Application of PSI techniques to slope instability detection in the Daunia mountains,

F. Bovenga(1), M.T. Chiaradia(1), R. Nutricato(1), A. Refice(2), J. Wasowski(3)

(1) Dipartimento Interateneo di Fisica, Politecnico di Bari, Bari (Italy), E-mail: [email protected], [email protected], [email protected] (2) CNR-ISSIA, Bari (Italy), E-mail: [email protected] (3) CNR-IRPI, Bari (Italy), E-mail: [email protected]

ABSTRACT

Persistent Scatterers Interferometry (PSI) techniques allow to detect and monitor millimetric displacements occurring on selected point targets exhibiting coherent radar backscattering properties (mainly buildings and other man-made structures). The technique is sensitive to the number and spatial distribution of point targets and, therefore, particular care must be used when dealing with scarcely-urbanized areas. In the present work we apply the SPINUA (Stable Point INterferometry over Un-urbanised Areas) PSI processing technique [1] to the Daunia region located in Southern Apennines, Italy. This region includes several isolated small hill- top towns affected by slope instability problems. We selected for the analysis an area of 25×25 km2, enclosing 10 urban centres. A dataset of both descending (84) and ascending (51) ERS-1/2 acquisitions has been processed to allow investigation of slopes with a wide distribution of facing directions. In order to ensure an adequate distribution of coherent points for a reliable estimation of the atmospheric signal, the analysis has been limited to small image windows which enclose urban areas. This strategy, which has been also applied to other similar test sites [2], is justified by the fact that landslides with the highest socio-economic impact are those involving the urban centres. The density of the detected stable targets resulted suitable for 8 of the 10 investigated town areas, with a very good coverage of urban structures in 5 cases. The remaining 2 towns show a low number of PS, making difficult the detection of displacements. These outcomes could depend on the geometrical distribution of the coherent structures potentially corresponding to the PS. Although in several cases the displacement fields show clear evidence of moving objects located on urban and peri-urban areas, local knowledge of the investigated area and in situ inspections are required in order to interpret correctly the significance of PS motion data and to identify the main mechanism of the detected deformations.

1 INTRODUCTION Persistent Scatterer Interferometry (PSI), originally developed at Politecnico di Milano [3], represents an innovative approach to detect Earth surface deformations via satellite-borne SAR. It relies on the identification and monitoring of single objects that remain highly coherent through time. PSI techniques have been successfully applied to many test sites affected by terrain displacements, mainly in or close to flat and widespread urban areas [3,4] where PS density is high and displacement sensitivities of up to about 2-3 mm/year can be achieved. Nevertheless, well documented examples of successful applications of these technique to slope instability studies are rather limited [5,6,7]. In fact, when investigating displacements on scarcely urbanised areas, such as small villages and rural zones, the PS spatial density, which is usually of hundreds per km2 for urban areas, may fall well below few tens per km2. In practice, two main problems arise: the objective scarcity of PS targets and the difficulty of detecting the few existing PS. The first problem is common to all applications of PSI techniques on areas characterized by low anthropization. Although for phenomena occurring on a regional scale the possibility of processing larger areas may ease this problem, in the case of landslides the deformations are slope-specific and typically of limited spatial extent. This, together with the fact that local atmospheric variations can be pronounced in regions with strong topographic relief, constitute a serious threat to successful PSI applications. The SPINUA (Stable Point INterferometry over Un-urbanised Areas) technique [1] is a PSI processing methodology developed with the specific aim of detection and monitoring of coherent PS targets in non or scarcely urbanized areas. Innovative aspects and some ad hoc solutions have been developed and tested, which render the tool flexible and suitable for applications to areas with by low densities of PS. In particular, a patch-wise processing scheme is adopted, which consists in processing small areas confined around the site under investigation. Sizes and locations of the patches are selected with the aim to optimize the density and the distribution of potential PS and, since the test sites are usually quite limited in size, this choice allows to approximate the APS as a bi-linear surface. The estimation of the APS bi-linear surface is performed through the periodogram method in order to take into account the phase wrapping. Focusing the attention on small image patches corresponding to built-up areas is also justified by the fact that landslides involving the urban centres are those with the highest socioeconomic impact. Moreover, an alternative method to identify candidate persistent scatterers (PSC), consisting in using urban pixels identified through an external classification procedure [2], was employed to select the initial fiducial pixels used to start the subsequent iterative phase processing. The method differs from conventional procedures based on thresholding the temporal amplitude stability measure and, on particularly difficult sites, allows to use a greater number of PSC, thus overcoming some problems deriving from the scarcity of man-made features in the area and performing with success the subsequent phase analysis. Finally, a stepwise approach for co-registration and relative calibration of multi-temporal data-sets has been developed and tested [8] with the aim to improve the precision of the pre-processing steps and consequently also the PS detection. Many of the processing solutions described so far, as well as the results described in the following, were developed in the framework of the European Community Project “Landslide Early Warning Integrated System” (LEWIS, http://www.silogic.fr/lewis). In particular, in the following we present the results relative to the study performed on the Daunia , an area located at the north-western border of the Region, in Southern Italy. We focus on the reasons why different densities of stable targets are detected in apparently similar urban/peri-urban settings, and on some strategies to deal with this problem.

