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The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment. [ABSTRACT FROM AUTHOR]

The integration of data from different sources can be very helpful in understanding the mechanism, the geometry, the kinematic, and the area affected by complex instabilities, especially when the available geotechnical information is limited. In this work, the suitability of different techniques for the study of a deep-seated landslide affecting a bridge in Alcoy (Spain) is evaluated. This infrastructure presents such severe damage that has rendered the bridge unusable, which prevents normal access to an important industrial area. Differential SAR Interferometry (DInSAR) and terrestrial Light Detection and Ranging (LiDAR) remote sensing techniques have been combined with ground displacement monitoring techniques, such as inclinometers and conventional geological and geotechnical investigation, electrical-seismic tomography, damage, and topographic surveys, to determine the boundaries, mechanism, and kinematics of the landslide. The successful case study that is illustrated in this work highlights the potential and the need for integrating multi-source data for the optimal management of complex landslides and the effective design of remedial measurements. [ABSTRACT FROM AUTHOR]

The complex landslide of Agnone, located in Molise Region (southern Italy), is a large deep-seated roto-translational slide, involving terrigenous flysch sequences and evolving in the lower portion, in an earth flow. First evidences of landslide activities date back to early 1900s and keep on moving in several time spans up to nowadays. This work illustrates the contribution of multiple optical and radar remote sensing data to landslide investigation approximately across a century as well as to mapping and monitoring ground displacements at the catchment scale.In particular, we firstly revised information from scientific literature and technical reports, compiling monitoring data acquired through inclinometers and geo-morphological surveys in preceding studies to characterize the geological setting of the area. Additionally, we used GPS data acquired from 2010 to 2015 to monitor ground surface motions on some benchmarks of the area. Then, we exploited remotely-sensed data in order to in-depth investigate the spatial and temporal evolution of the landslide-affected catchment.The use of historical air-photos enabled the generation of 3D models by means of the Structure for Motion (SfM) technique to detect, map and monitor unique geomorphological landscapes across several decades. Historical and recent sets of optical photographs from 1945 to 2003 were used in the study area to perform 3D reconstructions and analyze the geomorphological enlargement of the Agnone landslide over 60 years. Radar satellite images acquired by ERS1/2 and ENVISAT constellations at medium spatial resolution, in the spanning time 1992-2010, and by COSMO-SkyMed at high resolution, covering the period 2012-2015, were processed through Persistent Scatterer Interferometry (PSI) techniques. The derived PSI data provided yearly velocities and time series of ground deformation with millimetric accuracy and were exploited for back-monitoring slow surface displacements and for better tracing the boundaries of the mass movement. All data were discussed and used to increase the knowledge about the ground motions affecting the whole Agnone landslide and the surrounding territory. Remotely-sensed data were also compared through in situ field checks to evaluate landslide-induced damage on structures and infrastructures within and close to the landslide body. Outcomes of these analyses demonstrated the operative usefulness of the combination of multi-temporal and multi-technique conventional and remote sensing data for the assessment of spatial and temporal evolution of landslides at basin scale. Moreover, the collected data allowed to plan countermeasures and mitigation actions on the Agnone area according to the distribution and intensity of the detected landslide hazard. [ABSTRACT FROM AUTHOR]