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Infrared spectroscopy is a non-destructive mineralogical analysis with minimum sample treatment required. The mineral identification in this method is based on the energy reflected by molecular vibrational processes upon incoming infrared radiation which is a proxy for mineralogy and mineral chemistry. With this method, we can identify minerals that are difficult to distinguish using some other methods (i.e. methylene blue stain test, binocular microscopy, and petrography), particularly clay minerals which are the important temperature indicator minerals in geothermal systems. Therefore, it is a promising mineralogy analytic method to be applied in geothermal subsurface samples. However, the application of the method in geothermal studies is still limited, especially the recently-developed infrared imaging spectroscopy (IRIS), which is the extension of the conventional (non-imaging) infrared spectroscopy. This paper reviews the current use of infrared spectroscopy for geothermal exploration and present preliminary results of our work using infrared imaging spectroscopy. Our preliminary results show that infrared imaging spectroscopy can identify much more minerals compared to non-imaging infrared spectroscopy, including minerals which are present only in a small amount. Spatial relationships between hydrothermal alteration minerals might be visible in bigger cuttings grains. However, the initial mineral map classification using Spectral Angle Mapper (SAM) shows that it is difficult to determine the threshold which will give an accurate assignment of minerals.

Surface manifestations are an indicative sign of the geothermal activity in the subsurface. By far not all geothermal systems develop surface manifestations but where they are present, they are a good starting point for early stage exploration efforts. Hence, it is crucial to have a technique that can successfully and reliably detect and map (and possibly monitor) surface manifestations. Remote sensing has the potential to support the mapping of such geothermal surface manifestations, including deposited and altered mineralization and temperature anomalies. In particular, thermal infrared bands of multispectral satellite sensors, for example, have been shown to be effective in detecting surface temperature anomalies in ideal circumstances. Practical experience with thermal infrared data, however, often shows a more complex situation: thermal anomalies appear subdued in satellite data with only moderate spatial resolution (e.g. ASTER @ 90 metres) and are not easily discriminated from false anomalies that are created by solar heating and differences in e.g. land cover, rock properties or soil/vegetation moisture. Night time acquisitions suppress these false anomalies only partially since the acquisition time of most satellite sensors (e.g. ASTER @ 10.30pm local time) is too early in the night for the temperatures of the surrounding strata to have reached an equilibrium. In this paper we highlight some of the factors that influence the process of anomaly detection, compared to ground to airborne temperature measurements of thermal anomalies in a controlled test site area. We also introduce the new higher spatial resolution and multi-temporal ECOSTRESS sensor (JPL/NASA) as a possible step forward in the future detection of geothermal temperature anomalies.

The goal of this work is to investigate the potential of PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral data to predict the concentration of four macronutrients (K, P, N, S) and four micronutrients (Ca, Fe, Mg, Zn) in final wheat production. All investigated nutrients are essential to improving human nutrition. The initial findings indicate accurate predictions for Zn, P, Mg, S, K, Ca and Fe (R2 ranging from 0.57 to 0.74). N was less accurately estimated (R2 of 0.49). We conclude that the foliar chemical properties and temporal dynamics as detected by hyperspectral data translate successfully to the target micro- and macronutrients composition of the wheat production.

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H–OH absorption feature at 1900 nm and the poorly defined Al–OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.

Hydro-morphological processes (HMP, i.e. all processes contained within the spectrum defined between debris flows and flash floods) initiate in response to intense rainfall events. Efficient HMP hazard assessment over large regions is often hindered because of limited rainfall observations over mountainous areas. Real-time and easily accessible satellite rainfall products offer new opportunities to address the observational coverage problem in ungauged catchments. In this work, two satellite rainfall products, Global Precipitation Measurement (GPM) and Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), are investigated by taking the ground measurements as a reference. Based on a close agreement between GPM and rain gauge data, daily rainfall together with a series of antecedent rainfall values were calculated to investigate the spatio-temporal distribution of HMP occurred from 2001 to 2015 across the whole Chinese territory. Ultimately, rainfall thresholds for HMP initiation within different geomorphological settings in China were obtained by: (1) building antecedent rainfall sequences consisting of HMP and non-HMP via 100 nonparametric bootstrapped replicates; (2) testing several percentiles of the rainfall distribution as thresholds for HMP occurrence; (3) optimizing rainfall thresholds for six geomorphological macro-regions in China. This study confirmed the ability of satellite data in defining the rainfall conditions for the triggering of HMP, acknowledging the potential underestimation and/or bias that characterize any satellite rainfall products. Our findings provides new insight on rainfall conditions responsible for HMP initiation at the Chinese national scale.

