r.survey: a tool for calculating visibility of variable-size objects based on orientation

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes. This work was supported by the postdoctoral fellowship program of the Basque Government obtained by one of the authors [grant numbers POS_2019_1_0020] in collaboration with the Geological Survey of Canada; the research group IT1029-16 of the University of the Basque Country (UPV/EHU); and the geomorphology group of CNR IRPI

r.survey: a tool for calculating visibility of variable- size objects based on orientation

Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes.This work was supported by the postdoctoral fellowship program of the Basque Government obtained by one of the authors [grant numbers POS_2019_1_0020] in collaboration with the Geological Survey of Canada; the research group IT1029-16 of the University of the Basque Country (UPV/EHU); and the geomorphology group of CNR IRPI

Geo-LiM: a new geo-lithological map for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy) as a tool for the estimation of atmospheric CO2 consumption

We present a new geo-lithological map for Central Europe (Geo-LiM). It was prepared taking into account the chemical and mineralogical composition of the outcropping rocks and paying attention in discriminating metamorphic rocks, that were classified according to the chemistry of protoliths. The map was used for estimating the atmospheric CO2 consumed by the chemical weathering of silicates and carbonates. The map is made available in vector format [Donnini et al,. 2018. A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy) (Version 1.2) [Data set]. Zenodo Retrieved from https://zenodo.org/record/3530257], together with the computer code used to classify the lithologies and to join original maps. As a consequence, researchers can either replicate the product, or alter the code to derive a different lithological classification of the original geological maps, following the concept of Open Science

A new Alpine geo-lithological map (Alpine-Geo-LiM) and global carbon cycle implications

Abstract The chemical composition of river waters gives a measure of the atmospheric CO2 fixed by chemical weathering processes. Since the dominating factors controlling these processes are lithology and runoff, as well as uplift and erosion, we introduce a new simplified geo-lithological map of the Alps (Alpine-Geo-LiM) that adopted a lithological classification compliant with the methods most used in literature for estimating the consumption of atmospheric CO2 by chemical weathering. The map was used together with published alkalinity data of the 33 main Alpine rivers (1) to investigate the relationship between bicarbonate concentration in the sampled waters and the lithologies of the corresponding drained basins, and (2) to quantify the atmospheric CO2 consumed by chemical weathering. The analyses confirm (as known by the literature) that carbonates are lithologies highly prone to consuming atmospheric CO2. Moreover, the analyses show that sandstone (which could have a nonnegligible carbonate component) plays an important role in consuming atmospheric CO2. Another result is that in multilithological basins containing lithologies more prone to consuming atmospheric CO2, the contribution of igneous rocks to the atmospheric CO2 consumption is negligible. Alpine-Geo-LiM has several novel features when compared with published global lithological maps. One novel feature is due to the attention paid in discriminating metamorphic rocks, which were classified according to the chemistry of protoliths. The second novel feature is that the procedure used for the definition of the map was made available on the Web to allow the replicability and reproducibility of the product

Parameter-free delineation of slope units and terrain subdivision of Italy

Abstract Quantitative geomorphological and environmental analysis requires the adoption of well–defined spatial domains as basic mapping units. They provide local boundaries to aggregate environmental and morphometric variables and to perform calculations, thus they identify the spatial scale of the analysis. Grid cells, typically aligned with a digital elevation model, are the standard mapping unit choice. A wiser choice is represented by slope units, irregular terrain partitions delimited by drainage and divide lines that maximise geomorphological homogeneity within each unit and geomorphological heterogeneity between neighbouring units. Adoption of slope units has the advantage of enforcing a strong relation with the underlying topography, absent in grid cell–based analyses, but their objective delineation is still a challenge. A given study area admits different slope unit maps differing in number and size of units. Here, we devise an objective optimisation procedure for slope units, suitable for study areas of arbitrarily large size and with varying terrain heterogeneity. We applied the new approach to the whole of Italy, resulting in a map containing about 330,000 slope unit polygons of different sizes and shapes. The method is parameter–free due to objective optimisation using a morphometric segmentation function, and the map is readily available for general–purpose studies. A cluster analysis of slope units properties, compared with terrain elevation, slope, drainage density and lithology, confirmed that the terrain partition is geomorphologically sound. We suggest the use of the slope unit map for different terrain zonations, including landslide susceptibility modelling, hydrological and erosion modelling, geo–environmental, ecological, forestry, agriculture and land use/land cover studies requiring the identification of homogeneous terrain domains facing distinct directions

The contribution of weathering of the main Alpine rivers on the global carbon cycle

