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The role of biofilm in the Microbiologically Influenced Corrosion (MIC) of carbon steel by Sulfate Reducing Bacteria (SRB) in oil and gas exploitation, and geothermal installations is investigated. Simulated biofilm made of calcium alginate, abiotic sulfide (to mimic SRB metabolic end product H2S), CO2 (to mimic CO2 from SRB dissimilatory sulfate reduction) and simulated brine (3.0 wt% NaCl) are used to simulate the SRB environment. For reference experiments, distilled water is used instead of simulated brine. The electrochemical results show that the simulated biofilm in the reference at 120 min exposure time and in brine experiments neither inhibits nor accelerates corrosion. These results are strongly supported by corrosion kinetic adsorption parameters, statistical T-test, ICP-OES, pH, SEM-EDS and XRD. The results contradict with the existing literature on the role of biofilm and this is likely due to the presence of both H2S and CO2 as simulated SRB metabolites. Despite of this discrepancy, the obtained corrosion rates (0.25 to 1.6 mm/year) in the simulated SRB environment are comparable to published corrosion rates obtained in SRB experiments (0.20 to 1.2 mm/year). The results highlight the novelty of this research and have a direct impact on the role of other microbial metabolites on the corrosion of carbon steel.

The last three decades have witnessed substantial developments in data-driven models for landslide prediction. However, this improvement has been mostly devoted to models that estimate locations where landslides may occur, i.e., landslide susceptibility. Although susceptibility is crucial when assessing landslide hazard, another equally important piece of information is the potential landslide area once landslides initiate on a given slope. This manuscript addresses this gap in the literature by using a Generalized Additive Model whose target variable is the topographically-corrected landslide areal extent at the slope unit level. In our case, the underlying assumption is that the variability of landslide areas across the geographic space follows a Log-Normal probability distribution. We test this framework on co-seismic landslides triggered by the Kaikōura earthquake (7.8 Mw on November 13th 2016). The model performance was evaluated using random and spatial cross-validation. Additionally, we simulated the expected landslide area over slopes in the entire study area, including those that did not experience slope failures in the past. The performance scores revealed a moderate to strong correlation between the observed and predicted landslide areas. Moreover, the simulations show coherent patterns, suggesting that it is worth extending the landslide area prediction further. We share data and codes in a GitHub repository https://github.com/mmorenoz/GAM_LandslideSize to promote the repeatability and reproducibility of this research.

To cope with earthquakes, resistant housing is crucial. Too often essential construction techniques are not applied in the most vulnerable contexts. Local construction stakeholders have a major responsibility in reducing vulnerability of the built environment. This exploratory study investigates current seismic risk awareness in the region of Iringa (Tanzania) and discusses its implications for disaster resilience policy and practice. This medium seismic hazard risk area presents an interesting case-study to map risk awareness of key construction stakeholders. Our analysis covers seismic risk maps, governmental policy documentation and stakeholder awareness data. Data collection across 17 villages includes a poll with 21 mason and 15 school principals, and 5 interviews with a district engineer, a contractor, an architect and two homeowners. This sample illustrates the severe lack of risk awareness in Iringa. Our evidence reveals limited risk awareness across all community-based stakeholders, including absent risk awareness of local masons. High risk awareness was found in national governmental policy documents. We conclude that local seismic risk awareness is influenced by: (i) absent structural building codes that consider seismic forces, (ii) lack of risk communication campaigns by the government or NGOs, (iii) and limited freedom of speech linked to natural hazards. Our findings advocate for enhancement of risk awareness by: (i) development of a Tanzanian structural building code with recommendations for seismic resistance, (ii) better risk communication strategies to bridge the knowledge gap between governmental departments and communities, and (iii) exclusion of natural hazards from the list of prohibited communication topics in legislation.

Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster.

Geomorphological and sedimentary records near the confluences of the Tana River and major tributaries draining the eastern slopes of Mt. Kenya and the Nyambeni Range, indicate impacts of Late Quaternary volcanic activity in their fluvial records. The main reconstructed event was triggered by a 366.9 ka basalt flow (40Ar/39Ar dated) which flowed along Kazita River from the Nyambeni Range blocking both Kazita River and Tana River near Kibuka Grand Falls, causing a temporary lake. Consequently, Tana River and Kazita River started to build volcanoclastic Gilbert type deltas. The preserved pro-delta sediments rich in trachytic pumice fragments display a mineralogical and age match with known Ithanguni trachytic tuffs, indicating delta build up right after a contemporary Ithanguni eruption. This trachytic eruption caused the deposition of lahars and fluvial volcaniclastic sediments in all river records draining the Eastern side of Mt. Kenya. The multiple lahars seem to be triggered by eruptions under glacial conditions (basalt age indicates MIS 10). The lava dammed lake was only short lived (estimated to have lasted only a few years to decades) and breached before a complete lake infill could occur. The current Kibuka Grand Falls can be viewed as the delayed incisional response of this lava dam breach, indicating that after >366.9 ka, Tana River is still responding and adjusting to this short-lived disruptive phase. The current Kazita River has re-incised adjacent to a MIS 4 basalt flow down into the crystalline Basement System rocks. The MIS 10 pre-volcanic sedimentary record indicates that more sediments were in the fluvial system during glacial conditions than during the interglacial conditions. An implication of our reconstruction is that the Late Quaternary fluvial record of Tana River is of only limited use to reconstruct uplift rates because reconstructed Quaternary incision rates are reflecting both volcanic disruptions as climate change trends of aridification and decreasing glaciation extents.

The Wavelength Mapper is an algorithm that searches for the deepest absorption feature in each pixel of a hyperspectral image. On a per pixel basis, it extracts the wavelength position, which serves as a proxy of the mineralogy and the feature depth as a proxy for the relative abundance. This algorithm has been used with near and shortwave infrared data, but has not yet been tested on hyperspectral thermal infrared images. It is unclear what results are expected when the Wavelength Mapper algorithm is applied to hyperspectral thermal infrared data since reststrahlen features characteristically overlap in emissivity spectra. In this paper, the Wavelength Mapper is tested on a multi-flightline airborne hyperspectral TIR dataset acquired over the Yerington Batholith, Nevada. Observations were made in the 8.05–11.65 μm wavelength range to include thermal spectral features of major rock-forming minerals, and a new color ramp is created to separate quartz-rich rocks from plagioclase-rich rocks. Our results indicate that the Wavelength Mapper creates coherent spatial patterns across flightlines. The results displayed represent different types of igneous and sedimentary rocks, as well as the products of hydrothermal alteration via different colors, mainly based on the relative abundance of quartz, feldspar and garnet, as well as mica and epidote. Comparison with published maps indicate that the Wavelength Mapper represents for each pixel a parameter value that can be linked to the spectrally dominate rock-forming mineral of that area, as mapped with traditional fieldwork methods. In conclusion, the Wavelength Mapper can be applied to airborne hyperspectral TIR data to achieve a simple, repeatable, per-pixel overview map of the dominating rock-forming mineral occurrences.

Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.

Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system, which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC), whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geo-environmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggested.

Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks. A significant innovation is recognizing that risks can arise both from potential impacts due to climate change and from responses to climate change. This approach encourages thinking that traverses sectoral and regional boundaries and links physical and socio-economic drivers of risk. Advancing climate change risk assessment in these ways is essential for more informed decision making that reduces negative climate change impacts. © 2021 The Authors

Gully erosion is one of the most destructive geomorphological processes on relatively flat surfaces. This is exacerbated in the Arctic regions, where gullies are referred to as thermo-erosion gullies because of their unique connection to permafrost. As the surface of the permafrost freezes and thaws, soil particles destabilize, inducing erosion along preferential incisions, giving rise to widespread thermo-erosion gullies. In this study, we present the first thermo-erosion gully inventory in the Svalbard region (Nordenskiöld Land). The inventory was created using a combination of available aerial photographs from 2009 to 2011, direct field observations and measurements. The spatial distribution of thermo-erosion gullies is then exploited to investigate potential threats to the Arctic cultural heritage (CH). Analyses of thermo-erosion gullies are increasingly important for artic administrations, which require more detailed hazard assessments as the effect of climate change becomes increasingly evident across these landscapes. The inventory is comprised of 810 thermo-erosion gullies in Nordenskiöld Land, most of which are located in close proximity to coastlines. We assess the inventory size statistics and correlation with terrain characteristics to investigate potential predisposing factors. No gullies occurs at elevations greater than 200 m a.s.l., but gullies occur up to a maximum steepness of 37 degrees and along the whole topographic profile and, looking at the potential threat to CH, we found 44 of these sites within a 100 m buffer from the gullies. This distance is the reference that local administrations use to prioritize actions and safeguard the existence of artic CH sites. In fact, a 100 m distance implies that future evolution of thermo-erosion gullies, especially enhanced by climate change may eventually erode away soil from the CH surroundings, threatening their stability and existence.

Social vulnerability is a key component of the risk equation alongside the context of the hazard and exposure. Increasingly, social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. Humanitarian organisations particularly strive to capture social vulnerability in their decision processes relative to prioritisation of actions before disasters occur. This research supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action at the Parroquia level in Ecuador. This paper compares the results from the two most common approaches used to create composite indices, one using the weighting of variables from disaster experts’ judgments (referred to as Expert method) and the other using PCA analysis, with one or more components. While all outcomes reveal similar trends in areas where most indicators suggest the lowest (urban areas) or highest (the Amazon and northwest coastal regions) social vulnerability, the research shows that the choice of the method matters for assessing the social vulnerability in the rest of the country where there are less pronounced vulnerability signals. In those areas, PCA-driven indices suggest higher relative vulnerability levels than Expert outcomes. Further, in the Andes particularly, the PCA outcomes result in wider distribution than the Expert outcomes, and therefore more heterogeneity in the vulnerability assessment. While divergence in outcomes suggests particular attention with the use of composite indexes for decision making, our results provide support to understand the sensitivity in flood-specific social vulnerability outcomes spatially. To go further we emphasise the importance of using historical flood impact data to evaluate the contribution of each variable in the final social vulnerability scores.

An integrated, modeling method for shallow landslides, debris flows and catchment hydrology is developed and presented in this paper. Existing two-phase debris flow equations and an adaptation on the infinite slope method are coupled with a full hydrological catchment model. We test the approach on the 4 km2 Scaletta catchment, North-Eastern Sicily, where the 1-10-2009 convective storm caused debris flooding after 395 shallow landslides. Validation is done based on the landslide inventory and photographic evidence from the days after the event. Results show that the model can recreate the impact of both shallow landslides, debris flow runout, and debris floods with acceptable accuracy (91 percent inventory overlap with a 0.22 Cohens Kappa). General patterns in slope failure and runout are well-predicted, leading to a fully physically based prediction of rainfall induced debris flood behavior in the downstream areas, such as the creation of a debris fan at the coastal outlet.

In cold climate regions a significant fraction of annual soil erosion in agricultural land occurs during snowmelt and rain on partially frozen soils. Physically based and spatially distributed soil erosion models have proved to be good tools for understanding the processes occurring at catchment scale during rainfall erosion. However, most existing erosion models do not account for snow in a suitable way. A combination of the UEBGrid snow pack model and the LISEM erosion model was therefore used in this study. The aim was to test and validate this model combination and to assess its utility in relation to quantification and process understanding. Applying this model combination to simulate surface runoff and soil erosion showed that, in principle, it is possible to satisfactorily simulate surface runoff and observed soil erosion patterns during winter. The values for the calibration parameters were similar for the two chosen winter periods when the rainfall and snowmelt episodes occurred. However, the calibration procedure showed that the utility of this combination had several limitations. It is hoped that this study can help to improve existing models and trigger new developments in including snow pack dynamics and soil freezing and thawing in soil erosion models.

Unsafe conditions may increase the vulnerability of farmers natural hazards and reduce the capacity of farmers to prevent or recover from disaster impacts. This study aimed to investigate disruptions in cropping schedules to understand unsafe conditions that contribute to vulnerability in irrigated fields served by Ir. Djuanda (Jatiluhur) reservoir in West Java. Firstly, the deviation of ongoing cropping schedules from the official cropping calendar was evaluated using the time-series Enhanced Vegetation Index (EVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) imageries. Secondly, reasons for disruptions in cropping schedules were explored using an in-depth interview with farmers, extension officers, and water managers and analyzed using a qualitative content analysis. Thirdly, the progression from potential causes to consequences of the disruption was identified using a Bow-Tie analysis. Unsafe conditions were identified using the result of the Bow-Tie analysis. Finally, several ways to reduce vulnerability were suggested. This study has successfully showed that cropping schedules deviate from the official cropping calendar in the study area. Reasons for disruptions in cropping schedules include economic motives, weather variabilities, geographic locations, coping strategies, farmers’ interactions, and agricultural infrastructures. The Bow-Tie analysis has visualized the progression from potential causes, disruptions in cropping schedules, to potential disaster impacts. Unsafe conditions have been identified, categorized into the dangerous locations, unsustainable farming activities, unsuitable coping strategies, fragile infrastructures, and inaccurate perceptions, have been pinpointed. Addressing unsafe conditions is likely to able to reduce vulnerability in irrigated rice fields.

