Review of Satellite Interferometry for Landslide Detection in Italy

Abstract

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

A systematic exploration of satellite radar coherence methods for rapid landslide detection

“…Therefore small, low-coherence objects will be identified as landslides. This includes rivers, as demonstrated by Spaans and Hooper (2016). As the low coherence caused by a river is not temporary, this should have less of an effect on the other coherence methods tested here.…”

“…When a sibling-based method is used to estimate coherence, the coherence of a pixel becomes dependent on "siblings" that are not immediately adjacent to it but that are expected to behave similarly. In the method of Spaans and Hooper (2016) used here, an ensemble of siblings is selected for every pixel that have similar amplitude behaviour in a time series of pre-event imagery. For a landslide pixel, this means that its coherence is calculated from a more dispersed ensemble of pixels than with a traditional boxcar coherence estimate, and so proportionally less of the ensemble will also lie within the landslide.…”

“…In a "sibling" method, an assessment is carried out for every pixel to identify pixels that are statistically similar to it. For example, the sibling method of Spaans and Hooper (2016) identifies pixels that have similar amplitude behaviour through time.…”

“…The boxcar coherence estimate used in all methods was calculated using a 3 × 3 px moving window ( Table 1). The sibling-based coherence estimate used for the Bx-S method was calculated using the RapidSAR algorithm of Spaans and Hooper (2016). Siblings were calculated based on all preevent images shown in Fig.…”

Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

“…The highest resolution products (<10 m single-sided pixel dimension) are often used for Earth's surface deformation monitoring. Landslides, glaciers, earthquakes, and dune-field displacement tracking are examples of applications of satellite products that, covering small to large areas, have the potential to provide an exhaustive overview of the process [1][2][3][4].…”