2 THE STUDY AREA The area studied is located in the Daunia regio characterized by gentle hills and low mountains only locally exceeding 1000 m above sea level. The Daunia Apennines belong to the highly deformed transition area between the most advanced frontal thrusts of the Apennine chain and the western-most part of the foredeep ([10,11]). The chain units are characterised by a series of tectonically deformed turbiditic (flysch) formations of pre-Pliocene age. Some areas of the chain are also characterised by the presence of abundant slope deposits (colluvia) of Holocene age. The clay-rich flysch units are more prone to landsliding, compared to the formations containing higher proportion of lithoid intercalations (sandstones, limestones, marlstones). The widespread presence of clayey materials with poor geotechnical properties is the undelying cause of landsliding. Furthermore, as a result of the tectonic history of the Apennines, the geological materials are intensely deformed and hence also rock units are susceptible to slope movements. In general, the activity of landslides in the Daunia Apennines is characterized by seasonal remobilisations of slope movements, typically related to rainfall events and/or modifications of slopes by man. Individual meteoric events have been the most frequent triggers of landslides, even though the mean annual rainfall is rather modest (in the order of 700 mm per year). Although mass movements appear widespread throughout the entire region [12], there are few studies published on landslides in the Daunia Mountains. The documented events are concentrated within or in the immediate proximity of the urban areas. There are over 20 hilltop towns in Daunia, all of which have been involved in slope movement activity in the past. However, specific documentation concerning the exact temporal occurrence of slope failures is rarely available. In the 1990’s there has been an apparent increase in mass movement activity in several urban and peri-urban areas. Indeed, the stability of slopes bordering the hilltop towns has probably gradually worsened because of residential development over recent decades and the construction of buildings and infrastructures. This has led in some cases to re- activations of pre-existing old landslides. Furthermore, the urban expansion onto marginally stable hillslopes has led to the increases in first-time damaging failures.

