Web Map Service y Análisis de datos LiDAR y Ráster

En este documento se explica cómo trabajar con datos geográficos a través de un Web Map Service (WMS) en un Sistema de Información Geográfica - SIG (GIS en inglés). A su vez, también se explica cómo trabajar con datos de LiDAR a´`ereo, cómo obtener Modelos Digitales del Terreno de Alta Resoluación (HRDTM) y cómo analizar estas rásters para extraer información como modelos de sombra, redes de drenaje, cuencas hidrológicas, etc

Cartografia Digital amb QGIS

Guió per a la pràctica d'introducció a la cartografia digital de l'assignatura Geomorfologia, de 2on curs del Grau de Geologia de la UBEl document correspon al guió per a la realització d'una pràctica sobre introducció a la cartografia geomorfològica digital amb QGIS (programari lliure), per a l'assignatura Geomorfologia, de 2on curs del Grau de Geologia de la UB. En aquesta pràctica s'introdueixen les eines principals d'edició de dades vectorials amb QGIS per a la realització de cartografies digitals

Análisis de la variación de la línea de costa en el delta del río Llobregat con ArcGIS

Este Guion se elaboró para la realización de un ejercicio práctico planteado como un estudio de caso a partir del cual los estudiantes aprenden a preparar un proyecto SIG con ArcGIS, a distinguir diferentes tipologías de datos y a aplicar herramientas de análisis específicas para cada una de estas tipologías. Dada la complejidad de uso de ArcGIS, el guion está elaborado para que los estudiantes puedan reproducir las actividades planteadas en el aula de forma autónoma, mejorando así su atención en el aula i favoreciendo la discusión sobre la idoneidad de los datos y las herramientas utilizadas y fomentar la interpretación crítica de los resultados

Anàlisi SIG: Susceptibilitat als moviments de massa

Aprenentatge d'eines d'anàlisi SIG mitjançant el plantejament d'un projecte d'anàlisi de la Susceptibilitat als Moviments de Massa mitjançant ArcGISEl document correspon al Guió per a la realització d'un projecte d'anàlisi SIG basat en l'anàlisi de la susceptibilitat als moviments de massa en una regió del NW de Nicaragua. Aquest Guió correspon a un projecte proposat a l'assignatura GIS del Màster de Recursos Minerals i Riscos Geològics de la Universitat Autònoma de Barcelona i la Universitat de Barcelona

Millora de competències transversals mitjançant el treball col·laboratiu amb wikis – 1a fase

El treball en competències transversals a partir de les assignatures de grau és una línia docent plenament assentada des de la implementació dels crèdits ECTS. Tot i això, els estudiants que arriben als nostres ensenyaments mostren algunes mancances en el desenvolupament d’alguna d’aquestes competències, especialment pel que fa a l’expressió escrita i oral i a la gestió del temps i treball col•laboratiu. A partir de l’experiència docent de les professores implicades en el projecte en diversos anys anteriors s’ha detectat la dificultat de coordinació entre estudiants, per al desenvolupament de treballs en grup i de l’estrès que els provoca les presentacions orals. En aquest projecte es presenta una eina virtual que millora la gestió de la informació científica que es recull durant el desenvolupament de treballs col•laboratius i en facilita la disponibilitat a tots els membres del grup. Aquesta eina, a més, millora el seguiment i avaluació del treball i de les contribucions de cada membre del grup per part del professorat. La proposta contempla el desenvolupament del projecte a l’assignatura “Geologia General”, obligatòria de 1er curs del Grau d’Enginyeria Geològica (6 crèdits), en una primera fase, i a l’assignatura “Geoquímica”, obligatòria de 3er curs del Grau de Geologia (9 crèdits) en una segona fase. El denominador comú de les dues assignatures és el seu caràcter obligatori i general però els destinataris i els continguts són molt diferents. Tot i així, tots dos ensenyaments comparteixen competències tant transversals com específiques per a les quals aquestes assignatures constitueixen un punt d’encontre excel•lent per desenvolupar-les i aprofundir en el desenvolupament d'aquestes competències: treball en equip, comunicació oral i escrita i ús solvent dels recursos d’informació que han de millorar la gestió del temps i de dedicació al propi treball col•laboratiu

Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras

The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions

A feasible methodology for landslide susceptibility assessment in developing countries: A case study of NW Nicaragua after Hurricane Mitch

In October 1998, Hurricane Mitch triggered a large number of landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. In recent years, a number of risk assessment methodologies have been devised to mitigate natural disasters. However, due to scarcity of funds and lack of specialised personnel few of these methodologies are accessible to developing countries. To explore the potential application of relatively simple and affordable landslide susceptibility methodologies in such countries, we focused on a region in NW Nicaragua which was among the most severely hit during the Mitch event. Our study included (1) detailed field work to produce a high-resolution inventory landslide map at 1 : 10,000 scale, and (2) a selection of the relevant instability factors from a Terrain Units Map which had previously been generated in a project for rural development. Based on the combination of these two datasets and using GIS tools we developed a comparative analysis of failure-zones and terrain factors in an attempt to classify the land into zones according to the propensity to landslides triggered by heavy rainfalls. The resulting susceptibility map was validated by using a training and a test zone, providing results comparable to those reached in studies based in more sophisticated methodologies. Thus, we provide an example of a methodology which is simple enough to be fully comprehended by non-specialised technicians and which could be of help in landslide risk mitigation through implementation of non-structural measures, such as land planning or emergency measures

A cost-effective image-based system for 3D geomorphic monitoring: An application to rockfalls

Change-detection monitoring plays a crucial role in geoscience, facilitating the examination of earth surfaceprocesses and the mitigation of potential risks due to natural hazards. A significant aspect of this monitoringinvolves the use of images, enabling 2D to 4D monitoring approaches. Our objective is to bridge the knowledgegap in developing very low-cost camera units by providing insights into specific products, assembly processes,and utilized codes. The presented approach involves prioritizing cost reduction albeit a trade-off in systemquality. The results obtained in the study area of Puigcerc´os cliff in Spain demonstrates the system's efficacy indetecting rockfalls and pre-failure deformation with a notable level of detection of only 8 cm in the changedetection analysis. Additionally, two system versions are presented; one emphasizing real-time image transmission,while the other provides a simpler, energy-efficient approach conducive to long-term data capture usinga single battery. Both solutions showcase the potential of leveraging very low-cost technology in geohazardmonitoring

Dinámica, Factores condicionantes y posibles causas de la formación de la tartera de Cambrils (Solsonès, Lleida)

Landslides and rockfalls are a common hazard in mountain areas like the Pyrenees. However, due to the difficulty of access and therefore of data acquisition, and the low density of population they are poorly studied. The Tartera de Cambrils, is located in a small town in the Solsonès region, Catalonia, and is the product of ancient landslides and succeeding rockfalls. These processes can endanger different infrastructures in the village of Cambrils such as the road, the sports centre, the salt flats called 'El Salí' (currently also being used for tourist activity), two inns and several houses. This study aims to determine the processes that caused the initial landslides, those that occur at the rock slope nowadays and their causative factors. For this, we compiled information from the literature, conducted a field study building a geologic and geomorphologic cartography and acquired LiDAR data, with a Terrestrial Laser Scan, and photographs in order to produce three-dimensional point clouds. We also analyse the rock-cliff stability using photogrammetry and LiDAR data and direct measures of rock mass discontinuities. The bedding dips smoothly and contrary to the slope, making planar sliding an unprovable mechanism, favouring wedge sliding and toppling. The rock discontinuities are the main causative factor of rockfalls. Rockfall originates from rock fronts of decametric volume along the main scarp and on the scree. These rock fronts rotated respect to the rock in situ. The farther away from the main scarp, the larger the rotation of the blocks. The analysis of the fractures allows estimating an important possibility of rockfall directly affecting the inns and the road and provides fundamental data for the development of protection measures

End-to-End Intelligent Framework for Rockfall Detection

Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner or digital cameras. Multi-temporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertise and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework poses significant research challenges and justifies intensive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been benchmarked and compared applying well-known metrics to be incorporated into our system. Specifically, we developed statistical and machine learning techniques and applied them to analyze point cloud data extracted from Terrestrial Laser Scanner in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental data suggest that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally proven accuracy