Iceberg jam floods in Icelandic proglacial rivers: testing the self-organized criticality hypothesis

In this paper, we describe a fluvial marginal process associated with the formation of iceberg jams in Icelandic proglacial lakes. The floods triggered by the release of these iceberg jams have implications for the geomorphic evolution of the proglacial fluvial system. The process of iceberg jam floods share some conceptual characteristics with Self-Organized Criticality (SOC) approach of complex systems. Using a simple numerical model and field observations, we test the hypothesis that iceberg jam floods exhibit SOC. Field observations and aerial photo-interpretations in southeastern Iceland demonstrate the occurrence of icebergs jam in ice-contact lakes. The mapping of the south Vatnajökull margins between 2003 and 2012 reveals an increase of the calving potentiality and a rise in the likelihood of iceberg jam flood occurrence. Based on the results of the numerical model and field observations, we suggest that iceberg jam floods should be recognized as a SOC phenomenon. Analysis of the simulated time-series show that the iceberg jam floods become less frequent and more similar in magnitude over time. This global trend is related to the gradual enlargement of the lake outlet channel

Conception et mise en en oeuvre d'un outil pédagogique innovant pour l'approche par compétence en IUT : la réalité virtuelle au service d'une expérimentation plus réaliste

International audienceDans le cadre de la mission de professionnalisation des Instituts Universitaires deTechnologie (IUT), et sans attendre la réforme de l’approche par compétences, un outil pédagogiqueinnovant a été conçu à l’IUT Clermont Auvergne. Basé sur la réalité virtuelle, il permet à desétudiants qui découvrent les métiers de l’électricité de se former sans risque sur un dispositifsimulant un équipement industriel et les dangers associés. Une des originalités du projet réside dansl’implication des étudiants dans la conception de cet outil pédagogique. Afin de mesurer lesretombées de ce projet, une évaluation a été menée durant la conception de l’outil et sa premièreannée de déploiement. Les résultats montrent que les concepteurs ont gagné en compétence dansdifférents domaines techniques mais aussi transversaux, que les utilisateurs ont pu s’approprier cedispositif immersif et que l’équipe pédagogique a modifié sa posture dans le cadre de cette réforme

VirtuElec : A Tool Designed by and for Students for Training in Electrical Hazards

International audienceThe teaching of electrical hazards for future professionals is an important issue. This problem is complex, because to train students in risk, it is necessary to confront them with dangerous situations, but without making them take risks. The VirtuElec project was born in this context: coproducing, with a company specialized in virtual reality, an environment simulating electrical hazards and allowing to train according to different scenarios at different levels of competence. The originality of the project is to involve the students themselves in the construction of this environment. By integrating a project team, they worked in a design office to codevelop this tool and enrich it with video and virtual supports: a training support carried out by students and for students. Students who participated in this project gained knowledge in the areas of electrical hazards, virtual reality, and teamwork, but they felt they gained the most proficiency in the last two skills

VirtuElec : A Tool Designed by and for Students for Training in Electrical Hazards

International audienceThe teaching of electrical hazards for future professionals is an important issue. This problem is complex, because to train students in risk, it is necessary to confront them with dangerous situations, but without making them take risks. The VirtuElec project was born in this context: coproducing, with a company specialized in virtual reality, an environment simulating electrical hazards and allowing to train according to different scenarios at different levels of competence. The originality of the project is to involve the students themselves in the construction of this environment. By integrating a project team, they worked in a design office to codevelop this tool and enrich it with video and virtual supports: a training support carried out by students and for students. Students who participated in this project gained knowledge in the areas of electrical hazards, virtual reality, and teamwork, but they felt they gained the most proficiency in the last two skills

SAILORE is a toolbox of ESRI ArcGIS 10.x software using Spatial Analyst and 3D Analyst extensions. Those two extensions have to be licensed to make the utility work properly.