Within the humanitarian sector, there is a pressing need to scale up anticipatory action as climate change-related disasters increase. This article evaluates forecasts relating to extreme weather events – extreme rainfall, tropical cyclones, river flooding and storm surge – in Myanmar and the Philippines to assess the feasibility of using such forecasts to develop early warning systems and responses. To make best use of limited extant data, a variety of methods (reliability diagrams, hit rates, false alarm ratios, correlations) are employed. We review the skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) tropical cyclone forecasts and find that whilst errors in cyclone position are relatively small, forecasting intensity is more difficult. When a tropical cyclone has formed, the probabilities provided in the ECMWF track forecast are highly reliable and only slightly overconfident. A tropical cyclone activity product is relatively reliable for forecasts less than a week ahead for North Indian Ocean cyclones affecting Myanmar, but becomes very overconfident beyond this. Looking at flood forecasting models, a comparison of the Global Flood Awareness System (GloFAS, produced by the ECMWF and the European Commission as part of the Copernicus Emergency Management Services) with the Global Flood Forecasting Information System (GLOFFIS, produced by Deltares) demonstrates that both GloFAS and GLOFFIS have difficulty simulating 1 in 2 year return period flows or higher, although GloFAS performance is better than GLOFFIS. GloFAS reforecasts show significantly overconfident probabilities over Myanmar where discharge observations are availabel, whilst the lack of a GLOFFIS reforecast prevents evaluation of this forecast system directly. Evaluation of the ten-day operational storm surge forecast (the Global Storm Surge Information System, GLOSSIS) produced by Deltares was attempted but lack of any data prevented assessment. These findings present valuable insights into how well forecasts perform, which is crucial information for establishing effective humanitarian action mechanisms.

People affected by conflict are particularly vulnerable to climate shocks and climate change, yet little is known about climate change adaptation in fragile contexts. While climate events are one of the many contributing drivers of conflict, feedback from conflict increases vulnerability, thereby creating conditions for a vicious cycle of conflict. In this study, we carry out a systematic review of peer-reviewed literature, taking from the Global Adaptation Mapping Initiative (GAMI) dataset to documenting climate change adaptation occurring in 15 conflict-affected countries and compare the findings with records of climate adaptation finance flows and climate-related disasters in each country. Academic literature is sparse for most conflict-affected countries, and available studies tend to have a narrow focus, particularly on agriculture-related adaptation in rural contexts and adaptation by low-income actors. In contrast, multilateral and bilateral funding for climate change adaptation addresses a greater diversity of adaptation needs, including water systems, humanitarian programming, and urban areas. Even among the conflict-affected countries selected, we find disparity, with several countries being the focus of substantial research and funding, and others seeing little to none. Results indicate that people in conflict-affected contexts are adapting to climate change, but there is a pressing need for diverse scholarship across various sectors that documents a broader range of adaptation types and their results.

Since its discovery in 1909, the Moho was routinely studied by seismological methods. However, from the 1950s, a possible alternative was introduced by gravimetric inversion. Thanks to satellite gravity missions launched from the beginning of the 21st century, a global inversion became feasible, e.g., leading to the computation of the GEMMA model in 2012. This model was computed inverting the GOCE second radial derivatives of the anomalous potential by a Wiener filter, which was applied in the spherical harmonic domain, considering a two-layer model with lateral and vertical density variations. Moreover, seismic information was introduced in the inversion to deal with the joint estimation/correction of both density and geometry of the crustal model. This study aims at revising the GEMMA algorithm from the theoretical point of view, introducing a cleaner formalization and studying the used approximations more thoroughly. The updates are on: (1) the management of the approximations due to the forward operator linearization required for the inversion; (2) the regularization of spherical harmonic coefficients in the inversion by proper modelling the Moho signal and the gravity error covariances; (3) the inclusion of additional parameters and their regularization in the Least Squares adjustment to correct the density model by exploiting seismic information. Thanks to these updates, a significant improvement from the computational point of view is achieved too, thus the convergence of the iterative solution and the differences with respect to the previous algorithm can be assessed by closed-loop tests, showing the algorithm performance in retrieving the simulated “true” Moho.

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.

Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence—aided recognition of these surface processes. However, so far, the technological advancements have not produced robust automated mapping tools whose domain of validity holds in any area across the globe. For instance, capturing historical landslides in densely vegetated areas is still a challenge. This study proposed a deep learning method based on Light Detection and Ranging (LiDAR) data for automatic identification of historical landslides. Additionally, it tested this method in the Jiuzhaigou earthquake-hit region of Sichuan Province (China). Specifically, we generated a Red Relief Image Map (RRIM), which was obtained via high-precision airborne LiDAR data, and on the basis of this information we trained a Lightweight Attention U-Net (LAU-Net) to map a total of 1949 historical landslides. Overall, our model recognized the aforementioned landslides with high accuracy and relatively low computational costs. We compared multiple performance indexes across several deep learning routines and different data types. The results showed that the Multiple-Class based Semantic Image Segmentation (MIOU) and the F1_score of the LAU-Net and RRIM reached 82.29% and 87.45%, which represented the best performance among the methods we tested.

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

Permafrost-dependent landslides occur in a range of sizes and are among the most dynamic landforms in the Arctic in the warming climate. Retrogressive thaw slumps (RTSs) are enlarging landslides triggered by thawing and release of excess water from permafrost ground ice, causing smaller or larger collapses of ground surface, which in turn exposes new permafrost to rapid thawing and collapse. In this study, a preliminary assessment of previous thaw slump activity in Nordenskiöld Land area of Svalbard is made based on remote sensing digitisation of 400 slump-scar features from aerial images from the Norwegian Polar Institute (NPI). RTS properties and distribution are analysed with an emphasis on their implications for the preservation of the Svalbard’s cultural heritage (CH). Our analysis shows that the areas where RTS scars and CH co-exist in Nordenskiöld Land are, at present, limited and cover mainly areas distributed along north-west (Colesbukta, Grønfjorden, Kapp Starostin), north-east (Sassendalen and Sassenfjorden) and south-west (Van Muydenbukta) coastlines. Taking into consideration the preliminary aspect of this inventory and study, it can be stated that for now, RTS and CH sites do not have a high level of co-existence, except for eight sites which are located at less than 100 m to a RTS and one site that is located inside a currently inactive slump-scar. Further mapping of RTS will be undertaken in order to have a complete picture of these climate triggered landslides potentially threatening the Arctic CH. The results of this study, even if preliminary, can be used by local authorities and stakeholders in prioritising future documentation and mitigation measures and can thus present a powerful tool in disaster risk reduction.

