Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

first_pagesettingsOrder Article Reprints Open AccessArticle Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) by María Camila Herrera-Coy 1ORCID,Laura Paola Calderón 2,Iván Leonardo Herrera-Pérez 1,3,Paul Esteban Bravo-López 1,4ORCID,Christian Conoscenti 2ORCID,Jorge Delgado 1ORCID,Mario Sánchez-Gómez 5,6ORCID andTomás Fernández 1,6,*ORCID 1 Department of Cartographic, Geodetic and Photogrammetric Engineering, University of Jaén, 23071 Jaén, Spain 2 Department of Earth and Marine Sciences (DiSTeM), University of Palermo, 90123 Palermo, Italy 3 Department of Geographic and Environmental Engineering, University of Applied and Environmental Sciences (U.D.C.A.), Bogotá 111166, Colombia 4 Institute for Studies of Sectional Regime of Ecuador (IERSE), University of Azuay, Cuenca 010107, Ecuador 5 Department of Geology, University of Jaén, 23071 Jaén, Spain 6 Natural Hazards Lab of the Centre for Advanced Studies in Earth Sciences, Energy and Environment (CEACTEMA), University of Jaén, 23071 Jaén, Spain * Author to whom correspondence should be addressed. Remote Sens. 2023, 15(15), 3870; https://doi.org/10.3390/rs15153870 Received: 11 June 2023 / Revised: 24 July 2023 / Accepted: 31 July 2023 / Published: 4 August 2023 (This article belongs to the Special Issue Remote Sensing Techniques for Landslides Studies and Their Hazards Assessment) Download Browse Figures Versions Notes Abstract Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility.Incluye referencias bibliográfica

Predicting depositional areas of landslide susceptibility comparing four datasets extracted from landslide area: a case of study after rainfall-induced landslides by Ida Hurricane in 2009 on Ilopango Lake, El Salvador.

Hurricane Ida and low-pressure system 96E crossed Central American countries in 2009. However, in El Salvador, the torrential rainfalls caused many flooding and landslides. As a result, over 200 causalities and the destruction of several villages, and bridges occurred along the mountain slopes. The remote analysis allowed us to prepare an inventory of landslides that occurred after the Hurricane in a basin located in the northern part of Ilopango Caldera. Five groups of data sets were created using selected pixels of each landslide area in order to evaluate the capacity to predict the lowest and the entire landslide area. Multivariate Adaptive Regression Splines (MARS) were employed to model the spatial distribution of the following five data sets: i) the highest cell (data set MAX), ii) the highest 10% of cells (data set SUP), iii) the lowest cell (data set MIN), iv) the lowest 10% of cells (data set INF), and v) the entire landslide area (data set BODY). To calibrate and validate the models were selected randomly in groups of 75% and 25% of the mapped landslides, respectively. In order to evaluate the robustness of the results, ten calibration and validation samples were extracted for each instability data set. The analysis revealed that the most important predictors were Slope Length Factor, Normalized Difference Vegetation Index (NDVI), Terrain Ruggedness Index, Lithology (pyroclastic rocks), Topographic Position Index, and Aspects NE and NW. The receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), calculated for each of the five instability data sets, indicated that calibrating the models with the lowest landslide pixels (MIN data sets) allows to obtain the most accurate prediction of the validation the depositional area and the entire landslide bodies (BODY and INF data set), achieving AUC values ranging between 0.88 and 0.84

Stochastic assessment of landslide susceptibility by using five different instability datasets: a case study from the southern sector of the “Via al Llano” highway (Colombia)

In this study, the ability of stochastic models to predict landslide susceptibility in the southern sector of the “Via al Llano” highway (Colombia) was assessed. To this aim, an inventory of landslides occurred in the area was prepared by analyzing images available in Google Earth. Multivariate Adaptive Regression Splines (MARS) was employed to model the spatial distribution of the following five data sets of unstable cells selected within each landslide: i) the highest cell (data set MAX), ii) the highest 10% of cells (data set SUP), iii) the lowest cell (data set MIN), iv) the lowest 10% of cells (data set INF), and v) the entire landslide area (data set BODY). The goal of our experiment was to identify which of the calibration data set produces the best prediction of the landslide areas (BODY data set). The data sets were divided into calibration and validation groups of cells by randomly selecting 75% and 25% of the mapped landslides, respectively. In order to evaluate the robustness of the results, ten calibration and validation samples were extracted for each instability data set. The analysis revealed that the most important predictors were Normalized Difference Vegetation Index (NDVI), slope steepness, vertical distance to channel network, elevation and aspect. The receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), calculated for each of the five instability data sets, indicated that calibrating the models with the lowest landslide pixels (MIN or INF data sets) allows to obtain the most accurate prediction of the validation landslide bodies (BODY data set), achieving AUC values ranging between 0.80 and 0.85