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With the increase of frequency and intensity of heavy precipitation in the future, rainfall triggered landslides (RTL) can be one of the major threat to human life and property security. Early warning systems of natural hazards are one of the most effective measure for reducing disaster losses and risks. However, the forecast of RTL in near-real-time (NRT) is extremely difficult since the quality of NRT precipitation data is relatively poor. Quantile regression forest (QRF), a state-of-the-art statistical postprocessing method, has been proved to reduce the difference existing between NRT satellite precipitation estimates and ground-based rainfall data. When predicted rainfall maps are put side by side with raw NRT satellite product, the pattern of the first matches much more closely the locations where landslide events have been mapped in a test site in North-Eastern Turkey. This leave an optimistic perspective on the application of statistical postprocessing techniques in the field of weather science and in general for natural hazard assessment. Ideally, by correcting the continuous information in space and time provided by satellite rainfall estimates, one could create a new operational tool for landslide early warning system, not bound to the financial and deployment requirement typical of rain gauge and terrestrial radar stations.

Most shallow landslides are triggered by prolonged or short intense precipitation events. In dynamic physically-based model approaches for landslide susceptibility assessment, the input precipitation data is often derived from a single or a small number of rain gauges. However, precipitation patterns show a high variance in their spatial distribution that is insufficiently captured by standard rain gauge networks, particularly if inter-station distances are large. Spatially distributed weather radar-derived rainfall products have been used as input for physically-based landslide models to overcome the shortcomings of interpolated station measurements. However, the use of weather radar precipitation in physically-based modelling is not straightforward, since it represents an indirect measurement and thus requires pre-processing steps. With this in regard, the Integrated Nowcasting through Comprehensive Analysis (INCA) system (publicly released by GeoSphere Austria) provides historical (from 2011) hourly precipitation data at a 1 x 1 km resolution that combines weather radar data, station data and elevation data for the inclusion of elevation effects. The result is a pre-processed dataset that integrates the quantitative accuracy of station data with the spatial information provided by the radar data.In this study, we investigate whether the use of INCA precipitation data leads to improved model performance of TRIGRS compared to a conventional set-up using station data. We model slope stability in a 53 km2 sub-catchment located in South Tyrol (Italy) for an event that occurred in August 2016 with the INCA data and with precipitation data derived from a single station. The study compares the performances of the two model set-ups and their required parameter calibrations. First tests indicate that the model set-up using INCA data outperforms the station data set-up, as the spatial trend present in the INCA dataset of the modelled storm event follows the spatial trend present in the landslide inventory. In earlier studies and in a preliminary comparison with station data from South Tyrol, the historical INCA data was also shown to underestimate higher precipitation intensities, indicating that the two model set-ups require separate parameter calibrations. In future research, the calibrated model using the historical INCA dataset could be used with the nowcasting datasets from INCA to investigate if and how the INCA dataset can be used for landslide early warning systems.This study is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano (Südtirol/Alto Adige). In addition, the study also made use of the High-Performance Computing systems at the University of Innsbruck.

Local governments are faced with increasing levels of risk from extreme hydro-meteorological events such as (tropical) storms, flooding, landslides, drought, heatwaves, wildfires, etc. The frequency and interaction of these events, also in combination with other events that do not have a direct climate driver, makes that it is likely that many areas are faced with higher impacts from compounding events. Global trends such as population growth, urbanization, increased dependency on technology also contributed to larger exposure and vulnerability. In order to plan for future developments, and for reducing the increasing levels of risk, local governments require to plan ahead and evaluate the options available for reducing the risk under future scenarios. For this task Spatial Decision Support Systems are required that allow local governments to make informed decisions, considering the current and future levels of risk. RiskChanges is a Spatial Decision Support System for the analysis of current and future multi-hazard risk at a local level, in order to analyze optimal risk reduction alternatives. The system is developed by the University of Twente in collaboration with the Asian Institute of Technology, GeoInformatics Centre. RiskChanges ( http://www.riskchanges.org/ ) is an Open-Source, web-based tool, based on a series of python scripts, which are integrated into a Graphical User Interface. The tool includes several major features: multi-hazard, multiple assets, a vulnerability curve database, multi-user approach, comparison of risk, and spatial analysis. Users can upload their own datasets (in the form of hazard maps, elements-at-risk maps, administrative unit maps, and vulnerability curves). The tool contains an open-source vulnerability curve database, allowing to sharing of physical vulnerability curves among users. Multiple users can collaborate on the same project, and provide different input data. The multi-hazard feature allows performing the risk assessment for multiple natural and manmade hazard interactions. Exposure and vulnerability are combined in a loss calculation for each combination of element-at-risk and hazard. Loss maps are integrated into a risk map, where the user indicates the interaction between the hazard types. The system allows to analyze the risk of multiple asset types with different spatial characteristics. Users can compare the risk for the current situation and future scenarios and/or planning alternatives.

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.

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.

