Displays

A large-scale flood risk analysis that properly evaluates and quantifies the three components of risk (hazard, exposure and vulnerability) is essential in order to support national and global policies, emergency operations and land use management. For example, governments can use risk information for the prioritisation of investments to implement measures for flood damage reduction, for emergency operations and for land-use policies, while reinsurance companies can improve the estimation of the flood risk-based insurance premiums.

Nevertheless, limits in time and data represent significant limitation this kind of applications: i) the significant amount of data and parameters required for the calibration and validation of traditional model; ii) the moderate/coarse resolution of data available at global scale and the sparse availability of high-resolution data that may affect the accuracy of analysis results; iii) the high cost and computational demand of hydraulic models. However, the growing availability of data from new technologies of Earth observation (EO) and environmental monitoring combined with the advances in newly developed algorithms (e.g. machine learning) have extended the range of possibilities for geoscientists, updating and re-inventing the way highly resource- and data-intensive processes, such as risk management and communication, are carried out.

The present study proposes a cost-efficient method for large-scale analysis and mapping of direct economic flood damage at medium resolution in data-scarce environments. The proposed methodological framework consists of three main stages. The first step concerns the derivation of a water depth map through a Digital Elevation Model (30m resolution)-based geomorphic method that uses supervised linear binary classification. The second step aims to realize an exposure map on the basis of a supervised land use classification through the use of a machine learning technique: the information extracted from Landsat-8 remotely sensed optical images were utilized in combination with the discontinuous (i.e. available for a few large cities in Europe) existing high-resolution Urban-Atlas land use maps in order to obtain a land-use map with a resolution of 30 m. Finally, the flood economic damage mapping was carried out using the results of the two previous steps in a GIS algorithm, developed by authors, based on the vulnerability (depth-damage) curves method. The proposed integrated framework has been tested in Romania for a 100-years return time event. The resulting map (at 30 m resolution) covers the entire Romanian territory including minor order rivers, which are often neglected in large-scale analyses.

The demonstrative application shows how the description of flood risk may particularly benefit from the integrated use of geomorphic methods, machine learning algorithms and EO freely available monitoring data. The ability of the proposed cost-efficient model to carry out high-resolution and large-scale analyses in data-scarce environments allows performing future risk assessments keeping abreast of temporal and spatial changes in terms of hazard, exposure and vulnerability.

Acknowledgement: This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme.

How to cite: Albano, R., Sole, A., Manfreda, S., Samela, C., Craciun, I., Sivertun, A., and Ozunu, A.: Large Scale Flood Damage Mapping: the case study of Romania Country, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3434, https://doi.org/10.5194/egusphere-egu2020-3434, 2020.

Stromboli island (Italy) provides an outstanding record of volcanic island geomorphological evolution, and of ongoing volcanic phenomena with the example of the “Strombolian” types of eruption. The vegetation of Stromboli includes endemic species, some of which are exclusive to the Aeolian Islands. The western side of the island is characterized by olive trees that were cultivated by exploiting terraces up to high altitudes. All this makes an unique landscape, results of interaction between volcanic activity, geomorphological evolution, and traditional land management. Wildfires at the island of Stromboli are common phenomena related to the fallout of incandescent material on vegetation. Wildfires with small extensions are usually generated by explosions more intense “major” explosions, while large-scale wildfire have been triggered by larger scale activity, called “paroxysms”.

On 3^rd July 2019 a paroxysm without long-term precursors has occurred, followed by lava flows from a vent localized in the SW crater area and sporadically from the NE one. Afterwards, on 28^th August 2019, a new paroxysmal explosion has occurred followed by strong volcanic activity, culminating with a lava flow from the SW-Central crater area.

This study is focusing on environmental aftermath of the 2019 Stromboli eruptions. The analysis of Land Cover (LC) and Land Use (LU) changes is used to describe the impact on the environment of the island. The detection of impacted areas is mainly based on the integration of very high-spatial-resolution PLEIADES-1, moderate-spatial-resolution SENTINEL-2 satellite imagery, and field surveys. Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Relativized Burn Ratio (RBR) were used to map the areas covered by fires. NBR easily allows to easily identify the areas impacted by wildfire and the degree of severity of the damage. This index is calculated on two SENTINEL-2 images acquired on different dates before and after the fire (after a not excessively high number of days, especially if the area affected by the fire consists mainly of pasture or low bush). RBR is obtained as the difference between the NBR index of the images acquired before and after the event. LC and LU classifications has involved the detection of new classes whose details have been calibrated on different reduction scales from 1:2.000 to 1:10.000, following the environmental units that made up the Strombolian landscape. New LC and LU classifications are the result of the intersection between classes of CORINE Land Cover project (CLC) and local landscape patterns. Field survey has been useful to conduce semi-structured interviews to the local population; the purpose of the social investigation was to collect detailed and direct information about damages.

The most impacted areas by tephra fallout are located in the south-western and southern part of the island, nearby the village of Ginostra. The results of multi-temporal comparison show that fire-damaged areas amount to 39% of the total area of the island. Artificial areas have not been particularly impacted (max 14% of decrease), whereas agricultural and semi-natural vegetated areas show a much more consistent decrease of 34% and 81%, respectively.

How to cite: Turchi, A., Di Traglia, F., Luti, T., Zetti, I., and Fanti, R.: The effect of the 2019 eruption on the Island of Stromboli (Aeolian Islands UNESCO site, Italy)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4937, https://doi.org/10.5194/egusphere-egu2020-4937, 2020.

Insurers have been increasingly relying on space technologies to estimate flood-related damage more accurately for the last few years. CCR has developed and applied a specific workflow on three major flood events (with losses ranging from 200 million € to 1 billion € for the French insurance market) that occurred in mainland France: Seine and Loire in May-June 2016 (from 900 million € to 1 billion €), Seine and Marne in January-February 2018 (from 180 to 220 million €) and Languedoc in October 2018 (from 250 to 300 million €). Our methodology benefits from a strong validation thanks to thousands of claims collected and geolocalised on each flooded building which is usually a missing but key point. This methodology is based on EO data and remote sensing methods from medium (20 to 30 m) to high (10 m) resolution satellite data collected by Landsat-8, Sentinel-2, and Sentinel-1. The flooded areas inferred from satellite data are combined with CCR’s physical overflow model (1) to improve loss estimation that  are shared with insurance companies operating in France and public authorities.

Raw radar images are processed with the ESA SNAP remote sensing software. A radiometric threshold is estimated to distinguish water surfaces from surfaces without water. Moreover, coherence data derived from InSAR processing (2) provide additional data to detect flooded buildings in city centers. For multispectral images, the MNDWI index (3) was selected as it allows to delineate more precisely water surfaces. Finally, a Random Forest classification has proved effective in defining the spatial distribution of the flooded areas on river basins from the learning areas integrated to the algorithm. In the confusion matrix, implemented for validation, the Kappa index (4) reaches 96.2 % with an overall accuracy of 97.7 %.

A large focus is presented on the 2016 Seine and Loire basins flood event. With a loss estimated between 900 million € and 1 billion € and over 10 000 claims, this event allowed us to validate more precisely the remote sensing methodology which we developed. Insurance indicators such as probability of detection, probability of false detection, True Skill Score for both CCR overflow model and remote sensing data model were also calculated to estimate the benefits of this methodology.

(1) Moncoulon, D. and al., 2014. Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff. NHESS

(2) Chini, M. and al., 2019. Sentinel-1 InSar Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as a Test Case. Remote Sensing

(3) Baig, M.H.A., and al., 2013. Comparison of MNDWI and DFI for water mapping in flooding season. IEEE International Geoscience and Remote Sensing Symposium

(4) Landis and al., 1977. The Measurement of Observer Agreement for Categorical Data. International Biometric Society

How to cite: Onfroy, T., Tinard, P., and Nassih, A.: Coupling Hydrological Overflow Model and EO-data: Benefits on Hazard and Damage Estimation for Floods in France, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14957, https://doi.org/10.5194/egusphere-egu2020-14957, 2020.

With expeditions into glaciated regions on the planet becoming more commonplace there is a need to be able to make route assessments to identify potential hazards for safe operational planning. We use an example from the recently completed "the Longest Journey", a polar expedition that has broken the record for the longest solo unsupported polar journey in human history. The expedition route is in excess of 5,600 kilometres, commencing at the Russian Novolaskaya Station (Novo), to the Pole of Inaccessibility, to Dome Argus (Dome A), and returning to Novo. The estimation and provision of several derived quantities were provided along the route that included inferred crevassing potential of the, supplemented by reporting of additional terrain conditions and hazards. Here we present the route analysis and evaluation with what was actually found under field conditions with footage obtained during the traverse. We show significant success with apriori route planning can be obtained by careful analysis and expert interpretation of available data, that include satellite data based on visible and radar imagery. This approach to minimising hazard exposure can be usefully applied to other operations, including travel over remote and glaciated field locations for science and expedition purposes.