Within hydrological models, flow approximations are commonly used to reduce computation time. The validity of these approximations is strongly determined by flow height, flow velocity and the spatial resolution of the model. In this presentation, the validity and performance of the kinematic, diffusive and dynamic flow approximations are investigated for use in a catchment-based flood model. Particularly, the validity during flood events and for varying spatial resolutions is investigated. The OpenLISEM hydrological model is extended to implement both these flow approximations and channel flooding based on dynamic flow. The flow approximations are used to recreate measured discharge in three catchments, among which is the hydrograph of the 2003 flood event in the Fella river basin. Furthermore, spatial resolutions are varied for the flood simulation in order to investigate the influence of spatial resolution on these flow approximations. Results show that the kinematic, diffusive and dynamic flow approximation provide least to highest accuracy, respectively, in recreating measured discharge. Kinematic flow, which is commonly used in hydrological modelling, substantially over-estimates hydrological connectivity in the simulations with a spatial resolution of below 30 m. Since spatial resolutions of models have strongly increased over the past decades, usage of routed kinematic flow should be reconsidered. The combination of diffusive or dynamic overland flow and dynamic channel flooding provides high accuracy in recreating the 2003 Fella river flood event. Finally, in the case of flood events, spatial modelling of kinematic flow substantially over-estimates hydrological connectivity and flow concentration since pressure forces are removed, leading to significant errors.

Understanding alteration processes in geothermal systems through remote sensing can help assess the prospectivity of a geothermal field. As a result of which, alteration detection and mapping are vital in the early exploration stage. However, many geothermal surface manifestations, which are evidence of alteration processes, often occur in densely vegetated areas, particularly in tropical regions. Current lithological mapping techniques using passive remote sensing systems can have problems with vegetation canopy completely blocking the signal. LiDAR, an active remote sensing system, can be a solution since it can (partially) penetrate vegetation canopy. It collects information from the ground in 3D-coordinated points with laser return intensity (LRI) values. In this study, we test for the first time the possibility of differentiating hydrothermally altered rocks through their LRI values in a laboratory experiment. We scanned selected altered and unaltered rocks under different moisture and temperature conditions using a terrestrial laser scanner (at 1550 nm wavelength). Our results show LRI values follow the degree of alteration: strongly altered rocks have the highest LRI, unaltered rocks are at the lowest, and weakly altered rocks show an intermediate response. Varying moisture conditions decrease LRI values for all rocks, but the relative LRI order due to alteration remains intact. We recorded no significant temperature effect on LRI values for any of the alteration stages. Our results provide the first evidence that rocks with different hydrothermal alteration phases can be distinguished by their LRI values which opens up the potential for airborne mapping of geothermal surface manifestations with LRI.

The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.\ud \ud In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past.\ud \ud What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.

Infrared spectroscopy (IRS) is a method used to identify minerals based on their spectral response to infrared light. Spot-based IRS (also known as ‘reflectance spectroscopy’) has frequently been used as an analytical method by the geothermal industry, while infrared imaging spectroscopy (IRIS) has only recently been introduced. Research applying IRIS to the geothermal industry is still limited to academic trials, but sufficient progress has been made in algorithm development and application cases to make this technology ready for uptake by the geothermal industry. In contrast, the mineral exploration industry has embraced IRIS for a number of years and is driving its development forward. In this paper, we review the work that has been done in the geothermal industry with spot-based and imaging IRS, as well as review relevant examples from the mineral exploration realm to look for pathfinders for future uses of the method within the geothermal industry. The review focuses on the application of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectroscopy, as the application of long-wave infrared (LWIR) spectroscopy in the geothermal industry is still very limited. To cater for a wide range of audiences, we will explain the background of infrared spectroscopy as well as the commonly used geothermal index minerals and analytical techniques typically employed by the geothermal industry. Our review shows that IRS has a higher sensitivity in identifying kaolinite (along with its degree of crystallinity), ammonium-bearing minerals, and chemical variations of (spectrally-active) minerals compared to other methods. The ability of IRIS to obtain spectra with high spatial resolution enables the method to identify: (1) less common minerals, and (2) the potential to distinguish smectite and illite formed as interlayered minerals from those formed as two different grains. The latter identification typically cannot be done using spot-based IRS. Other parameters that can only be seen in IRIS include spatial relationships amongst minerals and more robust mineral abundance estimates. The mineral exploration industry has demonstrated that applying IRIS can go beyond just identifying minerals. It can also successfully identify host lithologies on intensely altered rocks, as well as identify and extract the position of veins and veinlets. The latter information is important for geothermal exploration, particularly to indicate permeability and cross-cutting relationships amongst alteration minerals. The IRIS applications that have been demonstrated by the mineral exploration community potentially represent the future trend in the geothermal industry. Spot-based IRS has already proven its added value in assisting geothermal exploration and exploitation. With the latest instruments and algorithm developments in place, imaging IRS is now on the brink of demonstrating its value to the geothermal industry.

For the first time, this paper develops an integrated and spatially-distributed modelling approach, linking atmospheric deposition, soil erosion and sewer transport models, to assess the transfer of traffic-related pollutants in urban areas. The modelling system is applied to a small urban catchment near Paris. Two modelling scenarios are tested by using experimentally estimated and simulated atmospheric dry deposits. Simulation results are compared with continuous measurements of water flow and total suspended solids (TSS) at the catchment outlet. The performance of water flow and TSS simulations are satisfying with the calibrated parameters; however, no significant difference can be noticed at the catchment outlet between the two scenarios due to the first flush effects. Considering the Cu, BaP and BbF contents of different particle size classes, simulated event mean concentration of each pollutant is compared with local in-situ measurements. Finally, perspectives to improve model performance and experimental techniques are discussed. An integrated and spatially-distributed air-surface-sewer model is developed.Modelling the transfer of traffic-related pollutants in urban areas.Simulations of TSS concentrations fit well with the continuous measurements.Realistic simulations of Cu, BaP and BbF are achieved at the catchment outlet.

