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Earthquakes do not only trigger landslides in co-seismic phases but also elevate post-seismic landslide susceptibility either by causing a strength reduction in hillslope materials or by producing co-seismic landslide deposits, which are prone to further remobilization under the external forces generated by subsequent rainfall events. However, we still have limited observations regarding the post-seismic landslide processes. And, the examined cases are rarely representative of tropical conditions where the precipitation regime is strong and persistent. Therefore, in this study, we introduce three new sets of multi-temporal landslide inventories associated with subsets of the areas affected by 1) 2016 Reuleuet (Indonesia, Mw = 6.5), 2) 2018 Porgera (Papua New Guinea, Mw = 7.5) and 3) 2012 Sulawesi (Indonesia, Mw = 6.3), 2017 Kasiguncu (Indonesia, Mw = 6.6) and 2018 Palu (Indonesia, Mw = 7.5) earthquakes. Overall, our findings show that the landslide susceptibility level associated with the occurrences of new landslides return to pre-seismic conditions in less than a year in the study areas under consideration. We stress that these observations might not be representative of the entire area affected by these earthquakes but the areal boundaries of our study areas.

The Himalayan rivers are glacier-fed and are vulnerable to devastating flash floods caused by damming of landslides and outbreak of glacial lakes. On 7 February 2021, around 10:30 am IST, a huge block of glacier mass broke from the Nanda Ghunti glacier. It is evident from the multi-temporal satellite imageries from Planet Scope that snow dust deposited in the affected area. During the course of the event, a huge amount of debris along with broken glacial fragments flooded the Rishi Ganga river and washed away the Hydropower plants; Rishi Ganga and Tapovan, more than 71 people were killed, and about 100 people are still missing. Detailed analysis of optical and radar data has been carried out to show the impact of the rockslide, changes in the surface characteristics of the source region, flood plains of the river and water quality of the Himalayan rivers (Alaknanda and Ganga). We have used five different indices Modified Normalized difference water index (MNDWI), Normalized difference vegetation index (NDVI), Enhanced vegetation index (EVI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), that show pronounced changes in water quality and flood plain at the four different sections of the river. The spectral reflectance and backscattering coefficients derived from high-resolution Planet scope and Sentinel 1 SAR data show characteristics behaviour of the flood plain and water quality. Further, we have also found changes in the water quality of several canals after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal.

The increasing demand of Rare Earth Elements (REE) in the industry and their economic importance plays a crucial role in the mining exploration. More knowledge on unconventional deposits such as dumps and tailings should be gathered to recover REEs. Purwadi et al. (2018) studied the concentration and visible and near-infrared reflectance spectroscopy of REEs-bearing tailings of a closed tin mine located on the Bangka Island (Indonesia) but detected no REEs bearing minerals due to their low abundance (

This paper discusses approaches to evaluate how landslide risk might change over time. Multi-hazard risk assessment (MHRA) is the quantitative estimation of the spatial distributions of potential losses for an area, of multiple natural hazards with different hazard interactions, with multiple event probabilities, for multiple types of elements-at-risks, and multiple potential loss components. The paper first discusses the various types of hazard interactions in which landslides are involved. An example is presented of a MHRA at district level in Tajikistan for a combination of seven hazard types (earthquakes, floods, windstorms, drought , landslides , mudflows and snow avalanches ) and five types of elements-at-risk (Built-up area, buildings, people, agriculture and roads). After discussing problems involved in such types of static MHRA projects, the paper continues with describing the need for analyzing changing multi-hazard risk as a basis for decision-making. These changes may be related to changes in triggering or conditional factors, increasing exposure of elements-at-risk, and their vulnerability and capacity. Dynamic risk can be evaluated on the long term because of changes in climate, land use, population density, economy, or social conditions. Climate change scenarios still contain large uncertainties in the changes in return periods of extreme rainfall events, and their spatial variation in mountainous areas. In addition, feedback mechanisms between climate change and land use change play an important role, but are difficult to incorporate in the risk assessment. Changes in landslide risk might also be occurring in a short time frame, and assessed as a basis for impact based forecasting, and to analyze the consequences of hazard interactions after major events, for instance after the occurrence of wildfires, major earthquakes, volcanic eruptions, or extreme rainfall events, which alter the conditional factors for the occurrence of landslides. An overview is given of the tools available for multi-hazard assessment, stressing the importance for developing integrated physically based multi-hazard models. One of such models, OpenLISEM Hazard, is presented in some more detail. In addition, an overview is given of the tools for multi-hazard risk assessment, stressing the need to incorporate landslide risk within the existing models, as currently this is not taken into account at a sufficient level. One example is given of a Spatial Decision Support System for dynamic multi-hazard analysis, which aims to assist stakeholders in decision making of optimal risk reduction alternatives under various future scenarios. This is further illustrated with a case study for the city of Envigado, near Medellin in Colombia. The OpenLISEM Hazard model is used to generate hazard scenarios for flood, landslides, and debris flows for different return periods of triggering rainfall. Building footprints are updated and classified into occupational types, and structural types, and absolute vulnerability curves were generated. Current multi-hazard risk is analyzed. After that several future scenarios of climate change and population change are outlined, for which the changing risk is analyzed. Finally, three alternatives for risk reduction are presented, their risk reduction is calculated, and eventually the optimally performing alternative under different future scenarios is selected.