How to cite: Galton-Fenzi, B., Winberry, J. P., Comery, J., and Wilson, G.: Approaches to minimising risk in glaciated terrain travel, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21831, https://doi.org/10.5194/egusphere-egu2020-21831, 2020.

Landslides are widespread landscape features in the Northern Apennine mountain chain and their activity frequently cause damages to settlements and infrastructures. In such context, slow-moving landslides are very common and typically affect fine-grained weathered rocks. Long periods of sustained slow-movements (cms/year) can be interrupted by rapid acceleration and catastrophic failures (ms/day) that are caused by intense rainfall events. Space-borne synthetic aperture radar interferometry (InSAR) proved effective to detect actively deforming phenomena and monitor their evolution in the periods before and after failures. We present InSAR results derived from the Sentinel 1 satellite constellation for landslide cases that underwent reactivation during 2019. In all cases, the catastrophic failures were unexpected and no ground-based monitoring data are available. We processed pre- and post-failure interferograms of SAR images acquired by Sentinel 1 A/B with time spans ranging from 6 to 24 days, removing those having low coherence by manual inspection. The conventional 2-pass technique allowed us to obtain measurements of surface displacement despite the fact that sparse to none infrastructures nor bare rock outcrops are present on the landslide bodies. Our interferograms show that surface displacements are visible well in advance of the actual failure. They display nearly continuous downslope motion with seasonal velocity changes. Time series between 2015 and 2019 shows that surface displacements can be appreciated throughout most part of the year with snow cover and summer peak of vegetation being the most notable exceptions. Distinct accelerations can be detected in space and time during the weeks and months preceding the reactivation.

We compare time-dependent deformations to precipitation patterns to explore their relationship and to document the transition from stable to unstable deformation. Our work suggests that InSAR interferometry can be successfully used to measure pre-failure displacements and detect slow-moving landslides that are more prone to reactivation in case of rainfall events.

How to cite: Simoni, A., Bayer, B., Ciuffi, P., Franceschini, S., and Berti, M.: Detection and measurement of landslide deformation prior to their failure by satellite radar interferometry., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7617, https://doi.org/10.5194/egusphere-egu2020-7617, 2020.

A severe hailstorm activity on 27^th July 2019 created significant damage to crops in the province of Styria, Austria. The hail reports from ESWD (European Severe Weather Database) shows with maximum diameter up to 8 cm was noticed in the vicinity of the storm occurred. Total 1040 crop damage reports were claimed from the Austrian Hail Insurance System due to this severe hailstorm event. A close inspection and understanding features of severe hailstorms is helpful for hail risk assessment. The present study investigates the associated synoptic weather conditions and life cycle of the thunderstorm, and its dynamics. Further analysis carried about hail detection methods and crop hail damage assessment based remote sending and crowdsourcing data. The spatial distribution and temporal development of severe thunderstorms details extracted from radar data. The 3D radar data and storm cell tracking software used to capture the thunderstorm life cycle from the beginning to the dissipating stage. Radar-derived parameters collected for each storm cells, i.e. Duration of the storm cell, volume and area the storm cell, the cloud top height and the maximum reflectivity. Hail detection algorithms (Waldvogel and Auer) used to identify hail event period. The spatial distribution total hail kinetic energy maps prepared to capture the swath and intensity of the hail storms to classify possible crop-hail damaged areas. Hail observational data from ESWD (European Severe Weather Database) and HeDi (Hail event Data interface) and crop damage reports from the Austrian Hail Insurance System are utilised as a ground truth information.  An event-based severe hailstorm analysis help to find proper risk transfer solutions for loss adjustment.

How to cite: Tani, S. and Paulitsch, H.: A case study on severe hailstorm on 27 July 2019 in the province of Styria, Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18626, https://doi.org/10.5194/egusphere-egu2020-18626, 2020.

Shoreline vectors are extracted from TerraSAR-X imagery based on the identification of peak backscatter intensity levels. The vectors are being catalogued and analysed to assess the accuracy/suitability of SAR imagery for identifying coastal erosion hotspots and for monitoring coastal change as input to forecasting models. The technique is being developed, tested and refined using data collected from three study sites on the west coast of Ireland (Brandon Bay; Clew Bay; Galway Bay).

The shoreline vectors are extracted from both archived and tasked TerraSAR-X imagery. The extracted shorelines are being validated using a combination of: 1) panchromatic and multispectral satellite imagery (VHR1 & VHR2), 2) panchromatic and RGB aerial imagery (VHR1), 3) LiDAR data and 4) repeat DGPS field survey data. In addition, these shoreline vectors are also being compared with equivalent extractions from other very high-resolution X-band SAR imagery (Cosmo-SkyMed) and high-resolution C-band and L-band SAR imagery (RADARSAT-2, ALOS PALSAR). The spatial accuracy of the extracted shorelines from tasked acquisitions will be further assessed using temporarily installed corner reflectors at a selection of the study sites.

SAR acquisition parameters (orbit pass direction, incidence angle, polarisation) and a selection of speckle noise reduction filters (e.g. Boxcar, Frost, Lee) were evaluated to determine the optimum combination for coastal sites with different physical characteristics.

Results are presented in high-definition video format using a combination of GIS, Earth browser and 3D visualisation platforms.

How to cite: Maguire, D. and Farrell, E.: Coastal Shoreline Extraction from Very High Resolution (VHR2) Satellite SAR Imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22187, https://doi.org/10.5194/egusphere-egu2020-22187, 2020.

The recent advances in the acquisition of aerial images using Remotely Piloted Aircraft Systems (RPAS) offer an efficient and low-cost solution for the assessment of geomorphologic changes in areas affected by landslides, gullies and rill erosion, river channel migration, through the creation of accurate Digital Elevation Models (DEM's). Despite many advantages of DEM's obtained through Structure from Motion (SfM) method (resources, availability, high resolution - spatial and temporal), they are suitable for reduced study areas, usually under 100-200 ha, where there is a significant intensity of geomorphic processes and where their effects threaten human assets or heritage.
This study focus on the area of Poiana Mănăstirii Thraco-Getic fortress (2550-2050 yr BP), located in the central part of Moldavian Plateau, Romania. Covering a surface of 12 ha, the fortress is surrounded by a 2-3 m high wall, with a 10 m wide base, and a 1 m deep and 4-6 m wide trench. In its southern part, the landslides destroyed these remnants, and due to the deforestation of the slope in the last 30 years, these processes recorded almost yearly reactivations. The main landslide scarp is affected by a gully system that contributes to the archaeological site degradation.
A DJI Phantom 4 Pro UAV was flown over the study area in October 2019 and acquired images with 80 % side and forward overlap at 20 MP resolution. Visual SFM open source software was used to obtain the point cloud and for georeferencing, a Ground Control Point network was measured with a Trimble GeoExplorer 6000 GPS. In order to detect and to map geomorphic changes, LiDAR point clouds (2012) were used as a reference dataset (with a spatial resolution of 0.25 m, and a vertical accuracy of 0.13 m).
A detailed map showing the changes in topography between 2012 and 2019 has been carried out, supplementing a geomorphological mapping. The most dynamic portions of the landslide are accompanied by dense micro-topographic features like secondary scarps, longitudinal and transversal cracks, which have been mapped using the ortophotoimage. The most dynamic parts of the hillslope are an earthflow, shallow and slumps along with the eastern gully system, piping sinkholes, and the main scarp gullies. The evolution of the landslides and gullies indicate that the southern part of the fortress will be affected in the near future. Alongside the identification of the most active parts of the landslide, we conclude that the entire recently deforested area must return as quick as possible to the initial land use (forest).

How to cite: Margarint, M. C., Niculita, M., Ciotina, M. C., Vaculisteanu, G., Linu-Stoilov, V., and Tarolli, P.: Using RPAS derived images and LiDAR DEM's for the assessment of geomorphic changes in a cultural heritage site affected by recent landslides, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7780, https://doi.org/10.5194/egusphere-egu2020-7780, 2020.