3 PS PROCESSING An area of 25×25 km2, enclosing 10 hill-top small urban centres affected by slope instability problems has been selected as a test area ( Fig. 1). A dataset of both descending and ascending ERS-1/2 acquisitions (84 and 51 scenes, respectively) has been processed, to allow monitoring of slopes with a wide distribution of facing directions. The dataset has been pre- processed adopting the optimized solutions for co-registration, relative calibration and resampling, as summarized in Sect. 1 and described in more detail in [1,8]. Differential interferograms have been obtained by using an external SRTM-derived DEM with spatial resolution of 3 arcsec (approx. 90 m × 90 m). The subsequent, more complex multi- temporal analysis required some preliminary investigations aimed at devising the best solutions in terms of area extent and PSC selection. By taking advantage of the experience gained in analogous test cases, e.g. those described in [9] and [2], in order to ensure an adequate distribution of PSC for a reliable estimation of the atmospheric signal, the SPINUA patch-wise approach has been adopted. In Fig. 1, the multi-image SAR amplitude reveals the hilly morphology of the site and the relative scarcity of urban settlements. The analysis was limited to small image windows of a few km2 in size, which enclose urban areas Fig. 1. In this case the exclusion of vegetated rural areas, which can be generally expected to contain very few PS, did not affect the significance of the results while allowing at the same time to use simpler and more robust model assumptions for atmospheric effects. The temporal coherence of a given pixel is defined as the coherent sum of its phase contributions along the temporal series of SAR images, corrected for all the modelled effects (atmosphere, deformation, DEM and orbital model errors): γ t = ∑k exp[i(φk − φk )]. The PS are selected as function of γt: higher values of the γt threshold ensure more reliability to the measurements, but also a less dense set of points. When PS densities are low, setting the γt threshold is a critical step. We put therefore emphasis on the derivation of optimal γt threshold values, based on statistical grounds, linking it to the accepted level of false-alarm probability, PFD, defined as the probability of having a non stable pixel erroneously identified as PS. Given a particular configuration of spatial and temporal baselines, it is possible to evaluate FD P as the probability that random phase values align fortuitously to give an estimated γt higher than a given threshold. FD P decreases as the γt threshold increases. It is also inversely proportional to the number of acquisitions available in the stack, although the particular distribution of spatial and temporal baselines influences the result. Given the configuration of spatial and temporal baselines of the available dataset and the expected average density FD of PS, the value of the γt threshold which guarantees an acceptable P can be evaluated by simulations. Using ERS acquisitions, the number of image pixels per km2 at single-look resolution (20 m in range and 5 m in azimuth) is about 50×200 =104. Thus, assuming as representative figure an average PS density of 10 per km2, and allowing for one false detection every 100 PS, the PFD in terms of number of pixels results of 1/100 × 10/104 = 10−5. Hence, at least 106 independent simulations were required for a reliable inference. Results of the simulations gave optimal γt threshold values around 0.74 for both ascending and descending configurations. This value was then used in the iterative computations necessary to remove atmospheric effects and topographic errors. Once a stable distribution of PS is reached, a higher threshold of 0.8 is adopted for the final map representation, to further increase the confidence level of the results. In a first processing phase, only a limited subset of acquisitions have been processed, in order to reduce the influence of large spatial and temporal baselines. Then, the procedure has been repeated by including new interferograms and using the estimated set of PS pixels from the previous phase to start the new estimation. The iteration process ends when the temporal coherence, γt, becomes stable. As the number of images increases, γt decreases due to several possible causes, among which the increasing nonlinearity of the deformation model, the loss of the structural coherence of the object, and the influence of high spatial and temporal baselines which add noisy samples in the estimation procedure. Results can be evaluated in terms of average γt values, number of final PS (which are the pixels whose γt overcomes a certain threshold) and reliability of detected deformations. The following discussion refers to the deformation maps obtained with above-described processing settings. The best results have been obtained by processing a subset of 40 and 35 images, respectively, for the ascending and the descending datasets.Different densities of coherent targets were detected in apparently similar urban/peri-urban settings. In particular, the detected PS density resulted suitable for 8 out of the 10 investigated town areas, with a very good coverage of urban structures of , , and . The remaining 2 towns ( and Volturara) show a very low number of PS, making difficult the detection of potential displacements. Moreover, in relation to variable facing directions of slopes, the PS density depends on the image acquisition configuration: for the towns of and , reliable results are provided only by descending acquisitions, while for and Volturino only by ascending acquisitions. On the remaining 4 towns, the PS obtained from ascending and descending data show complementary spatial distributions with densities always predominant in one of the two sets. In these cases, the results from ascending and descending processing have been integrated to provide a final displacement map with higher PS density. The outcomes are summarized in Table 1. The different densities of stable targets detected in apparently similar urban/peri-urban areas, are likely linked to the geometrical distribution and orientation of the coherent structures potentially corresponding to the PS. Moreover, the variable local facing direction of the slopes in the area impact differently on the geometrical deformations of the SAR image, thus leading to different detectability conditions. More in situ inspections are required to investigate this important aspect, which is also related to the scattering mechanisms associated to PS objects.

