7616, https://doi.org/10.5194/egusphere-egu22-7616, 2022.

Early warning for complex landslides is a difficult task since their evolution could depend on the combination of various predisposing and triggering geological (e.g. rock type, water circulation) and climatic factors (e.g. rainfall, snowmelt). Depending on the type of phenomenon, the temporal evolution of a landslide can be monitored in several ways, from classical to recent advances in remote sensing and in-situ measurements. The potential of real-time monitoring by ground-based radar interferometry (GB-InSAR) is exploited here to improve the understanding of the kinematic evolution of a complex landslide in the Italian Alps. To this end, the integrated use of long-term, spatially distributed GB-InSAR data and of a classical Robotic Total Station (RTS) monitoring is analyzed and discussed for the Sant’Andrea landslide, located in the municipality of Perarolo di Cadore (Belluno, Italy), a rotational slide in heterogeneous materials. Due to the landslide features, the use of these two different techniques is complementary: GB-InSAR measures a continuous field of motion, although along LOS, that is suitable for detecting unstable sectors and quantifying the space-time variations of the kinematics on the entire slope, whereas RTS is able to acquire tridimensional displacement data, very useful to monitor single points and to correctly interpret the GB-InSAR data. The landslide position, just upstream of the village center, represents a relevant hydrogeological risk for the inhabitants. This complex mass movement involves a clay-calcareous debris mass overlying an anhydrite-gypsum dolomitic bedrock. The kinematic activity exhibits an alternation of slow displacements, as long-term creep, and episodic or seasonal accelerations, strongly related to rainfall triggering in response to both heavy and lasting events. Based on the intensity and duration of rainfall, the significant accelerations are followed by a relaxation period with a slow regression of the displacement rate, usually without returning to the previous values.
The analysis carried out by combining the mapping of 3D point-based displacements and LOS surface velocity fields allows distinguishing mechanisms and sensitivity of the landslide sectors to rainfall inputs, as well as to understand the wide range of mechanical behaviors shown by the slope during the monitoring period. Such information aims to quantitatively evaluate the trigger-response signals to rainfall events to predict accelerating trends of the landslide displacements as well as possible failures. The proposed monitoring and modelling framework will be soon implemented in an operational early warning procedure using real-time, high-frequency GB-InSAR data together with RTS and weather forecasts, in accordance with local authorities of Civil Protection.

How to cite: Catani, F., Carraro, E., Galgaro, A., and Nava, L.: Integration of ground-based radar interferometry (GB-InSAR) and weather forecasts for real-time monitoring: kinematic evolution and early warning of the Sant’Andrea landslide (Eastern Italian Alps), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11731, https://doi.org/10.5194/egusphere-egu22-11731, 2022.

Large rock-ice avalanches have been observed in the past in the Mont Blanc Massif area, notably from the Grand Pilier d’Angle in 1920 and from the Brenva spur in 1997, which involved millions of cubic meters of material. More recently, a rockslide detached from the Brenva spur in 2016, involving 35000 m³ of material. In the context of monitoring, in the fall of 2020 and 2021, two Lidar campaigns were performed to obtain 3D models of the rock face and monitor future rockfall activity. Moreover, point clouds were obtained from the Structure from Motion technique, using aerial photos from helicopter. Comparing the point clouds of 2020 and 2021 in CloudCompare software, only a few small rockfalls of 10-30 m³ were observed. The three-dimensional model of the rock wall was used as an input for the structural analysis of the Brenva Spur and Grand Pilier d'Angle, using Coltop3D software. The analysis showed that the same families of discontinuities characterizing the Brenva Spur are also found in the Grand Pilier d’Angle and other granitic crops at lower altitudes, indicating that they all belong to the same regional set of discontinuities. To monitor the collapses of the Brenva spur, an accelerometer was installed in 2017 on the wall and a high-resolution camera was placed at a distance of about 6 km. In June and July 2018, two rockfalls and one rockslide were detected, by both the accelerometric signal and the visual inspection of the photos. A spectrogram was therefore created, which showed that both high and low-frequency contents are present. Low frequencies may correspond to the sliding and high frequencies may correspond to rock bounces.

How to cite: Vicari, J. H., Fei, L., Bertolo, D., Choanji, T., Derron, M.-H., Ferretti, G., Jaboyedoff, M., Thuegaz, P., Troilo, F., Uhlmann, D., and Wolff, C.: Rock instability hazard in high mountain area: the example of the Brenva spur (Mont Blanc massif), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8842, https://doi.org/10.5194/egusphere-egu22-8842, 2022.