For the past 20 years, the ASTER and MODIS instruments on Terra have acquired thermal infrared (TIR) data of the world’s volcanoes. These observations have improved our knowledge of long-term volcanic behavior, eruption monitoring, and post-eruption change. MODIS acquires images twice per day (later doubling this after the launch of Aqua) with 1 km TIR and mid-IR resolution. The volcano data from MODIS were later organized into global automated observation programs such as MODVOLC (USA) and later MIROVA (IT). These systems continually detect and track the amount of emitted energy at each active volcano, resulting in vast databases over time that are critically important for ongoing eruptions. Unlike MODIS, ASTER is scheduled and acquires TIR data at 90 m spatial resolution nominally every 5 – 16 days depending on the latitude. This can be improved to hours with proper scheduling and orbital dependencies using its expedited data system. For the past 15 years, an ASTER program called the Urgent Request Protocol (URP) has combined the rapid detection capability of MODIS with the high resolution expedited observations of ASTER in a sensor-web approach. The URP is operated by the University of Pittsburgh in conjunction with (and the support of) the Universities of Alaska, Hawaii, Turin (IT), Clermont Auvergne (FR), and Bristol (UK) as well as the USGS, the LP DAAC and the ASTER science team. The data are used for: operational response to new eruptions; determining thermal trends months prior to an eruption; inferring the emplacement of new lava lobes; and mapping the constituents of volcanic plumes, to name a few. This ASTER TIR archive of volcanic data is now being mined to provide statistics for future TIR orbital concepts being considered by NASA. As TIR instruments get smaller and more numerous with the use of uncooled detectors, they will become CubeSat compatible and could operate in a multi-platform, sensor-web architecture. This would improve response times to volcanic crises and enable new measurements such as the global inventory of volcanic degassing, thermal precursory trends at every volcano, and active flow temperatures at the minute timescale required for predictive flow and hazard assessment models. The combined spatial, spectral and temporal resolutions of ASTER and MODIS enabled a new multi-platform, multi-scale approach to volcanic remote sensing, a model which could be greatly improved depending on future instrument/mission selections.

How to cite: Ramsey, M.: Multi-platform volcanological imaging: Two decades of thermal infrared data from the ASTER and MODIS sensors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4274, https://doi.org/10.5194/egusphere-egu2020-4274, 2020.

The ongoing retreat of glaciers has formed several thousands of meltwater lakes in the Himalayas. Hundreds of these lakes have grown rapidly in area and volume in past decades, raising widely publicised concerns of an increasing hazard from sudden glacier lake outburst floods (GLOFs). Some 40 catastrophic lake outbursts have claimed thousands of fatalities and high losses in the Himalayas, mostly as a consequence of moraine-dam failures. Human and public safety along densely populated river reaches may thus be prone to changes in the lake size-distribution and the frequency of outburst floods. Yet multi-temporal inventories of Himalayan glacier lakes and associated outburst floods that we need for hazard appraisals have been collated only for selected basins with few standardised rules. Objectively tracing changes in regional GLOF hazard through time has thus remained elusive.

Here we meet this urgent demand for an improved GLOF hazard assessment. We estimate changes in the 100-year GLOF peak discharge from the late 1980s towards a scenario of completely ice-free Himalayas. We use a Random Forest model to predict land cover from seasonal Landsat images, and automatically extract glacier lakes for four time intervals. We obtain credible lake depths and volumes for each interval from a linear model learned from published bathymetric surveys. We further project possible sites for future Himalayan meltwater lakes from three published models of subglacial topography. We assume that these presently ice-covered depressions could fill completely with water though sediment and debris could decrease the storage space for future lakes. We simulate distributions of peak discharge for historic, present, and future lakes, accounting for different combinations of lake area, breach depth, and dam lithology. Most barrier types are unknown and could range from intact metamorphic bedrock to unconsolidated moraine debris. These two end members help to constrain the physically possible boundaries of GLOF peak discharges, which is supported by data from 82 natural dam breaks with known values of erodibility. To estimate the return periods of outburst floods, we used an extreme-value model to couple our simulations of peak discharge with mean annual rates of outburst floods, which remained unchanged in the Himalayas in the past three decades.

Given this constant rate of outburst floods, we report how hazard—expressed as the 100-year GLOF discharge—varied with regionally changing lake-size distributions in the past decades. We show that the southern Himalayas of Nepal and Bhutan had the largest increase of lake area, feeding notions of a rising GLOF hazard in this region. Hazard in the Western Himalaya, Karakoram, and Hindu Kush increased marginally, in line with the smallest historic abundance of glacier lakes and outburst floods. Future lake abundance and volumes may increase at least six-fold, with the largest lakes appearing in regions that have large glaciers today such as the Western Himalaya and the Karakoram. All other controls held constant, we find that hazard from these future lakes will largely rest on the erodibility of the barrier type, which needs to be acknowledged better in hazard appraisals.

How to cite: Veh, G., Garcia-Castellano, D., and Korup, O.: Evolving hazards from Himalayan glacier lake outburst floods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3486, https://doi.org/10.5194/egusphere-egu2020-3486, 2020.

Risk exposure adjournment in flood prone areas is usually limited by the unavailability of frequently updated information about urbanization and census. This limitation is produced mainly by the complexity of the long process that lead to thematic maps compliant with common product requirements.

Therefore, the mapping of exposed elements and population does not fully exploit the potential high refresh rate typical of remote sensing. This aspect may be particularly important in developing countries, where exposure may change at sub-yearly scale.

This work explores the potential of the combination of the high refresh rate of satellite night-time light products with the high precision of urban maps and census information. Target is the evaluation of the population exposure to the flood risk in urban areas.

The idea is to calibrate nightlight vs. urban density/population relations where contemporary estimations of both variables are available. These, combined with flood hazard maps, allows the estimation of the flood risk. Results will be validated using independent estimates of the population exposed to the flood risk in the same area.

Moreover, time series of nightlight products will be used to estimate the same variables at different times, demonstrating the possibility of rapid updates.

The work is based upon DMSP night-time light series, global urban footprint (GUF) maps by the German AeroSpace Center (DLR) and census data from the Italian institute of statistics (ISTAT). The independent data for the population exposed to risk are provided by the Italian Environmental Protection Agency (ISPRA).

How to cite: Boni, G., Taramasso, A. C., and Roth, G.: Estimation of flood risk exposure with cross fertilization between multi-platform remote sensing and census information., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9320, https://doi.org/10.5194/egusphere-egu2020-9320, 2020.

Floods and associated landslides account for a large number of natural disasters and affect many people wherever they occur (Hong et al., 2007). Sediment-free floods are rare, and in most cases, floods carry a notable amount of sediments (Acreman, 2016). Mass movement processes also transport a huge amount of sediments within a short period (Varnes, 1978). The mobilized sediments cause significant costs and damages as soon as they reach urban or rural environments. These damages and costs include (but are not limited to) cleaning or dredging cost, damage to contents of buildings (e.g. furniture, electric appliances), and blockage of drainage and sewer systems which get filled up with sediments (Einstein, 1950; Merz et al., 2010; Rodríguez et al., 2012).

This study aims to achieve a reliable sediment deposition quantification which is useful for assessing the risk of such events. Three methods were implemented for this purpose. First, the sediment deposition height was determined through in-situ investigation and the average height was estimated. Second, the deposition surface was simulated using trend interpolation and DEM was subtracted from that to get deposition height. Third, the deposition height and extent were determined by calculating the difference in elevation using pre- and post-event drone and LiDAR flights.

Dominica has experienced sediment deposition events in the past. It is significantly vulnerable to tropical storms and hurricanes. Dominica is a mountainous island covered by tropical rainforests and located about halfway between the French islands of Guadeloupe and Martinique in the Eastern Caribbean sea (Knutson et al., 2015; Malhotra et al., 2007; Wilkinson, 2018). Hurricane Maria made landfall on this island on September 18^th, 2017 and it heavily impacted the housing, transport infrastructure, tourism, agriculture, and education sectors (Dominica News Online, 2018). The intense rainfall caused flash floods, landslides, and debris flows resulting in a massive amount of sediments being deposited in urban and rural areas. The overall damages and losses are estimated at approximately USD 1.3 billion (The Government of the Commonwealth of Dominica, 2017). Dominica’s Ministry of Public Works reported that the total cost related to deposition of sediments (e.g. dredging rivers, cleaning streets and main roads, and clearing of airports and seaports)  exceeds USD 92 million which is a considerable portion of total damages and costs. This implies the significance of the risk imposed by sediment deposition.

The results of this research were compared with each other and with the findings of in-situ investigations. They indicate similar deposition heights and volumes, however, the pattern and extent of deposition are not the same. The practicality of the third method depends on the availability of data, but when data is available the outcomes provide a reliable assessment of sediment deposition volume. However, this cannot be trusted unless an in-situ investigation is performed.