Context: It is now widely accepted that the climate is changing, and that societal responses will need to be rapid and comprehensive to prevent the most severe impacts. A key milestone in global climate governance is to assess progress on adaptation. To-date, however, there has been negligible robust, systematic synthesis of progress on adaptation or adaptation-relevant responses globally. Aim: The purpose of this review protocol is to outline the methods used by the Global Adaptation Mapping Initiative (GAMI) to systematically review human adaptation responses to climate-related changes that have been documented globally since 2013 in the scientific literature. The broad question underpinning this review is: Are we adapting to climate change? More specifically, we ask ‘what is the evidence relating to human adaptation-related responses that can (or are) directly reducing risk, exposure, and/or vulnerability to climate change?’ This work responds to the recognition of the need for high-level syntheses of adaptation research to inform global and regional climate assessments.Methods: We review scientific literature 2013-2019 to identify documents empirically reporting on observed adaptation-related responses to climate change in human systems that can directly reduce risk. We exclude non-empirical (theoretical & conceptual) literature and adaptation in natural systems that occurs without human intervention. Included documents were coded across a set of questions focused on: Who is responding? What responses are documented? What is the extent of the adaptation-related response? What is the evidence that adaptation-related responses reduce risk, exposure and/or vulnerability? Once articles are coded, we conduct a quality appraisal of the coding and develop ‘evidence packages’ for regions and sectors. We supplement this systematic mapping with an expert elicitation exercise, undertaken to assess bias and validity of insights from included/coded literature vis a vis perceptions of real-world adaptation for global regions and sectors, with associated confidence assessments. Related protocols: This protocol represents Part 1 of a 5-part series outlining the phases of methods for this initiative. Part 1 provides an introduction to the Global Adaptation Mapping Initiative (GAMI) and an overview of methods.

Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-Term observations of heat and drought; and 11 large ensembles of state-of-The-Art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-Term warming trend. However, current climate models overestimate variability and tend to underestimate the long-Term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September-February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July-December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom dataset, it achieved 4.8% higher mean Average Precision (mAP) score, while being 91.4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes. Dedicated object tracking and speed estimation algorithms have been then optimized to track reliably objects from an UAV with limited computational effort. Different strategies to combine object detection, tracking, and speed estimation are discussed, too. From our experiments, the optimized detector runs at an average frame-rate of up to 29 frames per second (FPS) on frame resolution 512 × 320 on a Nvidia Xavier NX board, while the optimally combined detector, tracker and speed estimator pipeline achieves speeds of up to 33 FPS on an image of resolution 3072 × 1728. To our knowledge, the MultEYE system is one of the first traffic monitoring systems that was specifically designed and optimized for an UAV platform under real-world constraints.

Context: It is now widely accepted that the climate is changing, and that societal response will need to be rapid and comprehensive to prevent the most severe impacts. A key milestone in global climate governance is to assess progress on adaptation. To-date, however, there has been negligible robust, systematic synthesis of progress on adaptation or adaptation-relevant responses globally. Aim: The purpose of this review protocol is to outline the methods used by the Global Adaptation Mapping Initiative (GAMI) to systematically review human adaptation responses to climate-related changes that have been documented globally since 2013 in the scientific literature. The broad question underpinning this review is: Are we adapting to climate change? More specifically, we ask ‘what is the evidence relating to human adaptation-related responses that can (or are) directly reducing risk, exposure, and/or vulnerability to climate change?’ Methods: We review scientific literature 2013-2019 to identify documents empirically reporting on observed adaptation-related responses to climate change in human systems that can directly reduce risk. We exclude non-empirical (theoretical & conceptual) literature and adaptation in natural systems that occurs without human intervention. Included documents were coded across a set of questions focused on: Who is responding? What responses are documented? What is the extent of the adaptation-related response? What is the evidence that adaptation-related responses reduce risk, exposure and/or vulnerability? Once articles are coded, we conduct a quality appraisal of the coding and develop ‘evidence packages’ for regions and sectors. We supplement this systematic mapping with an expert elicitation exercise, undertaken to assess bias and validity of insights from included/coded literature vis a vis perceptions of real-world adaptation for global regions and sectors, with associated confidence assessments. Related protocols: This protocol represents Part 2 of a 5-part series outlining the phases of methods for this initiative. Part 2 outlines the methods used to conduct keyword searches and implement machine-assisted screening of documents based on inclusion and exclusion criteria. See Figures 1 and 2.

16 Pags.- 12 Figs.- 8 Tabls., Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper., Nejc Bezak and Matjaž Mikoš would like to acknowledge the support of the Slovenian Research Agency through grant P2-0180. Diana Vieira is funded by national funds (OE), through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen - DL57/2016 (CDL-CTTRI-97-ARH/2018 - REF.191-97-ARH/2018), and acknowledges CESAM financial support of through (UIDP/50017/2020+UIDB/50017/2020). Jae E. Yang and Pasquale Borrelli are funded by the EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea (Grant No. 2019002820004). Walter Chen is funded by the Ministry of Science and Technology (Taiwan) Research Project (Grant Number MOST 109-2121-M-027-001).

The effectiveness of damaged building investigation relies on rapid data collection, while jointly applying an unmanned aerial vehicle (UAV) and a backpack panoramic imaging system can quickly and comprehensively record the damage status. Meanwhile, integrating them for generating complete 3-D point clouds (3DPCs) is important for further assisting the 3-D measurement of the damaged areas. During the 2016 Meinong earthquake (Taiwan), the system collected multiview aerial images (MVAIs) and ground panoramic images of two collapsed buildings. However, due to the spatial offsets of the spherical camera result in nonideal panoramic images (NIPIs), an appropriate spherical radius has to be chosen to reduce the distance-related stitching errors. In order to evaluate the impact of using NIPIs for 3-D mapping, the geometric accuracy of the 3-D scene reconstruction (3DSR) and usability of the 3DPCs were assessed. This study introduces the stitching errors of panoramic images, uses sky masks for successful 3DSR, and obtains clean point clouds. It then analyzes the usability of point clouds that were obtained from only NIPIs, only MVAIs, and their integration. The analysis shows that NIPIs have more rapid processing efficiency than their unstitched original images and can increase the completeness of point clouds at the building’s lower floor, while MVAIs can reduce the stitching errors of NIPIs to an acceptable range. Therefore, integrating both images is necessary to achieve rapid and complete point cloud generation.

