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The assessment of temporal probability of landslides occurrence requires understanding the factors that control the stability of slopes. These factors are classified in predisposing (geomorphology, geology, etc.) and triggering factors (precipitation or earthquakes). Some of these factors remain constant over time (like geology), some are in a constant change (like land-use and land-cover) and some are rapidly changing their state (like rainfall intensity). If geology can be mapped in the field and rainfall can be measured with rain gauges, the changes in land-cover related to phenological phases are more difficult to measure and map. For the latter, the use of satellite images has been proven the most reliable solution. The present study uses Landsat archive for modelling the changes of rainfall interception over time as the result of phenological changes in land-cover. Over 300 Landsat scenes from 1973 until 2011 were used to calculate leaf area index (LAI). LAI has an important contribution in the rainfall interception model with direct impact on the landslides hydrological model. The spatial and temporal probability of landslides occurrence is calculated using Bayesian Dynamic Network (BDN). The factors for BDN are mapped or derived with deterministic analyses. The model is running in monthly time steps and the results are validated with recorded landslides, triggered in different time periods. For each occurrence of a landslide the local and temporal conditions (including the modelled and the observed values) are stored and statistically analysed.

Landsat Time Series Analysis for Modelling Temporal Probability for Landslide Occurrences in Curvature Subcarpathians, Romania