How to cite: Emtehani, S., Jetten, V., van Westen, C., and Shrestha, D.: Sediment Deposition Volume Assessment in Tropical Regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5081, https://doi.org/10.5194/egusphere-egu2020-5081, 2020.

An extraordinary flash-flood event occurred the 9^th October 2018 in the north-eastern part of Mallorca Island. The spatial distribution of main geomorphic changes were accurately mapped through field and aerial UAV campaigns in two contrasted small headwater catchments (i.e., < 2 km²) of the Begura de Saumà River. The first one was massively covered by step terraces over Lias limestone, whilst the second one was only covered by check-dam terraces over Miocene marls.

Two weeks after the event, a UAV was used to record aerial photographs and build high-resolution digital elevation models (HR-DEM; i.e., 5 cm). Geomorphic changes were assessed comparing this HR-DEM with LiDAR-derived DEM (i.e., 25 cm resolution) obtained in 2014. The Borselli index of connectivity (IC; version of Cavalli et al., 2013) was calculated from the LiDAR-derived DEM to compare the geomorphic changes triggered by the flash-flood with the structural sediment connectivity distribution.

At hillslope scale, the HR-DEM allowed the identification of geomorphic changes, such as the initiation of rills and the wall collapse of old agricultural terraces in the terraced limestone catchment. In the main headwater valley axis of the marls catchment, where natural streams had been historically reduced and deviated with the construction of check-dam terraces, huge geomorphic changes enabled the recovering of natural streams.

The spatial distribution of the observed geomorphic changes on hillslopes was compared with the spatial patterns of sediment connectivity. Geomorphic changes elucidated a good concordance with structural connectivity, both in the location and magnitude. The analysis of these concordances and some discordances allows the identification of hydrogeomorphological factors triggering the erosional response of hillslopes.

This work was supported by the research project CGL2017-88200-R “Functional hydrological and sediment connectivity at Mediterranean catchments: global change scenarios –MEDhyCON2” funded by the Spanish Ministry of Science, Innovation and Universities, the Spanish Agency of Research (AEI) and the European Regional Development Funds (ERDF).

How to cite: Calvo-Cases, A., Gago, J., Ruiz-Pérez, M., García-Comendador, J., Fortesa, J., Company, J., Nácher-Rodríguez, B., J. Vallés-Morán, F., and Estrany, J.: Spatial distribution of geomorphic changes after an extreme flash-flood compared with hydrological and sediment connectivity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21687, https://doi.org/10.5194/egusphere-egu2020-21687, 2020.

Temperature estimations of active lava flows are crucial to characterize volcanic eruptions and better understand their dynamic and evolution. EO data acquired by satellites, in the SWIR-TIR spectral range, allows to retrieve active lava flows temperature applying specific algorithms (e.g. TES). In particular, radiances emitted by the High Temperature targets, acquired by multispectral space sensors, represent the input parameter for temperature estimation methods; their incertitude influences the accuracy of the temperature retrieval. In the present work, a multi-temporal analysis of radiances acquired from different spaceborne imaging sensors, at several wavelengths in the SWIR-TIR spectral range, has been carried out in order to perform a cross-comparison of data and to estimate the error associated with the radiance of high temperature targets. We considered and analysed radiance data recorded by the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) on Mt. Etna volcano in the last twenty years. ASTER, launched on December 1999, is mainly used to study surface temperature and emissivity with a relatively high spatial resolution; ASTER measures radiance in the Visible and Near-InfraRed (0.52-0.86 μm) and Thermal InfraRed ranges (8.12 to 11.65 μm) with a pixel size of 15 m and 90 m, respectively, and a revisit time of 16 days. Landsat 8 is the most recent satellite of NASA Landsat program launched on February 2013. Its payload consists of two sensors: the OLI (Operational Land Imager) and the TIRS with two thermal bands. Specifically, daytime acquisitions over Mt. Etna volcano by ASTER from 2011 up to now and by Landsat 8 from 2013 up to now, are considered in the present study; the channels at 10.6 μm of both instruments are mainly investigated. The goal of the study is to analyse the migration of the thermal activity on Mt. Etna summit area.

How to cite: Musacchio, M., Lombardo, V., Romaniello, V., Silvestri, M., Spinetti, C., and Buongiorno, M. F.: Multi-temporal analysis of radiance acquired by ASTER and Landsat 8 on Mt. Etna volcano, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12791, https://doi.org/10.5194/egusphere-egu2020-12791, 2020.

Rapid damage mapping following a disaster event is critical to ensure that the emergency response in the affected area is prompt and efficient. Amongst major disasters, earthquakes are characterized as unpredictable and of high frequency of occurrence. Previous and current studies focus mainly on the mapping of damaged structures in urban areas after an event such as an earthquake disaster. Yet, research focusing on the damage level or its distribution in rural areas is absent. According to the UN, nearly half of the world's population lives in rural areas and is expected to rise. Furthermore, their resources and capabilities for disaster relief operations are limited. Therefore, there is a great importance to assess the damage following a disaster in these areas.

The primary aim of this study is to characterize and assess the damage (level and extent), temporally and spatially, following an earthquake event, in rural settlements. This will allow producing an algorithm suitable for rural area rapid mapping, which will contribute to our understanding and will provide insights of the damage extent and will allow a better response and access to the affected regions and remote population.

For this purpose, a damage assessment algorithm that will map the damage in both urban and rural environments is proposed. This algorithm makes use of combining SAR and optical data for rapid damage mapping.

As a case study we will demonstrate this algorithm using the areas affected by the Sulawesi earthquake and subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images pre and post the event, alongside a Sentinel-2 image pre- event are used as inputs.

The affected areas were analyzed with the SAR data using interferometric SAR (InSAR) coherence map. To overcome the loss of coherence caused by changes in vegetation cover, a vegetation mask was applied by using the NDVI to identify (and remove) vegetated areas from the coherence map. Then, thresholds were determined for the co-event coherence map (≤ 0.5) and the NDVI (≥ 0.4) and the two layers were combined into one. Based on the combined map, a damage assessment map was generated by using GIS spatial statistic tools (Fishnet and Zonal statistics). This map provides a quantitative assessment of the nature and distribution of the damage in rural and urban environments, as well the differences of damage features between them. The preliminary results show that while in urban area many structures were damaged, still in the rural areas the damage is larger, since most of the structures were damaged or even destroyed.

How to cite: Havivi, S., Maman, S., Rotman, S. R., and Blumberg, D. G.: Integrating data from different sensors for damage assessment after a natural disaster in rural areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1190, https://doi.org/10.5194/egusphere-egu2020-1190, 2020.

Himawari-8 is the next-generation geostationary meteorological satellite, which is developed by JMA and was been launched in October,2014. As the successor to the MTSAT series,Its spatial resolution, observation frequency and position accuracy are much better than the last generation, so it has large advantage in grassland fire monitoring. In this paper, we presentthe method of fire monitoring self-adaptive threshold based on Himawari-8 data, and takean example of using Himawari-8 data to monitor dynamically the grassland fire located near the border of China in April of 2016. The monitoring results show that the fire lasted about 22 hours, the size of burned area were large than 1500 km², the longest duration of a fire pixel was about 6 hours. Through analyzing a series fire information from successive  Himawari-8 10 minutes frequency observation,the result shows that the expanding speed of the fire is 5.4 km in the direction from west to east during some duration, which is up to the extent of fast speed fire type,. Using this method, analyzed the dynamic monitoring in the next day and other scattered fire point in different areas, which indicate that this method is universality in fire monitoring and Himawari-8 can be well used to monitor the fire dynamically changing, get the location, area and temperature of the fire, evaluate the expanding speed, estimate the trends of fire development and raise the ability of grass land fire monitoring and early warning.

How to cite: chen, J. and zheng, W.: Application of Grassland Fire Monitoring Based on Himawari-8 Geostationary Meteorological Satellite Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13143, https://doi.org/10.5194/egusphere-egu2020-13143, 2020.

The densely populated neighborhoods of Pinheiro, Bebedouro, Mutange, Bom Parto and Levada in the Municipality of Maceió (Brazil) are suffering serious geological instability.  Fractures, on both buildings and roads, have intensified since the beginning of 2018 and some of the areas were evacuated, due to safety reasons, by the local authorities during the second half of 2019. The preliminary investigation conducted by the Brazilian Geological Service (Serviço Geologico do Brazil - CPRM), suggested that the direct cause of the instability is connected to the salt mining activities carried out on near the cost of the Mundaú Lagoon. 

