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In the face of climate change, development and humanitarian practitioners increasingly recognize the need to anticipate and manage multiple, concurrent risks. One prominent example of this increasing focus on anticipation is the rapid growth of Forecast-based Financing (FbF), in particular within Red Cross and Red Crescent (RCRC). To evaluate how anticipatory efforts managed multiple compounding risks during the COVID-19 pandemic, we examine how 14 RCRC Societies adapted their Early Action Protocols to COVID-19. Though many National Societies successfully adapted to the onset of the additional hazard of COVID-19, we find that multi-hazard risk management can be improved by: proactively developing guidelines that enable rapid adaptation of existing plans; more flexible funding mechanisms; surge capacity to provide additional human resources; and increasing local capacity and ownership for implementation to ensure supplies, skills, and decision-making authority are available when communication or travel is restricted. These findings align with wider recommendations for improving development, humanitarian, and climate adaptation practice towards local capacity and agency. They also add urgency to broader calls for more flexible disaster financing and more practitioner-oriented investment in climate risk and multi-hazard management.

A first attempt of Electron microprobe (EMP) based Th-U-total Pb in situ dating of monazites in tailings from a placer deposit (quartz bearing sands) from Bangka Island, Indonesia, revealed for the first time from a single locality in Indonesia an age cluster around 231 Ma as well as two single grains with ages of 1133 Ma and 1916 Ma. The monazite ages from this study agree with detrital zircon ages and whole rock isochron ages from magmatic intrusions, which were reported from various localities all over the Malaysian peninsula, Myanmar and Thailand but not yet from Bangka Island. Perhaps intrusions in the closer vicinity, which could be the source of the monazite under study, have not been discovered so far or the sedimentary catchment area of the placer deposit is much larger than assumed so far. The existence of the monazite ages investigated in this study may also raise the question of the paleogeographic position of Bangka Island and the relative timing of sedimentation and Miocene rifting. However, the present work is an excellent example for the resistance of monazite and its Th-U-Pb system to sedimentary processes and hence is a useful tool for provenance studies and source of Rare Earth Element (REEs) recovery. An important outcome of this study is that sedimentary monazite relics often provide a wide range of unrealistic ages. Such dates may be wrongly explained in terms of recrystallization along with metasomatic process, instead these ages are only a methodic artefact because monazite relics are either not thick enough for dating or show poor surfaces with numerous cracks.

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.

Sahelian West Africa is a region of high year-to-year climate variability that can significantly impact on communities and livelihoods. Adaptive social protection (ASP) is being introduced in the region to support vulnerable people through enabling more effective responses to climate shocks, bridging social protection with disaster risk reduction and climate change adaptation. The ASPIRE (Adaptive Social Protection: Information for enhanced REsilience) project aimed to provide technical support to the World Bank’s Adaptive Social Protection Programme through demonstrating the use of climate forecasts in ASP and promoting dialogue between climate and social protection stakeholders. Here we discuss lessons learned in the project, highlighting challenges and opportunities for including climate forecasts in early warning systems to inform ASP. We provide recommendations to help achieve ASP systems designed to use climate forecasts, arguing that tailored seasonal forecast products have potential in some countries to improve the lead time of interventions to address climate-induced disasters. Critical to this is continued investment in underpinning science and capacity building of climate and social protection stakeholders, as well as continued dialogue and co-production between actors to develop climate forecasts that provide actionable information.

Ground motion scenarios exists for most of the seismically active areas around the globe. They essentially correspond to shaking level maps at given earthquake return times which are used as reference for the likely areas under threat from future ground displacements. Being landslides in seismically actively regions closely controlled by the ground motion, one would expect that landslide susceptibility maps should change as the ground motion patterns change in space and time. However, so far, statistically-based landslide susceptibility assessments have primarily been used as time-invariant.In other words, the vast majority of the statistical models does not include the temporal effect of the main trigger in future landslide scenarios. In this work, we present an approach aimed at filling this gap, bridging current practices in the seismological community to those in the geomorphological and statistical ones. More specifically, we select an earthquake-induced landslide inventory corresponding to the 1994 Northridge earthquake and build a Bayesian Generalized Additive Model of the binomial family, featuring common morphometric and thematic covariates as well as the Peak Ground Acceleration generated by the Northridge earthquake. Once each model component has been estimated, we have run 1000 simulations for each of the 217 possible ground motion scenarios for the study area. From each batch of 1000 simulations, we have estimated the mean and 95% Credible Interval to represent the mean susceptibility pattern under a specific earthquake scenario, together with its uncertainty level. Because each earthquake scenario has a specific return time, our simulations allow to incorporate the temporal dimension into any susceptibility model, therefore driving the results toward the definition of landslide hazard. Ultimately, we also share our results in vector format – a .mif file that can be easily converted into a common shapefile –. There, we report the mean (and uncertainty) susceptibility of each 1000 simulation batch for each of the 217 scenarios.

