Special Issue: Spatial landslide assessments and beyond: new challenges in mapping, modelling, validation, and scenario building

Spatial landslide assessments are of crucial importance in many parts of the world as their implementation can contribute to sustainable community development. Research on various topics such as landslide inventories, types of spatial models, validation techniques and scenario building incorporating land use and climate change, has been carried out for many decades. Since the final resulting landslide susceptibility maps, landslide intensity maps, dynamic susceptibility maps, or in some cases even hazard maps (considering the magnitude and frequency of potential landslide events), are of great interest to affected communities, their accuracy is most important to allow a reliable usage in any official application, e.g., within land use or intervention planning or as input for early warning.

In the last years, many efforts have been made to advance, strengthen, and move beyond the traditional concepts and models at the various research stages (e.g., inventory and data storage, modelling, accuracy assessment, map display). New challenges in these research domains include compound and cascading multi-hazards, extreme events, new and dynamic conditions due to global change, including climate change, as well as human modification of landscapes and how to introduce and reflect these correctly within spatial landslide assessments aiming towards hazard assessments.

Building from the “Spatial landslide assessments and beyond: new challenges in mapping, modelling, validation and scenario building” session of the 6th World Landslide Forum, but open to contributions from the wider landslide community, this Special Issue aims at bringing together the currently undertaken research concepts and methods, as well as the variety of solutions and applications. Contributions to novel technological and methodological concepts are most welcome, as well as examples of spatial landslide assessments. The core idea of this Special Issue is, to review the past and current methods, and to learn from each other to improve future approaches.