In this study we use radar interferomtery (InSAR) and 2D geomechanical modelling to characterize almost 16 years of continuous deformation in Municipality of Maceió (Brazil). We exploited the full potential of the well-known Multi Temporal Interferometry techniques (MTI) based on Advanced Synthetic Aperture Radar Differential Interferometry (A-DInSAR) and processed all available historical and currently operational SAR missions: the C-band ASAR ENVISAT, the L-band ALOS-1 POLSAR, L-band ALOS-2 POLSAR and C-band Sentinel-1 missions. The results show clear main deformation field over the neighborhood of Pinheiro with concentric pattern to the shore and increasing deformation intensity up to 25cm per year from 2003 to 2019. A minor deformation area is detected also south of the lagoon corresponding to the neighborhood of Bom Parto and Levada. A 2D geomechanical modelling of salt-cavern stability using Distinct Elements is developed to derive the relationship between the detected deformations and the salt mining activities. As a general conclusion, our study shows how MTI analysis is very efficient and reliable tool for emergency management purposes. Especially after the launch of the Sentinel-1 mission, which provides an acquisition in single pass every 12 days, we are able to detect when a surface displacement commence and monitor the deformation progress and status in time.

How to cite: Vassileva, M. S., Al-Halbouni, D., Dahm, T., Motagh, M., Walter, T., and Wetzel, H.-U.: Rapidly accelerating subsidence in Maceió (Brazil) analayzed by multi-temporal DInSAR analysis and 2d geomechanical modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19905, https://doi.org/10.5194/egusphere-egu2020-19905, 2020.

This study utilized multiple-temporal satellite imagery with UAV and IoT technology to evaluate and monitor the post-typhoon event remediation effectiveness of soil and water conservation of Shihmen Reservoir Watershed from 2015 to 2018.

A combination of the historical event-based landslide inventory and a collection and recent satellite imagery with coverage of the area pre- and post-typhoon MANGKHUT in 2018 were applied to evaluate landslide process, evolution and sediment environment change. In addition, two UAV operations were completed and captured over 160km2 in the 5 sub-watersheds to validate the remediation effectiveness and environmental change.

The results show that the landslide area within Shihmen Reservoir is less than that of the 1994 typhoon Aere and has no increased tendency. Effective conservation and remediation work can effectively reduce the sediment discharge of meteorological events and decrease the turbidity of the water at the storage point. In addition, the vegetation coverage rate of the entire Shihmen Reservoir watershed is close to 90%. Except for the occasional localized deforestation, the vegetation coverage has gradually stabilized.

Keywords: Shihmen reservoir, Remediation Efficiency, UAV and IoT Technology

How to cite: Chen, C.-K., Lin, B.-S., Chen, C.-H., and Pai, C.-C.: Application of Multiple-Temporal Satellite Imagery with UAV Technology to evaluate Post-typhoon Event Remediation Efficiency in Shihmen Reservoir Watershed, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6203, https://doi.org/10.5194/egusphere-egu2020-6203, 2020.

NHI (Normalized Hotspot Indices) is an original multichannel algorithm recently developed for mapping volcanic thermal anomalies in daylight conditions by means of infrared Sentinel 2 MSI and Landsat 8 OLI data. The algorithm, which uses two normalized indices analyzing SWIR (Shortwave Infrared) and NIR (Near Infrared) radiances, was tested with success in different volcanic areas, assessing results by means of independent ground and satellite-based observations.

Here we present and describe the NHI-based tool, which exploits the high computation capabilities of Google Earth Engine to perform the rapid mapping of hot volcanic features at a global scale. The tool allows the users to retrieve information also about changes of thermal volcanic activity, giving the opportunity of performing time series analysis of hotspot pixel number and total SWIR radiance. Advantages of using the NHI tool as a complement to current satellite-based volcanoes monitoring systems are then analysed and discussed, such as its future upgrades.

How to cite: Genzano, N., Marchese, F., Falconieri, A., Mazzeo, G., and Pergola, N.: A Google Earth Engine application for mapping volcanic thermal anomalies at a global scale by means of Sentinel 2 MSI and Landsat 8 OLI data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4683, https://doi.org/10.5194/egusphere-egu2020-4683, 2020.

Planning for disaster responses and targeting interventions to mitigate future problems requires frequent, up-to-date data on social, economic and ecosystem conditions. Monitoring socioeconomic conditions using household survey data requires national census enumeration combined with annual sample surveys on consumption and socioeconomic trends, the cost of which is prohibitive. We examine the role that Earth Observation (EO) data could have in mapping poverty in rural areas by exploring two questions; (i) can household wealth be predicted from RS data? (ii) What role can EO data play in future geographic targeting of resources? Here, we demonstrate that satellite data can predict the poorest households in a landscape in Kenya with 62% accuracy. When using a multi-level approach, a 10% increase in accuracy was achieved compared to previously used single-level methods which do not consider how landscapes are utilised in as much detail. EO derived data on buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead and the length of growing season were important predictor variables. A multi-level approach to link RS and household data allows more accurate mapping of homestead characteristics, local land uses and agricultural productivity. High-resolution EO data could provide a limited but significant contribution to geographic targeting of resources, especially when sudden changes occur that require targeted responses. The increasing availability of high-resolution satellite data and volunteered geographic data means this method can be modified and upscaled to larger scales in the future.

How to cite: Watmough, G., Campbell, A., Marcinko, C., Palm, C., and Svenning, J.-C.: Better data for geographic targeting of resources: the role of earth observation data for mapping social and economic conditions. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11670, https://doi.org/10.5194/egusphere-egu2020-11670, 2020.

In the satellite thermal remote sensing, the high-spatial resolution sensors may improve thermal constraining of volcanic phenomena, with direct implications on the comprehension of volcanic processes and monitoring purposes. Here we present a new hot-spot detection algorithm, developed for SENTINEL 2 (S2) data, which combines contextual spectral and spatial analysis, applied on the 8a-11-12 SWIR bands with 20 meters/pixel resolution. The algorithm is able to detect and count the number of hotspot-contaminated pixels (S2Pix), in a wide range of environments and for several types of volcanic activities. The S2-derived thermal trends, retrieved at different worldwide key-cases volcanoes, are than compared with the Volcanic Radiative Power (VRP) from MODIS images processed by the MIROVA system during the period 2016-2019. Dataseries showed an overall excellent correlation between the two imagery suites, enhancing the higher sensitivity of SENTINEL-2 to detect small size and subtle, low-temperature thermal signals. Results outline a relation between the S2Pix and VRP ratios and the volcanic processes (i.e. lava flows, domes, lakes, open-vent activity) producing a distinct pattern in terms of size and intensity of the thermal anomaly. Moreover, the high-spatial resolution of S2 imagery potentiality let to decrypt which is the thermal contribution of the different active volcanic portions, and to understand their evolution in terms of intensity and persistence. Our analysis indicates how the combination of high- (S2) and moderate- (MODIS) resolution thermal timeseries represent an improvement in the space-based volcano monitoring that can be useful for monitoring applications and communities which relate with active volcanoes.

How to cite: Massimetti, F., Coppola, D., Laiolo, M., Valade, S., Cigolini, C., and Ripepe, M.: Volcanic Hot-Spot detection using SENTINEL-2: results from the comparison with MODIS-MIROVA thermal signals., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5095, https://doi.org/10.5194/egusphere-egu2020-5095, 2020.

The Karaj-Chaloos road and the Tehran-North highway are two routes that connect the capital Tehran with the southern shores of the Caspian Sea. This contribution aims to study slope instabilities along these roads (Karaj-Gachsar and Tehran-Soleghan sections, respectively) using logistic regression method. In this regard, 14 layers of effective factors were created in the GIS environment and then correlated with the existing instabilities and their density was calculated. Results obtained by applying logistic regression model showed that the most important factors affecting the slope instabilities in the Karaj-Gachsar road area are distance from river, climate and SPI, while those for the Tehran-Soleghan road area are distance from fault and road and climate. According to the prepared maps, the southern and middle parts of the Karaj-Gachsar road, as well as another part in the northwest of the study area have the highest potential for the occurrence of landslides, whereas in the Tehran-Soleghan road area, the middle and southern parts and a small section in the north of the area have the highest potential for landslide occurrence. 34.95% of the Karaj road area has medium to high potential for the occurrence of slope instabilities and 4.97% of this area has very high potential. It is while 27.14% of the Soleghan road area possesses medium to high potential for instabilities and 4.57% of it exhibits very high risk. By comparing these two areas, it is conceivable that areas with medium to high potential of slop instabilities in the Soleghan road area are less than those of the Karaj road area (27.24% and 34.95%, respectively). However, the percentage of instabilities occurred in the Soleghan road area is much higher (86.26%) than the Karaj road area (54.87%). Finally, it can be mentioned that the logistic regression model was effectively applicable for preparing the zonation of the instability occurrence probability along the slopes overlooking the studied roads. It can also be concluded that in addition to natural factors, the human-made factors and particularly unsystematic road construction can play an important role in the landslide occurrences on the slopes overlooking the roads and in order to reduce the relative risks and increase the stability of the slopes, it is necessary to avoid manipulating the ecosystem and changing the current land use as much as possible, along with policy making for constructions in accordance with geomorphological and geological features of the area.