A recent study by Dalm et al. (2014) showed that alteration mineralogy acquired using SWIR point spectrometry could be linked to copper grade distribution for a group of samples from a South American copper mine. Since it was expected that SWIR hyperspectral imagery can provide more detailed information about the alteration mineralogy of these ores, we investigated whether this technique can be used to improve upon the indirect characterization of copper grades. Maps showing the distributions of SWIR-active minerals, white mica crystallinity, white mica composition, and chlorite composition were produced from SWIR hyperspectral images of 43 samples from the Dalm et al. (2014) study. Subsequently, a principle component analysis (PCA) was applied to the relative mineral abundances and the average white mica crystallinity and composition that were extracted from these maps. The PCA showed that this mineralogical data could be used to discriminate a significant portion of the samples with sub-economic copper grades. Furthermore, the study showed that SWIR hyperspectral imaging has the following advantages over SWIR point spectrometry: minerals that are present in relatively low quantities can be detected, the SWIR-active mineralogical composition at the surface of a sample can be quantified, and the texture of samples, such as grain sizes and cross-cutting vein structures, can be characterized. However, these advantages did not improve upon the indirect characterization of copper grades that was achieved using SWIR point spectrometry. This was attributed to the relatively small size of the sample set and the high textural variability between samples.

Airborne gamma-ray imagery (AGRI) provides coarse-resolution (approximately 400 × 400 m pixel) spatial information on gamma-ray emitting elements, i.e. potassium (K), thorium (Th) and uranium (U), in the upper half meter of the soil. These radioelements are a potential information source for soil mapping since their abundance is related to soil geochemistry, specifically the chemical composition of parent materials and their weathering products resulting from geomorphic and pedogenic processes. The aim of this study was to evaluate the potential of AGRI for improving digital soil survey process in two phases: (1) a preliminary phase, where hypotheses of soil-landscape relationships are developed, and (2) a phase where soil map unit boundaries are placed or revised. The study also evaluated the match between AGRI and existing lithologic, geopedologic, and soil series maps. The study was conducted in a well-characterized complex soil landscape: the 560 km2 upper Pa Sak watershed in Petchaboon province, Thailand. The relationship between gamma-ray data and geological units was examined with box-and-whisker plots and confirmed by rock and soil samples. Rock and soil sample classifications were compared with the gamma-ray image and to typical radioelement responses found in the literature. To interpret AGRI data in terms of regolith and soil genesis, we compared AGRI to two existing soil maps: geopedologic and soil series maps. First, the geopedologic map was split into four maps according to the geopedologic hierarchy: landscape, lithology, relief, and landform; at the latter (lowest) level, soil units are also associated. Secondly, soil series and geopedologic soil units were used to examine the distribution of radioelement response to selected soil characteristics: parent material, texture, mineralogy, and thickness. The interpretations from best correlation in both soil maps were described in terms of the radioelement changes during pedogenic and geomorphic processes, based on a review of literature and supported by soil samples. In the hypotheses-generating stage, AGRI provided useful information in three forms (single signal, ratio, and ternary images enhanced with a hill shaded DEM) by relating these to lithology, material transport, and internal pedogenetic processes. AGRI correlated well with the classes of the geopedologic map (1:50,000) at the two higher levels (landscape and lithology) but to a lesser degree at the two lower levels (relief and landform in geopedologic approach). In the mapping stage, AGRI showed deficiencies in the soil series map (1:50,000) made by conventional aerial photo analysis and limited field surveys, especially in inaccessible areas but also in low-relief terraces and flood plains, which provided a basis for future field sampling to correct these deficiencies. AGRI suggested new boundaries, differentiating topsoil properties and the presence of plinthite, despite its coarse resolution. Clustering of gamma ray and elevation data (DEM) was carried out using fuzzy logic to generate various classification layers. Final map was produced using total weighted inverse distance of all the classes in a 3 by 3 window. Class label was assigned to the one with the largest total inverse distance over the entire set of fuzzy classification bands. The result shows relatively higher classification accuracy for soil parent material differentiation (overall accuracy of 72%) as compared to the classification for soil types (67%).

The application of physically-based approaches for slope failure analysis at a catchment scale remains a difficult challenge, and several new models have been proposed in recent years. The assumptions of these models vary significantly. Tools such as random ellipsoid sampling provide detailed assessment of failure probability but due to numerical cost can not be applied beyond site-investigations. In this research, an iterative method for finding progressive slope failure surfaces is developed. Additionally, this method includes a description of lateral forces that occur due to weight of the fractured failure material. This development extends a similar approach that was developed as part of the OpenLISEM multi-hazard modelling tool. Study cases are presented for a set of hypothetical slopes and the storm-induced disaster that occurred in Messina (Italy), on the first of October 2009. Here, a large number of shallow slope failures transformed into debris flows. The model outcomes are compared with the outcomes of other free and open-source methods that are currently available within the scientific community (Infinite Slope, random ellipsoid sampling (r.slope.stability) and random spheroid sampling (Scoops3D)). Finally, finite element modelling is performed for the hypothetical slopes using ADONIS). Analysis of predicted failures show that the presented method is able to better predict the occurrence of smaller failures but ellipsoid sampling methods provide a more robust option for larger slope failures. Predicted failure surface patterns for the Messina 2009 event show complex variation between rotational and translational geometry depending on the topography. The ability to provide catchment-scale deterministic failure volume and geometry can potentially be used to quantify predicted source volumes during future disasters.

Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km2 area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance.

The idea behind any validation scheme in landslide susceptibility studies is to test whether a model calibrated on a certain data can predict an unknown dataset of the same nature (landslide presences/absences and covariates). Almost the entirety of landslide susceptibility studies are validated by subsetting a single dataset into a training and test sets. This dataset usually corresponds either to event-specific or to historical inventories. Very rarely, a multi-temporal inventory is available and, in the few cases where this condition is met, the validation practices involve training a model on a specific landslide inventory, deriving a single predictive equation and validating it on a subsequent landslide inventory. This commonly leads landslide predictive studies, even those with a strong statistical rigor, to neglect the uncertainty estimation in their modeling scheme. In statistics, validation can also be performed via statistical simulations. This means that after fitting a given model, one can generate any number of predictive functions and test their predictive skills on any type and number of unknown datasets. In this work, we take a similar direction and we apply it to model and validate three separate co-seismic inventories, including an uncertainty estimation phase. We mapped these inventories within the same area in Indonesia, for three earthquakes occurred in 2012, 2017 and 2018. Specifically, we build three event-specific Bayesian Generalize Additive Models of the binomial family. From each model we then simulate 1000 predictive realizations over the remaining two inventories, by using a plug-in scheme where all the morphometric covariates are kept fixed and only the ground motion is replaced according to the prediction target. By doing so, we introduce a new analytical tool for near-real-time landslide predictive purposes, which is able to produce a probabilistic model which stands in between the definitions of susceptibility and hazard. In fact, our model is able to accurately estimate “where” and “when” – although not “how frequently” – landslide have occurred by featuring the multitemporal information of the trigger. In our findings, the simulations are quite similar to the fitted models; and the nine combinations we analyse produce excellent performance. This result confirms the assumption that “the past is the key to the future”, as we show that the relative contribution of each variable and their interactions in each probabilistic model remains practically the same across temporal replicates. This information is not trivial because it supports the routines implemented in global near-real-time applications.

