Landslides constitute one of the most important natural hazards in Italy as they are widespread and result in considerable damage and fatalities every year. The Italian Landslide Inventory (IFFI) Project was launched in 1999 with the aim of identifying and mapping landslides over the entire Italian territory. The inventory currently holds over 480,000 landslides and has been available by means of Web services since 2005. The aim of this study is to define quality indices for evaluation of the homogeneity and completeness of the IFFI database. In order to estimate the completeness of the landslide attribute information, a heuristic approach has been used to assign weighting values to significant parameters selected from the landslide data sheet. The completeness and homogeneity of the landslide mapping has been evaluated by means of three different methods: an area-frequency distribution analysis; the proximity of the landslides surveyed to urban areas; variation of the landslide index within the same lithology. The quality indices have allowed identification of areas with a high level of completeness and critical areas in which the data collected have been underestimated or are not very accurate. The quality assessment of collected and stored data is essential in order to use the IFFI database for definition and implementation of landslide susceptibility models and for land use planning and management.
Landslides are one of the most widespread geohazards in Europe, producing significant social and economic impacts. Rapid population growth in urban areas throughout many countries in Europe and extreme climatic scenarios can considerably increase landslide risk in the near future. Variability exists between European countries in both the statutory treatment of landslide risk and the use of official assessment guidelines. This suggests that a European Landslides Directive that provides a common legal framework for dealing with landslides is necessary. With this long-term goal in mind, this work analyzes the landslide databases from the Geological Surveys of Europe focusing on their interoperability and completeness. The same landslide classification could be used for the 849,543 landslide records from the Geological Surveys, from which 36% are slides, 10% are falls, 20% are flows, 11% are complex slides, and 24% either remain unclassified or correspond to another typology. Most of them are mapped with the same symbol at a scale of 1:25,000 or greater, providing the necessary information to elaborate European-scale susceptibility maps for each landslide type. A landslide density map was produced for the available records from the Geological Surveys (LANDEN map) showing, for the first time, 210,544km 2 landslide-prone areas and 23,681 administrative areas where the Geological Surveys from Europe have recorded landslides. The comparison of this map with the European landslide susceptibility map (ELSUS 1000 v1) is successful for most of the territory (69.7%) showing certain variability between countries. This comparison also permitted the identification of 0.98Mkm 2 (28.9%) of landslide-susceptible areas without records from the Geological Surveys, which have been used to evaluate the landslide database completeness. The estimated completeness of the landslide databases (LDBs) from the Geological Surveys is 17%, varying between 1 and 55%. This variability is due to the different landslide strategies adopted by each country. In some of them, landslide mapping is systematic; others only record damaging landslides, whereas in others, landslide maps are only available for certain regions or local areas. Moreover, in most of the countries, LDBs from the Geological Surveys co-exist with others owned by a variety of public institutions producing LDBs at variable scales and formats. Hence, a greater coordination effort should be made by all the institutions working in landslide mapping to increase data integration and harmonization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.