Airborne laser scanning (ALS) is a tool now widely used in archaeology [1–8], geomorphology, and earth sciences [9–11] to detect natural landforms or remains of human activity, especially in forested areas, where other remote sensing techniques are unsuccessful or time-consuming. The main interest of this technology is to cover large areas while offering high spatial resolution capabilities. Research programs using LiDAR data are becoming more and more frequent. These studies are very often based on a multidisciplinary approach, involving specialists in archaeology, forestry, geomorphology, volcanology [12]. After ALS data acquisition, a point cloud classification has to be carried out, and the resulting Digital Terrain Model (DTM) and Digital Surface Model (DSM) areas are produced. Different visualization techniques are then generally applied to the DTM, to enhance micro-topography versus global topography and help to the detection of target features. The most common are multidirectional oblique weighting hillshade (MDOW), slope [15], Local Relief Model (LRM) [16,17], Sky-View Factor (SVF) [18], positive and negative openness [19,20]. These methods can be divided into two main categories: hillshade, Sky-View Factor, and openness are typically illumination techniques, based, respectively, on the sky portion visible from each position or on the openness characteristics of the relief at each position. They allow highlighting high-frequency components of the relief but remove all the elevation information. Indeed, these methods do not directly restore the topographic variations but rather the consequences of these variations, such as the portion of the visible sky. On the contrary, slope and LRM are DEM manipulating methods. They consist in computing elevation characteristics parameters, respectively, the slope or the high-frequency component of the relief. Recent studies proved that LRM is one of the most efficient visualization techniques [21,22]. The basic principle is to apply moving average filtering to the DTM, in order to remove the general trend of the natural relief: the local relief, characterized by sharp variations, is then revealed [16]. The extent of this filtering (kernel- or window-size) has to be defined by the user, according to both global relief characteristics and morphometric characteristics of the target features. The choice of the correct filtering extent is important but not critical when it is applied to the detection of well-preserved anthropogenic remains. This is because they are generally characterized by sharp changes in local relief, corresponding to the high-frequency component in the frequency domain, well separated from the lower frequency component (features of the natural relief). However, when the aim is to detect all the potentially interesting features, including geomorphological shapes or eroded anthropogenic remains, the filtering perimeter has to be adapted to the characteristics of the natural relief (e.g., slope), which influence the performance of the LRM significantly. Indeed, it is only possible to detect an artifact if it provides a sufficient contrast compared to the surrounding features, i.e., if its frequency signature is significantly higher than the one of the natural reliefs [23]. As LiDAR detection is now used on very large areas, several LRM configurations need typically to be used in order to detect both slight and sharp local relief variations in complex topography contexts, including flat areas and medium to steep slope areas, after what the results from the different models could be eventually merged. This process can be confusing and time-consuming, especially for inexperienced users, and also introduces significant bias, as the decision of the configurations to be tested depends on the skills (and the available time) of the operator. The Self-AdaptIve LOcal Relief Enhancer (SAILORE) approach present an evolution of the widely used Local Relief Model method, allowing the automatic adaptation of the filtering size according to natural relief, producing a single-model, which makes simpler, faster, more efficient, and more reliable detection of target features in large datasets with variegated topography. It automatically uses the best filter configuration, allowing the detection of all the types of anthropogenic remains, independently of the global relief context.Terms of use CreditsWhen using the toolbox, please cite: Toumazet, J.-P.; Simon, F.-X.; Mayoral, A. Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances according to Local Topography. Geomatics 2021, 1, 450–463. https://doi.org/10.3390/geomatics1040026Use limitationsBy downloading or using SAILORE toolbox, you agree to the following terms and conditions:SAILORE toolbox is open and free to use and modify by any user. Use, copy, share and do whatever you wish with this software only at your own risk. The author takes no responsibility of possible damage or other problems with your software, hardware or data caused by the use of SAILORE toolbox

SAILORE is a toolbox of ESRI ArcGIS 10.x software using Spatial Analyst and 3D Analyst extensions. Those two extensions have to be licensed to make the utility work properly.