Summary Ultrasound measurements are routinely used to evaluate the safe depth for ships navigation - nautical depth - at waterways and ports using single-beam dual-frequency echo-sounders. The nautical depth is routinely defined by suspension density in the range of 1100–1300 kg/m3 in the mud layer. While ultrasound measurements have a weak sensitivity to density variations, calibration is always needed to convert ultrasound measurements into reliable indicators for nautical depth levels in the mud layers using densely distributed density rheological in-situ measurements. We present a laboratory ultrasonic transmission experiment to monitor the fluid mud’s settling and consolidation processes using a sample from the Port of Rotterdam. We use P- and S-wave ultrasonic transducers in the frequency range between 200 to 1000 kHz. Our results show that the P-wave velocities slightly increase during the consolidation and settling process while the P-wave amplitudes decrease. On the other hand, we observe a high S-wave velocity that increases together with amplitudes over time. The P- and S-wave amplitude and S-wave velocity variation over time correlate well with the mud average density variation. The presented results can be very useful for fluid-mud monitoring at a lab scale, besides possible utilization for large-scale monitoring field campaigns.

Open Science (OS) is an umbrella term comprising principles to increase the transparency of research. Besides Open Access to scientific articles, these principles contain public availability of reusable methods (e.g., code and tools), data, and educational materials. This document outlines a plan to achieve the transition towards OS. ITC’s Strategic Plan for OS 2021-2025 - Towards an Open Future contains five initiatives: 1.OS at ITC aims to provide guidelines and OS capacity development to address the obstacles ITC researchers encounter when doing OS. 2.The ITC Knowledge Hub will provide services and tools to access, create, and publish open research, including scientific results based on qualitative/quantitative analyses using computational workflows. 3.Open Educational Resources will be addressed by exploring options to realise Open Educational Resources at ITC and providing lecturers with guidelines and support to create them. 4.The OS Community Twente serves as an inter-disciplinary, bottom-up community to promote, learn, share, and discuss OS practices. 5.Research & Funding aims to address challenges in OS through innovative developments and user studies. A further output is to generate funding to realise the ambitious aims presented in the plan. For a successful OS transition, the initiatives aim to address the Rewards & Recognition system, valorise Sharing & Collaboration, develop OS Knowledge & Skills, and foster Cultural change & Societal impact.

This study focus on the evaluation of scientific and industry-grade hyperspectral airborne sensors for the mapping of methane (CH4) emissions in the SWIR range. An imaging dataset from areas with known CH4 emissions was processed using the classic matched filter technique, and a new CH4 index. The airborne sensors were evaluated based on sensor design (spectral sampling and band centers), effectiveness of image processing, and impact of the signal-to-noise ratio (SNR) on CH4 mapping. The gas plume was mapped only in the images acquired with scientific-grade sensors. Results demonstrated that superior performance could be achieved when the position of band centers are closely located to the center of diagnostic CH4 absorption features. The impact of SNR was examined using a noise simulation, adding white noise to simulate images with varying SNR levels. Results indicate that the noisier signal of the industry-grade sensor is probably what prevented mapping the CH4 plume in this dataset. Simulations also demonstrated that as densest the plumes lower is the impact of SNR. Combined, results indicate that an imaging spectrometer with a scientific-grade SNR and band centers properly positioned to match the main CH4 features would improve the mapping of CH4 plumes with airborne sensors operating in the SWIR range.

The majority of geological remote sensing studies have been done in arid and semi-arid areas, leading to seamless compositional maps and a cross-continental geoscience product of Australia. A challenge remains when such products are to be made for continents with temperate and cultivated regions. For that, sensors with a continuous multitemporal operation such as Landsat 8 OLI and Sentinel-2 MSI may have to be used to reach an optimal seasonal acquisition. This paper tests the robustness of Sentinel-2 MSI spectral indices over a semi-arid area in southern Spain. Spectral indices (band ratios) for mapping green vegetation and iron oxide mineralogy were acquired over a 1-year period, which had alternating dry and wet periods. Results show that changing soil moisture and weather conditions over a period of 1 year affect the stability of spectral mineral indices.

