Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

Abstract

Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas.

Long and Short-Term Coastal Changes Assessment Using Earth Observation Data and GIS Analysis: The Case of Sperchios River Delta

“…Local tectonic movements and river sedimentation processes play the most important role in the coastal area development, mostly due to periodical flood flows and less to erosion caused by sea currents or sea-level changes [42,49,80,81]. The basin has undergone several serious earthquakes and flood events throughout the last couple of millenniums [43,48,50,82]. Flood events occur frequently due to the geomorphological characteristics of the basin (very mountainous and with steep slopes, especially at its southern part) and the coastal part (intense meandering and very gentle slopes) [43,48].…”

Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

“…Field observation is an effective procedure for landslide hazard assessment to collect the primary field data (distance to road, distance to river, and general evaluation of the location) for the study [39]. The mapping and landslide susceptibility analysis of landslideprone areas is the first stage in the field observation [40]. As part of the research, a field investigation was conducted in October 2020.…”

“…X 1 through X 7 = predictor or p distinct independent variables. ε is the aspect of the random error that reflects the difference between the predicted and fitted linear relationship; b1 through b7 are the estimated regression coefficients, which are estimates of the unknown population parameters explaining the correlation between the output response and dependent variable [40].…”

A GIS-Based Risk Assessment Approach for Evaluating Shelters in Crisis Areas: Case of Idleb/Syria

“…For example, while some studies focused on climate change risk assessment (Hawchar et al, 2020), ecological risk assessment (Eccles et al, 2019) or food safety risk assessment (Beni et al, 2012); some papers handled the related topic for natural disasters. In this context, some studies concentrated on multiple disasters (Chen et al, 2017;Chen et al, 2018); however, there are risk assessment researches on earthquake (Yariyan et al, 2020), fire (Benguerai et al, 2019), landslide (Huabin et al, 2005;Aye et al, 2016;Psomiadis et al, 2020), tropical cyclone (Sahoo and Bhaskaran, 2018), tsunami (Çankaya et al, 2016) and volcanic hazard (Alcorn et al, 2013). Still, it is observed that majority of existing studies concentrated on utilizing GIS for flood risk assessment (Abdalla et al, 2006;Fedeski and Gwilliam, 2007;Wang et al, 2011;Uddin et al, 2013;Abdalla et al, 2014;Aye et al, 2016;Al-Awadhi et al, 2018;Isahak et al, 2018;Rincón et al, 2018;Lyu et al, 2018;Cai et al, 2019;Hadipour et al, 2020;Zhang et al, 2020).…”