Landslides (2017) 14:1975–1991 DOI 10.1007/s10346-017-0851-6 Received: 19 February 2017 Accepted: 12 June 2017 Published online: 11 July 2017 © Springer-Verlag Berlin Heidelberg 2017 J. D. Jiménez-Perálvarez I R. El Hamdouni I J. A. Palenzuela I C. Irigaray I J. Chacón Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain) Abstract Landslide hazard in a region limited to data from a regional scale about triggering factors is assessed via cross tabulation between determining factors and landslides with recent activity. Firstly, landslide susceptibility was evaluated and validated through a bivariate statistical method between the previously identified stability conditioning factors and the mapped landslides. In this way, the most susceptible areas for assessing landslide hazards were selected. The main problem to solve in this type of research is the landslide activity. For this purpose, several techniques were applied: news reports, differ- ential interferometric synthetic aperture radar, digital photo- grammetry, light detection and ranging, photointerpretation, and dendrochronology. Both the strong and weak points of these techniques are also mentioned. The landslide return peri- od was computed via the association between landslide activity and triggering factors, in this case annual rainfall. Finally, land- slide hazard was mapped solely based on landslides with recent activity and their computed return period. The relationship between landslide occurrence and triggering factors shows that, according to both the considered assumptions and the observa- tions made, deep-seated landslides are triggered or reactivated together with superficial landslides once every 18 years, while superficial landslides as flows or falls occur once every 5 years. The results show that there is generally a low landslide hazard in the study zone, especially when compared to landslide sus- ceptibility. This means that landslides are mainly dormant from a natural evolution point of view, but could be reactivated as a result of geomorphological, climate, or human changes. In any case, the landslide hazard is successfully assessed, with a pre- diction of a 6% annual probability of a high hazard in 5% of the area, intersecting with the main infrastructures of the region; thus, control strategies are justified in order to avoid damage in extraordinary rainfall periods. Keywords Landslide hazard . Landslide activity . Statistical analysis . Return period . Betic Cordillera Introduction The impact of landslides in the social economical context of any country is high because of the consequences in term of losses of goods and services. The level of damages may be reduced when slope movement is identified in space and time, as landslides are natural phenomena that are more predictable and control- lable than many other destructive processes (Brabb 1991). One of the main types of strategy for reducing losses is the avail- ability of predictive maps with inventories of landslides and assessment of derived hazard, risk, etc. (Dai et al. 2002; Guzzetti et al. 2005, 2012; Chacón et al. 2006). The development of predictive maps has been widely followed in specialized literature (Carrara et al. 1991; Chung and Frabbri 1999; Irigaray et al. 2007; Van Den Eeckhaut and Hervás 2012; Vranken et al. 2015), and the need and limitations of normalized procedures for the generation of landslide hazard maps have been pointed out (Guzzetti et al. 2005, 2012) in order to achieve better land use planning and controlling policies. So depending on the needs, objectives, and scale of mapping, different guidelines, scopes, and methodologies have been recommended (Fell et al. 2008; SafeLand 2011). Landslide-hazard mapping should be based both on spatial- temporal data and on an assessment of destructive capacity, i.e., magnitude. Unfortunately, the large variety of landslide types means that the overall acceptance of a unique and definitive method of landslide-hazard assessment becomes difficult, there being no direct and universally accepted methods for defining landslide magnitude (Guzzetti et al. 2005, 2012). In these cases, the area covered by the landslide may be a reasonable approach (Guzzetti et al. 2005; SafeLand 2011). Therefore, mapping land- slide hazard frequently focused the determination of probability or spatial-temporal landslide occurrence frequency (Van Westen et al. 2006; Guzzetti et al. 2012) based on landslide area (as a proxy for magnitude) and landslide activity (WP/WLI 1993). The frequency of occurrence and evolution of landslides has nor- mally been established from inventories of historical events (Carrara et al. 2003; Guzzetti et al. 2005). Nevertheless, this procedure is of limited usefulness in difficult to access moun- tainous areas with social-economic value as natural heritage sites (Margottini and Vilímek 2014) due to the scarce availability of information on triggering factors and event records. It is therefore necessary to amplify this information through new techniques and procedures. The use of techniques based on direct data or historical aerial photographs in the assessment of return periods have been ana- lyzed in different papers, summarized by Carrara et al. (2003). In the last two decades, improvement of procedures for automatic data capture have been possible using precise techniques such as Global Positioning System (GPS), remote sensing, light detection and ranging (LiDAR), and satellite images, giving rise to remark- able advances in the estimation of terrain changes (Bonnard and Corominas 2005). The use of differential interferometric synthetic aperture radar (DInSAR) offers a high precision and permits the coverage of large areas (Casagli et al. 2010), but the historical record of SAR images is limited and the quality of results depends on optimal conditions concerning slope orientation (Fernández et al. 2009). Digital aerial photogrammetry and systems based on LiDAR permit the comparison of temporal series of images of given regions for the assessment of surface terrain changes (Fernández et al. 2011; Palenzuela et al. 2016a), although this requires a complex operation and the manipulation of point clouds is difficult. One of the main limitations of techniques based on remote sensing is that, because they are very recent, the return Landslides 14 & (2017) 1975 Original Paper