In this paper a Matlab tool called DiAna (Discontinuity Analysis), for the 2D and 3D geo-structural analysis of rock mass discontinuities on high resolution laser scanning data is presented.
The ...proposed approach is able to semi-automatically retrieve some relevant rock mass parameters, namely orientation, number of sets, spacing/frequency (and derived RQD), persistence, block size and scale dependent roughness, by analyzing high resolution point clouds acquired from terrestrial or aerial laser scanners.
In addition, with a specific DiAna option called
filterveg, we are able to remove vegetation or other disturbing objects from the point cloud, which is one of the main problems in LIDAR data processing.
Some examples of the proposed method have demonstrated its ability to investigate rock masses characterized by irregular block shapes, and suggest applications in the field of engineering geology and emergency management, when it is often advisable to minimize survey time in dangerous environments and, in the same time, it is necessary to gather all the required information as fast as possible.
Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory ...preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δNDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data.
•NDVI, PCA, and ICA are integrated into MRF for landslide mapping.•Improved CDMRF for landslide mapping from multi-sensor data•NDVI-, PCA-, and ICA-based MRF outperform CVA-based MRF significantly•Applicable to map rainfall-, typhoon-, and earthquake-triggered landslides
Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, ...especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.
: The measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe the relationship with triggering factors and to ...assess the effectiveness of the mitigation measures. Persistent Scatterer Interferometry (PSI) represents a powerful tool to measure landslide displacement, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of a landslide and allows a better understanding of landslide geometry and kinematics. In this work, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor the Santo Stefano d’Aveto landslide located in the Northern Apennines, Italy. This landslide can be defined as an earth rotational slide. PSI analysis has contributed to a more in-depth investigation of the phenomenon. In particular, PSI measurements have allowed better redefining of the boundaries of the landslide and the state of activity, while the time series analysis has permitted better understanding of the deformation pattern and its relation with the causes of the landslide itself. The integration of ground-based monitoring data and PSI data have provided sound results for landslide characterization. The punctual information deriving from inclinometers can help in defining the actual location of the sliding surface and the involved volumes, while the measuring of pore water pressure conditions or water table level can suggest a correlation between the deformation patterns and the triggering factors.
On 17 November 2016, an unexpected slope failure occurred in an undisclosed copper open-pit mine. The nature of the event urged for a thorough back-analysis of slope monitoring data in order to ...assess its size and temporal evolution, and to determine whether precursors potentially able to anticipate the failure were present. To this aim, satellite InSAR data spanning over the final 9 months before the event were, in retrospect, acquired and coupled with measurements from a ground-based radar that was in use at the time of the failure. Although progressive deformation was detected by the ground-based radar in correspondence of the two uppermost benches in the pit, the satellite InSAR data revealed that the vast majority of the instability actually involved a large part of natural slope above the mine crest. This sector was not visible by the ground-based radar. Thanks to the short revisit time of the Sentinel-1 mission, clear slope accelerating creep was observed for the first time in satellite InSAR measurements over an open-pit mine. The delimitation of the area featuring accelerating creep behavior matched remarkably the source area of the failure as mapped in the field after the event. Considerations on the volume of the instability and on the development of the failure process (both in space and time) were consequently derived. The results provided a clear example of the value of jointly using ground-based and satellite interferometry to reduce the uncertainties inherent to the identification and characterization of impending catastrophic slope failures.
•A slope failure occurred in an open-pit mine, causing several casualties•The event was not expected, despite active monitoring with a ground-based radar•Satellite InSAR data covered a key slope sector invisible to the ground-based radar•In retrospect, clear accelerating creep was observed in the satellite InSAR data•The main features of the failure were derived thanks to the joint data analysis
The management of unstable slopes is one of the most critical issues when dealing with safety in open-pit mines. Suitable notice of impending failure events must be provided, and at the same time the ...number of false alarms must be kept to a minimum to avoid financial losses deriving from unnecessary outages of the production works. Comprehensive slope monitoring programs and early warning systems are usually implemented to this aim. However, systematic procedures for their tuning are lacking and several key factors are often overlooked. Therefore the mitigation of slope failure risk is still a topic of great concern, especially in open-pit mines excavated through hard rock masses featuring markedly brittle behavior, which supposedly provide little or no measurable precursors to failure. In this paper, 9 instabilities occurred at an undisclosed open-pit mine, and monitored by ground-based radar devices, were reviewed with the goal of characterizing the typical slope deformation behavior and defining the appropriate strategy for the setup of alarms. The estimated mass of the case studies ranged from 1500t to 750,000t. 5 instabilities culminated to failure, whereas the other 4, although showing considerable amounts and rates of movement, ultimately did not fail. The analysis provided critical insights into the deformation of hard rock masses of high geomechanical quality, and allowed the identification of “signature” parameters of the failure events. General operative recommendations for effective slope monitoring and early warning were consequently derived.
•Radar monitoring data of 9 instabilities at an open-pit mine were reviewed.•The analysis included 5 failures and 4 instabilities that did not reach failure.•Slopes in the pit are prone to very rapid tertiary creep and brittle failure.•“Signature” parameters of the failure events were identified.•Recommendations for effective monitoring and alarm set-up at the pit were derived.
We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service ...through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement. Displacement time series, systematically updated with the most recent available Sentinel-1 acquisition, are analysed to identify anomalous points (i.e., points where a change in the dynamic of motion is occurring). The presence of a cluster of persistent anomalies affecting elements at risk determines a significant level of risk, with the necessity of further analysis. Here, we show that the Sentinel-1 constellation can be used for continuous and systematic tracking of ground deformation phenomena at the regional scale. Our results demonstrate how satellite data, acquired with short revisiting times and promptly processed, can contribute to the detection of changes in ground deformation patterns and can act as a key information layer for risk mitigation.
Intensity–duration rainfall thresholds are commonly used in regional-scale landslide warning systems. In this manuscript, 3D thresholds are defined also considering the mean rainfall amount fallen in ...each alert zone (MeAR, mean areal rainfall) in Emilia Romagna region (Northern Italy). In the proposed 3D approach, thresholds are represented by a plane instead of a line, and the third dimension allows to indirectly account for the influence of complex rainfall patterns. MeAR values are calculated according to different time periods ranging from 7 to 30 days, and all threshold parameters are calibrated independently for the 8 alert zones in which the region is divided. The approach was validated and compared with classical intensity–duration thresholds, finding that the 3D threshold may be used to get better performances, especially in terms of a consistent reduction of false alarms:− 20 to − 86%, depending on the alert zone and the selected MeAR duration. These results open new encouraging perspectives for the development of the regional warning system that is operated in the study area.
Landslide dams are rather common events in hilly and mountainous areas and they occur when a landslide reaches a valley floor closing the riverbed. If they form a lake basin, unstable landslide dams ...can have catastrophic consequences when they occur in upstream of populated regions. Landslide dam behavior is not completely understood yet, however several studies suggested implementing geomorphological index in order to assess their formation and evolution. These indexes result from the composition of two or more morphological attributes that characterize the landslide (e.g. landslide volume or length) and the involved river valley (e.g. valley width).
The objective of this work is the definition of a procedure, based on the joint use of different indexes, to assess landslide dams evolution over large areas (e.g. entire river catchment or even a region or a nation) and in short times, in order to be used for emergency response or for planning activities.
About 300 landslide dam events collected in Italy were analyzed and some state-of-the-art geomorphological indexes were applied to characterize the damming phenomena at the national scale. To overcome some limitations of the aforementioned indexes, we introduce two new indexes: the Morphological Obstruction Index and the Hydromorphological Dam Stability Index. The former combines the river width and the landslide volume, and it can be used to identify the conditions associated to dam formation discriminating between circumstances where a landslide dam is formed and circumstances where it is not. The latter uses a simplified stream power formulation (combining the upstream catchment area and the local slope gradient) to account for the river energy. This index allows evaluating the stability of a dam in near real time as soon as it occurs and can be used to discriminate between stable and unstable dams.
If compared with the reviewed state of the art indexes, the newly proposed ones show an improvement in the forecasting effectiveness and have the advantage of being based on morphometric input parameters that can be easily and quickly assessed on a distributed way even over large areas. We propose a tool that is based on the joint use of the newly proposed indexes and that can be used to provide fast and effective assessment on landslide dam formation and stability during emergency or planning activities.
•300 Italian landslide dams were analyzed using existing morphological indexes.•Two new indexes are proposed with improved forecasting effectiveness.•Practical tool for dam formation and stability is suggested for emergency/planning.
PDF
