Landslide Scarps Research Articles

Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

The concept of leveraging the predictive capacity of predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores the predictive capacity of different approaches to LS modelling using artificial intelligence. The key objective of this study is to estimate a LS map for the Taleghan-Alamut basin of Iran using Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost and CDT-SubSpace) hybrid machine learning approaches, which are state-of-the-art soft computing approaches that are hardly ever utilized in the assessment of LS. In this study, we used eighteen landslide predisposing factors (LPFs) that we considered to be the most important local morphological and geo-environmental factors influencing the occurrence of landslides. We calculated the significance of each of the LPFs in the landslide susceptibility assessment using the Random Forest Method. We also employed the Receiver Operating Characteristic curve, precision, performance, map robustness measurement and selection of the best-fitting models. The results shows that, compared to the other models, the CDT-Multiboost is the excellent model in this perspective with an average area under curve (AUC) of 0.993 based on a 4-fold cross-validation. We, therefore, consider the CDT-Multiboost models to be an effective method for improving spatial prediction of LS where landslide scarps or bodies are not clearly identified during the preparation of landslide inventory maps. Therefore, it will be helpful for preparing future landslide inventory maps and mitigate landslide damages.

36Cl exposure dating of the Mahu Giant landslide (Sichuan Province, China)

Abstract Age is of great significance to understanding the causes of landslide instability and movement, and therefore, it is important to know when conducting a hazard assessment. To date, no detailed and comprehensive determination of the age of the Mahu giant landslide has been conducted. The Mahu giant landslide is located in the lower reaches of the Jinsha River and has a volume of 2.38 km3 and a maximum elevation difference of 1080 m, making it the largest subaerial landslide found to date in China. In this study, we report the age of the landslide based on 36Cl exposure dating. Ten cosmogenic nuclide samples were collected from boulders on the landslide deposits and the bedrock on the landslide's scarp, and eight valid ages were derived in the range of 11.63 ± 0.96 to 29.14 ± 1.81 ka. Based on the lithologies and ages of the landslide's scarp and the deposits, the landslide is concluded to have been formed by two major events, with error weighted mean ages of 14.75 and 25.21 ka. The age results for the Mahu landslide obtained in this study provide a basis for understanding the causes of the Mahu landslide. Moreover, this case study indicates that the regional fault activity and earthquake risk can be analyzed from the perspective of landslide science.

Tsunami Squares modeling of the 2007 Dayantang landslide generated waves considering the effects in slide/water interactions

Landslide tsunami in reservoirs can inflict serious consequences and they have aroused social concern worldwide. Herein, we introduce a historical reservoir tsunami that occurred on June 2007 in Shuibuya Reservoir, China. After a progressive failure over 20 days, the Dayantang slope with a volume of 3 million cubic meters slid into the Shuibuya Reservoir and spawned a giant tsunami that ran up ~50 m on the opposite shoreline and caused disastrous consequences. To model the 2007 tsunami, we apply a meshless numerical approach called ‘Tsunami Squares’ (TS) that takes into account the landslide/water interactions. TS simulates both the landslide and its generated tsunami and introduces ‘Push Ahead’ (PA) and ‘Drag Along’ (DA) accelerations to mimic the physical mechanics in the process. The accuracy and efficiency of TS method are verified by a well-designed landslide tsunami experiment that compares the observed wave dynamics with the ones simulated by TS and achieves an excellent match between the two. Landslide simulations indicate a maximum impact velocity of ~20 m/s and that the computed post-slide mass stopped on the near river bed with a shape coinciding with the field observations. Wave simulations present a large region of wave impacts and a maximum wave runup height of 52.9 m at the opposite bank facing the landslide scarp that is comparable to the observation (~50 m). Sensitivity analysis that compares the simulations with, and without, PA and DA accelerations is carried out both for the experimental wave and the 2007 one. This comparison indicates that TS simulations that do not include PA and DA can seriously underestimate landslide tsunami size. • Investigation of the geometric and geological conditions of the 2007 landslide tsunami • Update of the TS method by introducing PA and DA accelerations in landslide/water interactions • TS Model validation by a lab-scale wave experiment and application to the data-rich 2007 event • Sensitivity analysis of PA and DA accelerations that affects the landslide tsunami generation

Методика изучения морфологии абразионно-аккумулятивных берегов Западного побережья Крыма с применением БЛА и ГНСС (на примере участка территории Большого Севастополя)