On geological time-scales the carbon fluxes from the solid Earth to the atmosphere mainly result from volcanism and metamorphic-decarbonation processes, whereas the carbon fluxes from atmosphere to solid Earth mainly depend on weathering of silicates and carbonates, biogenic precipitation and removal of CaCO3 in the oceans and volcanic gases – seawater interactions. Quantifying each contribution is critical. In this work, we estimate the atmospheric CO2 uptake by weathering in the Alps, using results of the study of the dissolved loads transported by 33 main Alpine rivers. The chemical composition of river water in unpolluted areas is a good indicator of surface weathering processes (Garrels and Mackenzie, 1971; Drever, 1982; Meybeck, 1984; Tardy, 1986; Berner and Berner, 1987; Probst et al., 1994). The dissolved load of streams originates from atmospheric input, pollution, evaporite dissolution, and weathering of carbonate and silicate rocks, and the application of mass balance calculations allows quantification of the different contributions. In this work, we applied the MEGA (Major Element Geochemical Approach) geochemical code (Amiotte Suchet, 1995; Amiotte Suchet and Probst, 1996) to the chemical compositions of the selected rivers in order to quantify the atmospheric CO2 consumed by weathering in Alpine region. The drainage basins of the main Alpine rivers were sampled near the basin outlets during dry and flood seasons. The application of the MEGA geochemical consisted in several steps. First, we subtracted the rain contribution in river waters knowing the X/Cl (X = Na, K, Mg, Ca) ratios of the rain. Next, we considered that all (Na+K) came from silicate weathering. The average molar ratio Rsil = (Na+K)/(Ca+Mg) for rivers draining silicate terrains was estimated from unpolluted French stream waters draining small monolithological basins (Meybeck, 1986; 1987). For the purpose, we prepared a simplified geo-lithological map of Alps according to the lithological classification of Meybeck (1986, 1987). Then for each basin we computed Rsil weighted average considering the surface and the mean precipitation for the surface area of each lithology. Lastly, we estimated the (Ca+Mg) originating from carbonate weathering as the remaining cations after silicate correction. Depending on time-scales of the phenomena (shorter than about 1 million year i.e. correlated to the short term carbon cycle, or longer than about 1 million years i.e. correlated to the long-term carbon cycle), we considered different equations for the quantification of the atmospheric CO2 consumed by weathering (Huh, 2010). The results show the net predominance of carbonate weathering on fixing atmospheric CO2 and that, considering the long-term carbon cycle, the amount of atmospheric CO2 uptake by weathering is about one order of magnitude lower than considering the short-term carbon cycle. Moreover, considering the short-term carbon cycle, the mean CO2 consumed by Alpine basins is of the same order of magnitude of the mean CO2 consumed by weathering by the 60 largest rivers of the world estimated by Gaillardet et al. (1999)

Landslide susceptibility maps of Italy: lesson learnt from dealing with multiple landslide classes and the uneven spatial distribution of the national inventory

Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to repare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and build one susceptibility map for each type. The analysis – carried out by using a Bayeian version of a Generalized Additive Model with a multiple intercept for each Italian region – revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the datases being assembled from twenty sub– inventories, each prepared by different administrations of the Italian regions. As a result, this spatial inhonomegenity may lead to a biased national–scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis suggested by the multiple intercepts results. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine. We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine sectors. The whole procedure is implemented in R–INLA. This allowed to examine fixed(linear) and random (nonlinear) effects from an interpretative standpoint and produced a full prediction equipped with an estimated uncertainty. We propose this overall modeling pipeline for any landslide datasets where a significant mapping bias may influence the susceptibility pattern over space.<br/

Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India)

Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality and completeness can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected in different landslide inventories prepared using different approaches in a study area. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that (1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low-visibility areas; and (2) the inventories obtained by photo interpretation of images suffer from a marked under-representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality and completeness of landslide inventories and then properly orient their use.This research has been supported by the Natural Environment Research Council (grant no. NERC/DFID NE/P000649/1) and the Eusko Jaurlaritza (grant no. POS_2020_2_0010)

Cloud TAC: OpenStack and Technology Learning and Knowledge for teaching IT Infrastructure

In today's university environment, most students are digital natives. Therefore, it is difficult to imagine their academic life without relating it to the various cloud tools for communication and collaborative work. In this context, university professors work in new scenarios of communication and collaborative work in the classroom. This represents a transformation in the teaching-learning process assisted by new ICTs s in the cloud. Working in the cloud offers the opportunity to transmit new knowledge when using pedagogical strategies supported by computer technologies. With the combination of ICTs and modern teaching-learning processes, the concept of Learning and Knowledge Technologies (TAC) is valuable. This work exposes the academic experience of researching and developing Cloud Computing using an OpenStack configuration so that students can empower themselves with the knowledge and use of cloud technologies. Thus, to be able to teach concepts and practices on IT Infrastructure including activities such as: design, configuration, implementation and administration of a private cloud for academic uses.Instituto de Investigación en InformáticaInstituto de Investigación en Informátic