This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy—in the order of 0.10 to 0.15 m—by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS).

A landslide susceptibility analysis has been made in the Río Aguas catchment (Almeria, Southeast Spain), using two statistical models, Maximum Entropy (MaxEnt) and Geographically Weighted Logistic Regression (GWLR). For this purpose, a previous landslide inventory has been used and re-elaborated, reaching a total incidence of 2.58% of the whole area. Different types of movements have been distinguished, being rock falls, slides and complex movements the predominant. From the inventory, the centroid of the rupture zone has been extracted to represent the landslides introduced in the models. A previous factor analysis has been made, using 12 predictors related to morphometry, hydrography, geology and land cover, with 5 m grid spacing, allowing the selection of factors to be used in the analysis and discarding those showing correlation between them. Then, MaxEnt and GWLR models are applied using different distributions of training and testing samples from the landslide inventory. For the validation, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) has been used but additionally, the degree of fit (DF) has allowed to validate the rupture zones themselves, not only the centroids. Results show an excellent prediction with both metrics in all the methods and samples, but the better results are obtained in the GWLR method for AUC and in the MaxEnt for the degree of fit. Therefore, a consensus model of both methods has been obtained, that improves even more the results reaching an AUC value of 0.99 and a degree of fit of 90%.

Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.