The method of studying the abrasion-accumulative coast of the Western coast of Crimea within the urban area of Sevastopol includes remote sensing using unmanned aerial vehicles (UAVs) and field studies of the morphology and structure of abrasion and landslide landforms of the coast. As a result of the research, the morphological zoning of the abrasion-accumulative coast was established. The formation of the morphological zoning of the abrasion-accumulative coast (according to I.S. Shchukin) during the last 150 years took place at a constant level of the Black Sea. Analysis of the coast from previously published multi-temporal maps and aerospace photographs revealed different stages in the movement of the coastline and landslide scarp. Since 1966, the coastal area has been used for low-rise residential development, which may have influenced the activity of landslide processes in the coastal strip. Remote sensing using UAVs consists of aerial photography of the research area along the planned flight route at altitudes of 20–100 m, with further compilation of a large-scale orthophotomap from a mosaic of images with geospatial fixation of images to the signs of the reference long-term local geodetic network, previously measured by the methods of global navigation satellite systems (GNSS ), as well as the formation of a digital elevation model (DEM) and the compilation of derived maps and plans on its basis in the environment of geographic information systems (GIS), for the analysis of the morphometry of the relief and modeling. The developed method of remote sensing of the Earth with the use of UAVs and simultaneous field studies makes it possible to organize operational monitoring of dynamically developing abrasion-accumulative shores.

A multidisciplinary approach for landslide residual risk assessment: the Pomarico landslide (Basilicata Region, Southern Italy) case study

On 25 and 29 January 2019, a large landslide destroyed an important part of the town of Pomarico (Basilicata Region, Southern Italy). Geological and geomorphological investigations provided a detailed description of the landslide features. Several geophysical surveys were carried out to deepen knowledge of the landslide and the residual risk assessment. Detailed electrical resistivity tomography (ERT), multichannel analysis of surface waves (MASW), and seismic refraction tomography (SRT) have been used to analyze geomorphological evidences of the failure and the potential kinematic evolution of the landslide scarp, a crucial factor to assess landslide residual risk. The joint analyses of the geophysical results, compared with geological and geomorphological data, allowed to obtain detailed information about the stratigraphic contact between clayey and sandy deposits in the crown area of the landslide, and to identify the post-failure stability condition changes in sands. The geophysical analyses confirmed the presence of multiple old degraded scarps developed over time and provided information on the decompression state of the different areas of the landslide crown. The results highlighted a subparallel stratification consisting of an anthropic surface carry-over material, which covers a layer of sands with silty intercalations, overlying clayey material that represents the bedrock of the investigated area. Furthermore, natural and anthropogenic caves, mainly developed in well-cemented layers of sands, were identified. This study emphasized how the integration of different geophysical methods constitutes a capable tool for characterizing landslides, contributing to assessing the landslide residual risk of the slope mass and evaluating the suitability of the methods in relation to the investigated landslide conditions.

Application Assessments of Using Scarp Boundary-Fitted, Volume Constrained, Smooth Minimal Surfaces as Failure Interfaces of Deep-Seated Landslides

More than 9000 potential deep-seated landslide sites in the mountain ranges of Taiwan have been identified by a series of renewed governmental hazard mitigation initiatives after the 2009 Morakot typhoon. Among these sites, 186 sites have protection targets where thorough mitigation strategies are to be implemented. One of the important tasks in the hazard mitigation initiative is to estimate the volume, fracture interface and related quantities of each landslide site. In addition, with this number of sites, an automated tool is needed to generate predictions at low operational costs. We propose to use volume-constrained smooth minimal surfaces to approximate the landslide fracture interfaces. A volume-constrained smooth minimal surface in the current context is defined as a differentiable surface that encloses a given landslide volume with the minimal surface area. Although the stratigraphy and geological structures are omitted, the smooth minimal surface method is verified with 24 known landslides and is shown to be able to generate acceptable, approximated fracture interfaces. A collection of assessment indices is employed to measure the fitness of the predictions. Finally, the prediction fitness versus the landslide scarp geometry is investigated.