Multi-hazard process chains are complex event involving multiple hazardous processes. Common examples are co-seismic landslides and simultaneous triggering of flash floods and landslides due to extreme precipitation. Due to the complexity, and lack of reliable modelling methods, relatively little is understood about the impact of multi-hazard interactions on risk assessment. We present an integrated multi-hazard analysis of impact from tropial cyclones on Dominica. Through general extreme value analysis, design storms with return periods between 1 and 100 years were produced. Using the LISEM model, hydrology, flash floods, slope stability, slope failure and landslide/debris flow runout was simulations for each potential event. The model was calibrated with high accuracy on the 2017 impact of Hurricane Maria. The results provide counter-intuitive insights into how multi-hazard interactions influence events. In a significant number of cases, increased trigger intensity does not equate to increased impact, or surface hazard intensity. For tropical cyclones in particular, insurance rules or policy-related documents can be based on the return period of the triggering event (e.g. hurricane peak wind velocity). Secondly, many types of interactions shaped the dynamics of the simulated events, as well as the impact from real events on Dominica. Dilution of mass movements increases runout, and additional drag leads to a very strong increase in deposition near urbanized areas. Investigation of physical processes in such situations should strongly consider the relevance of these interactions. Finally, the simulation ensemble highlights the spread in model outcomes due to intrinsic uncertainties. While these are significant due to complexity of the model, they are insignificant compared to the errors made in traditional approaches where hazards are considered in isolation. While this is partly due to the high intensity of the triggering events on Dominica, it provides evidence that the recent developments in integrated multi-hazard models have reached discussion of applicability.

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.

Floods are frequent hydro-meteorological hazards which cause losses in many parts of the world. In hilly and mountainous environments, floods often contain sediments which are derived from mass movements and soil erosion. The deposited sediments cause significant direct damage, and indirect costs of clean-up and sediment removal. The quantification of these sediment-related costs is still a major challenge and few multi-hazard risk studies take this into account. This research is an attempt to quantify sediment deposition caused by extreme weather events in tropical regions. The research was carried out on the heavily forested volcanic island of Dominica, which was impacted by Hurricane Maria in September 2017. The intense rainfall caused soil erosion, landslides, debris flows, and flash floods resulting in a massive amount of sediments being deposited in the river channels and alluvial fan, where most settlements are located. The overall damages and losses were approximately USD 1.3 billion, USD 92 million of which relates to the cost for removing sediments. The deposition height and extent were determined by calculating the difference in elevation using pre- and post-event Unmanned Aerial Vehicle (UAV) data and additional Light Detection and Raging (LiDAR) data. This provided deposition volumes of approximately 41 and 21 (103 m3) for the two study sites. For verification, the maximum flood level was simulated using trend interpolation of the flood margins and the Digital Terrain Model (DTM) was subtracted from it to obtain flooding depth, which indicates the maximum deposition height. The sediment deposition height was also measured in the field for a number of points for verification. The methods were applied in two sites and the results were compared. We investigated the strengths and weaknesses of direct sediment observations, and analyzed the uncertainty of sediment volume estimates by DTM/DSM differencing. The study concludes that the use of pre- and post-event UAV data in heavily vegetated tropical areas leads to a high level of uncertainty in the estimated volume of sediments.

The coupled effect of earthquakes and rainfall is rarely investigated in landslide susceptibility assessments although it could be crucial to predict landslide occurrences. This is even more critical in the context of early warning systems and especially in cases of extreme precipitation regimes in post-seismic conditions, where the rock masses are already damaged due to the ground shaking. Here, we investigate this concept by accounting for the legacy of seismic ground shaking in rainfall-induced landslide (RFIL) scenarios. We do this to identify whether ground shaking plays a role in the susceptibility to post-seismic rainfall-induced landslides and to identify whether this legacy effect persists through time. With this motivation, we use binary logistic regression and examine time series of landslides associated with four earthquakes occurred in Indonesia: 2012 Sulawesi (Mw = 6.3), 2016 Reuleut (Mw = 6.5), 2017 Kasiguncu (Mw = 6.6) and 2018 Palu (Mw = 7.5) earthquakes. The dataset includes one co-seismic and three post-seismic landslide inventories for each earthquake. We use the peak ground acceleration map of the last strongest earthquake in each case as a predisposing factor of landslides representing the effect of ground shaking. We observe that, at least for the study areas under consideration and in a probabilistic context, the earthquake legacy contributes to increase the post-seismic RFIL susceptibility. This positive contribution decays through time. Specifically, we observe that ground motion is a significant predisposing factor controlling the spatial distribution of RFIL in the post-seismic period 110 days after an earthquake. We also show that this effect dissipates within 3 years at most.

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.

Land use and land cover change can have effect on the land by increasing/decreasing landslide susceptibility (LS) in the mountainous areas. In the southwestern hilly and mountainous part of China, land use and land cover change (LUCC) has been taking place in the recent past due to infrastructure development and increase in economic activities. These development activities can also bring negative effects: the sloping area may become susceptible to landsliding due to undercutting of slopes. The study aims at evaluating the influence of land use and land cover change on landslide susceptibility at regional scale, based on the application of Geographic Information System (GIS) and Remote Sensing (RS) technologies. Specific objective is to answer the question: which land cover/land use change poses the highest risk so that mitigation measures can be implemented in time? The Zhushan Town, Xuanen County in the southwest of China was taken as the study area and the spatial distribution of landslides was determined from visual interpretation of aerial photographs and remote sensing images, as well as field survey. Two types of land use/land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained through the neural net classification of images acquired in 1992, the second through the object-oriented classification of images in 2002 and 2013. Landslide susceptible areas were analyzed using logistic regression models. In this process, six landslide influencing factors were chosen as the landslide susceptibility indices. Moreover, we applied a hydrologic analysis method achieving slope unit (SU) delineation to optimize the partitioning of the terrain. The results indicate that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land and the human engineering activities land (HEAL). The areas of these two kind of LUC increased by 34.3 % and 1.9 %, respectively. The comparison of landslide susceptibility maps in various periods revealed that human engineering activities was the most important factor to increase LS in this region. Such results underline that a more reasonable land use planning in the urbanization process is necessary.

