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In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations, and several approaches to evaluate the models' performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modeling and zonation is still missing. The literature search revealed that several software program and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically based landslide susceptibility modeling which integrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analysis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications. [ABSTRACT FROM AUTHOR]

Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality and completeness 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 in different landslide inventories prepared using different approaches in a study area. 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 areas; 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 and completeness of landslide inventories and then properly orient their use. [ABSTRACT FROM AUTHOR]

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the past 50 years, a large variety of statistically-based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations and several approaches to evaluate the models performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modelling and zonation is still missing. The literature search revealed that several articles describe tools that apply physically based models for susceptibility zonation, but only few use statistically-based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models, and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically-based landslide susceptibility modelling which integrate, extend and complete LAND-SE. LAND-SUITE is able to: i) facilitate input data preparation; ii) perform preliminary and exploratory analysis of the available data; iii) test different combinations of variables and select the optimal thematic/explanatory set; iv) test different model types and their combinations; and v) evaluate the models performance and uncertainty. LAND-SUITE provides a tool that can assist the user to reduce some common source of errors coming from the data preparatory phase, and to perform more easily, more flexible and more informed statistically-based landslide susceptibility applications. [ABSTRACT FROM AUTHOR]

Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged.We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models. [ABSTRACT FROM AUTHOR]