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses. Determining progress in adaptation to climate change is challenging, yet critical as climate change impacts increase. A stocktake of the scientific literature on implemented adaptation now shows that adaptation is mostly fragmented and incremental, with evidence lacking for its impact on reducing risk.

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

Landslide is a common natural disaster occurring in Indonesia during the rainy season from November to February. Attempts have been made to develop an early warning system based on the rainfall derived from satellite observation. It is essential to verify the accuracy level of the rainfall threshold in predicting the occurrence of rainfall, causing landslides and non-landslides to model the lower limit that can be used as an early warning device of the landslides. In this analysis, modelling was carried out with an empirical (intensity—duration/ID) approach using 220 data of rainfall that triggered landslide with satellite-based TRMM in Indonesia territory. The intensity and duration of antecedent rainfall were utilized in rainfall threshold modelling. The rainfall threshold was validated with ROC analysis. This method used seven statistics indices and ROC curve to determine the accuracy rate of the rainfall threshold. The results showed empirical equation I = 7.83D−0.328 within the interval time 2–18 days. The results of the analysis of the ROC on the rainfall threshold indicate that the model has a good accuracy rate and can be used in an early warning system of landslide even though it still has a fairly high error rate.

Defect-engineered or substoichiometric TiOx is of interest for use in photo- and electrocatalytic processes both as active material and catalyst support. Electrochemical doping of TiO2 via cathodic polarization is an appealing preparation method and frequently employed. Here, we explored the electrochemical preparation of TiOx in an undivided cell using iridium-based (iridium mixed-metal-oxide) and boron doped diamond (BDD) counter electrodes. Cyclic voltammetry and impedance spectroscopy revealed superior charge transfer properties of crystalline TiOx electrodes prepared with BDD (TiOx-BDD). It is shown that the electrochemical properties correlate well with intensities of the H-signals determined using Time of Flight - Secondary Ion Mass Spectrometry (ToF-SIMS). Thus, it is concluded that electrochemical preparation using BDD causes favourable H+ intercalation and/or H diffusion into the sub-surface layers of TiOx. Our extensive analysis using a combination of electrochemical and surface characterization (LEIS and XPS) techniques, additionally suggests that cathodic deposition of Ir, originating from the Ir-based counter electrode, present in sub-ppm concentrations only results in less-efficient doping. Instead in the presence of sub-ppm level Ir contamination hydrogen evolution is favoured during cathodic polarization. The results presented within this study highlight the necessity to use inherently stable counter electrodes for electrochemical preparation and reveal the pronounced influence of trace contamination in electrochemistry in general and the doping mechanism of TiOx electrodes in particular.

The Central Mediterranean, the area encompassing Italy, Sardinia, Tunisia and Libya, is characterised by multiple tectonic processes (plate convergence, subduction, and backarc extension). The evolution and interaction of the plate margins within this relatively small area are still being unravelled particularly at the adjacent region known as the Sicily Channel located between Sicily, Tunisia, Libya and Malta. This Channel is characterised by a seismically and volcanically active rift zone. Much of the observations we have today for the southern parts of the Calabrian arc are either limited to the surface and the upper crust, or are broader and deeper from regional seismic tomography, missing important details about the lithospheric structure and dynamics. The project GEOMED (https://geomed-msca.eu) addresses this issue by processing all the seismic data available in the region in order to understand better the geodynamics of the Central Mediterranean.We measure Rayleigh- and Love-wave phase velocities from ambient seismic noise recordings to infer the structures of the Central Mediterranean, from the Central Apennines to the African foreland, with a special focus on the Sicily Channel Rift Zone (SCRZ). The phase-velocity dispersion curves have periods ranging from 5 to 100 seconds and sample through the entire lithosphere. We invert the dispersion data for isotropic and polarised shear velocities with depth and infer crustal thickness and patterns of radial anisotropy. We find that continental blocks have thick crust (30-50 km), whereas beneath the SCRZ the crust is thin (SH>VSV) indicative of horizontal flow or extension, whereas the uppermost mantle is characterised by slow shear velocities indicative of warmer temperatures and strong negative radial anisotropy (VSH>VSV) indicative of vertical flow. We discuss the relevance of these findings together with other geophysical studies such as the regional seismicity and GPS velocity vectors to identify the rifting process type of the SCRZ.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 843696.

Identifying hydrothermal alteration minerals on both surface and subsurface (i.e. drill cores and cuttings) samples is essential to understand the hydrothermal processes and history of a geothermal system. With the development of infrared imaging spectroscopy, the future mineral identification analyses in geothermal exploration will likely use a lot of this new technique. The measurement collects the image of the samples as well as one infrared spectrum for every pixel of the image. As a result, one image contains a lot of infrared spectra. Thus, it is challenging to do a manual interpretation on individual spectra. The mineral identification is then expected to move towards a more autonomous way. Our research aims to test a classifier tool which is designed to mimics the knowledge and approach of spectral geology experts. We use the information on the commonly occurring minerals in geothermal systems and target their diagnostic spectral features to build the mineral classes. We combine the spectral characteristics of each of these minerals with the importance of them on deducing the hydrothermal conditions of geothermal systems for deciding the order of mineral classes. So in the case of pixel with a spectrum containing mineral mixtures, the pixel will be classified as the more important mineral class. In this paper, we would like to demonstrate how the classifier is built and what the expected outcomes look like. We aim for a classifier that can be applied reliably by non-experts working in any geothermal system.

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions.