Discovery of the Baldy toreva near urban areas along the southern Wasatch Range, Utah

ABSTRACTStructural and geomorphic studies, and lithostratigraphic and biostratigraphic mapping reveal that a giant toreva block (6.125 km3) slid off Mount Timpanogos toward what are now densely populated urban areas along the Wasatch Front of Utah. The block forms a prominent peak known as Big Baldy, which consists of steeply dipping and locally brecciated limestone and quartzarenite over nearly horizontal shale. Preferential erosion of this shale below overlying limestone and quartzarenite cliffs is most likely the cause of this particular landslide and potential future slides along the Wasatch Front. The low-angle contact at the base of the giant toreva block was initially mapped as a thrust, then as a low-angle normal fault. In both cases, these faults were inferred to have large amounts of displacement (900 meters), but no traces of such faults are found in adjacent canyons. The Baldy slide is associated with geomorphologic features, such as faceted spurs, landslide scarps, sackungen, and hummocky terrain. Limestone and quartzarenite beds in the block are back-rotated up to 80° and are locally broken and brecciated. No evidence of hydro-fracturing is found in the breccia or of multiple brecciation episodes, which indicates surficial rather than deep-crustal processes and perhaps a single event of slip. We speculate based on structural reconstructions of the slide block, and interpolation of maximum downcutting rates on nearby streams, that the slide initiated between 700 and 500 ka. Discovery of the Baldy slide attests to the importance of recognizing the influence of surficial processes in mountain front development and demonstrate the ongoing geologic hazard of mass wasting to communities along the seismically active Wasatch Front and similar horst blocks.

GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia

Simada area is found in the South Gondar Zone of Amhara National Regional State and it is 780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of Ethiopia. This area is part of the Guna Mountain which is characterized by weathered volcanic rocks, rugged morphology with deeply incised gorges, heavy rainfall and active surface processes. Many landslides have occurred on August 2018 after a period of heavy rainfall and they caused many damages to the local people. In this study, Frequency Ratio (FR) and Weights of Evidence (WoE) models were applied to evaluate the landslide causative factors and generate landslide susceptibility maps (LSMs). The landslide inventory map that consists of 576 active and passive landslide scarps was prepared from intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors including aspect, slope, curvature, lithology, land use, rainfall and distance to stream were combined with a training dataset using GIS tools to generate the LSMs of the study area. Then the area was divided into five landslide susceptibility zones of very low, low, moderate, high and very high. Later, the resulting maps have been validated by using area under the curve and landslide density index methods. The result showed that the predictive rate of FR and WoE models were 88.2% and 84.8%, respectively. This indicated that the LSM produced by FR model showed a better performance than that of WoE model. Finally, the LSMs produced by FR and WoE models can be used by decision-makers for land use planning and landslide mitigation purpose.

Characterization of displacement and internal structure of landslides from multitemporal UAV and ERT imaging

Preliminary knowledge of the surface movement and internal structure of a landslide is essential to improve the understanding of landslide behavior as an initial step to develop suitable mitigation measures. This paper characterizes displacement and internal structure of landslides using multitemporal unmanned aerial vehicles (UAV) and electrical resistivity tomography (ERT) imaging. Multitemporal orthophotos, digital elevation model (DEM) profile lines, DEMs of difference (DoD), and ERT images allowed us to measure the landslide morphometry and areal changes, detect the trend of surface displacement, calculate the surface movement rate and direction, characterize the spatial variability in surface movement rates, and relate spatial variation in displacement to the internal structure of the landslide. The Kalisari Landslide experienced retrogressive movement where the scarp of landslide moved backward 38 m from 2015 to 2019. The surface movement ranged from 0.7 to 8.1 m with the direction mostly to the northeast. The depletion and accumulation ranged from 0.1 to 5 m, with the most active change located northwest of the landslide. The variability in movement was influenced by the spatial variability in the subsurface material inferred from the ERT images, i.e., surface material, clay, weathered breccias (possibly altered), and breccias. The interpretation from multitemporal UAV and ERT images indicates that the Kalisari Landslide has a nonhomogeneous multiple retrogressive behavior with a curved slip surface located at the clay layer.

Tectonic evolution of strike-slip zones on continental margins and their impact on the development of submarine landslides (Storegga Slide, northeast Atlantic)

AbstractSubmarine landslides have affected the mid-Norwegian margin since the Last Glacial Maximum. However, the role of tectonic movements, and most especially fault reactivation, in generating landslides offshore Norway is largely unconstrained. This study uses high-quality three-dimensional seismic and borehole data to understand how landslide development is controlled by faults propagating within the uplifted south Modgunn arch. Variance and structural maps above the south Modgunn arch show that: (1) local scarps of recurrent landslides were formed close to the largest faults, and mainly above strike-slip faults; (2) distinct periods of fault generation were associated with tectonic events, such as the breakup of the northeast Atlantic Ocean, and those events forming the south Modgunn arch; and (3) important fluid-flow features coincide with faults and sill intrusions. In total, 177 faults were analyzed to demonstrate that fault throw values vary from 10 ms to 115 ms two-way traveltime (8 m to 92 m). We propose that the long-term activity of faults in the study area has contributed to fluid migration, weakened post-breakup strata, and controlled the development of submarine slope instability. In particular, strike-slip faults coincide with the locations of several Quaternary landslide scars near the modern seafloor. Similar processes to those documented in Norway may explain the onset of large-scale landslides on other continental margins.