How to cite: Jananeh, K.: Quantitative and Comparative Analysis of Slope Instability in Karaj-Chaloos Road (Karaj-Gachsar section) and the Under-Construction Highway of Tehran-North (Tehran-Soleghan section) Using Logistic Regression Method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-320, https://doi.org/10.5194/egusphere-egu2020-320, 2020.

Temperature is a key parameter controlling the evolution of lava flows. The hazardous behavior of eruptions prevents direct measurements of hot magmatic bodies. Hence, the temperature of magma is mostly retrieved by using non-contact methods (ground-based or satellite-based thermal cameras) based on measuring the infrared (IR) emission flux (E) of the body [1]. These well-established techniques are however subjected to important errors, ±100 °C, related to surrounding environment [2], large temperature gradients of cooling lavas [3], constant changes in composition and texture and especially an apparent lack of radiative emission properties during the lava emplacement. Despite that reducing the uncertainties of environmental and thermal gradients when measuring E is ultimately challenging, our study aimed to minimizing the uncertainty in one of the critical hitherto poorly known oversimplified parameters [3,4,5] namely spectral emissivity. Therefore, we performed optical measurements at relevant magmatic temperatures (up to 1200 °C) of representative basaltic dry magmas (MORB, alkaline, calc-alkaline). Emissivity has been systematically determined over a wide spectral (400-15000 cm^-1) and thermal range (from room up to 1200 °C) using a non-contact in situ IR emissivity apparatus [6]. SEM, EMPA and Raman spectroscopy techniques were also used in order to characterize and understand the complex radiative behavior of these natural magmatic compositions. Emissivity varies accordingly with temperature and wavenumber but our results also show that small changes in bulk-rock composition produce drastic changes in emissivity at given T, with iron content and its oxidation state being the main agents controlling this parameter. Appropriate emissivity values will then help to refine current field or (space) satellite IR monitoring data (i.e. Holuhraun 2014-2015, Iceland; [3]) and to implement the thermo-rheological models of lava flows [7] as to support hazard assessment and risk mitigation.

References: [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Ball and Pinkerton 2006. J. Geophys.Res., 111. [3] Aufaristama et al. 2018. Remote Sens. 10, 151 [4] Harris, A. 2013: Cambridge University press. 728. [5] Rogic et al. 2019 Remote Sens. 2019, 11, 662 [6] De Sousa Meneses et al. 2015. Infrared Physics & Technology 69. [7] Ramsey et al. 2019. Annals of Geophysics, 62, 2.

Keywords: Emissivity, temperature, vibrational spectroscopy, remote sensing, basalt

How to cite: Biren, J., del Campo, L., Cosson, L., Li, H., Slodczyk, A., and Andujar, J.: High temperature in-situ study of radiative properties of basaltic dry magmas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-546, https://doi.org/10.5194/egusphere-egu2020-546, 2020.

On November 14, 2016, a 7.8 magnitude earthquake struck the Kaikōura region on the South Island of New Zealand. The event triggered numerous landslides, which dammed rivers in the area and led to the formation of hundreds of dammed lakes. Landslide-dammed lakes constitute a natural risk, given their propensity to breach, which can lead to flooding of downstream settlements and infrastructure. Hence, detecting and monitoring dammed lakes is a key step for risk management strategies. Aerial photographs and helicopter reconnaissance are frequently used for damage assessments following natural hazard events. However, repeated acquisitions of aerial photographs and on-site examinations are time-consuming and expensive. Moreover, such assessments commonly only take place immediately after an event, and long-term monitoring is rarely performed at larger scales.

Satellite imagery can support mapping and monitoring tasks by providing an overview of the affected area in multiple time steps following the main triggering event without deploying major resources. In this study, we present an automated approach to detect landslide-dammed lakes using Sentinel-2 optical data through the Google Earth Engine (GEE). Our approach consists of a water detection algorithm adapted from Donchyts et al., 2016 [1], where a dynamic threshold is applied to the Normalized Difference Water Index (NDWI). The water bodies are detected on pre- and post-event monthly mosaics, where the cloud coverage of the composed images is below 30 %, resulting in one pre-event (December 2015) and 14 post-event monthly mosaics. Subsequently, a differencing change detection method is performed between pre- and post-event mosaics. This allows for continuous monitoring of the lake status, and for the detection of new lakes forming in the area at different points in time.

A random sample of lakes delineated from Google Earth high-resolution imagery, acquired right after the Kaikōura earthquake, was used for validation. The pixels categorized as ‘dammed lakes’ were intersected with the validation data set, resulting in a detection rate of 70 % of the delineated lakes. Ten key dams, identified by local authorities as a potential hazard, were further examined and monitored to identify lake area changes in multiple time steps, from December 2016 to March 2019. Taking advantage of the GEE cloud computing capabilities, the proposed automated approach allows fast time series analysis of large areas. It can be applied to other regions where landslide-dammed lakes need to be monitored over long time scales (months – years). Furthermore, the approach could be combined with outburst flood modeling and simulation to support initial rapid risk assessment.

 [1]   Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., & van de Giesen, N. (2016). A 30 m resolution surface water mask including estimation of positional and thematic differences using Landsat 8, SRTM and OpenStreetMap: A case study in the Murray-Darling basin, Australia. Remote Sensing, 8(5).

How to cite: Abad, L., Hölbling, D., Spiekermann, R., Dabiri, Z., Prasicek, G., and Argentin, A.-L.: Mapping and monitoring of landslide-dammed lakes using Sentinel-2 time series - a case study after the 2016 Kaikōura Earthquake in New Zealand, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-572, https://doi.org/10.5194/egusphere-egu2020-572, 2020.

In this study, the skill of TRMM Multi-Satellite Precipitation Analysis (TMPA) data to locate spatially and temporally extreme precipitation has been tested over Calabria, a region in southern Italy.

Calabria is a very challenging region for hydrometeorology studies, as i) it is a mainly mountainous region with complex orography; ii) it is surrounded by sea, providing  an abundance of available moisture; iii) it belongs to the Mediterranean region, a hot-spot for climate change.

TMPA, which provides daily data at a 0.25° resolution (i.e., about 25 km at southern Italy latitudes), was tested with regards to three extreme precipitation events that occurred between 1998 and 2019, i.e., the years of TMPA’s operational time frame. The first event, taking place on 07-12/09/2000, lasted for several days and involved most of Calabria. The second (01-04/07/2006) was a very localized midsummer event, which hit a very small area with destructive consequences. Finally, the 18-27/11/2013 event was a ten-day long heavy precipitation event that hit the region in spots.

TMPA daily data were compared against validated and homogenized rain gauge data from 79 stations managed by the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection. TMPA was evaluated both in relative and absolute terms: i) the relative skill was tested by checking if TMPA evaluated correctly the presence of extreme precipitation, defined as daily precipitation passing the 99th percentile threshold; ii) the absolute skill was tested by checking if TMPA reproduced correctly the cumulated precipitation values during the events.

TMPA proved sufficiently able to locate areas subject to heavy cumulated precipitation during large spatially distributed events over the region. However, it showed difficulties in reproducing very localized events, as the 2006 case study was not detected at all, showing that 25-km spatial resolution and daily time resolution proved inadequate to resolve this type of rainfall event.

Results might give insights into the possibility of using satellite data for real-time monitoring of extreme precipitation, especially since the transition from the old TMPA to the new Integrated Multi-satellitE Retrievals for GPM (IMERG) set was completed in January 2020.

Acknowledgments:

The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

How to cite: Caloiero, T., Coscarelli, R., and Caroletti, G. N.: Evaluating the skill of satellite data on the individuation of extreme precipitation events in Calabria (southern Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3071, https://doi.org/10.5194/egusphere-egu2020-3071, 2020.

