New Challenges in Landslides Studies by Exploiting SBAS-DInSAR Results: the Ivancich Landslide in Assisi, Central Italy
CalÚ, Fabiana1; Ardizzone, Francesca2; Castaldo, Raffaele1; Guzzetti, Fausto2; Lanari, Riccardo1; Ojha, Chandrakanta1; Tizzani, Pietro1; Manunta, Michele1
1IREA-CNR, ITALY; 2IRPI-CNR, ITALY

Landslides widely occur over the world, and result in high socio-economic impacts on the affected communities. Planning of efficient prevention/mitigation strategies plays a key role in the landslide risk management, and is based on a deep understanding of the phenomena, that can be reached if extensive information, in terms of both temporal and spatial coverage, is available. Use of traditional ground-based techniques, such as topographic surveys and in-situ measurements, obtained e.g. by inclinometers, extensometers and tilt-meters, can be technically difficult and not economically sustainable where the monitoring has to cover large areas and long periods. Data collected by satellite radar sensors and processed by Differential SAR Interferometry (DInSAR) techniques allow us to overcome such limitations and complement conventional in-situ methods, obtaining improvements in terms of temporal sampling and spatial coverage. Indeed, advanced DInSAR techniques allow generating time-series of ground deformation for large to very large areas (hundreds to thousands of squared kilometres) and for very long observation periods, thus providing long-term monitoring datasets valuable for the analysis of the behaviour of landslides over time. Among the advanced DInSAR techniques aimed at producing deformation time-series, the Small BAseline Subset (SBAS) approach (Berardino et al., 2002) exploits only SAR data pairs characterized by short separation (baseline) in time and space between the orbital satellite positions in order to limit the noise effect (decorrelation) and maximize the number of detected coherent measure points. Originally designed for investigating deformation phenomena extending over very large areas, as earthquakes, the SBAS algorithm has been subsequently extended in order to analyse localized phenomena affecting man-made features and individual slopes. As result, the SBAS analysis can be currently carried out at two spatial scales, namely at regional and local scale (Lanari et al. 2004). At the regional scale, the technique exploits average (multi-look) interferograms to produce deformation maps of wide areas at low spatial resolution scale; at the local scale, single-look interferograms are exploited in order to generate deformation maps at the full spatial resolution scale, thus allowing to focus on local deformation affecting single elements at risk. Furthermore, the SBAS approach is able to perform multi-sensor analyses by exploiting SAR data collected by different radar systems acquiring with the same illumination geometry as for the case of ERS-1/2 and ENVISAT satellites (Bonano et al. 2012). Such a characteristic allows us to effectively exploit the large SAR data archive currently available for the analysis of slow-moving deformation phenomena, as landslides, through the generation of continuous ERS/ENVISAT deformation time-series overall spanning almost 20 years. Such mapping and monitoring capability is further increased thanks to the availability of data acquired by the second generation SAR systems, e.g., TerraSAR-X, COSMO-SkyMed, characterised by reduced revisit time (down to a few days) and improved spatial resolution (down to few meters). Indeed, the improvement in temporal sampling allows us to study also deformation phenomena characterized by highly variable kinematics. Furthermore the high spatial resolution leads to a significant increase of the number of measurable points, thus improving the landslide mapping capability. Goal of this work is to investigate how the current SAR sensor scenario impacts on the landslide analysis and can be effectively exploited to improve the knowledge and understanding of the landslide phenomena. To this aim, we collected 91 ERS-1/2 and 39 ENVISAT SAR data acquired between 1992 and 2010 as well as 39 COSMO-SkyMed (CSK) scenes acquired between December 2009 and February 2012, relevant to the Umbria region, central Italy. SAR datasets have been processed through the multi-scale and multi-sensor SBAS approach. The results of our analyses show as the use of (a) large archives of existing ERS-1/2 and ENVISAT data can significantly contribute to back-analyses of landslides and (b) higher resolution images captured by COSMO-SkyMed sensors can improve mapping and monitoring of surface deformation induced by landslides. First of all, we investigate the long term behaviour by comparing the 1992-2010 SAR results and historical rainfall data recorded by a local rain gauge. Cross-correlation between the local monthly rainfall history and the SBAS deformation time-series revealed the lack of an immediate effect of rainfall on the ground deformation, suggesting a complex interaction between the local rainfall history and the acceleration or the deceleration of the ground deformation in the Ivancich landslide area. We take this as additional indication of the complexity of the deep-seated mass movement that affects the neighbourhood of the Assisi municipality. Moreover, we show that the integration of multi-sensor DInSAR analyses with traditional geomorphological data (inventory map) provides a comprehensive picture of the investigated landslide phenomenon, pointing out that areas, classified with the same state of activity through geomorphologic analyses, are interested by deformation fields with different velocities. Such result is relevant in term of hazard and risk zonation, and is confirmed by the damage assessment carried out through field surveys, that pointed out a more severe degree of urban damage along the boundary between the detected landslide sectors characterized by different state of activity. Finally, we present an innovative application of DInSAR data that consists in their exploitation for developing and constraining numerical models of the investigated phenomenon. Such approaches represent a new frontier in the landslide studies, due to the complexity and extent of the problem that can be effective tackled (and solved) only exploiting a large set of measure points, typically achievable by remote sensed data, properly integrated with information taken through conventional techniques and methodologies. References Berardino, P., Fornaro, G., Lanari, R. & Sansosti, E. 2002. A new algorithm for surface deformation monitoring based on Small Baseline Differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375-2383. Bonano, M., Manunta, M., Marsella, M. & Lanari, R. 2012. Long Term ERS/ENVISAT Deformation Time-Series Generation at Full Spatial Resolution via the Extended SBAS Technique. International Journal of Remote Sensing, 33:15, 4756-4783, doi: 10.1080/01431161.2011.638340. Lanari, R., Mora, O., Manunta, M., Mallorqui, J., Berardino, P. & Sansosti, E. 2004. A small baseline approach for investigating deformations on full resolution differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 42: 1377-1386.