Landslide characteristics in the Loess Plateau, northern China

Landslides play an important role in landscape evolution of the Loess Plateau, northern China, but they have not been systematically analysed across the region. The contribution to regional rates of erosion and sediment budgets is poorly known as a consequence. Co-seismic landslides are a significant natural hazard, and exacerbate the threat posed by active faults in the region, but this aspect of the landslides is also poorly quantified. This paper presents inventories for ~80,000 landslides in the Loess Plateau, and analyses the distributions and extents. Four study areas focus on the epicentres of major (M > 7) historical earthquakes, from 1303 CE (Hongdong), 1556 (Huaxian), 1718 (Tongwei) and 1920 (Haiyuan), which are likely to have been major triggers for landslides in these regions. A fifth study area focuses on landslides in the aftermath of localised storms in 2010 in the Tianshui region, to allow comparison of landslides clearly triggered by rainfall with landslides in the regional inventories. Landslides predominantly resulted from failure of the regional loess blanket that gives the region its name, or the soil profile developed above it. Rainfall-generated landslides are dominantly surficial soil landslides. Landslides preserved in the four epicentral areas are larger and deeper, and resemble global bedrock landslides in the dimensions. Total volume of landslide in each of the four epicentral areas are on the order of 3–7 km3; these volumes are likely to be the cumulative totals of repeated earthquakes in the same regions, with each event reactivating landslide scarps on hillsides. Comparison of landslide distributions and modern settlement patterns suggests that future M > 7 earthquakes striking the Loess Plateau may trigger landslides that could directly hit 30–50% of the communities.

Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning

Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC: 0.904 – 0.919). Whereas, testing with LR (AUC: 0.825 – 0.785) and NNET (AUC: 0.882 – 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not characterized properly in an inventory.

Methane gas emissions of the Black Sea—mapping from the Crimean continental margin to the Kerch Peninsula slope

This study focuses on gas bubble emissions across ~ 1800 km2 of the southeastern continental slope of the Crimean Peninsula in the Eastern Black Sea basin. In total, about 3500 gas emissions were mapped as so-called flares in single- and multibeam echosounder imaginary. Flares generally emanate from the seafloor, with a few exceptions, where water depths are less than 700 m, which represents the upper limit of the gas hydrate stability zone in this region. Methane in shallow sediments below 700 m is transferred to methane hydrate, and the migration of free gas into the water column is therefore inhibited in most cases. This study illustrates further that flare occurrence is linked to morpho-structural margin characteristics. Two morphologically different margin segments are distinguished, and flare distributions were found to relate to differing morphological structures along these segments. In the western province, where the upper continental margin is steep and incised by gullies, flares occur mainly along downslope-oriented ridge crests. In the eastern province, representing a part of the Don-Kuban paleo-fan, the slope is influenced by extensive mass wasting. Gas emissions in this area were mainly detected aligned along scarps of submarine landslides and related to sediment failures. Almost all flares disappear in the acoustic records of the water column below a water depth of ~ 100 m suggesting that the gas bubbles dissolve within the water body of the Black Sea and contribute to its methane inventory and the anoxic water column.

Using in situ-produced 10Be to constrain the age of the latest surface-rupturing earthquake along the Middle Kedrovaya fault (Baikal rift)