On 4 August 2015, a very high intensity storm, 31.5 mm in 20 min (94.5 mm/h), hit the massif of Mount Antelao on the Venetian Dolomites (eastern Italian Alps) triggering stony debris flow characterized by high magnitude. It routed along the Ru Secco Creek and progressively reached the resort area and the village of San Vito di Cadore, causing fatalities and damages. The aim of the present research is the study of this debris-flow event by means of pre and post-event topographic data derived by LiDAR (Light Detection and Ranging) and Structure-from-Motion (SfM) photogrammetry technique associated to its occurrence. This study analyzes the Digital Terrain Models (DTMs) derived from LiDAR survey carried out in July 2015 and UAV-SfM data obtained in September 2019. The most important step to compare these multi-temporal surveys was the co-registration process, fundamental to guarantee the coherence among the two different surveys. The post-event SfM-DTM of the area routed by debris flow subtracted to the pre-event LiDAR-DTM, provided a DoD (DTM of Difference) that was useful to assess the deposition-erosion patterns and estimate debris-flow volume. Multi-temporal topographical data are important to analyze the phenomenon and its characteristics. This allowed us to more in depth analyzed the debris-flow effects and provide valuable information for the planning of risk prevention measures.

How to cite: Chen, X., Cucchiaro, S., Bernard, M., Mauri, L., Chen, J., Tarolli, P., and Gregoretti, C.: Analyzing topographic changes through LiDAR and SfM techniques: assessing the deposition-erosion patterns and estimation of debris-flow volume in the eastern Italian Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3516, https://doi.org/10.5194/egusphere-egu2020-3516, 2020.

In the framework of the INAL/BRIC research contract #ID57 (2016) different remote sensing technologies, from proximal to remote (from airborne to satellite), and processing classification technique have been exploited to detect both manmade materials containing asbestos and natural occurring asbestos (NOA) formations.

Asbestos minerals show characteristic spectral features in the LWIR spectral regions centered at about 9.6 µm. The VNIR-SWIR spectral region was well explored by multi and hyperspectral airborne, while the LWIR spectral range, at present, is still less explored for the detection and identification of the NOA. The LWIR range should have a high potential as asbestos minerals absorption feature are far from the ones of the other minerals commonly associated with them (e.g., carbonates).

The area surveyed by the multispectral LWIR airborne TASI-600 corresponds to a peridotitic ophiolite of great thickness and extension referring to the ophiolitic complex (i.e. including Roccamurata) along the banks of the Taro river [1], [3]. The ultramafic rock outcrops occurring in the Taro Valley (Italy), belong to the External Ligurid Units of the Northern Apennines within Cretaceous-Eocene sedimentary formation [2]. These ultramafic rocks formations include natural asbestos minerals that have a high potential hazard to human health if inhaled [3].

The airborne survey has been carried out using the airborne hyperspectral TASI-600 sensor acquiring 32 spectral bands in the 8.0 - 11.5 µm spectral range with a spectral resolution of 100 nm. The airborne survey was performed on a test area NW to the Borgo Val di Taro town along the Taro Valley for about 50 km2 at an altitude of about 1000 m a.s.l.. The survey covers two quarries of massive ophiolites (almost serpentine) on which samples have been collected in view of a further spectral and chemical analysis.

This communication will present the preliminary results of multispectral LWIR TASI survey performed on the Roccamurata study area in terms of: (i) radiometric and geometric correction; (ii) LST, by using a split window technique, and emissivity calculation by using a TES algorithm (iii) a preliminary result of the serpentine mapping compared with the available 2016 geological map (http://www.isprambiente.gov.it/Media/carg/note_illustrative/216_Borgo_Val_di_Taro.pdf).

• [1] Boschetti, T., & Toscani, L. (2008). Springs and streams of the Taro–Ceno Valleys (Northern Apennine, Italy): reaction path modeling of waters interacting with serpentinized ultramafic rocks. Chemical Geology, 257(1-2), 76-91.
• [2] Marroni, M., Molli, G., Montanini, A., Ottria, G., Pandolfi, L., & Tribuzio, R. (2002). The external Ligurian units (Northern Apennine, Italy); from rifting to convergence of a fossil ocean-continent transition zone. Ofioliti, 27(2), 119-131.
• [3] Gaggero, L., Crispini, L., Isola, E., & Marescotti, P. (2013). Asbestos in natural and anthropic ophiolitic environments: a case study of geohazards related to the Northern Apennine ophiolites (Eastern Liguria, Italy). Ofioliti, 38(1), 29-40.
• [4] Beghè, D., Dall’Asta, L., Garavelli, C., Pastorelli, A. A., Muscarella, M., Saccani, G., ... & Chetta, A. (2017). Sarcoidosis in an Italian province. Prevalence and environmental risk factors.PloS one,12(5), e0176859.

How to cite: Pignatti, S., Bogliolo, M. P., Buongiorno, F., Despini, F., Ionca, V., Musacchio, M., Palombo, A., Panigada, C., Pascucci, S., Palombo, A., Santini, F., and Teggi, S.: Ophiolites mapping in the Taro Valley (Central Italy) by using an LWIR airborne TASI-600 survey: preliminary results on the Roccamurata complex, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5179, https://doi.org/10.5194/egusphere-egu2020-5179, 2020.

The alpine infrastructure of trails and huts is an essential asset for summer tourism in the Austrian Alps. Every year, around five million people use the trail network for hiking and other mountaineering activities. Mass movements such as shallow landslides, debris flows and rockfalls cause significant damages to the alpine infrastructure and may block access to certain mountain areas for weeks or even months. Such damages require repair and increased maintenance activity or even rerouting of trails. Climate change will exacerbate the problem as more frequent and severe mass movements can be expected. Therefore, the Alpine associations have to take natural hazards into account for their trail and hut management.

A promising opportunity for assessing the impact of natural hazards on alpine infrastructure arises through the new generation of Earth observation (EO) satellites of the European Copernicus programme. The high spatial and temporal resolution allows the detection of mass movements with an impact on trails and huts.

Therefore, we initiated the project MontEO (The impact of mass movements on alpine trails and huts assessed by EO data) to investigate the opportunities for EO-based mass movement mapping and hazard impact assessment for alpine infrastructure. We start with a user requirements analysis that describes the demand for consistent and appropriate information on mass movements for alpine infrastructure management. We perform interviews with the Alpine associations and other relevant stakeholders. They help us to identify significant mass movements, their impact on the alpine infrastructure, and the actions that trail keepers and hut facility managers take to deal with the impacts. Based on this, we assess the suitability of EO-derived mass movement information for alpine infrastructure management, and define requirements for its production and delivery.

Based on the user requirements, we develop a multi-scale approach and combine optical and synthetic aperture radar (SAR) satellite data (e.g. Sentinel-1/2, Pléiades) to comprehensively map mass movements and to detect mass movement hotspots. Further, we integrate the EO-based mapping results with ancillary data for landslide susceptibility mapping, and for modelling and simulating rockfalls and debris flows. Finally, we analyse the network of trails and huts in relation to the obtained mass movement information and thereby assess their impact on alpine infrastructure, i.e. identify the trails and huts that are (potentially) affected by mass movements.

We demonstrate the concept and methods for three study areas in the Austrian Alps: Großarl and Kleinarl Valley in Salzburg, Karwendel in Tyrol, and the Salzkammergut in central  Austria. For these areas, we will create EO-based mass movement inventory maps, hotspot maps, and hazard impact maps. We validate our results in close collaboration with the users and analyse their usefulness for alpine infrastructure maintenance and management. The outcomes of MontEO will contribute to improved maintenance efficiency and will lead to a safer alpine infrastructure with an increased value for hikers, the tourism industry and the society.

How to cite: Albrecht, F., Hölbling, D., Abad, L., Dabiri, Z., Reischenböck, G., Scheierl, G., Hipp, T., Resch, H., and Resch, G.: Assessing the impact of mass movements on alpine trails and huts using EO data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21325, https://doi.org/10.5194/egusphere-egu2020-21325, 2020.

The November 26, 2019 a Mw 6.2 earthquake struck the city of Durres as well as several towns in the Northwestern of Albania. The event killed 51 people, injured more than 900 and destroyed several buildings in the epicentral area. This area is dominated by active thrust tectonics due to the collision between Adriatic and Eurasian plates. This study shows the first results about the co-seismic displacements field estimated by the analysis of satellite SAR and GNSS data. In particular, GNSS observations were acquired by a network of 18 continuous GNSS stations located in the Albanian area. Using the GAMIT/GLOBK, GNSS data were processed within a time period ranging from January 1, 2016 to December 31, 2019 and time series produced. Moreover, a number of ascending and descending radar images acquired by the Sentinel-1 satellite in the period of the seismic event were processed using the ESA SNAP software. Pre-seismic, co-seismic and post-seismic interferograms provided the LOS displacement maps of the event and characterized the main deformation phenomena produced by such an event. The first preliminary results about the co-seismic displacements will be presented and compared with some theoretical co-seismic displacement fields provided thanks to the knowledge of the fault system affecting the area.