Extreme rainfall events are the main triggering causes for hydro-meteorological hazards in mountainous areas, where development is often constrained by the limited space suitable for construction. In these areas, hazard and risk assessments are fundamental for risk mitigation, especially for preventive planning, risk communication and emergency preparedness. Multi-hazard risk assessment in mountainous areas at local and regional scales remain a major challenge because of lack of data related to past events and causal factors, and the interactions between different types of hazards. The lack of data leads to a high level of uncertainty in the application of quantitative methods for hazard and risk assessment. Therefore, a systematic approach is required to combine these quantitative methods with expert-based assumptions and decisions. In this study, a quantitative multi-hazard risk assessment was carried out in the Fella River valley, prone to debris flows and flood in the north-eastern Italian Alps. The main steps include data collection and development of inventory maps, definition of hazard scenarios, hazard assessment in terms of temporal and spatial probability calculation and intensity modelling, elements-at-risk mapping, estimation of asset values and the number of people, physical vulnerability assessment, the generation of risk curves and annual risk calculation. To compare the risk for each type of hazard, risk curves were generated for debris flows, river floods and flash floods. Uncertainties were expressed as minimum, average and maximum values of temporal and spatial probability, replacement costs of assets, population numbers, and physical vulnerability. These result in minimum, average and maximum risk curves. To validate this approach, a back analysis was conducted using the extreme hydro-meteorological event that occurred in August 2003 in the Fella River valley. The results show a good performance when compared to the historical damage reports.

The 1984 Bhopal accident in India resulted in severe consequences with more than a thousand people dying in the immediate vicinity of the Union Carbide facility. After this tragedy, the implementation of landuse and zoning restrictions around hazardous installations got accepted worldwide as an important strategy reducing consequences from potential industrial accidents. Many European countries have already formulated specific landuse planning policies taking industrial risks into account. However, till date India is yet to effectively employ risk assessment techniques for landuse planning decisions around industrial clusters, as well as the relevant acceptability or tolerability criteria are yet to be formulated. In this paper, we have applied the classical quantitative risk assessment method to map cumulative risk levels arising from a number of hazardous installations located in Haldia, a densely populated area where several industrial plants storing and processing dangerous substances are located. The risk maps were prepared using common GIS tools and functions, and their sensitivity to various factors ascertained using uncertainty analysis techniques. Through the analysis of some reference plants, the aim of the paper is to underline the current difficulties an analyst has to face to determine confident risk maps as a basis for planning the uses of land due to deficiencies in the Indian legislation and the lack of guidelines. (C) 2015 Elsevier Ltd. All rights reserved.

This paper shows the results of Short-Wavelength Infrared (SWIR) spectroscopy investigations of volcanic rocks sampled from low-grade metamorphic greenstone belts of the Archean Pilbara Craton in Western Australia. From the reflectance spectra a range of spectrally active minerals were identified, including chlorites, hornblende, actinolite, epidote and white micas. The rock samples were grouped into mineral assemblages based on their spectrally identified minerals and stratigraphic positions. The metamorphic amphibolite and greenschist facies could be identified from the SWIR spectroscopic data as well as three sub zones of the greenschist facies: 1) a zone containing Fe-chlorite; 2) a zone containing intermediate chlorite and epidote; and 3) a zone containing intermediate chlorite, actinolite and hornblende. Spectral parameters were calculated from the reflectance spectra to assess the metamorphic grade and zones. Plots of the depth parameters of the Fe-OH feature near 2250 nm versus the Mg-OH feature near 2390 nm differentiate the metamorphic amphibolite and greenschist facies and a transition zone between the two. The wavelength position parameter of the Mg-OH absorption feature near 2340 nm also serves to discriminate between the various metamorphic sub zones. The identification of the metamorphic grades of the volcanic sequences in greenstone belts with SWIR spectroscopy is useful for regional geological field studies, exploration for metamorphic mineral deposits hosted in the greenstone belts and the interpretation of hyperspectral remote sensing data sets covering similar types of terranes.

Due to rapid industrialization, with high population density and constraints of land, it is expected that level of risks arising from the hazardous industries will increase in India in the coming decades. However, 30 years after the Bhopal accident (1984), except a few discrete regulations, there is as yet no integrated system for assessing and managing risks arising out of these hazardous industries in India. The gravity of aspects related to the management of industrial risk still remains crucially important. In particular, there is no standard guideline on risk analysis methodology, acceptability or tolerability criteria, nor is there an accident database or a risk reduction strategy for the areas where risk levels are already high. On top of this, there are technical and legislative gaps in the institutional framework to implement any of the above mentioned issues. With the backdrop of the Bhopal gas tragedy, the objective of this paper is therefore to evaluate the effectiveness of a comprehensive risk assessment framework for the emerging economy of India, in order to control and/or to reduce the risk level that exists. In this context, regulations and policies pertaining to industrial risk assessment were reviewed.

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method. HighlightsWe introduce a normalization approach for detection of extremes.We consider both the spatial and temporal dimensions of geophysical data.We apply the method and test its sensitivity on hyper-temporal satellite data.

