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Landslides Forecasting Analysis by Time Series Displacement Derived

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Landslides Forecasting Analysis by Time Series Displacement Derived LANDSLIDES FORECASTING ANALYSIS BY DISPLACEMENT TIME SERIES DERIVED FROM SATELLITE INSAR DATA: PRELIMINARY RESULTS P. Mazzanti (1,2,3), A. Rocca (2), F. Bozzano(1,2,3), R. Cossu(4), M. Floris (5) (1)NHAZCA S.r.l., Spin-off company of “Sapienza” Università di Roma, Via Cori snc, 00177, Rome, Italy, [email protected] (2)Department of Earth Sciences “Sapienza” Università di Roma, Piazzale A. Moro n.5, 00185, Rome, Italy, [email protected] (3)CERI, Research Centre “Sapienza” Università di Roma, Piazza U. Pilozzi n.9, 00038, Valmontone (RM), Italy, [email protected] (4)ESRIN - ESA via Galileo Galilei, 1, 00044, Frascati (RM), Italy, [email protected] (5)Università degli Studi di Padova – Dipartimento di Geoscienze, via Gradenigo, 6, 35131, Padua, Italy, [email protected] ABSTRACT forecasting solutions for landslides. The most used approach to address the problem is the semi- In this paper preliminary results of the Cat-1 project empirical one, since it is the most reliable in ”Landslides forecasting analysis by displacement forecasting analysis. Several semi-empirical models time series derived from Satellite and Terrestrial to predict the time of failure of slopes, as a function InSAR data” are presented. The project focuses on of velocity variation over time, have been proposed landslide forecasting analysis, based on the over the last decades [31, 30, 13, 38, 8, 3, 25]. application of slope creep models. Displacements Instrumental continuous collection of displacements data through time, related to landslides strain data through time is a basic requirement for the evolution, are usually derived by classical application of the above-mentioned approaches. monitoring methods. Here we propose the use of Among displacement monitoring techniques, remote spaceborne synthetic aperture radar (SAR) sensing ones are increasing in over the last years. differential interferometry (DInSAR) to achieve past Howeover, terrestrial and aerial remote sensing displacements information of already collapsed techniques can collect displacement data only after landslides. Persistent Scatterers (PS) InSAR and that suitable equipments have been installed. Small Baseline Subset (SBAS) InSAR are Differently, satellite SAR data are continuously considered and discussed in the perspective of collected by suitable satellites since 1992 and are proposed application. Furthermore, a rating method continuously increasing over time in terms of to evaluate the suitability of landslide prediction and quantity (sampling rate and number of SAR investigation by Satellite InSAR technique is satellites) and quality (ground resolution etc). presented and tested over two case studies. Hence, a large amount of displacement data can be obtained for several areas of the world. Furthermore, 1. INTRODUCTION by multi-stack analysis like Persistent Scatters Slope instabilities are caused by several complex Interferometry (PSI) [12], or Small BAseline Subset processes taking place at different levels: geological technique (SBAS) [1] time series of displacements and hydrogeological, climatic (e.g. rainfall with millimeter accuracy can be obtained. distribution or freeze-thaw cycles), earthquakes, These data can be a very useful tool for the temporal volcanism, human activities etc. The combination of prediction of landslides failure by applying semi- these factors and processes can lead to an instability empirical models. condition that is manifested by mass movements of In the frame of the ESA CAT-1 project – ID: 9099 - slopes occurring under the action of gravity. “Landslides forecasting analysis by displacement Landslides rarely behave as a rigid body that time series derived from satellite and terrestrial responds instantly to stresses. More frequently, InSAR data” long term SAR data stacks provided by especially in cases of large landslides (> 500.000 ERS and ENVISAT ASAR satellites will be m3), they are characterized by a strain pattern (i.e. processed and analyzed in order to investigate large displacements), increasing over time. This particular scale landslides (>1,000,000 m3) recently occurred behavior is known as “creep” [35, 36]. Starting from in Italy, with the aim to better define their slope the basic theory of creep, several efforts have been dynamics. Specifically, two slopes affected by large done over the last decades in order to develop landslides, already collapsed, are being back- _____________________________________________________ Proc. ‘Fringe 2011 Workshop’, Frascati, Italy, 19–23 September 2011 (ESA SP-697, January 2012) analyzed, in order to understand their predictability Materials involved represent another important by semi