Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d'Aosta Case Study, Northern Italy

Martina Cignetti, Andrea Manconi, Michele Manunta, Daniele Giordan, Claudio De Luca, Paolo Allasia, Francesca Ardizzone
2016 Remote Sensing  
This paper presents a methodology taking advantage of the GPOD-SBAS service to study the surface deformation information over high mountain regions. Indeed, the application of the advanced DInSAR over the arduous regions represents a demanding task. We implemented an iterative selection procedure of the most suitable SAR images, aimed to preserve the largest number of SAR scenes, and the fine-tuning of several advanced configuration parameters. This method is aimed at minimizing the temporal
more » ... ing the temporal decorrelation effects, principally due to snow cover, and maximizing the number of coherent targets and their spatial distribution. The methodology is applied to the Valle d'Aosta (VDA) region, Northern Italy, an alpine area characterized by high altitudes, complex morphology, and susceptibility to different mass wasting phenomena. The approach using GPOD-SBAS allows for the obtainment of mean deformation velocity maps and displacement time series relative to the time period from 1992 to 2000, relative to ESR-1/2, and from 2002 to 2010 for ASAR-Envisat. Our results demonstrate how the DInSAR application can obtain reliable information of ground displacement over time in these regions, and may represent a suitable instrument for natural hazards assessment. ]. This information is crucial to increase our capability to assess landslide hazards, and to manage the associated risks [15] [16] [17] [18] . In recent decades, surface deformation in mountain environments has been measured by means of diverse monitoring approaches, ranging from classical in situ instruments [19] [20] [21] [22] [23] [24] to more advanced remote sensing methods [25] [26] [27] [28] . In situ instruments are typically installed to obtain a time series with high temporal resolution, which allows for a better analysis of local phenomena over time, while remote sensing techniques are more suitable for the evaluation of deformation trends at regional scales. Among different techniques, space-borne differential synthetic aperture radar (SAR) interferometry (DInSAR) has gained an important role in measuring ground displacements over the last few years [29] . DInSAR is a consolidated method used to provide data with extensive spatial coverage and to investigate different types of phenomena simultaneously, including those hindered by limited or difficult access. For example, DInSAR was successfully applied to detect and monitor slow movements of mountain slopes in the order of few centimeters/year in the Austrian and Swiss Alps, and the Pyrenees, confirming its operational potential in high mountain areas [30] [31] [32] . Advanced DInSAR methods have been developed to derive ground velocity maps and displacement time series [33] [34] [35] . The small baseline subset (SBAS) technique [34] , which combines sets of interferograms with small orbital separation (baseline) and short revisiting time to reduce the temporal decorrelation and maximize the number of coherent SAR targets, has proven to be suitable in different deformation scenarios [36] [37] [38] [39] . Accuracies are in the order of 1 mm/year for mean surface velocities and 5 mm for displacement measurements [40] . Nowadays, the increasing availability of multi-temporal satellite acquisitions allows for the generation of time series of ground deformation spanning periods as long as 20 years. This information is particularly suitable for studying the long-term behavior of landslides, rock glaciers, and glaciers. However, the analysis of surface deformation via DInSAR is challenging for several reasons, including (i) the high topographic gradients associated with the complex orography; (ii) abundant vegetation affecting the temporal correlation of the SAR signal; and (iii) unsuitable valley flank orientations relative to the SAR view angle [41] [42] [43] [44] . Moreover, DInSAR ground deformation monitoring for systematic analyses is complicated due to the intrinsic limitations of the technique (i.e., coherence loss due to large revisit time, phase decorrelation due to large or rapid displacement, and line-of-site (LOS) measurements only) [33, [45] [46] [47] . Additional issues are caused by the atmospheric phase screen (APS), which is amplified by high topographic gradient in high mountain regions. The APS, caused by the atmospheric pressure, temperature, and water vapor variations between two SAR acquisitions, may cause artifacts on the surface deformation results [48] . Moreover, SAR images acquired in winter periods are highly affected by snow cover, which causes temporal coherence loss [47, 49, 50] . All these factors have to be carefully taken into account for SAR data processing to thoughtfully interpret the ground deformation in high mountain regions. In several cases, however, the complexity of SAR data processing, as well as the large number of attempts to be performed before obtaining reliable results, hinders the achievement of surface deformation results. Currently, the application and the treatment of SAR data in high mountain regions requires specific background knowledge of the user and demand the application of complex processing algorithms and software. Moreover, the increasing amount of SAR data available from different satellites missions leads to rising needs of storage and computing resources. Recently, a relevant service was released within the ESA GRID-based operational environment [51], i.e., the unsupervised implementation of the Parallel-SBAS (P-SBAS) algorithm [52] . GPOD is coupled with high-performance and sizeable computing resources managed by GRID technologies, and it provides flexibility for building an application virtual environment with quick accessibility to data, computing resources, and results. The users access various services useful in the EO applications through a web interface, which guides the users from the creation of a new task until the results publication, passing through the data selection and the job monitoring. The P-SBAS algorithm has been implemented to exploit the GPOD resources, and to process the SAR data archived by ESA to perform the full SBAS-DInSAR processing chain in an unsupervised fashion from the
doi:10.3390/rs8100852 fatcat:um6ucy6hkze73k2h42rqv7jlzy