Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas

Téhrrie König (), Hermann J. H. Kux () and Rodolfo M. Mendes ()
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Téhrrie König: National Institute for Space Research (INPE)
Hermann J. H. Kux: National Institute for Space Research (INPE)
Rodolfo M. Mendes: National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN/MCTIC)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 97, issue 3, No 9, 1127-1149

Abstract: Abstract Natural hazards, occurring all over the world, may become a disaster when humans and nature interact. In Brazil, landslides triggered by heavy rainfall are the most common phenomenon that affects the population. Due to the economic and social losses and deaths, the identification and monitoring of risk areas are extremely important. Therefore, this study aims to identify the landslide-susceptible areas in Vila Albertina and Britador neighborhood, located in Campos do Jordão city in São Paulo state, Brazil. Using the Shalstab mathematical model, which analyzes the slope stability, and satellite images from WorldView-2 sensor with data mining techniques, it was identified the most susceptible areas for this phenomenon and the main characteristics of human occupation that might induce landslides. To achieve this goal, three scenarios were simulated for each neighborhood, changing the values of the geotechnical parameters, used as input on Shalstab. The results of susceptibility areas were consistent with the reality observed in these neighborhoods and the landslide scars corroborate with the assumption that anthropic changes induce landslides. The satellite image allowed the identification of different types of human interaction and its changes in steep slope areas.

Keywords: Landslide; Susceptibility; Shalstab; WorldView-2; Data mining (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-019-03691-4

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