The 2008 Wenchuan earthquake (China, M w 7.9) highlighted the importance of assessing and mitigating the hazards from co-seismic landslides and landslide dams. The seismic shaking triggered hundreds of thousands of landslides, about 800 of which dammed the course of rivers. To understand whether distinctive factors concurred with the river-damming events, we analyzed the spatial patterns of the river-damming landslides and the non-damming landslides separately, with reference to a number of possible controlling factors. Then, we quantified the significance of these factors using the weight of evidence method, and we used the results to perform a susceptibility assessment in a portion of the earthquake-affected region to verify the effectiveness of the method. We find that the distance to the fault surface rupture, peak ground acceleration (PGA) and lithology play a controlling role for co-seismic landslides of any type. The occurrence of river-damming landslides, rather than by a specific lithology or topography, is more related to hydrological factors, while topographic controls (slope, internal relief and terrain roughness) are more significant for the non-damming landslides.

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.

New broadband seismic data from Botswana and South Africa have been combined with existing data from the region to develop improved P and S wave velocity models for investigating the upper mantle structure of southern Africa. Higher craton‐like velocities are imaged beneath the Rehoboth Province and parts of the northern Okwa Terrane and the Magondi Belt, indicating that the northern edge of the greater Kalahari Craton lithosphere lies along the northern boundary of these terranes. Lower off‐craton velocities are imaged beneath the Damara‐Ghanzi‐Chobe Belt, and may in part reflect thinning of the lithosphere beneath the incipient Okavango Rift. Lower velocities are also imaged to the north and northwest of the Bushveld Complex beneath parts of the Okwa Terrane, Magondi Belt, and Limpopo Belt, indicating that cratonic upper mantle in some areas beneath these terranes may have been modified by the 2.05‐Ga Bushveld and/or 1.1‐Ga Umkondo magmatic events.

The persistent natural hydrocarbon seepage in onshore basins challenges observation and exploration technologies, which are required to document and assess these valuable indications of the presence of oil and gas in the subsurface. This paper aims at demonstrating the relationship between the compositional variation of an evaporite cap rock and the types of seeps occurring at the surface. For this purpose, the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was utilized for mapping mineral variations of a petroleum system in the Zagros oil fields. Relative absorption-band depth (RBD), band rationing and the boosted regression trees (BRT) were applied to enhance and classify the mineral composition of evaporite, sandstone, and marly limestone formations. The gas seeps were associated with the areas of gypsum-bearing evaporite cap rock while oil seeps were mostly associated with calcite and clay zones within the cap rock, which was more prone to fracturing during the tectonic activities of the basin. It is suggested that the application of remote sensing in the oil and gas industry could be widened by detection of sleep-induced alteration to assess the efficiency of cap rock and to evaluate the productivity of reservoirs at a regional scale.

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.

Often disasters cause structural damages and produce rubble and debris, depending on their magnitude and type. The initial disaster response activity is evaluation of the damages, i.e. creation of a detailed damage estimation for different object types throughout the affected area. First responders and government stakeholders require the damage information to plan rescue operations and later on to guide the recovery process. Remote sensing, due to its agile data acquisition capability, synoptic coverage and low cost, has long been used as a vital tool to collect information after a disaster and conduct damage assessment. To detect damages from remote sensing imagery (both UAV and satellite images) structural rubble/debris has been employed as a proxy to detect damaged buildings/areas. However, disaster debris often includes vegetation, sediments and relocated personal property in addition to structural rubble, i.e. items that are wind- or waterborne and not necessarily associated with the closest building. Traditionally, land cover classification-based damage detection has been categorizing debris as damaged areas. However, in particular in waterborne disaster such as tsunamis or storm surges, vast areas end up being debris covered, effectively hindering actual building damage to be detected, and leading to an overestimation of damaged area. Therefore, to perform a precise damage assessment, and consequently recovery assessment that relies on a clear damage benchmark, it is crucial to separate actual structural rubble from ephemeral debris. In this study two approaches were investigated for two types of data (i.e., UAV images, and multi-temporal satellite images). To do so, three textural analysis, i.e., Gabor filters, Local Binary Pattern (LBP), and Histogram of the Oriented Gradients (HOG), were implemented on mosaic UAV images, and the relation between debris type and their time of removal was investigated using very high-resolution satellite images. The results showed that the HOG features, among other texture features, have the potential to be used for debris identification. In addition, multi-temporal satellite image analysis showed that debris removal time needs to be investigated using daily images, because the removal time of debris may change based on the type of disaster and its location.

