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An updated and complete landslide inventory is the starting point for an appropriate hazard assessment. This paper presents an improvement for landslide mapping by integrating data from two well-consolidated techniques: Differential Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the normalised channel steepness index (ksn). The southwestern sector of the Sierra Nevada mountain range (Southern Spain) was selected as the case study. We first propose the double normalised steepness (ksnn) index, derived from the ksn index, to remove the active tectonics signal. The obtained ksnn anomalies (or knickzones) along rivers and the unstable ground areas from the DInSAR analysis rapidly highlighted the slopes of interest. Thus, we provided a new inventory of 28 landslides that implies an increase in the area affected by landslides compared with the previous mapping: 33.5% in the present study vs. 14.5% in the Spanish Land Movements Database. The two main typologies of identified landslides are Deep-Seated Gravitational Slope Deformations (DGSDs) and rockslides, with the prevalence of large DGSDs in Sierra Nevada being first revealed in this work. We also demonstrate that the combination of DInSAR and Landscape Analysis could overcome the limitations of each method for landslide detection. They also supported us in dealing with difficulties in recognising this type of landslides due to their poorly defined boundaries, a homogeneous lithology and the imprint of glacial and periglacial processes. Finally, a preliminary hazard perspective of these landslides was outlined. [ABSTRACT FROM AUTHOR]

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures. [ABSTRACT FROM AUTHOR]

Slope failures occur in open-pit mining areas worldwide, producing considerable damage in addition to economic loss. Identifying the triggering factors and detecting unstable slopes and precursory displacements —which can be achieved by exploiting remote sensing data— are critical for reducing their impact. Here we present a methodology that combines digital photogrammetry, satellite radar interferometry, and geo-mechanical modeling, to perform remote analyses of slope instabilities in open-pit mining areas. We illustrate this approach through the back analysis of a massive landslide that occurred in an active open-pit mine in southwest Spain in January 2019. Based on pre- and post-event high-resolution digital elevation models derived from digital photogrammetry, we estimate an entire sliding mass volume of around 14 million m3. Radar interferometry reveals that during the year preceding the landslide, the line of sight accumulated displacement in the slope reached − 5.7 and 4.6 cm in ascending and descending geometry, respectively, showing two acceleration events clearly correlated with rainfall in descending geometry. By means of 3D and 2D stability analyses we located the slope instability, and remote sensing monitoring led us to identify the likely triggers of failure. Las Cruces event can be attributed to delayed and progressive failure mechanisms triggered by two factors: (i) the loss of historical suction due to a pore-water pressure increase driven by rainfall and (ii) the strain-softening behavior of the sliding material. Finally, we discuss the potential of this methodological approach either to remotely perform post-event analyses of mining-related landslides and evaluate potential triggering factors or to remotely identify critical slopes in mining areas and provide pre-alert warning. [ABSTRACT FROM AUTHOR]