Abstract The 30-km-long Middle Kedrovaya (MK) fault, on the northwestern flank of Lake Baikal, is one of the most impressive late Quaternary seismogenic structures in the Baikal rift. In places, surface fault scarps expose striated bedrock fault planes and offer some opportunities to examine the faulting history. Using cosmogenic 10Be exposure dating, we measured the exposure ages of one of the MK scarps (54.44630°N, 108.51524°E, elevation 985 m). This ~12-m-high normal fault scarp is composed of cataclasites in early Proterozoic granitoids of the Irel complex. We analyzed 12 of 18 samples, collected every 0.5 m, from 0.45 m above the toe of the exposed fault plane, which dips 55° in its lower half (samples 1–6) and 45° in upper half (samples 7–12). Exposure ages are the following from the toe upward: sample 1, 2.26 ± 0.53 ka; 2, 1.54 ± 0.55 ka; 3, 0.80 ± 0.55 ka; 5, 1.44 ± 0.55 ka; 6, 2.13 ± 0.62 ka; 8, 1.45 ± 0.48 ka; and 9, 1.40 ± 0.52 ka. The relationship between the oldest calculated ages (samples 1 and 6) and the relative sampling height along the scarp suggests that a recent surface-rupture event with a vertical component of 2.05 m (i.e. net slip of 2.5 m) occurred on the cliff ~2.2 ± 0.6 ka. Striations on the fault plane in the sampling points 1 and 6, as well a close date of 2145–2463 cal years BP calculated from radiocarbon age of a paleoseismic rupture within the Cape Shartly, ~62 km further southwestward, support this interpretation. The close ages assume that the dated deformations were related to a single Mw = 7.4 earthquake, the coseismic effects of which extended at least 82 km (the rupture length between the Cape Shartly and the NNE termination of the MK paleoseismic rupture zone). A more concentrated age mode of about 1.46 ± 0.53 ka (samples 2, 5, 8 and 9), which is in an incorrect stratigraphic order in reference to the older ages, is assumed to result from local erosion or partial shielding. Cosmogenic dating of fault scarps and seismogenic landslide scarps in bedrock potentially offers an independent method of reconstructing the late Quaternary paleoseismology of the northwestern flank of Lake Baikal.

Fluidized landslides triggered by the liquefaction of subsurface volcanic deposits during the 2018 Iburi\u2013Tobu earthquake, Hokkaido

The 6.6 Mw Iburi–Tobu earthquake struck southern Hokkaido, Japan on 6 September 2018. The earthquake triggered widespread slope collapses in the hills near the epicenter, resulting in destructive landslides that killed 36 people. Volcanic deposits covering the region slid downhill in a flow-like manner suggestive of fluidized landslides. Here, we report a distinctive example of liquefaction in the field, which could be a prerequisite for the generation of fluidized landslides triggered by large earthquakes. In the scarp of a typical landslide, an altered halloysite-bearing volcanic layer is observed at a level almost coincident with the sliding surface. The layer is intensely undulating and can be divided into an upper clay-rich layer and a lower pumice-rich layer, suggesting that the altered layer had liquefied as a result of the strong coseismic ground motion. The layer had been soaked by heavy rainfall just one day before the earthquake and could have liquefied, producing a weak and slippery plane, resulting in the catastrophic landslides in this area.

Scaling land-surface variables for landslide detection

As geomorphological processes operate at various spatial scales, their morphological expressions, i.e., land-surface variables (LSVs) should be scaled accordingly. Most approaches on landslide susceptibility modeling and landslide detection have been performed based on arbitrarily scaled LSVs. We propose a methodology to improve automated landslide detection by fitting each LSV to its optimal scale. We test our approach on two landslide inventories, with different landslide morphology. First, we derive seven LSVs from a DEM in a standard 3 × 3 moving window. Then, we rescale each LSV using focal mean statistics in increasingly larger moving windows until the optimal scale is found, i.e., scale at which logistic regression shows the best fit between the existence of landslide scarps and individual LSVs. The LSVs at the optimal scale are used as input data in a random forest (RF) model. In order to calculate the effect of scaling predictors on the accuracy of the model, we compare the results, using the area under the curve (AUC), against the results from an RF model with unscaled LSVs as input data. The results show (i) that different LSVs have different optimal scales, and (ii) the multi-scale approach improved the models significantly, from AUC = 0.73 to 0.80 for the first study area and from AUC = 0.59 to 0.73 for the second study area. Based on these results, we conclude that a multi-scale approach should be considered when automated models are used in order to detect landslides, in complex terrain settings.