Airborne laser scanning (ALS) is a tool now widely used in archaeology [1–8], geomorphology, and earth sciences [9–11] to detect natural landforms or remains of human activity, especially in forested areas, where other remote sensing techniques are unsuccessful or time-consuming. The main interest of this technology is to cover large areas while offering high spatial resolution capabilities. Research programs using LiDAR data are becoming more and more frequent. These studies are very often based on a multidisciplinary approach, involving specialists in archaeology, forestry, geomorphology, volcanology [12]. After ALS data acquisition, a point cloud classification has to be carried out, and the resulting Digital Terrain Model (DTM) and Digital Surface Model (DSM) areas are produced. Different visualization techniques are then generally applied to the DTM, to enhance micro-topography versus global topography and help to the detection of target features. The most common are multidirectional oblique weighting hillshade (MDOW), slope [15], Local Relief Model (LRM) [16,17], Sky-View Factor (SVF) [18], positive and negative openness [19,20]. These methods can be divided into two main categories: hillshade, Sky-View Factor, and openness are typically illumination techniques, based, respectively, on the sky portion visible from each position or on the openness characteristics of the relief at each position. They allow highlighting high-frequency components of the relief but remove all the elevation information. Indeed, these methods do not directly restore the topographic variations but rather the consequences of these variations, such as the portion of the visible sky. On the contrary, slope and LRM are DEM manipulating methods. They consist in computing elevation characteristics parameters, respectively, the slope or the high-frequency component of the relief. Recent studies proved that LRM is one of the most efficient visualization techniques [21,22]. The basic principle is to apply moving average filtering to the DTM, in order to remove the general trend of the natural relief: the local relief, characterized by sharp variations, is then revealed [16]. The extent of this filtering (kernel- or window-size) has to be defined by the user, according to both global relief characteristics and morphometric characteristics of the target features. The choice of the correct filtering extent is important but not critical when it is applied to the detection of well-preserved anthropogenic remains. This is because they are generally characterized by sharp changes in local relief, corresponding to the high-frequency component in the frequency domain, well separated from the lower frequency component (features of the natural relief). However, when the aim is to detect all the potentially interesting features, including geomorphological shapes or eroded anthropogenic remains, the filtering perimeter has to be adapted to the characteristics of the natural relief (e.g., slope), which influence the performance of the LRM significantly. Indeed, it is only possible to detect an artifact if it provides a sufficient contrast compared to the surrounding features, i.e., if its frequency signature is significantly higher than the one of the natural reliefs [23]. As LiDAR detection is now used on very large areas, several LRM configurations need typically to be used in order to detect both slight and sharp local relief variations in complex topography contexts, including flat areas and medium to steep slope areas, after what the results from the different models could be eventually merged. This process can be confusing and time-consuming, especially for inexperienced users, and also introduces significant bias, as the decision of the configurations to be tested depends on the skills (and the available time) of the operator. The Self-AdaptIve LOcal Relief Enhancer (SAILORE) approach present an evolution of the widely used Local Relief Model method, allowing the automatic adaptation of the filtering size according to natural relief, producing a single-model, which makes simpler, faster, more efficient, and more reliable detection of target features in large datasets with variegated topography. It automatically uses the best filter configuration, allowing the detection of all the types of anthropogenic remains, independently of the global relief context.Terms of use CreditsWhen using the toolbox, please cite: Toumazet, J.-P.; Simon, F.-X.; Mayoral, A. Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances according to Local Topography. Geomatics 2021, 1, 450–463. https://doi.org/10.3390/geomatics1040026Use limitationsBy downloading or using SAILORE toolbox, you agree to the following terms and conditions:SAILORE toolbox is open and free to use and modify by any user. Use, copy, share and do whatever you wish with this software only at your own risk. The author takes no responsibility of possible damage or other problems with your software, hardware or data caused by the use of SAILORE toolbox

Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography

International audienceThe use of Light Detection and Ranging (LiDAR) is becoming more and more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection of features of interest, visualization filters have to be applied to the raw Digital Elevation Model (DEM), to enhance small relief variations. Several filters have been proposed for this purpose, such as Sky View Factor, Slope, negative and positive Openness, or Local Relief Model (LRM). The efficiency of each of these methods is strongly dependent on the input parameters chosen in regard of the topography of the investigated area. The LRM has proved to be one of the most efficient, but it has to be parameterized in order to be adapted to the natural slopes characterizing the investigated area. Generally, this setting has a single value, chosen as the best compromise between optimal values for each relief configuration. As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it

Les forêts charentaises (France) : un objet d’étude, un contexte de recherche

International audienc

Automatic detection of complex archaeological grazing structures using airborne laser scanning data

International audienceThe use of Light Detection And Ranging (LiDAR) for archaeological purposes is becoming more prevalent in order to detect and to document remains located in forested areas. One of the main interests of airborne laser scanning is to put the archaeological information in their context, and to allow a better understanding of the relation between each item and its environment. This concept of archaeological landscape generally results in a too large amount of data to permit a manual analysis. This paper describes an approach for the automatic detection of elementary archaeological grazing structures, found in high concentration in some places of Auvergne (France). These elementary structures are generally connected, creating complex archaeological grazing sets. The detection process is based on the design of a model of an elementary grazing structure. The automatic detection is then carried out, based on the evaluation of the matching degree of each element with the model and on their belonging to complex archaeological grazing structures. The efficiency of the method is tested, by comparison with the manual digitalisation of an expert, on a restricted zone, and the results show that the success rate of the automatic detection reaches higher values than classical template matching approaches. The additional criterion, based on the belonging of each elementary structure to a more complex one, improves the detection success: In a complementary way, this approach offers new opportunities: it is also possible to detect complex structures with a template matching approach, if they contain some simple forms, that can be modelled

Les forêts charentaises : un objet d'étude, un contexte de recherche. Le LiDAR topographique aéroporté : un outil indispensable, une méthodologie renouvelée.

International audienc