Due to the raw images of multi-lens multispectral (MS) camera has significant misregistration errors, performing image registration for band co-registration is necessary. Image matching is an essential step for image registration, which obtains conjugate features on the overlapped areas, and use them to estimate the coefficients of a transformation model for correcting the geometrical errors. However, due to the none-linear intensity of spectral response, performing feature-based image matching (such as SURF) can only obtain only a few conjugate features on cross-band MS images. Different to SURF that extracts local extremum in a multi-scale space and utilizes a threshold to determine a feature, we proposed a normalized SURF (N-SURF) that extracts features on single scale, calculates the cumulative distribution function (CDF) of features, and obtains consistent features from the CDF. In this study, two datasets acquired from Tetracam MiniMCA-12 and Micasense RedEdge Altum are used for evaluating the matching performance of N-SURF. Results show that N-SURF can extract approximately 2–3 times number of features, match more points, and have more efficient than original SURF. On the other hand, with the successful of MS image matching, we can therefor use the conjugates to compute the coefficients of a geometric transformation model. In this study, three transformation models are used to compare the difference on MS band co-registration, i.e. affine, projective, and extended projective. Results show that extended projective model is better than the others as it can compensate the difference of lens distortion and viewpoint, and has co-registration accuracy of 0.3–0.6 pixels.

Geologists have been instrumental in shaping Earth observation satellite missions; likewise, geology has been the subject of many remote sensing studies [1]. Applications of optical remote sensing in geology date back to some early studies using the Earth Resources Technology Satellite-1 , the predecessor of the Landsat satellite program [2]. In the 1980s, the seventh channel in the short-wave infrared (SWIR) of the Landsat thematic mapper program was added, as a result of spectroscopic mineral studies by geologists [58]. A subsequent satellite-borne instrument, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), launched in 1999, had specific bands in the SWIR and thermal infrared dedicated to mapping mineral groups [3].

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.

Agricultural field boundary information is important and often required for the geosciences and the agricultural sector. In this paper, a novel method is developed to extract sub-boundaries within the permanent boundaries of agricultural land parcels from high-resolution optical satellite imagery using an improved cluster-based snake model. The method takes the advantage of the results of an automatic fuzzy c-means (FCM) clustering and edge detection to compute external forces for an improved gradient vector flow (GVF) snake model. The GVF snake algorithm is improved by using an automatic seeding model based on clustering results and image moment functions. To seed the improved GVF algorithm, an ellipse is automatically delineated for each cluster within agricultural parcel by utilizing image moment functions (in particular silhouette moments). The GVF snake model is then implemented for each seed, one seed at a time. Active contours tend to have curve shapes rather than straight lines due to their structure that consists of several connected nodes within each contour. Therefore, the final accurate results are obtained after performing a three-stage line simplification operation. The experiments of the method were conducted on 20 test fields in a study area located near to the town of Karacabey, Turkey, using the 4-m resolution IKONOS multispectral (xs) image, the 2.44-m resolution QuickBird xs image, and the 0.61-m resolution QuickBird pan-sharpened (PS) image. Experimental results demonstrate that using both the clustering and edge detection results as external forces for the improved GVF snake model increases the accuracy of the results. In addition, the developed method showed a fairly good performance in extracting sub-boundaries for the fields comprising crops with an inherent high inner heterogeneity, such as rice and corn. The method can potentially be applied in the extraction of within-field sub-boundaries from high-resolution satellite imagery in agricultural areas.

Rapid assessment of spatial distribution of earthquake-induced landslides could provide valuable information in the emergency response phase. Previous studies proposed global analyses with the aim of predicting earthquake-induced landslide distributions in near real-time. However, in all those studies, mapping units are constituted by pixels, which do not reflect homogeneously distributed physical property for a given terrain unit and whose size do not match the resolution of existing thematic data at global scale. Moreover, none of the existing analyses considers sampling balance between different inventories or categorizing the inventories to construct a training set with higher statistical representativeness. We develop an improved global statistical method to address these drawbacks. We use slope units, which are terrain partitions attributed to similar hydrological and geomorphological conditions and to processes that shape natural landscapes. A set of 25 earthquake-induced landslide-events are selected and categorized based on the similarity between causal factors to determine the most relevant training set to make a prediction for a given landslide-event. As a result, we develop a specific model for each category. We sample an equal number of landslide points from each inventory to overcome the dominance of some inventories with large landslide population. We use seven independent thematic variables for both categorizing the inventories and modeling, based on logistic regression. The results show that categorizing landslide-events introduces a remarkable improvement in the modeling performance of many events. The categorization of existing inventories can be applied within any statistical, global approach to earthquake-induced landslide events. The proposed categorization approach and the classification performance can be further improved with the acquisition of new inventory maps.

Marine gravimetry provides high-quality gravity measurements, particularly in coastal areas. After the update of new sensors in GFZ’s air-marine gravimeter Chekan-AM, gravimetry measurements showed a significant improvement from the first new campaign DENEB2017 with an accuracy of 0.3/2=0.21 mGal @ 1 km along the tracks, which is at the highest accuracy level of marine gravimetry. Then, these measurements were used to assess gravity data derived from satellite altimetry (about 3 mGal) and a new finding is that a bias of −1.5 mGal exists in the study area. Additionally, ship soundings were used to assess existing seafloor topography models. We found that the accuracy of SRTM model and SIO model is at a level of 2 m, while the accuracy of the regional model EMODnet reaches the lever of sub-meters. Furthermore, a bias of 0.7 m exists and jumps above 5 m in the SRTM model near the coast of Sweden. Finally, new combined gravity anomalies with sounding data are used to reveal the fine structure of ocean topography. Our estimated seafloor topography model is more accurate than existing digital elevation data sets such as EMODnet, SRTM and SIO models and, furthermore, shows some more detailed structure of seafloor topography. The marine gravimetry and sounding measurements as well as the estimated seafloor topography are crucial for future geoid determination, 3D-navigation and resource exploration in the Baltic Sea.