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

The 3 April 2017 Moiyabana intra-plate earthquake in central Botswana occurred in a region that, until then, had been assumed to be seismically quiet. Its location away from the East African Rift system in a Proterozoic mobile belt between Archean Cratons has raised questions on the triggering mechanism and sparked various studies investigating the crustal and mantle structure in the region, the focal mechanism and the displacement associated with the event. Aeromagnetic and magnetotelluric data indicate movement on a NW striking and NE dipping fault. However, the details of the fault geometry differ when analysing each dataset independently. The geophysical inversion results plus reconstructions of fault movement from InSAR data are all compatible with normal movement and reactivation of a previous thrust fault. An open question though is to which degree fluids are responsible for triggering the event. Here we present first results of reconciling the different available datasets in an integrated analysis. We will show an updated geophysical model of the region around the hypocenter. Such a model can help to shed light on the rupture processes during the earthquake and forms a first step to unravel the genesis of this intra-plate event.

The tectonics of the Sicily Channel, located in the Central Mediterranean, are thought to be driven by the Calabrian back-arc system moving south-eastwards and the north moving African plate. The Channel is characterized by a seismically and volcanically active rift zone, which extends for more than 600 km in length offshore from the south of Sardinia to the south-east of Malta. Much of the observations we have today are either limited to the surface and the upper crust, or are broader and deeper from regional seismic tomography, missing important details about the lithospheric structure and dynamics. The project GEOMED () addresses this issue by processing all the seismic data available in the region in order to understand better the geodynamics of the Central Mediterranean.We use seismic ambient noise recorded on more than 50 stations located on Algeria, Italy (Lampedusa, Linosa, Pantelleria, Sardinia (LISARD seismic network), Sicily), Libya, Malta, and Tunisia to generate high-resolution seismic tomography maps for the region at different depths. We measure Rayleigh-wave phase velocities with periods ranging from 5 to 100 seconds sampling through the entire lithosphere. We find that at short periods ( 20 s comparable to those beneath the basins suggesting that the SCRZ has a thinner crust. The seismic velocity maps are compared with the regional tectonics, seismicity, volcanic activity and other geophysical studies to present a more holistic understanding of the processes involved.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 843696.

Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015) with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.

Flood management is adopting a more risk-based approach, whereby flood risk is the product of the probability and consequences of flooding. Two-dimensional flood inundation modeling is a widely used tool to aid flood-risk management. The aim of this study is to develop a flood inundation model that uses historical flow data to produce flood-risk maps, which will help to identify flood protection measures in the rural areas of Sri Lanka. The LISFLOOD-FP model was developed at the basin scale using available historical data, and also through coupling with a hydrological modelling system, to map the inundation extent and depth. Results from the flood inundation model were evaluated using Synthetic Aperture Radar (SAR) images to assess product accuracy. The impacts of flooding on agriculture and livelihoods were analyzed to assess the flood risks. It was identified that most of the areas under paddy cultivation that were located near the middle and downstream part of the river basin are more susceptible to flood risks. This paper also proposes potential countermeasures for future natural disasters to prevent and mitigate possible damages.

To study near-surface melt changes over the Greenland ice sheet (GrIS) since 1979, melt extent estimates from two regional climate models are compared with those obtained from spaceborne microwave brightness temperatures using two different remote sensing algorithms. Results from the two models are consistent with those obtained with the remote sensing algorithms, at both daily and yearly time scales, encouraging the use of the models for analyzing melting trends before the satellite era (1958–1979), when forcing data is available. Differences between satellite-derived and model-simulated results still occur and are here used to identify (i) biases in the snow models (notably in the albedo parametrization, in the thickness of a snow layer, in the maximum liquid water content within the snowpack and in the snowfall impacting the bare ice appearance in summer) and (ii) limitations in the use of passive microwave data for snowmelt detection at the edge of the ice sheet due to mixed pixel effect (e.g., tundra or rock nearby the ice sheet). Results from models and spaceborne microwave sensors confirm a significant (p-value = 0.01) increase in GrIS surface melting since 1979. Melt extent recorded over the last years (1998, 2003, 2005 and 2007) is unprecedented in the last 50 years with the cumulated melt area in the 2000's being, on the average, twice as that occurring during the 1980's.

This paper presents a prototype of an interactive web-GIS tool for risk analysis of natural hazards, in particular for floods and landslides, based on open-source geospatial software and technologies. The aim of the presented tool is to assist the experts (risk managers) in analysing the impacts and consequences of a certain hazard event in a considered region, providing an essential input to the decision making process in the selection of risk management strategies by responsible authorities and decision makers. This tool is based on the Boundless (OpenGeo Suite) framework and its client side environment for prototype development, and it is one of the main modules of a web-based collaborative decision support platform in risk management. Within this platform, the users can import necessary maps and information to analyse areas at risk. Based on provided information and parameters, loss scenarios (amount of damages and number of fatalities) of a hazard event are generated on-the-fly and visualized interactively within the web-GIS interface of the platform. The annualized risk is calculated based on the combination of resultant loss scenarios with different return periods of the hazard event. The application of this developed prototype is demonstrated using a regional data set from one of the case study sites, Fella River of North Eastern Italy, of the Marie Curie ITN CHANGES project.