<p>Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, or landslide susceptibility, hazard, and risk evaluation. Their quality can depend on the data and the methods with which they were produced. The poor visibility of the territory to investigate offered by the point of observation from which landslides are interpreted is not frequently considered as a source of error in manually produced inventories. In this work, we present an approach to relate visibility and spatial distribution of the information collected in field work based inventories and inventories obtained through interpretation of satellite images.<br>We first used the r.survey tool and a digital elevation model to model and classify the visibility of the territory explored by field work based inventories. Furthermore, we assumed uniform visibility for inventories obtained through interpretation of satellite images.<br>Then, we measured the landslide density in the different visibility classes of the field based inventories. Last, we simulated visibility classes for the image based inventories using the road net of the area as virtual observation points, and we measured the relative landslide density.<br>We applied this approach to four inventories: one was produced by photo-interpretation, another one concerns to a regional multi-temporal database and the other 2 were done by direct field-mapping.<br>Our results show that 1) the density of the information is strongly related to the visibility in inventories obtained through field work, where landslides are abundant in high visibility classes but rarely reported in low visibility classes; and 2) the density of information is almost constant in inventories obtained by photo-interpretation of images, but they suffer from a marked under representation of small landslides in areas with potentially high visibility, e.g. close to roads. We maintain that the proposed procedure can be useful to evaluate the quality of landslide inventories and drive their correct use.</p>

Abstract. Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected by four landslide inventories prepared using different approaches in two study areas. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides, and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that 1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low visibility classes; and 2) the inventories obtained by photo-interpretation of images suffer from a marked under representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality of landslide inventories and then properly orient their use.

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<p>Forecasting rainfall-induced landslides, whilst challenging, is increasingly important due to the impact these hazards can have on society. The difficulty in forecasting arises from the inherent variability of geo-environmental factors and the scale at which underlying processes operate. The availability of data required to develop and validate thresholds for operational purposes is often limited. In regions where data (e.g. meteorological, or geotechnical) is sparse or incomprehensive, it is important to have a framework to systematically fuse the incomplete datasets to aid the development of a threshold model or to supplement an existing preliminary trigger threshold model.</p><p>For this study, a bespoke conceptual hydrological model called the ‘BGS water balance model’ is used in Nilgiris (Tamil Nadu state, India) to integrate the ground and meteorological information for informed decision making on the landscape saturation condition. This simple conceptual model with applicability over a large area provides an approximation of the degree of saturation value that can be used to map the potential antecedent wetness pathway leading to the initiation of landslides.</p><p>In this session, the BGS water balance model features along with the study area geological characteristics, landslide controls, input datasets and sensitivity analysis will be discussed. Further, we will show the results of the back-analysed landslides and explore the value of this approach in the context of landslide forecasting.</p>