Landslide susceptibility assessment in mountainous and tropical scarce-data regions using remote sensing data : a case study in the Colombian Andes
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Landslides triggered by rainfall are one of the most frequent causes for natural disasters in the tropical and mountainous countries, such as Colombia. However landslide susceptibility assessments are often limited due to the scarcity of reliable observations and available information, particularly in remote high-mountain regions. Although Colombia is a tropical and mountainous terrains dominated by landslide prone region, it has little availability of data for landslide susceptibility assessment. This study presents the application of a logistic regression model to assess landslide susceptibility in the La Liboariana catchment. It is a basin on a tropical inaccessible terrain in northern Colombian Andes, where on May 18th, 2015, more than 40 landslides and an associated flash flood and debris flow afterwards killed 104 inhabitants. The applied approach is based on free access remote sensing tools to complete and obtain the missing landslide causative factors. To select key factors related to landslide occurrence the prediction and successes performance of the susceptibility maps for each combination of landslide causative factors was estimated using the Receiver Operating Characteristics (ROC). The results show that only three factors gave the best predicting accuracy. All the factors were obtained by free remote sensing tools, indicating they can provide enough information to achieve a successful approach to landslide susceptibility assessment in complex terrains as the study area. This suggests that the proposed approach could be implemented in several tropical regions with similar characteristics based only in free access information.
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