Transportation networks are severely affected by natural hazards, including landslides. The prioritization of maintenance works is required to preserve the efficiency and functionality of road infrastructure. To overcome the subjectivity of traditional visual inspections for road pavement condition assessment, advanced (semi-)automatic approaches have been emerging. Still, the quantitative and objective description of damage typology and extent, and its severity classification remain the major issues for the assessment of landslide impacts on transportation routes. The objective of this work is to provide a ready-to-use tool for semi-automatic damage assessment of asphalt-paved roads in landslide-affected areas to support risk analysis and planning of mitigation measures. The use of 3D models and 2D images as reconstructed from UAV-based photogrammetry is investigated to detect longitudinal and transverse cracks on the road pavement and assess their severity in landslide areas, as a rapid, systematic, objective and less laborious alternative to traditional field surveys. A semi-automatic procedure is proposed to i) select asphalt-paved roads exposed to landslides, ii) rapidly map distresses on selected road sections, iii) quantitatively detect and describe longitudinal and transverse cracks, and iv) classify their severity according to the International Roughness Index (IRI). The procedure is applied to the Province of Como (northern Italy), where three test sites are selected for detailed analyses. The results indicate that the proposed procedure is useful for road management purposes at different levels of details by providing four outputs: i) a road damage hotspot map to detect deformations, ii) a multi-criteria binary classifier to detect pavement damage, iii) an IRI-based criterion to rate the pavement quality, and iv) a road damage severity map.

The study of Land Use Land Cover (LULC) is essential to understanding how land has been altered in recent years and what has caused the processes behind the change. This is significant for the future development of the area, particularly on the campus of the Universitas Padjadjaran Jatinangor. The purpose of this study was to apply remote-sensing techniques to map a university campus and vicinity by comparing the area of urban green space (UGS) and floor area ratios (FARs) of the campus in 2015 and 2017. Additionally, surface runoff analysis was also conducted. For our research, we used WorldView-2’s high-resolution satellite imagery with a resolution of 0.46 m in the Universitas Padjadjaran (Padjadjaran University, or Unpad) Jatinangor campus, Jawa Barat, Indonesia. Our approach was to interpret the imagery by running the normalized difference vegetation index (NDVI) to distinguish UGS and FAR and using digital elevation model (DEM) interferometric synthetic aperture radar (SAR) data with hydrologic analysis to identify the direction of surface runoff. The results obtained are as follows: the UGS remained more extensive compared with FAR, but the difference decreased over time owing to infrastructure development. Surface runoff has tended to flow toward the southeast in direct relation to the slope configuration.

Information on spectral emissivity (SE) is vital when retrieving and evaluating land surface temperature (LST) estimates from remotely sensed observations. SE measurements often come from spectral libraries based upon laboratory spectroscopic measurements, with uncertainties typically derived from repeated measurements. To go further, we organised a &ldquo, round-robin&rdquo, inter-comparison exercise involving SE measurements of three samples collected at seven different international laboratories. The samples were distilled water, which has a uniformly high spectral emissivity, and two artificial samples (aluminium and gold sheets laminated in polyethylene), with variable emissivities and largely specular and Lambertian characteristics. Large differences were observed between some measurements, with standard deviations over 2.5&ndash, 14 &mu, m of 0.092, 0.054 and 0.028 emissivity units (15.98%, 7.56% and 2.92%) for the laminated aluminium sheet, laminated gold sheet and distilled water respectively. Wavelength shifts of up to 0.09 &mu, m were evident between spectra from different laboratories for the specular sample, attributed to system design interacting with the angular behaviour of emissivity. We quantified the impact of these SE differences on satellite LST estimation and found that emissivity differences resulted in LSTs differing by at least 3.5 K for each artificial sample and by more than 2.5 K for the distilled water. Our findings suggest that variations between SE measurements derived via laboratory setups may be larger than previously assumed and provide a greater contribution to LST uncertainty than thought. The study highlights the need for the infrared spectroscopy community to work towards standardized and interlaboratory comparable results.

Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using this data along with each and all topographic factors across the west coast of the Trishuli River in Nepal. For the first time, the Dempster—Shafer (DS) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features such as barren lands and consequently increase the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the DS model can be considered as an optimizer method to combine the results from different scenarios.

We used existing high-resolution gravity and aeromagnetic data to map the crustal units underneath Botswana. The thick sedimentary cover in this region has always been a challenge to understand the geological and tectonic configuration of unexposed crustal terranes using traditional geological methods. We utilized a standard physical mapping technique to convert previously obtained gravity and aeromagnetic data into apparent density and magnetic susceptibility maps, respectively, of the crustal tectonic terranes. Then, the derived maps were fused into a single apparent physical map to facilitate its geotectonic interpretation. The results showed that most of the tectonic boundaries were inaccurately mapped due to limited availability of high-resolution geophysical information. Moreover, we found that some terranes were missing from the current tectonic map. We used the apparent physical property map and the basement rocks information from literature to significantly update the tectonic map of Botswana, which shows the spatial extent of the geological units beneath the Kalahari sedimentary cover. The new map gives an insight into the geodynamics of Botswana crust.

We present an unsupervised deep learning approach for post-disaster building damage detection that can transfer to different typologies of damage or geographical locations. Previous advances in this direction were limited by insufficient qualitative training data. We propose to use a state-of-the-art Anomaly Detecting Generative Adversarial Network (ADGAN) because it only requires pre-event imagery of buildings in their undamaged state. This approach aids the post-disaster response phase because the model can be developed in the pre-event phase and rapidly deployed in the post-event phase. We used the xBD dataset, containing pre- and post- event satellite imagery of several disaster-types, and a custom made Unmanned Aerial Vehicle (UAV) dataset, containing post-earthquake imagery. Results showed that models trained on UAV-imagery were capable of detecting earthquake-induced damage. The best performing model for European locations obtained a recall, precision and F1-score of 0.59, 0.97 and 0.74, respectively. Models trained on satellite imagery were capable of detecting damage on the condition that the training dataset was void of vegetation and shadows. In this manner, the best performing model for (wild)fire events yielded a recall, precision and F1-score of 0.78, 0.99 and 0.87, respectively. Compared to other supervised and/or multi-epoch approaches, our results are encouraging. Moreover, in addition to image classifications, we show how contextual information can be used to create detailed damage maps without the need of a dedicated multi-task deep learning framework. Finally, we formulate practical guidelines to apply this single-epoch and unsupervised method to real-world applications.

