Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park,Ohio

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.     

Effects of grain morphology on critical state: a computational analysis

We introduce a new DEM scheme (LS-DEM) that takes advantage of level sets to enable the inclusion of real grain shapes into a classical discrete element method. Then, LS-DEM is validated and calibrated with respect to real experimental results. Finally, we exploit part of LS-DEM potentiality by using it to study the dependency of critical state (CS) parameters such as critical state line (CSL) slope \(\lambda \), CSL intercept \(\varGamma \), and CS friction angle \(\varPhi _{\mathrm{CS}}\) on the grain’s morphology, i.e., sphericity, roundness, and regularity. This study is carried out in three steps. First, LS-DEM is used to capture and simulate the shape of five different two-dimensional cross sections of real grains, which have been previously classified according to the aforementioned morphological features. Second, the same LS-DEM simulations are carried out for idealized/simplified grains, which are morphologically equivalent to their real counterparts. Third, the results of real and idealized grains are compared, so the effect of “imperfections” on real particles is isolated. Finally, trends for the CS parameters (CSP) dependency on sphericity, roundness, and regularity are obtained as well as analyzed. The main observations and remarks connecting particle’s morphology, particle’s idealization, and CSP are summarized in a table that is attempted to help in keeping a general picture of the analysis, results, and corresponding implications.     

Authigenic rhodochrosite from a gas hydrate-bearing structure in Lake Baikal

International Journal of Earth Sciences - Early diagenetic carbonates are rare in Lake Baikal. Siderite (Fe carbonate) concretions in the sediments were discovered only recently. Here, we discuss...     

The Oeschinensee rock avalanche,Bernese Alps,Switzerland: a co-seismic failure 2300 years ago?

Landslide deposits dam Lake Oeschinen (Oeschinensee), located above Kandersteg, Switzerland. However, past confusion differentiating deposits of multiple landslide events has confounded efforts to quantify the volume, age, and failure dynamics of the Oeschinensee rock avalanche. Here we combine field and remote mapping, topographic reconstruction, cosmogenic surface exposure dating, and numerical runout modeling to quantify salient parameters of the event. Differences in boulder lithology and deposit morphology reveal that the landslide body damming Oeschinensee consists of debris from both an older rock avalanche, possibly Kandertal, as well as the Oeschinensee rock avalanche. We distinguish a source volume for the Oeschinensee event of 37 Mm³, resulting in an estimated deposit volume of 46 Mm³, smaller than previous estimates that included portions of the Kandertal mass. Runout modeling revealed peak and average rock avalanche velocities of 65 and 45 m/s, respectively, and support a single-event failure scenario. ³⁶Cl surface exposure dating of deposited boulders indicates a mean age for the rock avalanche of 2.3 ± 0.2 kyr. This age coincides with the timing of a paleo-seismic event identified from lacustrine sediments in Swiss lakes, suggesting an earthquake trigger. Our results help clarify the hazard and geomorphic effects of rare, large rock avalanches in alpine settings.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Late Pleistocene baldcypress (Taxodium distichum) forest deposit on the continental shelf of the northern Gulf of Mexico

Approximately 13 km south of Gulf Shores, Alabama (United States), divers found in situ baldcypress (Taxodium distichum) stumps 18 m below the ocean surface. These trees could have only lived when sea level fell during the Pleistocene subaerially exposing the tectonically stable continental shelf. Here we investigate the geophysical properties along with microfossil and stratigraphical analyses of sediment cores to understand the factors that lead to this wood’s preservation. The stumps are exposed in an elongated depression (~100 m long, ~1 m deep) nested in a trough of the northwest–southeast trending Holocene sand ridges and troughs with 2–5 m vertical relief and ~0.5 km wavelength. Radiocarbon ages of the wood were infinite thus optically stimulated luminescence (OSL) dating was used to constrain the site’s age. Below the Holocene sands (~0.1–4 m thick), separated by a regional erosional unconformity, are Late Pleistocene mud-peat (72±8 ka OSL), mud-sand (63±5, 73±6 ka OSL), and palaeosol (56±5 ka OSL) facies that grade laterally from west to east, respectively. Foraminiferal analysis reveals the location of the terrestrial-marine transitional layer above the Pleistocene facies in an interbedded sand and mud facies (3940±30 (1σ) ¹⁴C a BP), which is part of a lower shoreface or marine-dominated estuarine environment. The occurrence of palaeosol and swamp facies of broadly similar ages and elevation suggests the glacial landscape possessed topographic relief that allowed wood, mud and peats to be preserved for ~50 ka of subaerial exposure before transitioning to the modern marine environment. We hypothesize that rapid sea-level rise occurring ~60 or ~40 ka ago provided opportunities for local flood-plain aggradation to bury the swamp thus preserving the stumps and that other sites may exist in the northern Gulf of Mexico shelf.     

When hazard avoidance is not an option: lessons learned from monitoring the postdisaster Oso landslide,USA

On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning.

     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.