Previous prospectivity modelling for epithermal Au–Ag deposits in the Deseado Massif, southern Argentina, provided regional-scale prospectivity maps that were of limited help in guiding exploration activities within districts or smaller areas, because of their low level of detail. Because several districts in the Deseado Massif still need to be explored, prospectivity maps produced with higher detail would be more helpful for exploration in this region. We mapped prospectivity for low- and intermediate-sulfidation epithermal deposits (LISEDs) in the Deseado Massif at both regional and district scales, producing two different prospectivity models, one at regional scale and the other at district-scale. The models were obtained from two datasets of geological evidence layers by the weights-of-evidence (WofE) method. We used more deposits than in previous studies, and we applied the leave-one-out cross validation (LOOCV) method, which allowed using all deposits for training and validating the models. To ensure statistical robustness, the regional and district-scale models were selected amongst six combinations of geological evidence layers based on results from conditional independence tests. The regional-scale model (1000 m spatial resolution), was generated with readily available data, including a lithological layer with limited detail and accuracy, a clay alteration layer derived from a Landsat 5/7 band ratio, and a map of proximity to regional-scale structures. The district-scale model (100 m spatial resolution) was generated from evidence layers that were more detailed, accurate and diverse than the regional-scale layers. They were also more cumbersome to process and combine to cover large areas. The evidence layers included clay alteration and silica abundance derived from ASTER data, and a map of lineament densities. The use of these evidence layers was restricted to areas of favourable lithologies, which were derived from a geological map of higher detail and accuracy than the one used for the regional-scale prospectivity mapping. The two prospectivity models were compared and their suitability for prediction of the prospectivity in the district-scale area was determined. During the modelling process, the spatial association of the different types of evidence and the mineral deposits were calculated. Based on these results the relative importance of the different evidence layers could be determined. It could be inferred which type of geological evidence could potentially improve the modelling results by additional investigation and better representation. We conclude that prospectivity mapping for LISEDs at regional and district-scales were successfully carried out by using WofE and LOOCV methods. Our regional-scale prospectivity model was better than previous prospectivity models of the Deseado Massif. Our district-scale prospectivity model showed to be more effective, reliable and useful than the regional-scale model for mapping at district level. This resulted from the use of higher resolution evidential layers, higher detail and accuracy of the geological maps, and the application of ASTER data instead of Landsat ETM + data. District-scale prospectivity mapping could be further improved by: a) a more accurate determination of the age of mineralization relative to that of lithological units in the districts; b) more accurate and detailed mapping of the favourable units than what is currently available; c) a better understanding of the relationships between LISEDs and the geological evidence used in this research, in particular the relationship with hydrothermal clay alteration, and the method of detection of the clay minerals; and d) inclusion of other data layers, such as geochemistry and geophysics, that have not been used in this study.

In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.

Monitoring soil surface dynamics in semi-arid agricultural landscapes becomes increasingly important due to the vulnerability of these ecosystems to desertification processes. Observations using remote sensing via the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions can be limited due to the special characteristics of such soils (e.g. rich in quartz, poor in clay minerals, coarse textured, and grain coatings). In this laboratory-based work we demonstrate the capabilities of the thermal infrared between 8 and 14 μm (longwave infrared) to detect and quantify small ranges of the soil properties sand-, clay, and soil organic carbon (SOC) content, as they appear in the semi-arid agricultural landscapes of the Mullewa region in Western Australia. All of the three soil properties could be predicted using the longwave infrared (LWIR) spectra with higher accuracy and precision than from the VNIR-SWIR wavelength region. The study revealed the complex relationships between the soil properties and the VNIR-SWIR soil spectra, which were caused by the spectral influence of the soils' grain coatings (based on iron and clay minerals). These difficulties could be handled more appropriately by the prediction models based on the LWIR soil spectra. Our results indicate that in order to quantitatively monitor farming areas for such erosion-related soil properties; remote sensing using the LWIR wavelength region would produce better estimates than using the wavelength ranges in the VNIR-SWIR.

In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.

Rolling resistance is one of the main forces resisting wheelchair propulsion and thus affecting stress exerted on the upper limbs. The present study investigates the differences in rolling resistance, propulsion efficiency and energy expenditure required by the user during power-assisted and manual propulsion. Different tire pressures (50%, 75%, 100%) and two different levels of motor assistance were tested. Drag force, energy expenditure and propulsion efficiency were measured in 10 able-bodied individuals under different experimental settings on a treadmill. Results showed that drag force levels were significantly higher in the 50%, compared to the 75% and 100% inflation conditions. In terms of wheelchair type, the manual wheelchair displayed significantly lower drag force values than the power-assisted one. The use of extra-power-assisted wheelchair appeared to be significantly superior to conventional power-assisted and manual wheelchairs concerning both propulsion efficiency and energy expenditure required by the user. Overall, the results of the study suggest that the use of power-assisted wheelchair was more efficient and required less energy input by the user, depending on the motor assistance provided.

To assess the usability of satellite rainfall estimates for erosion studies in Cape Verde, the 3B42 and 3B43 products from TRMM, and the Multi-Sensor Precipitation Estimate MPE from Meteosat are compared to daily and monthly ground rainfall measured between 1998 and 2010. TRMM estimates from 1998 and MPE from 2009 were studied and it was found that they detect the general trend and direction of rainy clouds but underestimate the amount of rainfall compared to rain gauges. To obtain an improved rainfall estimate from Meteosat data, the cloud top temperature derived from the 10.8 μm infrared channel was correlated to rainfall intensity measured at a 3-minute interval between 2008 and 2010 by a Parsivel optical disdrometer. A modified exponential equation of daily rainfall intensity as a function of cloud top temperature was obtained. The equation was applied to 15-minute intervals from the infrared channel to obtain 15‐minute rainfall and summed up as daily rainfall. The daily rainfall depths estimated from cloud top temperature showed a better correspondence with the amounts measured by ground gauges on the island (r = 0.75), and are a first approach to cope with lack of short duration rainfall data for erosion studies in island states such as Cape Verde.

Land degradation processes in hilly areas are often directly related to land cover changes. At the same time, many hilly areas in the tropics are inaccessible and thus lack proper data. As a result, data scarcity is a crucial problem when trying to assess soil erosion. In this paper a method is described showing the use of digital elevation, remote sensing and field measurement techniques for deriving necessary land cover parameters as required for erosion modeling. The study shows that correction of topography-induced illumination variation in remote sensing data helps improve classification accuracy while field measurements help in accurate estimation of cover factor. Similarly, the incorporation of DEM-derived parameters such as upslope catchment area and flow direction network in erosion modeling helps not only to improve the estimation of soil losses but also to identify the dominant erosion process active in the area. This makes the formulation of sound conservation measures for minimizing soil losses and reducing off-site erosion effects in low lying areas much easier.