Hydro-morphological processes (HMPs) are some of the most destructive natural disasters. Understanding the spatiotemporal characteristics of HMPs across China is important for enabling better disaster estimation and prevention at the national scale. However, few studies have focused on the spatiotemporal HMP characterization under various geomorphic settings in China, which may shed light on their regional/national evolution. To bridge this research gap, we analysed the longest HMP time series available in China, which including 47,483 HMP records, to detect spatiotemporal patterns. Specifically, we run several tests namely, Mann–Kendall test, wavelet analysis, Monthly Frequency, and Index of Dispersion, with the objective of detecting the temporal evolution, trends, period, and clustering of HMPs in six geomorphological regions (geomorphic-regions): Eastern Plain, South Eastern Mountain (SEM), North Central Plateau, North Western Basin, South Western Mountain (SWM), and Tibetan Plateau. Our results show that in the last decades have been associated with a marked increase in HMPs and this should be accounted for, especially in those areas where we have retrieved high spatiotemporal clustering (e.g., SEM, SWM). Besides, the main periodicity of HMPs is approximately 12–25 years for most of China since the 1980s, which showed analogous patterns with precipitation anomalies. This study provides a preliminary reference for revealing the spatiotemporal characteristics of HMPs in the context of climate change; therefore, the information provided can be crucial to plan engineering applications with specific return period.

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.

Kathmandu Kyirong Highway (KKH) is one of the most strategic Sino-Nepal highways. Low-cost mitigation measures are common in Nepalese highways, however, they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road, and thus less resources are made available for its maintenance. It is subjected to frequent landslide events in an annual basis, especially during monsoon season. The Gorkha earthquake, 2015 further mobilized substantial hillslope materials and damaged the road in several locations. The aim of this research is to access the dynamic landslide susceptibility considering pre, co and post seismic mass failures. We mapped 5,349 multi-temporal landslides of 15 years (2004–2018), using high resolution satellite images and field data, and grouped them in aforementioned three time periods. Landslide susceptibility was assessed with the application of ‘certainty factor’ (CF). Seventy percent landslides were used for susceptibility modelling and 30% for validation. The obtained results were evaluated by plotting ‘receiver operative characteristic’ (ROC) curves. The CF performed well with the ‘area under curve’ (AUC) 0.820, 0.875 and 0.817 for the success rates, and 0.809, 0.890 and 0.760 for the prediction rates for respective pre, co and post seismic landslide susceptibility. The accuracy for seismic landslide susceptibility was better than pre and post-quake ones. It might be because of the differences on completeness of the landslide inventory, which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations. The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents. This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk, which will further help to design the efficient mitigation measures along the mountain highways.

For many regions on Mars, the surface composition and its geological history have been debated in the literature. Because of the limited surface coverage of in situ measurements, either new data or new processing methodologies are required to get a better understanding of the Martian geology. This paper presents the results of a multivariate, unsupervised, analysis on underutilized CRISM (Compact Reconnaissance Imaging Spectrometer) multispectral mapping mode data set for surface type analysis. The devised summary products of Pelkey et al. (2007) and Viviano-Beck et al. (2014) are averaged for the CRISM ~5° × 5° mosaic grids and used to analyze the variability in this data set. The averaged summary product values are studied using correlation coefficients calculated between summary products, as well as the correlation with dust coverage and elevation. The degree of correlation is used to interpret the summary products for the global distribution of mafic and secondary minerals and the effect of external factors such as ice, atmosphere, and dust coverage. With unsupervised clustering, all grid pixels are classified based on the spectral variability. These clusters are plotted as global maps and interpreted for geological variations in the CRISM data. Some clusters spatially correspond with previously recognized compositional distinct regions Northern Lowlands, Southern Highlands, Meridiani Planum, Syrtis Major, and Nili Fossae. Several other clusters are described here for the first time such as Solis Planum, Ophir Planum, and Hellas Basin. These regions cover known geological units, but the interpretation of the spectral variability is uncertain whether it relates to geological or external factors.