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.

The Geothermal Capacity Building program GEOCAP, a bilateral program (6MEURO) between Netherlands and Indonesia, has been running since 2013 and is foreseen to continue until 2022. In this public-private partnership we thus have a decade of experience which we share in this session. The session addresses sustainable solutions for geothermal exploration including environment friendly methods, looks at the synergy between surface and subsurface exploration methods, and how to upscale local geologic information to regional prospectivity. In this risk analysis and mitigation, geology as well as economy and the environment are integral components of a multi-disciplinary workflow developed by GEOCAP. Methods for modern exploration of geothermal resources include those derived from remote sensing and earth observation, 3D subsurface/surface (thermo) GIS and modelling, geophysics, geochemistry and engineering. We explore synergies between exploration methods directed towards derisking operations and increasing drilling success rates. In this session through Q&A and open discussion we intend to identify scientific white spots, leading to a preliminary science and policy agenda for future bilateral collaboration on geothermal exploration and education. By emphasizing the focus on (solving) real-world problems we feel there is broad scope for the private sector participation in the session and in setting the agenda. Our target audience are geoscientists, policy makers and private enterprises. This session emphasizes the use of remote sensing public domain images (SAR and thermal) for geothermal exploration, especially in high enthalpy volcanic geothermal systems which generally have significant surface manifestations and are mostly controlled by geological structures, both primary and secondary structures. Some of the developments in remote sensing techniques and methods that have been carried out and the case study of remote sensing analysis in geothermal exploration will also be presented as learning and discussion materials.

Estimating landslide volume based on pre- and post-event elevation models without detailed field investigations is still a major challenge due to hidden failure surfaces. A MATLAB model named “SLIPFITTER” was developed to model failure surfaces using polynomial surface to fit the geometry of exposed scarps extracted from Digital Elevation Models (DEMs). To test our model, a series of analog experiments were designed to acquire data for model testing in a controlled environment. The test indicates the only option to simulate failure surfaces with only remotely sensed data, is to assume that the failure surface was outcropping at the toe of slopes. It is not possible to model landslides that do not reach the bottom of slopes without survey data. Using the assumption, the model was tested on real landslides, showing its capability of modeling for multiple landslides over an area in a single simulation, and the possibility to predict potential failure surfaces from a reported crack. The major limitations of the model include the high requirement for the input DEM quality, the incapability to consider underlying bedrock structures, and the difficulty of simulating landslides with complex movements.

This paper gives an overview of recent research on generating landslide inventories of triggering events by earthquake and extreme rainfall in the Himalayan context, and how these influence subsequent landslide susceptibility, hazard and risk assessment. Within a collaboration project between between ITC, GSI and NRSC , a number of techniques were developed for landslide inventory mapping, ranging from using Object Based Image Analysis, to the use of available records and collaborative mapping using Google Earth Images for the generation of event-based landslide inventories. The completeness of landslide inventories and its spatial and thematic accuracy are discussed in relation to the effect they have on the evaluation of landslide susceptibility and hazard assessment. Also a collaboration with the USGS is discussed for the generation of a web-based portal of earthquake-induced landslide inventories for major historical earthquakes. It is very important to collect as many of these event-based inventories in order to better correlate them with the specific characteristics of the earthquake that triggered them and the terrain conditions, in order to develop methods for earthquake-induced landslide hazard. Event based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Examples are given of the work that was carried out after the 2015 Gorkha earthquake in Nepal. Object Based Image Analysis (OBIA) combined with machine learning methods were used to generate landslide inventories from before, during and after the earthquake, and the characteristics of earthquake-induced landslide inventories were compared with rainfall-induced landslide inventories. The contributing factors related to topography, geological and land cover were analyzed for both inventories for different landslide types and sizes. In order to develop rainfall thresholds as a basis for Landslide Early Warning a study was carried out in the Rasuwa district of Nepal, using a combination of satellite based rainfall estimates, rainfall station data, and physically-based modelling of soil water conditions and slope stability. Also multi-hazard relationships (earthquake-landslides-flooding-debrisflows) were modelled using a physically-based model in comparable environments affected by earthquakes, in order to analyze the post-earthquake changes in landslide hazard intensity.