A new method is presented for the exploratory analysis of hyperspectral OMEGA imagery of Mars. It involves mapping the wavelength position and depth of the deepest absorption feature in the range between 2.1 and 2.4 µm, where reflectance spectra of minerals such as phyllosilicates, carbonates and sulphates contain diagnostic absorption features. For each pixel of the image, the wavelength position maps display the wavelength position of the deepest absorption feature in color and its depth in intensity. This can be correlated with (groups of) minerals and their occurrences. To test the validity of the method, comparisons were made between wavelength position maps calculated from OMEGA images of the Nili Fossae area at two different spatial resolutions, of 0.95 and 2.2 km, and five CRISM images in targeted mode, at 18 m spatial resolution. The wavelength positions and their spatial patterns in the two OMEGA images were generally similar, except that the higher spatial resolution OMEGA image showed a larger diversity of wavelength positions and more spatial detail than the lower resolution OMEGA image. Patterns formed by groups of pixels with relatively deep absorption features between 2.250 and 2.350 µm in the OMEGA imagery were in agreement with the patterns calculated from the CRISM imagery. The wavelength positions of clusters of similar pixels in the wavelength position maps are consistent with groups of minerals that have been described elsewhere in the literature. We conclude that mapping the wavelength position of the deepest absorption features between 2.1 and 2.4 µm provides a useful method for exploratory analysis of the surface mineralogy of Mars with hyperspectral OMEGA imagery. The method provides a synoptic spatial view of the spectral diversity in one single image. It is complementary to the use of summary products, which many researchers have been using for assessment of the information content of OMEGA imagery. The results of the exploratory analysis can be used as input for the construction of surface mineralogical maps. The wavelength position mapping method itself is equally applicable to other terrestrial and planetary data sets and will be particular useful in areas where field validation is sparse and with imagery containing shallow spectral features.

Sentinel-2 is ESA's medium spatial resolution (10–60 m) super-spectral instrument aimed at ensuring data continuity for global land surface monitoring of Landsat and SPOT. Several simulation studies have been conducted that show the potential of Sentinel-2 for estimating biophysical and biochemical parameters such as leaf area index, chlorophyll and nitrogen, and spectral products such as the red edge position and NDVI time series, providing data continuity for a number of other operational sensors. This paper aims at simulating Sentinel-2 products that are relevant to the geology and soil science community and we compare these to well established band ratio products from ASTER. As the basis for the simulation we use airborne hyperspectral imagery from the HyMAP sensor which were spectrally and spatially resampled to the resolutions of ASTER and Sentinel-2 using actual spectral response functions and pixel aggregate, respectively. The simulated image products demonstrate a good correspondence between ASTER and Sentinel-2 VNIR and SWIR bands. A number of band ratios are proposed for Sentinel-2 to derive the following products: ferric iron, ferrous iron, laterite, gossan, ferrous silicate and ferric oxides. These are compared to established band ratios of real ASTER data as well as simulated (from HyMAP) ASTER data and they correlate favourably. Investigating the spatial patterns reveals that there is a good match between the proposed Sentinel-2 band ratio products and those of ASTER. The resulting band ratio products are compared to the local geologic map of the imaged hydrothermal area (the Rodalquilar mining area, Cabo de Gata volcanic field, SE Spain). It is shown that they support the existing conceptual geologic model of the epithermal deposit.

A graphics processing unit (GPU) can perform massively parallel computations at relatively low cost. Software interfaces like NVIDIA CUDA allow for General Purpose computing on a GPU (GPGPU). Wrappers of the CUDA libraries for higher-level programming languages such as MATLAB and IDL allow its use in image processing. In this paper, we implement GPGPU in IDL with two distance measures frequently used in image classification, Euclidean distance and spectral angle, and apply these to hyperspectral imagery. First we vary the data volume of a synthetic dataset by changing the number of image pixels, spectral bands and classification endmembers to determine speed-up and to find the smallest data volume that would still benefit from using graphics hardware. Then we process real datasets that are too large to fit in the GPU memory, and study the effect of resulting extra data transfers on computing performance. We show that our GPU algorithms outperform the same algorithms for a central processor unit (CPU), that a significant speed-up can already be obtained on relatively small datasets, and that data transfers in large datasets do not significantly influence performance. Given that no specific knowledge on parallel computing is required for this implementation, remote sensing scientists should now be able to implement and use GPGPU for their data analysis.

This study focused on the sensitivity of flood dynamics to soil hydraulic properties derived from three different soil databases in urbanized area: (1) upscaled locally observed soil texture data based on the soil-landscape relationships (SMLS); (2) the SoilGrids250m open data source (SMSG); and (3) the FAO soil map (SMFAO). However, soil in the urban areas has two major conditions related to land cover and soil physical structure for which the information is not available in soil databases: the effect of fragmented vegetation cover and the effect of compaction of bare soil. Non-built-up areas can be covered by fragmented vegetation (grass and shrubs) which generally has high infiltration rates. In contrast, bare areas such as dirt roads and footpath can be heavily compacted and have typically low infiltration rates. We used Pedotransfer functions (PTFs) with satellite-derived vegetation cover and bare soil to predict soil hydraulic properties related to the uncompacted and compacted scenarios. Infiltration dynamics was derived from the predicted soil hydraulic properties for these soil information sources, which then determined the flood dynamics in the catchment. The flash flood modeling was done using the integrated flood modeling system using openLISEM (Bout and Jetten, 2018) for the whole of Kampala (Uganda) using the 25th of June 2012 flood event. In the distributed openLISEM hydrological model, these two urban soil conditions have been treated separately. We have evaluated the sensitivity of flood dynamics to three different soil databases under both uncompacted and compacted urban soil conditions by using different flood indicators such as catchment water balance, infiltration rate, flood depth and duration, flooded area and flood volume, and the average number of structures affected. The result of the study indicates that soil hydraulic properties needed for the distributed hydrological model are better predicted when using the SMSG and SMLS, which resulted in better infiltration simulation. Consequently, compared to an earlier simulation that was verified with stakeholders and accepted for drainage system design, the accuracy of the simulated flood extent map was better when using SMSG and SMLS. Moreover, soil compaction significantly reduces infiltration and consequently increases the flood depth and duration, and therefore, must be included in the urban flash flood modeling study.

Crustal structure in South America is one of the least understood among the Earth's continental areas. Variations in crustal thickness are still poorly constrained over large portions of the continent because of scarce or unevenly distributed crustal thickness estimates throughout South America. To address this scarce and inhomogeneous data cover we explore the possibility to derive crustal thickness from satellite gravity data. In this study, we utilize the combined gravity model EIGEN-6C, which is composed of GOCE and other gravity data. The Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite has a much more uniform spatial resolution than any land-based gravity or seismic survey in South America. The gravity data inversion is for a simple two-layer model with fixed density contrast over the interface, the Moho. The method is not relying on point constraint data and assumes that all of the signal is related to topography of the Moho. Model quality can therefore be assessed by a comparison with point observations on crustal thickness. We show that for the stable part of the continent 90% of our estimates are similar, within error bounds, to seismic observations. Variations occur in active orogenic zones or regions with suspected non-standard Moho density contrasts. A comparison with seismological models shows a high correlation with the most recent model. Especially in areas where continental and global models of crustal structure have limitations in terms of wave paths or point constraints the gravity based model provides a unique continuity of crustal structure providing new insights on structure and tectonics and increase our understanding of the Earth's structure underneath South America.