The assessment of the surface spatially-distributed three-dimensional (3D) deformation is crucial in landslide monitoring, as it represents the landslide kinematics. However, there is a lack of technologies that can provide this datum effectively and they are often limited by financial and/or logistic issues. We have developed a methodology to fuse displacement data obtained by robotic total station (RTS) and time-lapse camera, whose images we processed with digital image correlation (DIC). Our technique adopts the 3D RTS measurements at specific points (i.e., corresponding to reflective prisms) to calibrate a transformation from the two-dimensional (2D) spatially-distributed DIC observations into 3D data. The algorithm involves a series of steps: i) DIC measurements are orthorectified on an available digital elevation model and represented in the local coordinate system of the time-lapse camera, obtaining the 2D displacement vectors that lie on the image plane (z and x components). ii) The RTS data are rototranslated into the camera coordinate system. iii) The ratio α between the z component of the RTS displacement vector and the module of the RTS displacement vector is calculated in the available measurement points. iv) The point values of α are spatially interpolated over the landslide active domain. v) The DIC displacement map of the z component is divided by α to obtain the spatially-distributed module of displacement (the third displacement component is simply derived using the Pythagoras Theorem). vi) The results are rototranslated from the camera coordinate system into the geographic coordinate system. The most critical element of the data fusion is the spatial interpolation of α across the landslide domain. Actually, the availability of a dense network of RTS measurement points, compared to the landslide extension, is not common in real monitoring. Therefore, α might suffer strong approximation in the presence of complex kinematics. Nevertheless, since α is a composition of non-independent displacement components, it is expected to vary smoothly and, therefore, it should be efficiently interpolated even with a limited number of measurement points. We conducted simulations with synthetic data to quantify the uncertainty contribution of α interpolation, which is generally <10%. We successfully applied the RTS-DIC data fusion to the monitoring dataset of the Mont de La Saxe Rockslide, during a period of strong reactivation, with displacement rates from ~0.1 m day^-1 to >10 m day^-1. We proved the efficacy of the methodology by comparing the obtained results with the independent measurements of a ground-based interferometric synthetic aperture radar, obtaining a median deviation < 0.09 m. The proposed monitoring solution has the advantage of involving low-cost and widely-used technologies, therefore it can be easily adopted in many other sites and monitoring contexts.

How to cite: Dematteis, N., Wrzesniak, A., and Giordan, D.: Data fusion of robotic total station and time-lapse camera to assess the surface three-dimensional deformation of a landslide., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9919, https://doi.org/10.5194/egusphere-egu22-9919, 2022.

RIEGL Laser Measurement System GmbH has been manufacturing laser scanners for a wide range of applications for more than 20 years. The high accuracy and reliability of their long-range measurement is based on RIEGL’s unique technology of echo digitization and online waveform processing, which means that the laser scanners operate even in poor visibility and demanding multitarget situations caused by dust, haze, rain, snow.

The RIEGL surveying equipment provides highly accurate 3D data for a wide range of applications, including bathymetry, monitoring, archaeology, topography and many more. For all these applications, RIEGL provides various sensors to carry out surveys from an aircraft, from a UAV, from boats, mobile mounted on a car or classically from a tripod as a terrestrial laser scanner.

We would like to take a closer look at the latter and the latest developments in the field of terrestrial laser scanners, especially with regard to surface monitoring.

The latest hardware processing architecture enables execution of different background tasks (such as point cloud registration, geo-referencing, orientation via integrated Inertial Measurement Unit, etc.) on-board in parallel with simultaneous scan data acquisition.

This on-board data processing capability can also be utilized within apps running on the scanner for customized data-processing workflows. RIEGL offers the so-called “Mining Apps” as a bundle, including the Monitoring App, the Design Compare App and the Slope Angle App.

The Monitoring App calculates changes to a given reference scan. This allows to detect movements of e.g., highwalls long before they are visible to the human eye. The interpretation of these movements through a time series of scans allows the prediction of a possible slope failure. The Design Compare App works similar to the Monitoring App. Instead of a reference scan a given design model defines the reference. As a result over- and under-cut to the given design model are visualized. The Slope Angle App calculates the local slope angle from the scan data and visualizes the results color-encoded.

All of these apps produce a web browser-based result (Fig. 1). The web server runs on the scanner hardware, allowing the user to view the results with any standard web browser without installing additional software. Alternatively, the result data can be automatically synchronized to the cloud for worldwide publication on a website.