A power‐law relation for the frequency‐area distribution (FAD) of medium and large landslides (e.g., tens to millions of square meters) has been observed by numerous authors. But the FAD of small landslides diverges from the power‐law distribution, with a rollover point below which frequencies decrease for smaller landslides. Some studies conclude that this divergence is an artifact of unmapped small landslides due to lack of spatial or temporal resolution; others posit that it is caused by the change in the underlying failure process. An explanation for this dilemma is essential both to evaluate the factors controlling FADs of landslides and power‐law scaling, which is a crucial factor regarding both landscape evolution and landslide hazard assessment. This study examines the FADs of 45 earthquake‐induced landslide inventories from around the world in the context of the proposed explanations. We show that each inventory probably involves some combination of the proposed explanations, though not all explanations contribute to each case. We propose an alternative explanation to understand the reason for the divergence from a power‐law. We suggest that the geometry of a landslide at the time of mapping reflects not just one single movement but many, including the propagation of numerous smaller landslides before and after the main failure. Because only the resulting combination of these landslides can be observed due to a lack of temporal resolution, many smaller landslides are not taken into account in the inventory. This reveals that the divergence from the power law is not necessarily attributed to the incompleteness of an inventory. This conceptual model will need to be validated by ongoing observation and analysis. Also, we show that because of the subjectivity of mapping procedures, the total number of landslides and total landslide areas in inventories differ significantly, and therefore the shapes of FADs also differ considerably.

Layered lithium transition-metal oxides, such as LiCoO2 and its doped and lithium-rich analogues, have become the most attractive cathode material for current lithium-ion batteries due to their excellent power and energy densities. However, parasitic reactions at the cathode–electrolyte interface, such as metal-ion dissolution and electrolyte degradation, instigate major safety and performance issues. Although metal oxide coatings can enhance the chemical and structural stability, their insulating nature and lattice mismatch with the adjacent cathode material can act as a physical barrier for ion transport, which increases the charge-transfer resistance across the interface and impedes cell performance at high rates. Here, epitaxial engineering is applied to stabilize a cubic (100)-oriented TiO layer on top of single (104)-oriented LiCoO2 thin films to study the effect of a conductive coating on the electrochemical performance. Lattice matching between the (104) LiCoO2 surface facets and the (100) TiO plane enables the formation of the titanium mono-oxide phase, which dramatically enhances the cycling stability as well as the rate capability of LiCoO2. This cubic TiO coating enhances the preservation of the phase and structural stability across the (104) LiCoO2 surface. The results suggest a more stable Co3+ oxidation state, which not only limits the cobalt-ion dissolution into the electrolyte but also suppresses the catalytic degradation of the liquid electrolyte. Furthermore, the high c-rate performance combined with high Columbic efficiency indicates that interstitial sites in the cubic TiO lattice offer facile pathways for fast lithium-ion transport.

The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.

The development of a failure plane in the subsurface can result in movement of large volumes of solids. Depending on the type of rock and initiation mechanism, this material can partially contain cohesive structure. Such semi-structured landslides or rock avalanches feature alternate dynamics compared to debris flows and other types of granular movements. The way in which this structure influences the impact on structures, and thus the resulting hazard, remains largely unknown. Recently, a two-phase semi-structured generalized mass movement model was developed. This model implements a full stress-strain relationship for the moving mixture. In this work, this model is applied to a set of hypothetical and real mitigation measures and structures. The effect of various types of checkdams and blocking pillars is investigated. The resulting impacts are converted to impact pressures and potential damage fractions. Finally, the model results are compared with traditional unstructured debris flow and landslide runout models. Results indicate the strong increase of structural damage with increased cohesive structure. The model furthermore predicts complex interactions between the semi-structured mass movements and mitigation measures. Moving aggregates can break, after which individual rocks or particles might continue in diverging trajectories. Depending on the physical parameters of the material, fragmentation can also occur before impact, after which movement is similar to granular flows.

We present a 3D shear-wave velocity model of the southern African upper mantle developed using 30–200 s period Rayleigh waves recorded on regional seismic networks spanning the subcontinent. The model shows high velocities (∼4.7–4.8 km/s) at depths of 50–250 km beneath the Archean nucleus and several surrounding Paleoproterozoic and Mesoproterozoic terranes, placing the margin of the greater Kalahari Craton along the southern boundary of the Damara Belt and the eastern boundaries of the Gariep and Namaqua-Natal belts. At depths ≥250 km, there is little difference in velocities beneath the craton and off-craton regions, suggesting that the cratonic lithosphere extends to depths of about 200–250 km. Upper mantle velocities beneath uplifted areas of southern Africa are higher than the global average and significantly higher than beneath eastern Africa, indicating there that is little thermal modification of the upper mantle present today beneath the Southern African Plateau.

