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<p>Landslides are widespread geomorphic processes in mountainous regions, whose occurrence varies over time in response to both conditioning and triggering factors. Nevertheless, the majority of spatial prediction (susceptibility) models remain static and do not explicitly account for the spatiotemporal evolution of the factors governing slope stability. To overcome this limitation, we developed a multi-temporal landslide inventory based on very high-resolution (50&#xa0;cm) orthophotos and used it to produce dynamic landslide susceptibility maps that capture temporal changes in terrain instability. The study focuses on the Belluno area (northeastern Italian Alps) and applies a spatiotemporal Generalized Additive Model (GAM) to analyze non-linear relationships between landslide occurrence and key environmental variables, including slope, aspect, curvature, elevation, and cumulative rainfall calculated over multiple temporal windows. Landslide occurrences derived from historical records and image interpretation were used as the dependent variable, while predictor variables were obtained from DEM-based terrain analysis and rainfall data. The GAM framework allows flexible modeling of non-linear effects and temporal dependencies, particularly those associated with rainfall-triggered landslides. Results indicate that slope steepness and short- to mid-term cumulative rainfall exert the strongest control on landslide susceptibility in the study area. Model performance evaluation using ROC–AUC metrics shows that the spatial–temporal GAM outperforms conventional cross-sectional susceptibility models. The proposed approach highlights the importance of incorporating temporal dynamics into landslide susceptibility assessment and provides a refined framework for spatially explicit evolution of evolving slope instability.</p>

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From static to dynamic landslide susceptibility: a multi-temporal inventory-based approach in the Belluno Alps (NE Italy)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Rajeshwari Bhookya,
  • Sansar Raj Meena,
  • Aadityan Sridharan,
  • Georg Gutjahr,
  • Gayathri G. Reshma,
  • Saurabh Singh,
  • Ascanio Rosi,
  • Filippo Catani,
  • Mario Floris

摘要

Landslides are widespread geomorphic processes in mountainous regions, whose occurrence varies over time in response to both conditioning and triggering factors. Nevertheless, the majority of spatial prediction (susceptibility) models remain static and do not explicitly account for the spatiotemporal evolution of the factors governing slope stability. To overcome this limitation, we developed a multi-temporal landslide inventory based on very high-resolution (50 cm) orthophotos and used it to produce dynamic landslide susceptibility maps that capture temporal changes in terrain instability. The study focuses on the Belluno area (northeastern Italian Alps) and applies a spatiotemporal Generalized Additive Model (GAM) to analyze non-linear relationships between landslide occurrence and key environmental variables, including slope, aspect, curvature, elevation, and cumulative rainfall calculated over multiple temporal windows. Landslide occurrences derived from historical records and image interpretation were used as the dependent variable, while predictor variables were obtained from DEM-based terrain analysis and rainfall data. The GAM framework allows flexible modeling of non-linear effects and temporal dependencies, particularly those associated with rainfall-triggered landslides. Results indicate that slope steepness and short- to mid-term cumulative rainfall exert the strongest control on landslide susceptibility in the study area. Model performance evaluation using ROC–AUC metrics shows that the spatial–temporal GAM outperforms conventional cross-sectional susceptibility models. The proposed approach highlights the importance of incorporating temporal dynamics into landslide susceptibility assessment and provides a refined framework for spatially explicit evolution of evolving slope instability.