Frequent landslide events affect the Kathmandu Kyirong Highway (KKH), one of the most strategic Sino-Nepal highways, with multiple social effects. Amongst them, the impacts on local tourism, although being substantial, have not been studied so far. The aim of this research is to analyze the characteristics of such landslides and their influence on road damages and/or blockages as well as on local tourism industry. We analyzed the co-seismic landslides triggered by the Gorkha Earthquake, 2015 (7.8 Mw), the post-seismic landslides that occurred during the monsoons following the earthquake, as well as landslides which occurred or reactivated in 2018, with relation to the damage that they caused to the highway. High resolution satellite images from 2015 to 2018, and field data were used for the analysis. The Langtang avalanche that locates off the highway was also mapped due to its high impacts on tourism. Between 2015 and 2018, the number of road damaging landslides in the Betrawati-Rasuwagadhi section of KKH (where Dhunche and Syafrubesi towns are located) was 101 in the main track (MT) and 103 in the new track (NT), with respective average density of 1.46/km and 3.63/km. The dominant observed landslide types were debris slides and rock falls. Landslides were mostly concentrated in the locations with the following characteristics: 1) having higher elevated area, 2) being located with the ‘main central thrust’ and other lineaments’ belts, 3) belonging to the Proterozoic lesser Himalayan rocks, 4) having a slope gradient of 25°–45°, 5) having northern, western and southern slope aspect, 6) being subjected to average annual rainfall of higher than 1,000 mm, and 7) having less than 4 km distance from the past earthquake epicenters. The results further indicated that 7 rain-induced and 4 co- and post-seismic landslides have great impact on tourist flows. An impact analysis was also assessed through a door to door questionnaire survey with local hotel operators from Dhunche and Syafrubesi towns (n = 29 + 31). The results reveal that out of six rigorously affected sectors by landslides leading to road blockage, tourism business is the most impacted livelihood sector in these towns. The reduction of visitors in different hotels ranged from 50%–100% in Dhunche and 70%–100% in Syafrubesi for the first year aftermath of the tremor. This is higher than the respective 5%–50% tourist reduction due to rain-induced landslides. Using as a reference the base year 2014, the income loss of hotels in both towns was found to be 50%–100% in 2015, 20%–100% in 2016, 5%–75% in 2017, and similar to 35% in 2018. These results provide insights on the synergic effect of contributing factors for cut slope as well as down slope instability along mountainous motorways and their impact on income sources for local communities.

Botswana experienced a Mw 6.5 earthquake on 3rd April 2017, the second largest earthquake event in Botswana's recorded history. This earthquake occurred within the Limpopo-Shashe Belt, ~350 km southeast of the seismically active Okavango Rift Zone. The region has no historical record of large magnitude earthquakes or active faults. The occurrence of this earthquake was unexpected and underscores our limited understanding of the crustal configuration of Botswana and highlight that neotectonic activity is not only confined to the Okavango Rift Zone. To address this knowledge gap, we applied a regularized inversion algorithm to the Bouguer gravity data to construct a high-resolution crustal thickness map of Botswana. The produced crustal thickness map shows a thinner crust (35–40 km) underlying the Okavango Rift Zone and sedimentary basins, whereas thicker crust (41–46 km) underlies the cratonic regions and orogenic belts. Our results also show localized zone of relatively thinner crust (~40 km), one of which is located along the edge of the Kaapvaal Craton within the MW 6.5 Botswana earthquake region. Based on our result, we propose a mechanism of the Botswana Earthquake that integrates crustal thickness information with elevated heat flow as the result of the thermal fluid from East African Rift System, and extensional forces predicted by the local stress regime. The epicentral region is therefore suggested to be a possible area of tectonic reactivation, which is caused by multiple factors that could lead to future intraplate earthquakes in this region.

Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used.

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.

There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies. This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.

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.

A landslide inventory is indispensable for determination of landslide susceptibility, hazard, risk assessment and disaster mitigation strategies. These inventories were traditionally developed using manual digitization of remote sensing images and aerial photographs, and pixel-based image classification. Recently, Object-Based Image Analysis (OBIA) supersedes visual interpretation and pixel-based methods. OBIA utilizes spectral, textural, contextual, morphological and topographical information in remote sensing images. However, OBIA-based landslide detection methods are often designed for specific areas and remote sensing dataset. The aim of this study is to evaluate the transferability of three published OBIA landslide detection methods for semi-automated landslide detection in the Himalaya mountainous region of northern Pakistan. A SPOT-6 multi-spectral image with Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM) derivatives, i.e. slope, aspect, hill-shade, relief, elevation and stream network are used for landslide detection using eCognition developer software. The three published methods scale parameters for image segmentation and parameter thresholds are evaluated first. It is observed that the aforementioned methods are not directly applicable to our study area and remote sensing datasets. Therefore, an alternate (proposed) method is developed for semi-automated landslide detection. Accuracy assessment of the selected methods and proposed method is assessed by Precision, Recall and F1 measures. Using the proposed method, a total of 357 landslides are detected with 91.46% Precision, 93.31% Recall and 92.38% F1 measure accuracy.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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