Technical Note: Use of remote sensing for landslide studies in Europe

Abstract. The presented questionnaire study summarizes an evaluation of approaches, techniques and parameters of slope-instability investigation and monitoring of their occurrence, reliability and the applicability of the monitoring techniques for early warning. The study is based on information collected from 86 monitored landslides in 14 European and Asian countries. Based on the responses, lidar ALS (airborne laser scanners), geophysical logging, aerial photographs, resistivity surveying, GB InSAR (ground-based synthetic aperture radar interferometer) and the refraction seismic were considered the most reliable methods for investigation of structure and character of landslides. Especially lidar ALS and geophysical logging were ranked high despite their application at relatively few landslides. Precipitation amount, pore-water pressure and displacement monitored by wire extensometers, dGPS and total stations, followed by air temperature and EM-emissions monitoring and displacement monitored by the TM 71 crack gauge were considered the most promising parameters for early warning.

Section: Introductionsupporting

confidence: 78%

Application and reliability of techniques for landslide site investigation, monitoring and early warning – outcomes from a questionnaire study

Baroň

Supper

2013

“…The validation of the results is not simple because it is very difficult to find an area where the landslides are both spatially and temporally correctly located. Landslides can be accurately detected and mapped by remote sensing techniques or field surveys, but these activities can be very time consuming and sometimes they represent the main objective of a work (Tofani et al, 2013). The time of occurrence, in turn, is very rarely known with hourly precision, and usually landslides are just reported to be related to a rainstorm, without any more precise timing.…”

Section: Hiresss Testing and Preliminary Resultsmentioning

confidence: 99%

HIRESSS: a physically based slope stability simulator for HPC applications

Rossi

Catani

Leoni

et al. 2013

Self Cite

143

115

Abstract. HIRESSS (HIgh REsolution Slope StabilitySimulator) is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. The hydrological model receives the rainfall data as dynamical input and provides the pressure head as perturbation to the geotechnical stability model that computes the factor of safety (FS) in probabilistic terms. The hydrological model is based on an analytical solution of an approximated form of the Richards equation under the wet condition hypothesis and it is introduced as a modeled form of hydraulic diffusivity to improve the hydrological response. The geotechnical stability model is based on an infinite slope model that takes into account the unsaturated soil condition. During the slope stability analysis the proposed model takes into account the increase in strength and cohesion due to matric suction in unsaturated soil, where the pressure head is negative. Moreover, the soil mass variation on partially saturated soil caused by water infiltration is modeled.The model is then inserted into a Monte Carlo simulation, to manage the typical uncertainty in the values of the input geotechnical and hydrological parameters, which is a common weak point of deterministic models. The Monte Carlo simulation manages a probability distribution of input parameters providing results in terms of slope failure probability. The developed software uses the computational power offered by multicore and multiprocessor hardware, from modern workstations to supercomputing facilities (HPC), to achieve the simulation in reasonable runtimes, compatible with civil protection real time monitoring.A first test of HIRESSS in three different areas is presented to evaluate the reliability of the results and the runtime performance on large areas.

“…Moreover, the types of slow-moving landslides (Varnes, 1978) investigated by DInSAR data analyses in the aforementioned studies are mainly: slides and earth flows, deep-seated gravitational movements and creep phenomena (Tofani et al, 2013).…”

Section: The Dinsar Techniquesmentioning

confidence: 99%

“…In this regard, remote sensing techniques can offer a valuable contribution, as it is testified by the rapidly growing number of scientific works dealing with landslide inventory mapping achieved by means of the analysis of the information gathered by either passive or active air-and space-borne sensors or the integration of traditional ground-based monitoring with remote sensing data (Catani et al, 2005;Nichol and Wong, 2005;Van den Eeckhout et al, 2007;Tofani et al, 2013). In particular, as it is pointed out in the report "Deliverable 4.4" (SafeLand Deliverable 4.4, 2011) of the EU-funded SafeLand Project (http://www.safeland-fp7.eu), Synthetic Aperture Radar Differential Interferometry (DIn-SAR) techniques may represent suitable tools for updating inventory maps dealing with slow-moving landslides, namely mass movement phenomena whose typical velocity values range from few mm yr −1 up to 1.6 m yr −1 (Cruden and Varnes, 1996).…”

Section: Introductionmentioning

confidence: 99%

The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale

Cascini

Peduto

Pisciotta³

et al. 2013

Testing innovative procedures and techniques to update landslide inventory maps is a timely topic widely discussed in the scientific literature. In this regard remote sensing techniques – such as the Synthetic Aperture Radar Differential Interferometry (DInSAR) – can provide a valuable contribution to studies concerning slow-moving landslides in different geological contexts all over the world. In this paper, DInSAR data are firstly analysed via an innovative approach aimed at enhancing both the exploitation and the interpretation of remote sensing information; then, they are complemented with the results of an accurate analysis of survey-recorded damage to facilities due to slow-moving landslides. In particular, after being separately analysed to provide independent landslide movement indicators, the two datasets are combined in a DInSAR-Damage matrix which can be used to update the state of activity of slow-moving landslides. Moreover, together with the information provided by geomorphological maps, the two datasets are proven to be useful in detecting unmapped phenomena. The potentialities of the adopted procedure are tested in an area of southern Italy where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria