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Housing in informal settlements often lacks construction techniques that adopt criteria of resilience to natural hazards. Smartphones are rapidly diffusing in economically developing countries. The aim of this study was to assess the current use of smartphones by the masons of the informal settlements of Iringa, Tanzania, and to identify pathways for improving their construction practices. Data were collected through a mixed method approach that includes in-depth interviews, surveys, and a focus group with masons. The results reveal that only a few masons received formal training, most of them have never interacted with a qualified engineer nor consulted trained professionals when needed. Most masons own a smartphone and they extensively use it to gather technical information from the web, transfer money through mobile payments, share images of construction details, and promote their work on social media. The broad use of smartphones shows potential for enhancing construction quality. This article presents a unique analysis of the use of smartphones in the construction of informal settlements in Tanzania, which could be extended to other countries. Based on the findings, new strategies are proposed to engage with local stakeholders and foster the exchange of technical knowledge for safer settlements via smartphones.

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

The 2008 Wenchuan earthquake lead to various complex multi-hazard chains that included seismically triggered landslide initiation, landslide runout, river damming, dam breaching, and flooding. The modeling of the interactions between such hazardous processes is challenging due to the complexity and uncertainty. Here we present an event-based physically based model that is able to simulate multi-hazard land surface process chains within a single unified simulation. The final model is used to simulate a multi-hazard chain event in the Hongchun watershed, where co-seismic landslides led to a landslide dam and, 2 years later, a debris flow that breached the landslide dam. While most aspects of the multi-hazard chain are predicted well, the correct prediction of slope failures remains the biggest challenge. Although the results should be treated carefully, the development of such a model provides a significant progress in the applicability of multi-hazard chain simulations.

The intensity, frequency, and duration of heatwaves are increasing worldwide. Still, heatwaves are systematically underreported and underresearched across the African continent. This study examines heatwaves across Mozambique, a country highly vulnerable to a variety of climate risks yet where heatwaves have received little to no attention. A spatio-temporal analysis was conducted for five heatwave characteristics (heatwave number, frequency, duration, amplitude, and magnitude) and corresponding trends from 1983 to 2016. This was done using the remotely sensed CHIRTS-daily, which presents one of the most accurate and highest resolution (5 × 5 km) daily temperature product currently available, especially for data-scarce regions. Three heatwave definitions were analyzed and compared, which are based on (1) the 90th percentile of daily maximum temperature (TX90), (2) the 90th percentile of daily minimum temperature (TN90), and (3) the Excess Heat Factor (EHF). Results were overlayed with high-resolution population data to obtain heatwave exposure and likely potential implications. Our findings show that Mozambique has experienced many heatwaves over the past decades. On average, 2–18.6 annual heatwave days (HWF) were recorded with the longest heatwaves (HWD) lasting X- 11.5 days. More and longer heatwaves were observed in the North and along the coast of Mozambique. Heatwave magnitude (HWM) ranged from 0.3 to 6.8 °C and amplitude (HWA) from 0.8 to 11.7 °C, with highest values in South and Central Mozambique. Heatwave events, days, and duration were found to be significantly increasing (p

Rapid urbanization and land use transformation coupled with climate change impact are witnessed for development urban heat island worldwide and make it major environmental concerns for human health. National Capital Region (NCR) is one of the fast sprawling National Capital city of India. The aim of the present study is to explore the spatial changes in major biophysical parameters of city over last 20 years from 2000 to 2020 using satellite data. The spatial variation in the land surface temperature and its relation with Land use, NDVI and Built-up area were evaluated and discussed in the present work. The major changes were observed in minimum and maximum temperature of the city, in 2000 minimum temperature was 23.20 °C and maximum 34.85 °C, which increased to 26.31 and 39.92 °C in 2010; and 31.70 to 44.74 °C in 2020 . Normalized differential vegetation index (NDVI) decrease from 82 to 62%, Non-Vegetated area has reduced to 62% from 82% due to fast urbanization and land transformation in last two decades. Barren and fallow land also reduces about 27% in last two decades and built-up area also shown increasing trend . The spatial changes in biophysical parameter of the city clearly indicated that NDVI, NDBI and Land use/land cover charges are the main driving forces for development of urban heat island in the city and many health issues related to heat waves are already reported in the city along with many other negative impacts on natural landscape and resources. The observations from present work will be very useful scientific information to understand the causes of elevated land surface temperature and mitigation measures. Results could be used by policy makers to develop the suitable model for long term sustainability of the cities.

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term “triggering” precipitation, medium-term “preparatory” precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small platforms that are more agile to move in confined spaces, such as during emergencies. However, this task is still challenging because of the absence of a GNSS (Global Navigation Satellite System) signal that limits the localization (and scaling) of the UAV. The reduced density of points in feature-based monocular SLAM (Simultaneous Localization and Mapping) then limits the completeness of the delivered maps. In this paper, the real-time capabilities of a commercial, inexpensive UAV (DJI Tello) for indoor mapping are investigated. The work aims to assess its suitability for quick mapping in emergency conditions to support First Responders (FR) during rescue operations in collapsed buildings. The proposed solution only uses images in input and integrates SLAM and CNN-based (Convolutional Neural Networks) Single Image Depth Estimation (SIDE) algorithms to densify and scale the data and to deliver a map of the environment suitable for real-time exploration. The implemented algorithms, the training strategy of the network, and the first tests on the main elements of the proposed methodology are reported in detail. The results achieved in real indoor environments are also presented, demonstrating performances that are compatible with FRs’ requirements to explore indoor volumes before entering the building.

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.

The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TSs) and thermo-erosion gullies (TEGs) could take place and rapidly develop under the influence of climatic variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming, and therefore, a deeper understanding of TSs and TEGs is necessary to understand and foresee the dynamics behind local cryospheric hazards' occurrences and their global implications. We present the latest update of two polygonal inventories where the extent of TSs and TEGs is recorded across Nordenskiöld Land (Svalbard Archipelago), over a surface of approximately 4000 km2. This area was chosen because it represents the most concentrated ice-free area of the Svalbard Archipelago and, at the same time, where most of the current human settlements are concentrated. The inventories were created through the visual interpretation of high-resolution aerial photographs as part of our ongoing effort toward creating a pan-Arctic repository of TSs and TEGs. Overall, we mapped 562 TSs and 908 TEGs, from which we separately generated two susceptibility maps using a generalised additive model (GAM) approach, under the assumption that TSs and TEGs manifest across Nordenskiöld Land, according to a Bernoulli probability distribution. Once the modelling results were validated, the two susceptibility patterns were combined into the first multi-hazard cryospheric susceptibility map of the area. The two inventories are available at https://doi.org/10.1594/PANGAEA.945348 (Nicu et al., 2022a) and https://doi.org/10.1594/PANGAEA.945395 (Nicu et al., 2022b).

As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). The lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using a 30-year record of 42,833 satellite images (1990–2019), and we discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2 in the last 3 decades, respectively. We noticed that different regions of the TP exhibited varying change rates in glacial lake size; most regions show a trend of expansion and increase in glacial lakes, while some regions show a trend of decreasing such as the western Pamir and the eastern Hindu Kush. The mapping uncertainty is about 17.5%, which is lower than other available datasets, thus making our inventory reliable for the spatio-temporal evolution analysis of glacial lakes in the TP. Our lake inventory data are publicly published, it can help to study climate change–glacier–glacial lake–GLOF interactions in the Third Pole and serve as input to various hydro-climatic studies.

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories.

In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and the Red Cross distributed veterinary kits for 50 heads of cattle per family, tarpaulins, tool kits to install a temporary awning for alpacas to protect them from the cold wind and snow, protection kits for alpaca herders and warm clothing for children under five, pregnant women, the elderly, and people with chronic and infectious diseases in 430 highly vulnerable households. This article presents the results of a household survey following the impact of the extreme snowfall. We document the early actions taken by these communities to protect their livelihoods, health, and assets. The evaluation also presents descriptive statistics of household-level outcomes for households receiving pre-snowfall support and those that did not receive any intervention or only received post-disaster assistance. While most households took action to protect their assets, there were fewer extreme losses of alpaca herds reported in the communities who received the early support, and these communities also reported fewer adults suffering from respiratory illnesses. More in-depth research on this type of early action is necessary on a wider scale, especially to evaluate the utility of different support measures and the necessary quantity of support needed. This case study can inform government, civil society, and humanitarian actors of how early action is happening before disasters occur and provide a direction for further investment in research and practice to make use of hydro-meteorological forecasts for the benefit of the most vulnerable. Copyright © 2021 Bazo, de Perez, Jacome, Mantilla, Destrooper and Van Aalst.

The gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data management, modeling and conversion into predictive maps. This in turn has limited the scope to catchment-scales. In this manuscript, we opt to propose a tool where the whole modeling procedure is unified within the same cloud computing system, allowing one to get rid of potential errors caused by input/output operations but also to extend the study areas indefinitely, as cloud data-management tools easily offer access to global data. Specifically, we present an interactive tool for susceptibility modeling in Google Earth Engine (GEE), the Susceptibility Tool for GEE (STGEE). Our tool requires few input data and makes use of the breadth of predictors’ information available in GEE. In this cloud computing environment, binary classifiers typical of susceptibility models can be called and fed with information related to mapping units and any natural hazards’ distribution over the geographic space. We tested our tool to generate susceptibility estimates for gully erosion occurrences in a study area located in Sicily (Italy). The tool we propose is equipped with a series of functions to aggregate the predictors’ information in space and time over a mapping unit of choice. Here we chose a Slope Unit partition but any polygonal structure can be chosen by the user. Once this information is derived, our tool calls for a Random Forest classifier to distinguish locations prone to gully erosion from locations where this process is not probabilistically expected to develop. This is done while providing a modeling performance overview, accessible via a separate panel. Such performance can be calculated on the basis of a exploratory analysis where all the information is used to fit a benchmark model as well as a spatial k-fold cross-validation scheme. Ultimately, the predictive function can be interactively used to generate susceptibility maps in real time, for the study area as well as any study area of interest. To promote the use of our tool, we are sharing it in a GitHub repository accessible at this link: https://github.com/giactitti/STGEE.

The evolution of the Sicily Channel Rift Zone (SCRZ) is thought to accommodate the regional tectonic stresses of the Calabrian subduction system. Much of the observations we have today are either limited to the surface or to the upper crust or deeper from regional seismic tomography, missing important details about the lithospheric structure and dynamics. It is unclear whether the rifting is passive from far-field extensional stresses or active from mantle upwelling beneath. We measure Rayleigh-and Love-wave phase velocities from ambient seismic noise and invert for 3-D shear-velocity and radial anisotropic models. Variations in crustal S-velocities coincide with topographic and tectonic features. The Tyrrhenian Sea has a similar to 10 km thin crust, followed by the SCRZ (similar to 20 km). The thickest crust is beneath the Apennine-Maghrebian Mountains (similar to 55 km). Areas experiencing extension and intraplate volcanism have positive crustal radial anisotropy (V-SH > V-SV); areas experiencing compression and subduction-related volcanism have negative anisotropy. The crustal anisotropy across the Channel shows the extent of the extension. Beneath the Tyrrhenian Sea, we find very low sub-Moho S-velocities. In contrast, the SCRZ has a thin mantle lithosphere underlain by a low-velocity zone. The lithosphere-asthenosphere boundary rises from 60 km depth beneath Tunisia to similar to 33 km beneath the SCRZ. Negative radial anisotropy in the upper mantle beneath the SCRZ is consistent with vertical mantle flow. We hypothesize a more active mantle upwelling beneath the rift than previously thought from an interplay between poloidal and toroidal fluxes related to the Calabrian slab, which in turn produces uplift at the surface and induces volcanism.

The Sciara del Fuoco (SdF) collapse scar at Stromboli is an active volcanic area affected by rapid morphological changes due to explosive/effusive eruptions and mass-wasting processes. The aim of this paper is to demonstrate the importance of an integrated analysis of multi-temporal remote sensing (photogrammetry, COSMO-SkyMed Synthetic Aperture Radar amplitude image) and marine geophysical data (multibeam and side scan sonar data) to characterize the main morphological, textural, and volumetric changes that occurred along the SdF slope in the 2020–2021 period. The analysis showed the marked erosive potential of the 19 May 2021 pyroclastic density current generated by a crater rim collapse, which mobilized a minimum volume of 44,000 m3 in the upper Sciara del Fuoco slope and eroded 350,000–400,000 m3 of material just considering the shallow-water setting. The analysis allowed us also to constrain the main factors controlling the emplacement of different lava flows and overflows during the monitored period. Despite the morphological continuity between the subaerial and submarine slope, textural variations in the SdF primarily depend on different processes and characteristics of the subaerial slope, the coastal area, the nearshore, and “deeper” marine areas.

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes.

The persistence over space and time of flash flood disasters – flash floods that have caused either economical losses or loss of life or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyze the national inventory of flash flood disasters in China that occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first based on Ripley's K function, and the second on scan statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect to the precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic change.

Consumer-grade Unmanned Aerial Vehicles (UAVs) are poorly suited to monitor complex scenes where multiple analysis tasks need to be carried out in real-time and in parallel to fulfil time-critical requirements. Therefore, we developed an innovative UAV agnostic system that is able to carry out multiple road infrastructure monitoring tasks simultaneously and in real-time. The aim of the paper is to discuss the system design considerations and the performance of the processing pipeline in terms of computational strain and latency. The system was deployed on a unique typology of UAV and instantiated with realistic placeholder modules that are of importance for infrastructure inspection tasks, such as vehicle detection for traffic monitoring, scene segmentation for qualitative semantic reasoning, and 3D scene reconstruction for large-scale damage detection. The system was validated by carrying out a trial on a highway in Guadalajara, Spain. By utilizing edge computation and remote processing, the end-to-end pipeline, from image capture to information dissemination to drone operators on the ground, takes on average 2.9 s, which is sufficiently quick for road monitoring purposes. The system is dynamic and, therefore, can be extended with additional modules, while continuously accommodating developments in technologies, such as IoT or 5G.

While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases.

Human detection in images using deep learning has been a popular research topic in recent years and has achieved remarkable performance. Training a human detection network is useful for first responders to search for trapped victims in debris after a disaster. In this paper, we focus on the detection of such victims using deep learning, and we find that state-of-the-art detection models pre-trained on the well-known COCO dataset fail to detect victims. This is because all the people in the training set are shown in photos of daily life or sports activities, while people in the debris after a disaster usually only have parts of their bodies exposed. In addition, because of the dust, the colors of their clothes or body parts are similar to those of the surrounding debris. Compared with collecting images of common objects, images of disaster victims are extremely difficult to obtain for training. Therefore, we propose a framework to generate harmonious composite images for training. We first paste body parts onto a debris background to generate composite victim images and then use a deep harmonization network to make the composite images look more harmonious. We select YOLOv5l as the most suitable model, and experiments show that using composite images for training improves the AP (average precision) by 19.4% (15.3%→34.7%). Furthermore, using the harmonious images is of great benefit to training a better victim detector, and the AP is further improved by 10.2% (34.7%→44.9%). This research is part of the EU project INGENIOUS. Our composite images and code are publicly available on our website.

Mass movements such as debris flows and landslide differ in behavior due to their material properties and internal forces. Models employ generalized multi-phase flow equations to adaptively describe these complex flow types. However, models commonly assume unstructured and fragmented flow after initiation of movement. In this work, existing work on two-phase mass movement equations are extended to include a full stress-strain relationship that allows for runout of (semi-) structured fluid-solid masses. The work provides both the three-dimensional equations and depth-averaged simplifications. The equations are implemented in a hybrid Material Point Method (MPM) which allows for efficient simulation of stress-strain relationships on discrete smooth particles. Using this framework, the developed model is compared to several flume experiments of clay blocks impacting fixed obstacles. Here, both final deposit patterns and fractures compare well to simulations. Additionally, numerical tests are performed to showcase the range of dynamical behavior produced by the model. Important processes such as fracturing, fragmentation and fluid release are captured by the model. While this provides an important step towards complete mass movement models, several new opportunities arise such as ground-water flow descriptions and application to fragmenting mass movements and block-slides.

Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

The development of instruments and methods to assist in methane (CH4) emissions mapping is essential to the fossil fuel industry. Early identification of local CH4 sources can benefit both petroleum hydrocarbon prospecting and environmental monitoring. Generic airborne imaging spectrometers operating in the shortwave infrared (SWIR) wavelength range (1000−2400 nm) have shown their suitability for this task. However, to date, there is no airborne scanner specifically designed for the detection of CH4 plumes. To overcome current handicaps and achieve better results at local scale, further investigation in sensor design is needed to evaluate which components can be adjusted to improve CH4 mapping with airborne sensors. Here, we focus on the evaluation of currently operational airborne imaging spectrometers for CH4 mapping in the SWIR range. Data acquired over areas with known CH4 emissions by scientific and industry-grade airborne hyperspectral sensors were examined. The research was conducted in three steps: analysis of sensor design, image processing and noise simulation. In the first step, differences in the spectral sampling between the sensors were analyzed. A reference CH4 signature from HITRAN spectral database was resampled to the spectral sampling of each airborne sensor. The center wavelength of diagnostic CH4 absorption features (identified in the convolved signatures) in relation to the position of band centers from each equipment was examined. For the image processing stage, a new CH4 index and a classic matched filtering were used to map CH4 plumes. To assess the impact of the signal-to-noise ratio (SNR) of the airborne sensors on CH4 plume mapping, white noise was added to the data to simulate images with varying SNR levels. Results demonstrated that the wavelength position of band centers is a key for CH4 mapping. The CH4 plumes could be mapped only with scientific-grade sensors, in which the band centers were closer to the center of CH4 features. Simulations with the addition of random noise demonstrated that a noisier signal is probably the reason why the industry-grade sensor tested here failed to map CH4 plumes, given that all instruments have a comparable spectral sampling. Furthermore, the simulations also demonstrated that the density of the plume has also a weight on the mapping of CH4 sources, once the image that captured the densest plume requested a higher addition of noise to be lost. The overall investigation indicates that a hyperspectral airborne sensor with bands properly positioned and scientific-grade SNR would better resolve the narrow CH4 features in the SWIR range.

The use of drones to explore indoor spaces has gained attention and popularity for disaster management and indoor navigation applications. In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces. We compared the point clouds obtained by a multi-ranger deck, a multi-layer LIDAR scanner and a stereo camera, and assessed each against ground truth obtained with a terrestrial laser scanner. Results showed that both drones are capable of mapping relatively cluttered indoor environments and can provide point clouds that are sufficient for a quick exploration. Furthermore, the LIDAR scanner-based system can handle a relatively large office environment with an accumulated drift less than 0.02% (1 cm) on the Z-axis and 0.77% (50 cm) on the X and Y axes over a length trajectory of about 65 m. Despite the limited features of the sensor configuration of the Crazyflie, its performance is promising for mapping indoor spaces, given the relatively low deviation from the ground truth: cloud-to-cloud distances measured were generally less than 20 cm.

Accurate determination of clay minerals can be challenging due to the natural occurrence of interlayered phases, i.e., layers of different clay species such as illite and smectite. The overlap of peaks of the constituent minerals (e.g., illite and smectite), and the similarity of diffraction patterns when not treated with ethylene glycol, hampers identification, especially when the clay content is low. We investigated the occurrence of interlayered illite/smectite in a rock sample from Rodalquilar, Spain, using X-ray diffraction, scanning electron microscopy and reflectance spectroscopy in the short-wave infrared wavelength range. For the first time, a precise determination of interlayered I/S conducted on the extracted clay fraction treated with ethylene glycol using such different approaches was provided. X-ray diffraction results demonstrated the presence of an I/S peak at around 8.4° in the untreated fraction coupled with a peak splitting at 6.7° and 9.4° 2θ when solvated with ethylene glycol. While spectroscopy indicated the occurrence of interlayered structures as a mixture of the two constituent minerals, the results of X-ray analysis showed that the interlayered clay consisted of two discrete phases (illite and smectite). The two discrete phases were observed in both the whole rock analysis and in the extracted clay fraction. This study shows that X-ray diffraction and validation with a scanning electron microscope is a mandatory, integrating tool for detecting interlayered phases since reflectance spectroscopy alone cannot be used to differentiate between interlayered clay minerals and non-interlayered mixtures. This work highlights the limits and advantages of three sensors (X-ray diffraction, scanning electron microscopy and reflectance spectroscopy) to investigate clay mixtures and interlayering, representing a significant contribution to confidence in the interpretation of interlayered clays, this being essential in mineral exploration and prospecting.

The dominant factors in Microbial Influenced Corrosion (MIC) are hard to determine because normally several individual species and their metabolites are involved, and, moreover, different metabolites may cause opposing effects. To address this problem, the effects of individual metabolites from different species should be elucidated when at the same time other metabolites are held constant. In this study, the role is investigated of simulated organic acid metabolites, namely, acetic and L–ascorbic acids, on corrosion of geo-energy pipelines (carbon steel) in a simulated Sulfate Reducing Bacteria (SRB) environment. The SRB environment is simulated using a calcium alginate biofilm, abiotic sulfide, CO2, and NaCl brine. The electrochemical results show that both simulated organic acid metabolites accelerate corrosion in a simulated SRB environment. The results are further supported by electrochemical weight losses, kinetic corrosion activation parameters, multiple linear regression, ICP-OES, pH, and XRD. However, a comparison of electrochemical results with those published in the literature for a simulated SRB environment without acetic or L-ascorbic acid under similar experimental conditions shows that the presence of acetic in this study results in lower corrosion current densities while in presence of L-ascorbic acid results into higher corrosion current densities. This implies that acetic and L-ascorbic acids inhibit and accelerate corrosion, respectively. In addition, the results highlight that H2S is a key role of corrosion in the presence of organic acid. The results of this study are important new and novel information on the role of acetic and L-ascorbic acids in corrosion of geo-energy pipelines in the SRB environment.

Hydro-morphological processes (HMP; any process in the spectrum between debris flows and flash floods) threaten human infrastructures and lives; and their effects are only expected to worsen in the context of climate change. One of the ways to limit the potential damage of HMP is to take preventive or remedial actions probabilistically knowing where and how frequently they may occur. The expected information on where and how frequently a given earth surface process may manifest itself is referred to as susceptibility. And, for the whole Chinese territory, a susceptibility model for HMP is currently not available. To address this issue, we propose a yearly space-time model consisting of a Generalized Linear Model of the binomial family. The target variable of such model is the annual presence/absence information of HMP per catchment across China, from 1985 to 2015. This information has been accessed via the Chinese catalogue of HMP, a data repository the Chinese government has activated in 195X and which is still currently in use. This binary spatio-temporal information is regressed against a set of time-invariant (catchment shape indices and terrain attributes) and time-variant (urban coverage, rainfall, vegetation density and land use) covariates. Furthermore, we include a regression constant for each of the 31 years under consideration and also a three-years aggregated information on previously occurred (and not-occurred) HMP. We consider two versions of our modeling approach, an explanatory benchmark where we fit the whole space-time HMP data, including a multiple intercept per year. Furthermore, we also extend this explanatory model into a predictive one, by considering four temporal cross-validation schemes (Forward-All, Forward-Sequence, Backward-All, and Backward-Sequence), removing the yearly multiple intercept. In the first of 31 temporal replicates, Forward-All is calibrated for 1985 and then used to predict from 1986 to 2015. In the second step, a model is calibrated for 1985 and 1986 combined and used to validate the rest of the space-time series. This is replicated up to the last model where the combined data from 1985 to 2014 is calibrated to predict the last year of the HMP presence/absence data. Forward-Sequence also moves in the same temporal direction but the sampling scheme sequentially extracts two years at a time, one for calibration and one for validation. For instance, the first step is trained for 1985 and used to predict 1986; then the second step is trained for 1986 and used to predict 1987. As for Backward-All, and Backward-Sequence, their structure is the same but the temporal direction goes from 2015 to 1985. Our explanatory model suggests that the overall number of HMP events per year has increased in the last decade and that the annual susceptibility has subsequently followed the same trend. As for the four cross-validation routines, Forward-Sequence shows excellent performance with an average AUC of 0.83, slightly better than Forward-All, Backward-All, and Backward-Sequence. From an interpretative standpoint, this implies that the best spatio-temporal prediction we obtained is associated with short-term variations of the HMP distribution and that such variations should be considered in a forward temporal direction. Furthermore, we portrayed the annual susceptibility models into 30 maps, where the south-east of China is shown to exhibit the largest variation in the spatio-temporal probability of HMP occurrence. Also, we compressed the whole spatio-temporal prediction into three summary maps. These report the mean, maximum and 95\% confidence interval of the spatio-temporal susceptibility distribution per catchment, per year. The information we present has a dual value. On the one hand, we provide a platform to interpret environmental effects on HMP at a very large scale, both spatially (the whole Chinese country) and temporally (31 years of records). On the other hand, we provide information on which catchments are more prone to experience a HMP-driven disaster. Hence, a step further would be to select the more susceptible catchment for detailed analysis where physically-based models could be tested to estimate the potentially impacted areas.

Hazardous surface processes such as floods and mass movements are often induced by a common trigger such as extreme precipitation. The relationship between the intensity of the trigger and the surface hazard is generally assumed to be monotonically increasing (increasing precipitation never decreases hazard intensity). The validity of this assumption of complex multi-hazard events has not been thoroughly investigated. In this research, the relationship between cumulative precipitation and hazard intensity was investigated by a simulation of 50 return period precipitation events on the Carribean island Dominica. Here, several tropical hurricanes have induced events with (flash) floods, slope failure, debris flows and landslides within the past decades. Results show that complex multi-hazard interactions break the common assumption for the relationship between trigger and hazard intensity. In particular, landslide dam formation and mass movement dilution result in hazard intensities that are not a one-to-one increasing function of trigger intensity. Spatial variability in this behavior is quantified using a rank-order correlation coefficient between trigger return period and hazard return period. Since trigger and hazard return periods are, in the study case, not approximately equal, the hazard for a specific location can not be classified based on trigger return period. This has implications for risk calculation and decision making related to disaster risk reduction.

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.

This study compares the performance of three analytical methods, hyperspectral imaging in the short-wave infrared range (SWIR), X-ray powder diffraction (XRD), and electron-microprobe analysis (EMPA) for the identification of minerals frequently formed during natural and technological geothermal processes. The samples from three geothermal sites in Indonesia contain a spectrum of minerals, which are characteristic for geothermal activities (alunite, jarosite, cristobalite, tridymite) and/or are involved in scaling-forming processes in geothermal plants (barite, celestine, common opal, calcite). We show and discuss the strengths and weaknesses of the individual techniques with respect to properly identifying these minerals and to approximate their relative proportions. The SWIR camera permits an unambiguous identification of only a small selection of the geothermally relevant minerals, which restricts its employment as routine tool in the context of geothermics. XRD and EPMA are more powerful techniques and deliver complementary information. In case that SWIR inactive species occur, (b) the entire spectrum of minerals present in a sample has to be unambiguously identified, (c) amorphous substances are present, (d) accurate mineral compositions are required, and (e) detailed information on minerals occurring in low abundance and grain size is warranted, the combined use of XRD and EMPA is mandatory.

Preprint currently under review for the journal Natural Hazards and Earth System Sciences; Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide headscarp location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.

Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 operativity. To address the issue, we explore the usage of SAR data over the eastern Iburi sub-prefecture of Hokkaido, Japan. In the area, about 8000 co-seismic landslides were triggered by an Mw 6.6 earthquake on 6 September 2018, at 03.08 local time (JST). In the following study, we modify a Deep Learning (DL) convolutional neural network (CNN) architecture suited for pixel-based classification purposes, the so-called Attention U-Net (Attn-U-Net) and we employ it to evaluate the potential of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite and slope angle to map landslides even under thick cloud cover. Four different datasets, composed of two different band combinations per two satellite orbits (ascending and descending) are analyzed. Moreover, the impact of augmentations is evaluated independently for each dataset. The models’ predictions are compared against an accurate landslide inventory obtained by manual mapping on pre-and post-event PlanetScope imagery through F1-score and other common metrics. The best result was yielded by the augmented ascending tri-temporal SAR composite image (61% F1-score). Augmentations have a positive impact on the ascending Sentinel-1 orbit, while metrics decrease when augmentations are applied on descending path. Our findings demonstrate that combining SAR data with other data sources may help to map landslides quickly, even during storms and under deep cloud cover. However, further investigations and improvements are still needed, this being one of the first attempts in which the combination of SAR data and DL algorithms are employed for landslide mapping purposes.

To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.

In this study, a new tool for quantitative, data-driven susceptibility zoning (SZ) is presented. The SZ plugin has been implemented as a QGIS plugin to maximize its operational use within the geoscientific community. QGIS is in fact a commonly used open-source geographic information system. We have scripted the plugin in Python, and developed it as a collection of functions that allow one to pre-process the input data, calculate the susceptibility, and then estimate the quality of the classification results. The susceptibility zoning can be carried out via a number of classifiers including weight of evidence, frequency ratio, logistic regression, random forest, support vector machine, and decision tree. The plugin allows one to use any kind of mapping units, to fit the model, to test it via a k-fold cross-validation, and to visualize the relative receiving operating characteristic (ROC) curves. Moreover, a new classification method of the susceptibility index (SI) has been implemented in the SZ plugin. A typical workflow of the SZ plugin is described, and its application for landslide susceptibility zoning in Northeast India is reported. The data of the predisposing factors used are open, and the analysis has been carried out using a logistic regression and weight of evidence models. The corresponding area under the curve of the relative ROC curves reflects an optimal model prediction capacity. The user-friendly graphical interface of the plugin has allowed us to perform the analysis efficiently in few steps.

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

Recovering from major earthquakes is a challenge, especially in mountainous environments where postearthquake hazards may cause substantial impacts for prolonged periods of time. Although such impacts were reported in the 1923 Great Kantō earthquake and the 1999 Chi-Chi earthquake, careless reconstruction in hazard-prone areas and consequently huge losses were witnessed following the 2008 Wenchuan earthquake in the Sichuan province of China, as several reconstructed settlements were severely damaged by mass movements and floods. In order to summarize experiences and identify problems in the reconstruction planning, a monitoring of one of the settlements, the town of Longchi, was carried out by image interpretation and field investigation. Seven inventories containing buildings, farmlands, roads and mitigation measures were made to study the dynamics of elements at risk and exposure over a period of 11 years. It was found that the total economic value of the newly reconstructed buildings was several times more than in the preearthquake situation in 2007, because of enormous governmental investment. Postseismic hazards were not sufficiently taken into consideration in the recovery planning before the catastrophic debris flow disaster in 2010. As a result, the direct economic loss from postseismic disasters was slightly more than the loss caused by the Wenchuan earthquake itself. The society showed an impact-adapt pattern, experiencing losses from disasters and then gaining resistance by abandoning buildings in hazard-prone areas and installing mitigation measures. The locations potentially exposed to postearthquake hazards were summarized, and a possible timetable for reconstruction was proposed. Problems might be encountered in hazard assessment, and possible solutions were discussed.

Ground surface topography influence the spatial distribution of earthquake induced ground shaking. This study shows the influence of topography on seismic amplification during the 2005 Kashmir earthquake. Earth surface topography scatters and reflects seismic waves, which causes spatial variation in seismic response. We perform a 3D simulation of the 2005 Kashmir earthquake in Muzaffarabad with spectral finite element method. The moment tensor solution of the 2005 Kashmir earthquake is used as the seismic source. Our results show amplification of seismic response on ridges and de-amplification in valleys. It is found that slopes facing away from the source receive an amplified seismic response, and that 98 % of the highly damaged areas are located in the topographically amplified seismic response zone.

Geospatial information acquired with Unmanned Aerial Vehicles (UAV) provides valuable decision-making support in many different domains, and technological advances coincide with a demand for ever more sophisticated data products. One consequence is a research and development focus on more accurately referenced images and derivatives, which has long been a weakness especially of low to medium cost UAV systems equipped with relatively inexpensive inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) receivers. This research evaluates the positional accuracy of the real-time kinematics (RTK) GNSS on the DJI Matrice 600 Pro, one of the first available and widely used UAVs with potentially surveying-grade performance. Although a very high positional accuracy of the drone itself of 2 to 3 cm is claimed by DJI, the actual accuracy of the drone RTK for positioning the images and for using it for mapping purposes without additional ground control is not known. To begin with, the actual GNSS RTK position of reference center (the physical point on the antenna) on the drone is not indicated, and uncertainty regarding this also exists among the professional user community. In this study the reference center was determined through a set of experiments using the dual frequency static Leica GNSS with RTK capability. The RTK positioning data from the drone were then used for direct georeferencing, and its results were evaluated. Test flights were carried out over a 70 x 70 m area with an altitude of 40 m above the ground, with a ground sampling distance of 1.3 cm. Evaluated against ground control points, the planimetric accuracy of direct georeferencing for the photogrammetric product ranged between 30 and 60 cm. Analysis of direct georeferencing results showed a time delay of up to 0.28 seconds between the drone GNSS RTK and camera image acquisition affecting direct georeferencing results.

Events characterized by widespread landslides provide rare but valuable opportunities to investigate the spatial and size distributions of landslides in relation to seismic, climatic, geological and morphological factors. This study presents a unique event inventory for the co-seismic landslides induced by the February 25, 2018 Mw 7.5 Papua New Guinea earthquake. The mainshock rupture was dominated by reverse fault motion, and this was also the case for the aftershocks. The latter also triggered widespread landslides in combination with rainfall during the period between February 26 and March 19. We mapped approximately 11,600 landslides of which, more than 10,000 were triggered by the mainshock, with a total failed planimetric area of about 145 km². Such a large area makes this inventory the world's second-largest recorded landslide event after the 2008 Mw 7.9 Wenchuan earthquake, where the motion changed from predominantly thrust to strike-slip. Large landslides are abundant throughout the study area located within the remote Papua New Guinea Highlands. Specifically, more than half of the landslide population is larger than 50,000 m² and overall, post-seismic landslides are even larger than their co-seismic counterparts. To understand the factors controlling the distribution of landslides' occurrence and size, we combine descriptive statistics as well as more rigorous bivariate and multivariate analyses. We statistically show that the 15-day antecedent precipitation plays a role in explaining the spatial distribution of co-seismic landslides. Also, we examine four strong aftershocks (Mw ≥ 6.0) within 9 days after the mainshock and statistically demonstrate that the cumulative effect of aftershocks is the main factor disturbing steep hillslopes and causing the initiation of very large landslides, up to ~5 km². Overall, the dataset and the findings presented in this paper represent a step towards a holistic understanding of the seismic landslide hazard assessment of the entire Papua New Guinea mainland.

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

Landslides are one of the most disastrous natural hazards that frequently occur in Indonesia. In 2017, Balai Sabo developed an Indonesia Landslide Early Warning System (ILEWS) by utilizing a single rainfall threshold for an entire nation, leading to inaccuracy in landslide predictions. The study aimed to improve the accuracy of the system by updating the rainfall threshold. We analyzed 420 landslide events in Java with the 1-day and 3-day effective antecedent rainfall for each landslide event. Rainfall data were obtained from the Global Precipitation Measurement (GPM), which is also used in the ILEWS. We propose four methods to derive the thresholds: the first is the existing threshold applied in the Balai Sabo ILEWS, the second and third use the average and minimum values of rainfall that trigger landslides, respectively, and the fourth uses the minimum value of rainfall that induces major landslides. We used receiver operating characteristic (ROC) analysis to evaluate the predictability of the rainfall thresholds. The fourth method showed the best results compared with the others, and this method provided a good prediction of landslide events with a low error value. The chosen threshold was then applied in the Balai Sabo-ILEWS.

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect the hydro-mechanical behavior of geomaterials, which, in turn, controls slope stability. Here, we develop a slope unit-based multitemporal susceptibility model for the epicentral region of the 2008 Wenchuan earthquake to explore how land surface temperature (LST) relates to landslide patterns over time. We find that LST can explain post-earthquake landsliding while it has no visible effect on the coseismic scene, which is dominated by the strong shaking. Specifically, as the landscape progressively recovers and landslide rates decay to pre-earthquake levels, a positive relationship between LST and landslide persistence emerges. This seems consistent with the action of healing processes, capable of restoring the thermal sensitivity of the slope material after the seismic disturbance. Although analyses in other contexts (not necessarily seismic) are warranted, we advocate for the inclusion of thermal information in geostatistical modeling as it can help form a more physically consistent picture of slope stability controls.

Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities.

Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation (SPDE) approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where Hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.

Mapping existing landslides is a fundamental prerequisite to build any reliable susceptibility model. From a series of landslide presence/absence conditions and associated landscape characteristics, a binary classifier learns how to distinguish potentially stable and unstable slopes. In data rich areas where landslide inventories are available, addressing the collection of these can already be a challenging task. However, in data scarce contexts, where geoscientists do not get access to pre-existing inventories, the only solution is to map landslides from scratch. This operation can be extremely time-consuming if manually performed or prone to type I errors if done automatically. This is even more exacerbated if done over large geographic regions. In this manuscript we examine the issue of mapping requirements for west Tajikistan where no complete landslide inventory is available. The key question is: How many landslides should be required to develop reliable landslide susceptibility models based on statistical modeling? In fact, for such a wide and extremely complex territory, the collection of an inventory that is sufficiently detailed requires a large investment in time and human resources. However, at which point of the mapping procedure, would the resulting susceptibility model produce significantly better results as compared to a model built with less information? We addressed this question by implementing a binomial Generalized Additive Model trained and validated with different landslide proportions and measured the induced variability in the resulting susceptibility model. The results of this study are very site-specific but we proposed a very functional protocol to investigate a problem which is underestimated in the literature.

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.