empirical methods. factor in terms of different responses to stress and Time series of displacement derived from InSAR different mechanical and kinematic behaviour [36]. multi-stack data processing are analyzed in order to obtain graphs of inverse of the velocity vs. time. 2.1. Slope-creep approach Hence, efficacy of satellite InSAR data for temporal Landslides rarely behave as a rigid body that prediction of landslides is analyzed and discussed, responds instantly to stresses. More frequently, and the main limitations of InSAR data for the especially in cases of large landslides (> 500.000 application of landslide time of failure forecasting m3), they are characterized by a strain pattern (then models is assessed and investigated in detail. displacement), increasing over time [34]. Therefore, The expected results of this project are the time-dependent failure relationships must be following: a) identification of limits and potentiality considered in order to correctly describe the long- of PSI and SBAS methods for landslide forecasting; term deformation pattern, known as “slope creep” b) identification of type of landslides that can be [35, 15], that represents the slow deformation of investigated by InSAR techniques (for forecasting slopes involving soil and rock, taking place under purposes); c) development of new landslide gravity and external loadings [10]. forecasting models (or adaptation of existing ones) In the creep model, materials show a particular specifically based on Satellite InSAR data; iv) evolution of the strain pattern characterized by three comparison of predictive landslide capabilities of phases: a primary creep, a secondary one and finally, Satellite and Terrestrial InSAR data a tertiary creep in which a continuous strain acceleration leads the material to failure (see fig.1). 2. LANDSLIDES FORECASTING Forecasting the collapse of a landslide is a complex matter because of the high complexity of landslide phenomena. As a matter of fact, “landslide” is a generic term that assembles gravity-controlled slope instabilities characterized by different features in terms of geometry, size, material etc [2]. Existing classifications for landslides are usually based on parameters such as: type of process, morphology, geometry, type and rate of movement, type of material and state of activity [36, 20, 9, 19]. The size is rarely used as a classification parameter, since the contribution to knowledge, provided by it Figure 1. Example of typical creep behaviour of a is considered too small if compared with above- natural material mentioned ones. A classification based on the volume was proposed (tab.1) by [11] while a size Three main approaches can be considered in classification for debris flows have been proposed studying a landslide on the basis of the creep by [21] in order to address specific practical issues. behaviour: Size class Size description Volume (m3) - “micromechanical” approach, which relates the 1 Extremely small <500 stress-strain mechanism to molecular scale process 2 Very small 500–5000 [26], therefore not particularly useful to describe a 3 Small 5000–50,000 creep deformation at the slope scale; 4 Medium 50,000–250,000 - rheological-mechanical approach, that is based on 5 Medium–large 250,000–1,000,000 ideal models (e.g. viscoelastic, viscoplastic, mod. 6 Very large 1,000,000–5,000,000 Burger, mod. Maxwell etc.) able to mathematically 7 Extremely large >5,000,000 explain the process and that can be solved Table 1. Size classification for landslides (after Fell, numerically by suitable codes also at the slope scale; 1994) (from Jacob, 2005) - empirical approach in which the time series of displacement are used to describe the behaviour and In the authors opinion the interest on the landslides evolution in terms of material’s failure on the basis size as a physical parameter, is not only related to of empirical and semi-empirical relations. classification purposes. As a matter of fact, different The latter is the most commonly used since it is the processes can be recognized if small-scale or large- most constrained, being closely related to scale landslides are accounted for, especially if we observational methods (it is based on real observed look at the pre-failure stage. displacement data for predicting the future behaviour). The models of landslide prediction by starting from conventional ones to more advanced displacement time series [31, 30, 13, 38, 8, 3, 25] are ones (like Satellite InSAR). based on the assumption that the slopes collapse are preceded by a creep behavior characterized by an 3.1. “Future oriented” monitoring techniques acceleration of movement in time. The displacements monitoring of structures and By large scale experiments results, observing ground surface in slopes affected by landslides can surficial displacements, [13], demonstrated
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