BANK EROSION AND INSTABILITY MONITORING WITH A LOW COST TERRESTRIAL LASER SCANNER

ABSTRACT: Among the dominant processes taking place in a river basin, especially mountain ones, sediments creation and transport play a key role in morphological processes. Studies usually focus on big mass movements, such as landslides and debris flows, or on wide spread slope erosion due to rainfalls, while bank erosion is neglected or not considered essential for sediment budget at basin scale. Nevertheless, authors consider bank erosion a process that deserve more careful studies; not only the sediment share from bank erosion is not negligible in steep mountain rivers, but also the process can threat structures on river sides due the possibility to have limited, but still significant, mass collapse of bank sections during intense events. The paper present an attempt to monitor bank erosion in a section of a river in Northern Italy Alps and to put it in relation with weather and water discharge. Survey campaign was set up at regular time intervals, or after particularly intense rainfalls, and uses a Terrestrial Laser Scanner (TLS) to acquire the bank surface. The tool was developed internally, at Politecnico di Milano, to meet requirements about low cost level and good accuracy. Successive acquisitions of point clouds were elaborated, via an ad-hoc MatLab code, to determine erosion, or deposition, volumes of sediments. These volumetric results have been evaluated in relation with rainfalls and freeze-thaw cycles looking for a relationship between environmental conditions and bank failures. Some interesting results are shown, such as a relation between erosion rates and temperature or water flow in the river. The path to a complete process understanding and modelling is long, however the results reported can be considered a first step towards objective

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

Critical hydrometeorological events are generally triggered by heavy precipitation. In complex terrain, precipitation may be perturbed by the upslope raising of the incoming humid airflow, causing in some cases extreme rainfall. In this work, the application of LUME-Linear Upslope Model Extension-to a group of extreme events that occurred across mountainous areas of the Central Alps and Apennines in Italy is presented. Based on the previous version, the model has been "extended" in some aspects, proposing a methodology for physically estimating the time-delay coefficients as a function of precipitation efficiency. The outcomes of LUME are encouraging for the cases studied, revealing the intensification of precipitation due to the orographic effect. A comparison between the reference rain gauge data and the results of the simulations showed good agreement. Since extreme precipitation is expected to increase due to climate change, especially across the Mediterranean region, LUME represents an effective tool to investigate more closely how these extreme phenomena originate and evolve in mountainous areas that are subject to potential hydrometeorological risks

IMAGE-BASED RECONSTRUCTION AND ANALYSIS OF DYNAMIC SCENES IN A LANDSLIDE SIMULATION FACILITY

The application of image processing and photogrammetric techniques to dynamic reconstruction of landslide simulations in a scaled-down facility is described. Simulations are also used here for active-learning purpose: students are helped understand how physical processes happen and which kinds of observations may be obtained from a sensor network. In particular, the use of digital images to obtain multi-temporal information is presented. On one side, using a multi-view sensor set up based on four synchronized GoPro 4 Black® cameras, a 4D (3D spatial position and time) reconstruction of the dynamic scene is obtained through the composition of several 3D models obtained from dense image matching. The final textured 4D model allows one to revisit in dynamic and interactive mode a completed experiment at any time. On the other side, a digital image correlation (DIC) technique has been used to track surface point displacements from the image sequence obtained from the camera in front of the simulation facility. While the 4D model may provide a qualitative description and documentation of the experiment running, DIC analysis output quantitative information such as local point displacements and velocities, to be related to physical processes and to other observations. All the hardware and software equipment adopted for the photogrammetric reconstruction has been based on low-cost and open-source solutions

The homotopy invariance of the string topology loop product and string bracket

Let M be a closed, oriented, n -manifold, and LM its free loop space. Chas and Sullivan defined a commutative algebra structure in the homology of LM, and a Lie algebra structure in its equivariant homology. These structures are known as the string topology loop product and string bracket, respectively. In this paper we prove that these structures are homotopy invariants in the following sense. Let f : M_1 \to M_2 be a homotopy equivalence of closed, oriented n -manifolds. Then the induced equivalence, Lf : LM_1 \to LM_2 induces a ring isomorphism in homology, and an isomorphism of Lie algebras in equivariant homology. The analogous statement also holds true for any generalized homology theory h_* that supports an orientation of the M_i 's.Comment: 21 pages, 2 figures final version published in Journal of Topolog

Long-term hydrogeophysical monitoring of the internal conditions of river levees

To evaluate the vulnerability of the earthen levee of an irrigation canal in San Giacomo delle Segnate, Italy, a customized electrical resistivity tomography (ERT) monitoring system was installed in September 2015 and has been continuously operating since then. Thanks to a meteorological station deployed at the study site, we could investigate the relationship between the inverted resistivity values and different parameters, namely air temperature, rainfall and water level in the canal. Air temperature seems to have a minor but not negligible influence on resistivity variations, especially at shallow depth. A model of soil temperature versus depth was used to correct resistivity sections for air temperature variations through the different seasons. Changes of the water level in the canal and rainfall significantly affect measured resistivity values. At the study site, the most important variations of resistivity are related to saturation and dewatering processes in the irrigation periods. Although we explored the effect of drawdown procedures on resistivity data, this process, causing rapid variations of resistivity values, is still not completely understood because the canal is rapidly emptied during rainfall events. Therefore, the effect of variations of the water level in the canal on levee resistivity cannot be distinguished from the effect of rainfalls. To study the effect of water level variations alone, we considered the beginning of the irrigation period when the dry canal is gradually filled and we observed a smooth trend of resistivity changes. The effect of rainfall on the data was studied during different periods of the year and at different depths of the levee so that the resistivity variations could be evaluated under different conditions. To convert the inverted resistivity sections into water content maps, an empirical and site-dependent relationship between resistivity and water content was obtained using core samples. Water content data can then be used for the implementation of stability analysis using custom modeling. This study introduces an efficient technique to monitor earthen levees and to control the evolution of seepage and water saturation in pseudo-real time. Such a technique can be exploited by Public Administrations to reduce hydrogeological risks significantly

APPLICATION OF LUCAS-KANADE DENSE FLOW FOR TERRAIN MOTION IN LANDSLIDE MONITORING APPLICATION

Landslides are natural hazards that can cause severe damage and loss of life. Optical cameras are a low-cost and high-resolution alternative among many monitoring systems, as their size and capabilities can vary, allowing for flexible implementation and location. Computer vision is a branch of artificial intelligence that can analyze and understand optical images, using techniques such as optical flow, image correlation and machine learning. The application of such techniques can estimate the motion vectors, displacement fields, providing valuable information for landslide detection, monitoring and prediction. However, computer vision also faces some challenges such as illumination changes, occlusions, image quality, and computational complexity. In this work, a computer vision approach based on Lucas-Kanade optical dense flow was applied to estimate the motion vectors between consecutive images obtained during landslide simulations in a laboratory environment. The approach is applied to two experiments that vary in their illumination and setup parameters to test its applicability. We also discuss the application of this methodology to images from Sentinel-2 satellite optical sensors for landslide monitoring in real-world scenarios

A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream

The problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy is addressed. This study is based on a large set of measurements collected from real pebbles, traced along the stream through radio-frequency identification tags after precipitation events. Two classes of data-driven models based on machine learning and functional geostatistics approaches are proposed and evaluated to predict the probability of movement of single pebbles within the stream. The first class built upon gradient-boosting decision trees allows one to estimate the probability of movement of a pebble based on the pebbles' geometrical features, river flow rate, location, and subdomain types. The second class is built upon functional kriging, a recent geostatistical technique that allows one to predict a functional profile-that is, the movement probability of a pebble, as a function of the pebbles' geometrical features or the stream's flow rate-at unsampled locations in the study area. Although grounded in different perspectives, both models aim to account for two main sources of uncertainty, namely, (1) the complexity of a river's morphological structure and (2) the highly nonlinear dependence between probability of movement, pebble size and shape, and the stream's flow rate. The performance of the two methods is extensively compared in terms of classification accuracy. The analyses show that despite the different perspectives, the overall performance is adequate and consistent, which suggests that both approaches can provide modeling frameworks for sediment transport. These data-driven approaches are also compared with physics-based ones that are classically used in the hydrological literature. Finally, the use of the developed models in a bottom-up strategy, which starts with the prediction/classification of a single pebble and then integrates the results into a forecast of the grain-size distribution of mobilized sediments, is discussed

SUSCEPTIBILITY MAPPING OF SHALLOW LANDSLIDES INDUCING DEBRIS FLOWS: A COMPARISON OF PHYSICS-BASED APPROACHES

The assessment of timing and potential locations of rainfallinduced shallow landslides through mathematical models represents a challenge for the assessment of landslide hazard, especially in cases with limited or not available data. In fact, modeling slope hydrological response and stability requires accurate estimates of unsaturated/saturated hydraulic and geotechnical properties of materials involved in landsliding, as well as climate and topography. Such aspect is relevant for the prediction of location and timing of landslide events, which is greatly needed to reduce their catastrophic effects in terms of economic losses and casualties. To such a scope, we present the comparison of results of two physics-based models applied to the assessment of susceptibility to shallow rainfall-induced landslides in Valtellina region (northern Italy). The analyses were carried out considering effects of availability, resolution and type of data concerning spatial distribution, thickness and properties of soils coverings. For such a scope, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) and the Climatic Rainfall Hydrogeological Modeling Experiment (CHRyME) models were considered. The study emphasizes issues in performing distributed numerical slope stability modeling depending on the availability of spatially distributed soil properties which hamper the quality of physic-based models. Further analyses aimed at the probabilistic assessment of landslide spatial distribution, related to a specific value of rainfall threshold, can be considered as potentially applicable to multi-scale landslide hazard mapping and extendable to other similar mountainous frameworks