International audience; The development of surface fissures is an important indicator for understanding and forecasting slopemovements. Landslide investigations therefore frequently include the elaboration and interpretation of maps representing their spatial distribution, typically comprising intensive field work and instrumentation. It is only recently that aerial photography with sub-decimetre spatial resolution is becoming more commonly available and opens a window to analyse such features from a remote sensing perspective.While these data are in principle helpful to elaborate maps from image interpretation techniques, there is still no image processing technique available to extract efficiently these geomorphological features. This work proposes a largely automated technique for the mapping of landslide surface fissures from very-high resolution aerial images. The processing chain includes the use of filtering algorithms and post-processing of the filtered images using object-oriented analysis. The accuracy of the resulting maps is assessed by comparisons with several expert maps in terms of affected area, fissure density and fissure orientation. Underhomogenous illumination conditions, true positive rates up to 65% and false positive rates generally below 10% are achieved. The resulting fissure maps provide sufficient detail to infer mechanical processes at the slope scale and to prioritize areas for more detailed ground investigations or monitoring.

Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular.

Earthquake-triggered landslide dams are potentially dangerous disrupters of water and sediment flux in mountain rivers, and capable of releasing catastrophic outburst flows to downstream areas. We analyze an inventory of 828 landslide dams in the Longmen Shan mountains, China, triggered by the M-w 7.9 2008 Wenchuan earthquake. This database is unique in that it is the largest of its kind attributable to a single regional-scale triggering event: 501 of the spatially clustered landslides fully blocked rivers, while the remainder only partially obstructed or diverted channels in steep watersheds of the hanging wall of the Yingxiu-Beichuan Fault Zone. The size distributions of the earthquake-triggered landslides, landslide dams, and associated lakes (a) can be modeled by an inverse gamma distribution; (b) show that moderate-size slope failures caused the majority of blockages; and (c) allow a detailed assessment of seismically induced river-blockage effects on regional water and sediment storage. Monte Carlo simulations based on volumetric scaling relationships for soil and bedrock failures respectively indicate that 14% (18%) of the estimated total coseismic landslide volume of 6.4 (14.6) x 10(9) m(3) was contained in landslide dams, representing only 1.4% of the >60,000 slope failures attributed to the earthquake. These dams have created storage capacity of similar to 0.6x 10(9) m(3) for incoming water and sediment. About 25% of the dams containing 2% of the total river-blocking debris volume failed one week after the earthquake; these figures had risen to 60% (similar to 20%), and >90% (>90%) within one month, and one:year, respectively, thus also emptying similar to 92% of the total potential water and sediment storage behind these, dams within one year following the earthquake. Currently only similar to 0.08 x 10(9) m(3) remain available as natural reservoirs for storing water and sediment, while similar to 0.19 x 10(9) m(3), i.e. about a third of the total river-blocking debris volume, has been eroded by rivers. Dam volume and upstream catchment area control to first order the longevity of the barriers, and bivariate domain plots are consistent with the observation that most earthquake-triggered landslide dams were ephemeral. We conclude that the river-blocking portion of coseismic slope failures disproportionately modulates the post-seismic sediment flux in the Longmen Shan on annual to decadal timescales.

In this paper, the threat posed to cultural heritage by landslides and avalanches is analysed for two communities (Ushguli and Mulakhi) in the Upper Svaneti region in Georgia. The vulnerability of 60 cultural heritage objects has been evaluated through a conservation calculation based on an existing methodology using a State of Conservation Index (SCIx), which served as an input in a Spatial Multicriteria Evaluation (SMCE). Factors that are considered important for the occurrence of landslides (slope, landcover, lithology and drainage density) and snow avalanches (slope, insolation, slope curvature and landcover) have been used to generate a susceptibility map. A qualitative risk assessment was carried out by combining susceptible areas and cultural heritage objects. As there were very limited historical data available on the occurrence of landslides and snow avalanches, a combination of local and expert knowledge has been used to extract information on both cultural heritage and natural hazards. Existing management plans were also analysed to evaluate how natural hazards could be incorporated. Finally, some recommendations are given related to the analysis of the impact of natural hazards on cultural heritage in Georgia.

Airborne hyperspectral imagery was used to study the distribution of white mica minerals in Archean (3.2 Ga) submarine hydrothermal systems associated with volcanogenic massive sulfide mineralization in a well-exposed volcanic sequence of the Soansville greenstone belt in the Pilbara, Western Australia. White mica mineral abundance and distribution maps were compared with published hydrothermal alteration maps and differences were interpreted using whole-rock geochemistry and temperature estimates from oxygen isotope geothermometric studies of hydrothermally altered rocks. Three different zones were identified from the hyperspectral imagery: 1) Al-rich white mica zones in the upper parts of the volcanic sequence which are related to recharge of unevolved seawater, 2) Al-poor white mica zones at middle and upper levels of the volcanic sequence predominantly related to K alteration by more-evolved hydrothermal fluids, and 3) high to intermediate Al-content white mica zones in lower levels of the sequence and in cross-cutting zones related to intense alteration by laterally flowing and upwelling evolved fluids. The integrated study of the spatial distribution of hyperspectrally detected white mica minerals together with published maps and rock analyses allowed characterization of the hydrothermal systems and reconstruction of paleo fluid pathways.

Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.

Event-based landslide maps display landslide events of different magnitudes that are associated with a single major trigger. Event-based landslide maps allow the estimation of spatio-temporal probabilities of landslide events that are required for the assessment of landslide hazard. In this paper, we discuss the generation of event-based landslide inventory maps for a region in Darjeeling Himalayas (India) with limited historical landslide information. We used eight different sets of source data, spanning the period from 1968 to 2007, which include topographic maps, satellite images and post-event field maps. For each period we mapped the various landslide types, and calculated landslide density and activity in individual terrain units to analyze changes in the spatial behavior of landslides through time. For the assessment of the temporal landslide probability, we used landslide event-days and associated daily and antecedent rainfall amounts in a multivariate statistical analysis to model the temporal relationship between landslide events and the amount of triggering rainfall. For estimating the magnitude probability, we applied magnitude–frequency analysis for the different event-based landslide maps using landslide area (m2) as a proxy. Despite the incompleteness of the source data sets used, the resulting landslide inventory maps can be used successfully in determining temporal and magnitude probabilities of future landslides.