The Kalsi-Chakrata road corridor, located in the Lesser Himalayas, experiences several landslides every year, resulting in a considerable amount of damage to roads, assets, and other infrastructure and even loss of lives. During the monsoon season (June–August), the disruption of routes due to landslide resulted in economic losses and barred villagers from accessing critical and essential facilities, thus impacting the livelihood of the communities residing along the road corridor. Hence immediate requirement was to systematically assess the landslide susceptibility for the study area that would support in the preparation of the planning and mitigation goal, both short and long term. The present study adopted the fuzzy analytic hierarchy process (fuzzy AHP) method integrated with geospatial technology that may be highly effective for landslide susceptibility assessment in the landslide-prone Lesser Himalayas. The use of validated landslide inventory data and high-resolution remote sensing images for selection and mapping of landslide conditioning factors gratifies the geospatial aspect of local variations across the Kalsi-Chakrata road corridor. For fuzzy AHP model setup, prominent landslide contributing factors viz., slope, aspect, altitude, lithology, proximity to road, fault & drainage, Stream Power Index (SPI), Topographical Wetness Index (TWI), rainfall, land use/land cover (LULC), soil, and seismicity, were mapped and classified into significant classes. The resultant landslide susceptibility map (LSM) shows that about 55% (45.23 km2) of the study area was categorized as a very high and high landslide susceptibility zone. Of this, about 21% (17.7 km2) was within very high LSM zone, while 33% (27.5 km2) fell under high landslide susceptibility categories. Approximately 17.6% (14.5 km2) areas fall within the moderately susceptible zone, where chances of future landslides may be amplified without periodic observation and prospective study. At the village level, it was observed that Jhutaya village, located nearer foothill of Lesser Himalayas, is most susceptible to landslide followed by Dhaira, Chapanu, and Sairi villages. The fuzzy AHP model shows 86.52% accuracy in landslide prediction evaluated through the ROC curve (at 95% confidence level). Hence, the output LSI may be referred by the planners and engineers for monitoring, prevention, and mitigation of landslide hazards and development of infrastructure in the Kalsi-Chakrata road corridor, and the fuzzy AHP methodology adopted may be applied in other areas of the Lesser Himalayas.

We have investigated the feasibility of using lab-based short-wave infrared (SWIR) hyperspectral imagery to identify and map the mineralogical composition of selected sedimentary drill core from the subsurface of the Netherlands. We aimed at i) identifying and mapping spectrally active minerals, such as white mica, clay and carbonate minerals, ii) the quantification of clay content in the core and their detection limits, iii) the possibility of mapping minerals in core samples with uneven surfaces with out-of-focus hyperspectral images, and iv) the added value of using high-resolution (26 µm pixel-size) compared to intermediate-resolution images (at 200 µm pixel-size). Twenty-six core samples from wells L18-06A, L16-02A, L16-03 and L16-14 were imaged with a SWIR hyperspectral scanner. The images were processed to interpretable products, such as spectral parameters and classified maps that enhanced the variation in mineralogical composition. Results showed that nearly all image-spectra of all core samples were dominated by white mica (muscovite-illite) and clay minerals (kaolinite-dickite and illite-smectite). The variation in white mica and clay minerals could be described by i) the wavelength position of the Al-OH feature near 2200 nm, ii) the muscovite-illite crystallinity value which is calculated by division of the Al-OH feature near 2200 nm and the water feature near 1900 nm, and iii) the ratio of illite relative to kaolinite-dickite. Secondary minerals that were observed using the SWIR hyperspectral images were Fe-Mg carbonates and Fe-oxides. The total clay content (the sum of white mica and clay minerals) could be estimated from the SWIR hyperspectral data by comparison with compositional data of previous mineralogical studies (with CHEMSCAN and SEM methods) on the same core samples. Regression models showed that the total clay content can be modeled from the depth of clay features near 1400 and 2200 nm with a mean error of 4.1 % in the preferred regression model. Since the depth of clay absorption features are direct indicators of the total clay content, the models are likely to be portable to other sedimentary core samples. However, the models should be tested on other core samples with a wider range of clay contents than the ranges of 6.7 to 27.9 % and 88.9 to 91.0 % of the sample set. The limit of detection is likely to be significantly lower than 6.7%, the lowest clay content encountered in the core sample set. Comparison with hyperspectral mineralogical maps obtained from semi-continuous core over the same intervals shows similar mineralogical patterns and variation as in the core samples. The spectral response and detail in hyperspectral images that are out-of-focus (Labscanner from -5 cm to +5 cm and SisuCHEMA from -2.6 cm to +0.6 cm) are still sufficient to identify the main mineralogical composition and total clay content. This makes it possible to image samples with uneven surfaces without adjusting the distance to the hyperspectral camera. Although images acquired at 200 μm pixel-size provide sufficient detail to identify the dominant white mica, clay and carbonate mineralogy and quantify total clay content, higher resolution images at provide more mineralogical and textural detail that may be required for more detailed investigations of drill core. Finally, results show that washing and drying have an effect on the SWIR spectra of the core samples. Drying and subsequent hydration of the samples affect the shape and depth of the water feature. The samples must be measured after similar treatment and under similar moisture conditions in order to obtain reproducible illite-crystallinity estimates. Initial attempts to develop spectral indicators for the identification of very-low permeability rocks are encouraging. Spectra of illite minerals, with relatively short wavelengths of OH and OH/H2O features near 1400 and 1900 nm, show significant correlations with the presence of very low permeability rocks. Further investigation is required to further define the relationship and to explain inconsistencies between the classification results of the core samples and larger intervals of archived core. We conclude that laboratory-based SWIR hyperspectral imaging at 200 μm provides a useful, non-destructive method to identify and map the white mica, clay and carbonate minerals in the investigated sedimentary drill core. Furthermore, this allows for quantification of the total clay content and offers the possibility to identify zones of very-low permeability.

We report new P and S wave velocity models of the upper mantle beneath southern Africa using data recorded on seismic stations spanning the entire subcontinent. Beneath most of the Damara Belt, including the Okavango Rift, our models show lower than average velocities (−0.8% Vp; −1.2% Vs) with an abrupt increase in velocities along the terrane's southern margin. We attribute the lower than average velocities to thinner lithosphere (~130 km thick) compared to thicker lithosphere (~200 km thick) immediately to the south under the Kalahari Craton. Beneath the Etendeka Flood Basalt Province, higher than average velocities (0.25% Vp; 0.75% Vs) indicate thicker and/or compositionally distinct lithosphere compared to other parts of the Damara Belt. In the Rehoboth Province, higher than average velocities (0.3% Vp; 0.5% Vs) suggest the presence of a microcraton, as do higher than average velocities (1.0% Vp; 1.5% Vs) under the Southern Irumide Belt. Lower than average velocities (−0.4% Vp; −0.7% Vs) beneath the Bushveld Complex and parts of the Mgondi and Okwa terranes are consistent with previous studies, which attributed them to compositionally modified lithosphere resulting from Precambrian magmatic events. There is little evidence for thermally modified upper mantle beneath any of these terranes which could provide a source of uplift for the Southern African Plateau. In contrast, beneath parts of the Irumide Belt in southern and central Zambia and the Mozambique Belt in central Mozambique, deep‐seated low velocity anomalies (−0.7% Vp; −0.8% Vs) can be attributed to upper mantle extensions of the African superplume structure.