4 INTERPRETATION OF THE RESULTS Although in several cases the displacement fields show clear evidence of moving objects located on urban and peri- urban areas, local knowledge of the investigated town areas and in situ inspections are required in order to interpret correctly the significance of PS motion data and to identify the main mechanism of the detected deformations. In general the majority of detected PS are stable and lay on the historical centres of the towns, whereas the moving points showing very similar average displacement are grouped in clusters located along the towns borders. Thiscan be linked to the fact that in hilltop towns landslide events occur mainly in the towns peripheries, where the urban expansion took place in more recent times and the manmade structures corresponding to PS are located on or close to the steeper and potentially unstable slopes. The only exception is the town of Pietramontecorvino where the deformations are present also in the urban centre. Indeed, this town centre has been known for the presence of many buildings with instability problems. We focus now on the town of Volturino, where previous knowledge has been acquired in the framework of the LEWIS project. The selection of preliminary coherent points performed on the amplitude dispersion index resulted in a 2 suitable PSC distribution (several tens per km ) for the ascending dataset. The final mean velocity field for PS with γt above 0.8 is shown superimposed on an orthophoto of the area in Fig. 2. As can be seen, the coherent points are located mainly on the urban area; their average spatial density is 236 PS per km2. The great majority of the PS, which coincide with the town's centre, do not show any significant movement. In this area a carbonate flysch sequence crops out (Flysch di Formation), which is relatively less prone to landsliding with respect to the nearby clay-rich units. Instead, the south-western and more recently developed part of the town includes an isolated but homogeneous group of PS showing significant velocity of displacement (≤ –3 mm/y). These PS allow to delimit a small built up zone (about 1 ha in extent), which belongs to a local morphological low and falls in the head portion of a minor valley. The bordering reliefs made of carbonate flysch supply water to the valley, which is drained by a small watercourse flowing towards north. This area is known under the local name of “Fontana a Monte” (Up-valley Fountain), which is indicative of the presence of water. The valley is characterised by the presence of a clay-rich unit. The built up head portion of the valley locally includes also fill material, which most likely amounts up to a few meters in thickness. Although the valley descends approximately towards the north, the facing directions of local slopes in the built up area vary considerably and range from north-west to north-north-east. Also slope inclinations are variable, but seldom exceed 10 degrees. This variability is in part a consequence of fill and cut engineering modifications of the area during the housing development, which began in the 1950's. The average annual LOS displacement velocities in the unstable area range from –5 to –3 mm/yr for the period 1992-1999. The inset plot in Fig. 2 shows an example of the average LOS displacement trend of a PS in the area. The trends of other moving PS are similar. There is no detailed information regarding the landslide activity in the “Fontana a Monte” area. The records available in the indicate that one documented landslide event occurred in 1988. It caused damage to some houses, retaining walls and roads. Furthermore, an unpublished report of the Comune from June 1998 reveals the occurrence of some modest size and thickness landslides associated with rainfall events in the first half of 1998, as well as similar failures in the preceding years. The inspection of slope stability conditions in Volturino in 2004 (conducted by one of the authors on behalf of the Department of Civil Protection), as well as the most recent visits in 2005, revealed that several buildings and retaining structures show signs of distress and have suffered recurrent damage (appearance of cracks) since the first half of the 1990's. At least in one case the first occurrence of cracks in a private house appears to be well remembered and dates back to 1994. The reported recurrence of cracks, which after the initial opening are generally filled with cement for aesthetic reasons, their progressive widening and the appearance of new ones indicate conditions of persisting instability of the ground. Therefore, the above information strongly suggests that the PS displacement results are realistic. Indeed, because of the marginal stability conditions, the Comune imposed severe restrictions for housing development in the “Fontana a Monte” area.

ID Urban Center Ascending Descending 1 Casalnuovo Monterotaro 224 51 2 Casalvecchio 65 22 3 Carlantino - - 4 Castelnuovo della Daunia 145 48 5 Celenza Valfortore - 62 6 Pietramontecorvino 20 108 7 San Marco La Catola - 80 8 Motta Montecorvino 28 - 9 - - 10 Volturino 236 - Tab. 1. Number of PS per km2 obtained by processing through the SPINUA algorithm the ascending and descending dataset on the 10 urban centres in the Daunia test site. Dash symbols indicate unsuccessful processing.

Fig. 1. Average amplitude image of the Daunia Test Area (28 km × 27 km): the white-bordered rectangles enclose 10 town areas selected for the PS processing. In the case of the town of Volturino the patch is 4 km wide in range and 3.7 km in azimuth. Note the prevailingly moderate relief hillslope topography of the area.

Fig. 2. PS map superimposed on an orthophoto of the Volturino area. The red line enclosing the red dotted pattern marks the area where landsliding occurred in the past. The inset plot represents the LOS displacement times series of the PS pixel marked by the red arrow (actual vertical deformation depends on local slope geometry). 5 CONCLUDING REMARKS In the present work we apply the SPINUA PSI processing technique to the Daunia region located in the Southern Apennines. This region includes several isolated small hill-top towns affected by slope instability problems. The vegetation coverage and the scarcity of man made structures hinder the application of standard large area PSI approach, while the ad hoc solutions adopted by SPINUA provided encouraging results. In several cases the displacement fields show clear evidence of moving objects located on urban and peri-urban areas. However, local knowledge of the investigated area and in situ inspections are required in order to interpret correctly the significance of PS motion data and to identify the main mechanism of the detected deformations. In the case of Volturino the moving PS fall in a location that, unlike the remaining part of the town, is characterised by the presence of many distressed buildings and structures. This area is very close to the head portion of a small valley where landslides occurred in the recent past.

ACKNOWLEDGEMENTS This work was supported in part by the European Community (Contract No. EVGI 2001-00055 - Project LEWIS). Images were provided by ESA under the CAT-1 project number 2653, “Advanced SAR Interferometry techniques for landslide warning management”.

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