1887

Abstract

Summary

This research presents a GIS-based approach to construct a comprehensive landslide susceptibility map for the Romagna region in Italy, an area characterized by high seismic activity and a significant number of historical landslides. The study employs quantitative methods to evaluate the factors influencing landslide occurrence, including elevation, slope, slope direction, rainfall, proximity to water bodies and highways, soil type, Normalized Difference Vegetation Index (NDVI), and Topographic Wetness Index (TWI).

By analyzing landslides that occurred over the past two decades, the frequency ratio method was employed to establish relationships between landslide-prone areas and causal factors. Weighting factors for each factor class were mathematically determined based on the distribution of landslide points. Additionally, specific criteria were considered to prioritize certain factors.

The resultant landslide susceptibility map offers valuable insights for geologists and decision-makers in Emilia-Romagna to better assess and forecast landslides, contributing to the preservation of life and property in this seismic-prone region.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023500009
2023-09-18
2025-11-07
Loading full text...

Full text loading...

/deliver/fulltext/2214-4609/2023/landslide2023/Landslide23-09.html?itemId=/content/papers/10.3997/2214-4609.2023500009&mimeType=html&fmt=ahah

References

  1. Bhat, I. M., & Nusrath, A.Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) Analysis of Chamarajnagara District Karnataka India Using Landsatdata. (2023)
    [Google Scholar]
  2. Kose, D. D., & Turk, T. (2019). GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods.Physical Geography, 40(5), 481–501.
    [Google Scholar]
  3. Mattivi, P., Franci, F., Lambertini, A., & Bitelli, G. (2019). TWI computation: a comparison of different open-source GISs.Open Geospatial Data, Software and Standards, 4(1), 1–12.
    [Google Scholar]
  4. Shano, L., Raghuvanshi, T.K. & Meten, M.Landslide susceptibility evaluation and hazard zonation techniques – a review.Geoenviron Disasters7, 18 (2020).
    [Google Scholar]
  5. Squarzoni, G., Bayer, B., Franceschini, S., & Simoni, A. (2020). Pre-and post-failure dynamics of landslides in the Northern Apennines revealed by space-borne synthetic aperture radar interferometry (InSAR).Geomorphology, 369, 107353.
    [Google Scholar]
  6. Zatserkovny, V. I., Tishaev, I. V., & Shishenko, O. I. (2016). Application of remote sensing materials in forest fire monitoring and vegetation quantitative assessment tasks.Science-based technologies, 29(1), 42–47.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023500009
Loading
/content/papers/10.3997/2214-4609.2023500009
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error