Soil is the outmost layer of earth that supports plant growth and many living creatures depending on it. It provides a suitable environment for forest growth, crop production, securing human food supplies, filtering and storing water and a carbon reservoir. However, the soil is subject to degradation as a result of natural and human factors. Soil degradation weakens an ecosystem’s capacity to function appropriately, affects the climate by changing the water and energy balances, and disrupts the carbon, nitrogen or sulphur cycles. Soil stability is defined as the aggregates’ ability to maintain their bonds under stresses that might trigger their disintegration. While we may understand the factors dominating soil stability, the spatial and temporal variations of these factors controlling the soil stability dynamics are still missing. There is limited knowledge of the soil mineralogical behaviour at varying temperatures and soil moisture contents occurring in a short period at the microscale. Therefore, the research’s objective is to investigate the seasonal effect on soil surface stability using proximal remote sensing. The effect of soil surface mineralogy alterations due to moisture variations under laboratory conditions using imagine spectroscopy is one of this study’s objectives. Similarly, freeze-thaw cycles might encourage migration and alter the soil matrix’s chemical constituents exposed to different moisture conditions. The Spectral Information Divergence was applied to quantify the soil image occupied by Mg-clinochlore, goethite, quartz coated 50% by goethite, hematite dimorphous with maghemite. The results showed that the minerals’ percentage changed over time, depending on soil type and moisture conditions. Mineralogical alterations of soils with organic matter occur at field capacity. Likewise, these minerals behaved differently under freeze-thaw cycles, depending on the soil type and soil condition. Also, the amount and the type of organic matter are vital in soil experiencing freeze-thaw cycles. Monitoring soil aggregate breakdown was analysed using the shadow ratio showing a gradual change over time, with no details about weather conditions. Both the entropy and the Huang thresholding technique show variations of soil aggregate breakdown responding to weather conditions. Using data obtained with a regular camera, the results show that freeze-thaw cycles cause soil aggregate breakdown.

Remote sensing tools have proven to be effective for direct detection of petroleum hydrocarbons (PHC) in continental areas. PHC yield diagnostic absorption features in the near, shortwave, midwave, and thermal infrared (NIR-SWIR-MWIR-TIR) ranges. Thus far, the NIR-SWIR range has been primarily exploited for remote sensing PHC detection, whereas the application of MWIR and TIR data has remained underexplored. In this work, we aim to close this gap by using the Spatially-Enhanced Broadband Array Spectrograph System (SEBASS) (7.6–13.5 μm) to map PHC-impregnated soil substrates. A laboratory experiment was performed to identify the features in pure crude oil samples, in dry/wet mineral substrates, and in their physical mixtures using attenuated total reflectance (ATR) and directional-hemispherical reflectance (DHR) measurements. A similar setting was designed in an open environment and imaged by the SEBASS sensor. Close-range spectroscopy indicated that PHC features between 7 and 15 μm are subtle in comparison to the features of the mineral substrates, thus hindering direct identification of the PHC spectrally. However, the PHC coating of the grains increased the emissivity of the background features - a finding that was supported by SEBASS data processing using a matched filtering technique. The SEBASS data indicated that besides an increase in the emissivity of the underlying substrates, the presence of PHC also induces a change in the temperature of the contaminated targets. The combination of these two factors highlighted the contaminated sites indirectly. Since the mapping of clean, moist and PHC contaminated sites with TIR data depends on variations in the emissivity and temperature of the targets, the application can be extended to orbital sensors with lower spectral resolution (such as ASTER and ECOSTRESS), enabling the monitoring of larger areas.

Extreme weather and climate events associated with El Niño and La Niña cause massive societal impacts. Therefore, observations and forecasts are used around the world to prepare for such events. However, global warming has caused warm El Niño events to seem bigger than they are, while cold La Niña events seem smaller, in the commonly used Niño3.4 index (sea surface temperature (SST) anomalies over 5∘ S–5∘ N, 120–170∘ W). We propose a simple and elegant adjustment, defining a relative Niño3.4 index as the difference between the original SST anomaly and the anomaly over all tropical oceans (20∘ S–20∘ N). This relative index describes the onset of convection better, is not contaminated by global warming and can be monitored and forecast in real-time. We show that the relative Niño3.4 index is better in line with effects on rainfall and would be more useful for preparedness for El Niño and La Niña in a changing climate and for El Niño—Southern Oscillation research.

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

The integration of soil value in agronomic micro‐level decision‐making and macro‐scale policy development has grown immensely in recent years. Major threats to soil resources and their impact on human well‐being require a comprehensive estimation of soil's economic worth, highlighting the need for sustainable and pragmatic conservation strategies. However, the absence of formal markets for numerous soil amenities, coupled with the heterogeneity of stakeholder cognition and spatio‐environmental factors, obfuscates the valuation process for soil and similar public goods. This paper aims to address such concerns by evaluating stakeholders’ willingness to pay (WTP) for soil conservation as a proxy indicator for its explicit value. Two contingent valuation method (CVM) formats, the payment card (PC) and the dichotomous choice (DC), were used to analyse WTP in Norzagaray, Philippines. Results suggest farmers’ income, education, land tenure type, level of environmental consciousness and proximity to amenities influence their inclination to spend on soil conservation measures. Econometric analyses indicate a compensating surplus corresponding to the mean WTP estimate of ₱79.98 (PC‐CVM) and a Turnbull WTP estimate of ₱99.47 (DC‐CVM). Estimated WTP values can be used in future benefit‐transfer studies, and the findings of the econometric models can be used in developing strategies that would promote greater stakeholder acceptance and participation. The approach presented here provides another step towards a more comprehensive characterization of soil value that integrates environmental valuation and econometric modelling with geospatial data.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0–0.4 m) and subsoil (0.4–1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.