Global simulations of hydrology and hydraulics combine network-type routing with hydrological simulations on large Hydrological Response Units (HRUs). In order to efficiently simulate the hydrological response of HRUs, empirical equations are generally applied to averaged physical parameters or empirical constants. The usage of physically-based equations on a spatial description of the HRU could provide benefits in accuracy and scenario assessment. In this research we develop a multi-parameter inter-variable terrain description to simulate HRU hydrology and flow. Water is distributed over these classifications and used for application of physically-based equations. A mass-balanced scheme is presented that uses parameter inter-variability matrices to update the distributions of water. Furthermore, the method is used to simulate processes using physically-based equations that result in fluxes from and to other classes within a distribution, resulting in a semi-spatial process description within an HRU. Finally, physically-based equations are thus applied to a dynamic semi-spatial HRU description using multiple inter-variable physical parameters. A variety of study cases on hypothetical and real events are performed to highlight the behavior and sensitivity of the model, as well as comparison to existing methods. Un-calibrated comparison with full spatial flow simulations show reasonable agreement with a Nash-Sutcliffe coefficient of 0.47. Comparison to simulations of real events on catchments in Northern China and Central Spain show good comparability with an average Nash-Sutcliffe coefficient of 0.56. The performance of the model indicates good applicability when considering the efficiency of the method. Simulation time is in the ranges from fractions of seconds on consumer hardware without dependency of the area of the catchment. Additionally, the model uses physical parameters as input and physically-based descriptions of processes. This results in interactions between infiltration and flow that alter flow dynamics and hydrograph shapes in similar manners to full spatial hydrology and flow models. Finally, the method provides the possibility of linked additional processes such as groundwater and erosion, and could be applied on a global scale.

Physical vulnerability is a challenging and fundamental issue in landslide risk assessment. Previous studies mostly focus on generalized vulnerability assessment from landslides or other types of slope failures, such as debris flow and rockfall, while the long-term damage induced by slow-moving landslides is usually ignored. In this study, a method was proposed to construct physical vulnerability curves for masonry buildings by taking the Manjiapo landslide as an example. The landslide's force acting on the buildings' foundation is calculated by applying the landslide residual-thrust calculation method. Considering four rainfall scenarios, the buildings' physical responses to the thrust are simulated in terms of potential inclination by using Timoshenko's deep-beam theory. By assuming the landslide safety factor to be landslide intensity and inclination ratio to be vulnerability, a physical vulnerability curve is fitted and the relative function is constructed by applying a Weibull distribution function. To investigate the effects of buildings' parameters that influence vulnerabilities, the length, width, height, and foundation depth and Young's modulus of the foundation are analysed. The validation results on the case building show that the physical vulnerability function can give a good result in accordance with the investigation in the field. The results demonstrate that the building length, width, and foundation depth are the three most critical factors that affect the physical vulnerability value. Also, the result shows that the higher the ratio of length to width of the building, the more serious the damage to the building. Similarly, the shallower the foundation depth is, the more serious the damage will be. We hope that the established physical vulnerability curves can serve as tools for the quantitative risk assessment of slow-moving landslides.

Soil liquefaction is one of recognized nonlinear devastating types of ground failures associated with earthquakes. The analyses frameworks for this phenomenon have been addressed using different methods and correlated triggering factors in case histories. In the current paper, a hybrid model using imperialistic competitive metaheuristic algorithm (ICA) incorporated with multi-objective generalized feedforward neural network (MOGFFN) for the purpose of liquefaction potential analysis was assessed. The optimum hybrid ICA-MOGFFN model was applied on a diversified database of 296 compiled case histories comprising nine of the most significant effective parameters on liquefaction. The result of ICA-MOGFFN model demonstrated for 3.01%, 2.09% and 7.46% progress in the success rates for the safety factor, liquefaction occurrence and depth of liquefaction. Accordingly, the conducted precision–recall curves showed 5.08%, 1.73% and 3.92% improvement compared to MOGFFN. Further evaluations using different statistical metrics represented superior progress in performance of hybrid ICA-MOGFFN. The capability of the developed method then was approved from observed agreement with other accepted procedures. The results implied that the developed hybrid model was a flexible and accurate enough tool that can effectively be applied for the liquefaction potential analyses. Using sensitivity analyses, the most and least effective inputs on the predicted liquefaction parameters were identified.

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change.However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0–2 years) compared to the late recovery phase (2–3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of -concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.

There is a gap between lab experiments where resistivity&ndash, soil moisture relations are generally very good and field studies in complex environmental settings where relations are always less good and complicated by many factors. An experiment was designed where environmental settings are more controlled, the best outside laboratory, to assess the transferability from lab to outdoor. A field experiment was carried out to evaluate the use of electric resistivity tomography (ERT) for monitoring soil moisture dynamics over a period of 67 days. A homogeneous site in the central part of The Netherlands was selected consisting of grass pasture on an aeolian sand soil profile. ERT values were correlated to gravimetric soil moisture samples for five depths at three different dates. Correlations ranged from 0.43 to 0.73 and were best for a soil depth of 90 cm. Resistivity patterns over time (time-lapse ERT) were analyzed and related to rainfall events where rainfall infiltration patterns could be identified. Duplicate ERT measurements showed that the noise level of the instrument and measurements is low and generally below 3% for the soil profile below the mixed layer but above the groundwater. Although the majority of the measured resistivity patterns could be well explained, some artefacts and dynamics were more difficult to clarify, even so in this homogeneous field situation. The presence of an oak tree with its root structure and a ditch with surface water with higher conductivity may have an impact on the resistivity pattern in the soil profile and over time. We conclude that ERT allows for detailed spatial measurement of local soil moisture dynamics resulting from precipitation although field experiments do not yield accuracies similar to laboratory experiments. ERT approaches are suitable for detailed spatial analyses where probe or sample-based methods are limited in reach or repeatability.