Object-Based Point Cloud Analysis for Landslide and Erosion Monitoring

Today point clouds from close-range sensing are used for operational erosion and landslide monitoring. Distances between points from multi-temporal acquisitions can indicate surface deformation, while a designation of the underlying geomorphological processes is often handicapped by complex terrain structures and vegetation. We present an approach to landslide monitoring that integrates semantic information and three-dimensional deformation detection automatically. Surface changes are assigned to (i) semantic object classes (landslide scarp, eroded area, deposit) and (ii) to spatially contiguous, individual objects (like parts of the landslide scarp and moving clods of turf and soil). We demonstrate this object-based approach with a time series of 13 topographic Light Detection and Ranging point clouds, covering a site affected by shallow landsliding. The results of this case study illustrate how the presented methods translate the unstructured point clouds into information on geomorphological process dynamics to support erosion and landslide assessment.

Location and Energy Saving of Underwater Sensor Network for Monitoring Seabed Landslide

Xu, Y.; Shan, H., and Jia, Y., 2018. Location and energy saving of underwater sensor network for monitoring seabed landslide. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 29–34. Coconut Creek (Florida), ISSN 0749-0208.The traditional positioning system of underwater sensor network for monitoring submarine landslide could complete the location of submarine landslide, but the positioning offset was large and it was unable to work for a long time. Therefore, the research on positioning and energy saving of underwater sensor network for monitoring submarine landslide was proposed. The gradient of water depth was calculated based on multibeam bathymetric data, and the gradient change was recognized. Combined with topography features, the scarp of submarine landslide was recognized, and the identification of position of submarine landslides was determined. Based on SLMP positioning algorithm, the mechanism of UPS framework was used to realize the accurate coordinate positioning of submarine landslide. DCT transform, LDPC coder-decoder and Key frame coder-decoder of encoding algorithm of underwater sensor were used to transform Wyner-Ziv domain code and thus to realize energy-saving operation of underwater sensor network monitoring. Experiment proves that the positioning accuracy of method in this paper is improved by 58.33%, and the energy can be saved more than 55.75%. Meanwhile, it has high efficiency.

Tree ring based estimation of landslide block displacement: Evaluation of a new experimental approach

The mechanisms and velocities of landslide movements are important information for assessing hazards associated with landslides. This contribution introduces a new approach in reconstructing the past rate of landslide displacement using dendrogeomorphic methods. The principle of this method is based on the analysis of exposed roots in tension cracks or scarps of active landslides. The identification and dating of anatomical changes in root ring series in various root segments, induced by gradual root exposure due to crack spreading, enable the estimation of the rate of root exposure and the calculation of the displacement rate of landslide blocks. We analysed anatomical reactions of 87 root cross sections in tension cracks of an active landslide the Moravskoslezské Beskydy Mts. in the Outer Western Carpathians. Microscopical analysis of microcuts from cross sections showed the reduction in the earlywood vessel sizes, indicating the year of root exposure. We identified 17 years of exposure between 1997 and 2015. We calculated the individual displacement rates of the landslide expressed as the segment rate between the two adjacent samples of tension crack spreading and the mean spreading rate for all roots crossing one tension crack. The average total spreading rate for each tension crack was 3.2 cm year−1, with a maximum of 4.3 cm year−1 and a minimum of 1.7 cm year−1. Although this new approach has some uncertainties and limitations, it remains a low-cost technique that allows us to reconstruct the minimal past rate of landslide displacement without the need of long-term monitoring.

Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide

Detecting slope movement before landslides occur in mountain regions is crucial for disaster reduction. In October 2018, a gigantic landslide occurred on the Jinsha River, causing dammed-lake breach flood 500 km downstream. In this work, we used 25 Sentinel-2 images from November 2015 to August 2018 to explore the capability of this high temporal resolution optical images in detecting slope movement before the Jinsha River landslide. Normalized difference vegetation index (NDVI) was calculated to composite temporal profiles using all Sentinel-2 images. With this NDVI time series, unsupervised K-means classifier was applied to initially classify the study area and find the best thresholds for automatically extracting landslide scars in the image series. These extracted landslide scars were validated using interpreted results from two high spatial resolution images of similar dates in 2015 (user’s accuracy 89.7%, producer’s accuracy 83.6%) and 2018 (user’s accuracy 90.8%, producer’s accuracy 74.9%). After validation, extracted landslide scars of different years were counted and displayed in an RGB composite image to highlight slope movement. In addition, monotonous decrease/increase of NDVI was also observed, indicating continuous expansion of landslide scarps and movement of landslide head along the slope on the landslide surface. This work demonstrated the capability of Sentinel-2 time series images to capture slope movement with short revisit time at low cost. By incorporating other environmental information (such as elevation), this proposed method has the potential to consistently map pre-landslide slope movements over a large region.