Environmental critical thresholds based on statistical analysis for modelling landslide susceptibility in Continental Basaltic Provinces

The study aims to estimate the environmental critical thresholds using statistical approaches to understand the landslide conditioning factors that can trigger landslides in the Continental Basaltic Provinces a landslide-prone area, using as reference the landslides that occurred in an extreme rainf...

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Veröffentlicht in:ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2024-11, Vol.X-3-2024, p.463-470
Hauptverfasser: Renk, Jennifer Fortes Cavalcante, Mendes, Tatiana Sussel Gonçalves, Simões, Silvio Jorge Coelho, de Andrade, Marcio Roberto Magalhães, Pampuch Bortolozo, Luana Albertani, Junqueira, Adriano Martins, Silva, Melina Almeida
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Sprache:eng
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Zusammenfassung:The study aims to estimate the environmental critical thresholds using statistical approaches to understand the landslide conditioning factors that can trigger landslides in the Continental Basaltic Provinces a landslide-prone area, using as reference the landslides that occurred in an extreme rainfall event. The study area is a region that was the scene of an extreme hydrological event in January 2017, with an accumulated volume of rain of 163.9 mm in 8 hours, causing a widespread event of shallow planar landslides with more than 400 scars detected. Hydrological, anthropic, geological, geomorphological, and topographical features of this region were analyzed considering landslides and non-landslides samples set, and their influence in the event was carried out using the Frequency Ratio method, followed by Pearson's Linear Correlation Coefficient and Linear Regression. The results showed that this process helped us to understand environmental critical thresholds based on classes of conditioning factors that have a greater influence on rainfall-triggered landslide occurrences and, consequently, higher predictive capacity in the landslide susceptibility models with the same geoenvironmental parameters which is a valuable insight for risk management.
ISSN:2194-9050
2194-9042
2194-9050
DOI:10.5194/isprs-annals-X-3-2024-463-2024