Seismic hazard analysis is carried out in this study by estimating ground motion for hypothetical earthquakes in the area of Muzaffarabad, Pakistan, with the MT solution of the 2005 Kashmir earthquake. The earth’s topography influences seismic waves by scattering and reflecting it, thereby causing spatial variation in seismic response. Using the moment tensor solution of the 2005 Kashmir earthquake, we perform 25 spectral element method (SEM)-based 3D simulations along major faults in the study area. The SEM model incorporates the topography and homogeneous half-space characteristics. Our results show that, beside topography, the relative location of the source with respect to slopes also has an influence on the observed variation in ground shaking amplitudes. By integrating the mean and standard deviation of estimated ground shaking from 25 simulations, we present a seismic hazard map for the study area. The map summarizes the topographic and potential source location effect on seismic-induced ground shaking in the study area. It provides a classification from hazardous to safe in relative terms and can be used as a guide in earthquake preparedness.

Two extreme heatwaves hit Western Europe in the summer of 2019, with historical records broken by more than a degree in many locations, and significant societal impacts, including excess mortality of several thousand people. The extent to which human influence has played a role in the occurrence of these events has been of large interest to scientists, media and decision makers. However, the outstanding nature of these events poses challenges for physical and statistical modeling. Using an unprecedented number of climate model ensembles and statistical extreme value modeling, we demonstrate that these short and intense events would have had extremely small odds in the absence of human-induced climate change, and equivalently frequent events would have been 1.5 °C to 3 °C colder. For instance, in France and in The Netherlands, the July 3-day heatwave has a 50–150-year return period in the current climate and a return period of more than 1000 years without human forcing. The increase in the intensities is larger than the global warming by a factor 2 to 3. Finally, we note that the observed trends are much larger than those in current climate models., Environmental Research Letters, 15 (9), ISSN:1748-9326, ISSN:1748-9318

The Academy of ICT Essentials for Government Leaders module series has been developed by the Asian and Pacific Training Centre for Information and Communication Technology for Development (APCICT). This module introduces disaster risk management (DRM) and provides an overview of how information and communication technologies (ICTs) can be used for DRM. A large number of examples and case studies on the applications of ICTs in DRM have been included in the module. The main objective of the module is to introduce the basic concepts of DRM and the applications of ICTs in disaster mitigation and prevention, preparedness, response, and recovery. The target audiences of this module are policymakers and civil servants at the national and local government levels who are responsible for or engaged in DRM activities.

Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of landslides over time rarely been documented due to limitations in available data sources to consistently map such events. We monitored the evolution of a road network and assessed its effect on slope stability for a ten year window in Arhavi, Turkey. The main road construction projects run in the area are associated with a hydroelectric power plant as well as other road extension works and are clearly associated with the vast majority (90.1%) of mass movements in the area. We also notice that the overall number and size of the landslides are much larger than in the naturally-occurring comparison area. This marks a strong and negative effect of human activities on the natural course of earth surface processes. Our findings show that the damage generated by the road construction is compatible with the possible effect of a theoretical earthquake with a magnitude greater than Mw=6.0. Overall, better co- and post-construction conditions should be ensured during and after road works to mitigate the risk to local communities. We also notice a significant variation in sediment transport as a result of road construction. As a result, our study fits in the big picture of Anthropocene related changes and specifically points out at problems in mountainous areas that could undoubtedly be better managed to reduce the risk to local communities.

Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

A large number of landslides in the Three Gorges Reservoir area in China have been reactivated by reservoir impoundment since 2003. Many of them were activated as slow-moving landslides, which caused severe damages to buildings and also pose a significant risk for residents. In this study, a quantitative procedure has been proposed to analyze and assess the landslide risks to buildings and lives by considering the Outang landslide as an example. First, the landslide segmentation was done by considering the sliding history, macroscopic deformation, and monitoring data of displacement. The SLOPE/W and SEEP/W modules of the Geostudio software were used to establish the geotechnical model and analyze the seepage, respectively. Under different scenarios of annual reservoir regulation and return period of rainfall events as determined by Pearson type III (P3) distribution, failure probabilities of every segmented part were obtained with Monte Carlo simulation. Second, the vulnerability of the elements at risk was quantitatively estimated using a model that incorporated the landslide intensity and resistance of the exposed elements. Then, through the analysis and comparison of scenario-based landslide risks, the evolution of landslide risk influenced by different combinations of triggering factors (i.e., reservoir water level and rainfall) was examined. Finally, landslide risks before and after the implementation of several risk reduction alternatives were compared to determine the benefits of alternatives. The risk maps displayed that the risk was the greatest for the scenario of the rapid decline of the reservoir water level in combination with heavy rainfall; the corresponding total annual economic and population risks of the landslide were estimated in 96.88 million ¥ and 216 lives, respectively. In this scenario, the economic risk level of the west deformation zone was the highest, and the total population risk was the highest in the lower unit of the landslide. Results of benefit-cost analysis suggested that real-time monitoring and resident relocation seem to be the most effective alternatives for risk reduction concerning the Outang landslide.

Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.