Landslide inventory and main geomorphological features affecting slope stability in the Picentino river basin (Campania, southern Italy)

Abstract

The landslide inventory of the Picentino basin was realized at 1:25,000, with focus on main geomorphological features affecting slope stability. It is based on different sets of air-photos (scales 1:33,000-1:18,000, dated up to 1998), and on field surveys. Among shallow landslides, channelized debris flows strongly prevail, originated as debris slides on moderately steep slopes. Further sectors are affected by deeper slope movements of greater extent. Items related to tectonics, erosion processes, and anthropized sectors are also mapped. Relevant predisposing factors to slope instability are shown in 1:100,000 thematic maps. In addition, shallow landslides of the 1998 inventory were compared to those derived from two eventbased inventories (

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

“…As the study region is small and we only focus on one area, the regional background condition, the voltage U, is defined as a fixed value of 1 V. The combined action of trigger factors and internal factors to prevent disaster occurrence is R. In this paper, R is assumed to be 1 and the resistance of the trigger and internal parameter systems are each thought to be 0.5. The importance of all related factors for the disaster events was determined based on previous studies of landslides and debris flows [46][47][48][49][50][51][52][53]. Where the importance was greater, the resistance value was smaller.…”

A spatiotemporal object-oriented data model for landslides (LOOM)

“…Current approaches in temporal characterization of landslide inventories can be traced back to three main strategies: (i) static inventory, as a single snapshot representing all the recognized landslides at a specific time point in a single dataset (Guerricchio et al 2000;Carrara et al 2003;Van Westen et al 2003;Conoscenti et al 2008;Yalcin 2008;Brunetti et al 2014;Schlögel et al 2018;Marinos et al 2019); (ii) multi-temporal snapshot-based inventory, as a collection of at least two snapshots contained in different datasets, where each snapshot has its own time reference (Corbi et al 1999;Guida et al 2006;Coico et al 2013;Lupiano et al 2019); (iii) multi-temporal inventory time frame-or event-based, as different datasets containing landslides occurred in a specific time frame (from dd/mm/yyyy to dd/mm/yyyy) or triggered by a specific event (in such a case, usually the exact date of occurrence is known). In some cases, time frames characterization is qualitative, based on geomorphological properties of landslides (relict, very old, old, recent) (Cascini et al 2008;Murillo-García et al 2015;Martino et al 2017;Samia et al 2017a;Roback et al 2018;Lupiano et al 2019).…”

Landslide Scars Detection using Remote Sensing and Pattern Recognition Techniques: Comparison Among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers

“…For monitoring, landslide inventory maps are used, identifying mass movement scars, providing information about past events, such as location, types and patterns, and assisting to build landslide susceptibility models (RAMOS-BERNAL et al, 2018). Thus, landslide inventory maps are crucial to support urban planning and disaster risk reduction (LUPIANO et al, 2019).…”