How to cite: Grassi, F., Cenni, N., and Mancini, F.: Combination of satellite SAR and GNSS data of co-seismic deformation after the November 26, 2019 Albania earthquake: first results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4553, https://doi.org/10.5194/egusphere-egu2020-4553, 2020.

Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a thermal infrared sensor, developed by NASA-JPL, launched in June 2018. ECOSTRESS acquires five LWIR spectral channels between 8 and 12 μm, with 70 m of spatial resolution at different times of the day and night.

The availability of multispectral TIR bands allows the retrieval of Land Surface Temperature (LST) and Land Surface Emissivity (LSE) by using well known procedures, like Temperature and Emissivity Separation (TES). The availability of LSE images in the LWIR atmospheric window at a medium resolution allows to estimate some topsoil/rock properties, for example those related to quartz diagnostic absorption features.

Furthermore, recent studies have shown that multispectral data in the LWIR region allows to retrieve quantitative information on topsoil properties, such as texture, carbon and nitrogen content, especially when applying multivariate statistical models [1] [2]. This study intends to verify the potential of night and day ECOSTRESS images for topsoil properties estimation.

To this aim, on specific experimental fields in Central Italy, soil sampling campaigns have been conducted to assess the topsoil properties like soil texture (clay, silt, sand) and soil organic carbon (SOC).

First, on these experimental fields, ECOSTRESS archive images were explored to identify the images in which the sampled fields are ploughed (i.e. bare soil conditions). Second, the ECO2LSTE products [3], containing the land surface temperature and emissivity, were downloaded from the USGS web site (https://ecostress.jpl.nasa.gov/data) and atmospherically corrected. Third, the TES algorithm was applied providing emissivity images at a spatial resolution of 70 m.

Last, the emissivity images were used to define a prediction model (calibration and validation) by using both Partial Least Squares Regression (PLSR) and Random Forest (RF).

The preliminary results seem to confirm: i) the potential of ECOSTRESS LWIR data to retrieve topsoil properties valuable for agronomical purposes at the regional scale, ii) the preliminary result of the multivariate analysis like PLSR and RF to derive model for topsoil properties (mainly clay and organic content) prediction  at a medium resolution scale.

References

• [1] Notesco, G., Weksler, S., & Ben-Dor, E. (2019). Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data. Remote Sensing, 11(12), 1429.
• [2] Pascucci, S., Casa, R., Belviso, C., Palombo, A., Pignatti, S., & Castaldi, F. (2014). Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study.European journal of soil science, 65(6), 865-875.
• [3] Silvestri, M., Romaniello, V., Hook, S., Musacchio, M., Teggi, S., & Buongiorno, M. F. (2020). First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas. Remote Sensing, 12(1), 184.

How to cite: Rangisetty, A., Casa, R., Ionca, V., Laneve, G., Pascucci, S., Silvestri, M., and Pignatti, S.: Application of ECOSTRESS multispectral LWIR images to assess topsoil properties: preliminary results on agricultural test sites in Central Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5319, https://doi.org/10.5194/egusphere-egu2020-5319, 2020.

Heat waves have tremendous ecological and socioeconomic consequences for many countries and initiate complex event chains that reach from the land surface to the upper atmosphere. Although it is well known that global change affects the Earth and environment on many different time and length scales, currently, only very limited knowledge is available on the importance of such distinct dynamic events for the long-term development of the Earth system. To investigate the impact of extended heat periods and droughts on our terrestrial ecosystems and natural resources, the Helmholtz MOSES project implements a modular infrastructure that is designed to capture such highly dynamic events in event-driven campaigns. As part of this infrastructure initiative a new hyperspectral thermal instrument, the Telops Hyper-Cam LW, was recently acquired at the Potsdam German Research Centre for Geosciences (GFZ) and capabilities for airborne surveys, laboratory and field deployment, as well as data processing in the context of heat wave impacts are currently developed.

The Telops Hyper-Cam LW is a Fourier-transform imaging spectrometer (~8–12 μm) with adjustable spectral resolution from 0.25 to 150 cm⁻¹ that can be operated at various scales from ground and airborne platforms. The hyperspectral longwave infrared shows great potential for the characterization of soil and vegetation properties and their variability related to heat wave impacts. However, this spectral imagery can only be used to fullest advantage when the signal is corrected, e.g. path radiance of the atmosphere, as well as the downwelling radiance component have been removed from the measured signal and temperature is separated from emissivity.

In this context, this contribution describes the recent developments at GFZ toward (i) The development of suitable field sampling strategy & protocols related to the acquisition of field thermal hyperspectral data including calibration and validation measurements, (ii) Establishment of preliminary protocols for field data processing to temperature and emissivity, (iii) Test and mounting of the Hyper-cam on the Cessna-T207A airborne platform from the Free University Berlin (FUB) and (iv) Flight testing and calibration, and establishment of preliminary protocols and strategies for the development of a processing chain from raw data to temperature and emissivity imagery and extraction of relevant thematic parameters.

In particular, first results will be shown based on the MOSES/ScaleX-2019 campaign where field Hyper-Cam measurements were acquired in different configurations at the Fendt grassland test site located in the German Pre-Alpine foreland. Different approaches for temperature emissivity separation are tested and compared, e.g. normalization emissivity method and spectral smoothness based emissivity separation. Furthermore, calibration and validation activities are presented in the frame of several airborne surveys over different targets to correct and validate the thermal signal. Preliminary airborne results will be shown over different locations in Germany and Greece that indicate good geometric and radiometric data accuracy, as well as high potential for the differentiation of surface materials from the spectral emissivity and surface temperature.

How to cite: Chabrillat, S., Milewski, R., Brell, M., Hohmann, C., Ruhtz, T., Zöllner, M., and Gagnon, J.-P.: Development of hyperspectral thermal infrared mapping capabilities at field and airborne level within MOSES heat wave event chain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8860, https://doi.org/10.5194/egusphere-egu2020-8860, 2020.

The understanding of the long-term influence of hurricanes on the coastal zone deal with the monitoring the geomorphological evolution and the socioeconomic changes. In this study we analyze, the evolution of the Saint-Martin Island (West Indies, Caraïbean Sea) from 1947 to 2019. During the last 70 years, several hurricanes occurred and Saint-Martin has seen huge economic and environmental changes due to (1) fiscal laws, (2) a huge population increase, (3) coastal urbanization. Based on aerial photos and satellite images, we have analyzed this development. We have described the urban, agricultural and natural change. The transformation of the agricultural economy into an economy dominated by tourism, has significantly changed the coastal zones. The new spatial and economical configuration of the island has led to a higher risk of marine flooding.

Hurricane Irma (2017) seriously damaged coastal infrastructures and dwellings, caused fatalities as well as triggered the mangrove partial destruction. Field study comparison with satellite images observation show that non-negligible mistakes on the dwelling damage could be done. The damage quantification is often use to elaborate plan risk and must be carefully taken into account. In the Saint-Martin Island, the population disagree with plan risk focusing only on natural hazard without integrating socioeconomic risk and difficulties that strongly affect the inhabitants since several decade. Solution proposed to manage natural risk often trigger the conditions that favored the occurrence of social crisis and social crisis management has often generated an increase of the vulnerability to natural hazard (Gargani and Jouannic, 2015 ; Gargani, 2016 ; Jouannic et al., 2017). As a consequence of Irma Hurricane, social inequalities are expected to increase (Gargani, 2019).

Gargani J., G. Jouannic. Les liens entre Société, Nature et Technique durant les derniers 200 ans : analyse dans deux vallées françaises. VertigO, V. 15, n.3, 2015.

Gargani J., Crises environnementales et crises socio-économiques. L’Harmattan, Paris, 156p, 2016.

Gargani J., Prévenir les catastrophes naturelles ou alibi de réorganisation urbaine en faveur des plus riches ? Le journal du MAUSS, 28 octobre 2019.

Jouannic G., Gargani J., Legendre T., Gastaud P., Kolli Z., Crozier D., Arki F., Stratégie d’adaptation et réduction de la vulnérabilité : exemple de l’évolution des rives dans la vallée du Rhône et de la Saône. Espace populations sociétés, 2016/3, 2017.

How to cite: Gargani, J., Pasquon, K., and Jouannic, G.: How hurricanes influence social and economic changes ?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8974, https://doi.org/10.5194/egusphere-egu2020-8974, 2020.

Due to the high seismicity and high annual precipitation, numerous landslides have occurred and caused severe impact in Taiwan. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precise geoinformation. The Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. To access potential hazards we combine sUAS, field survey, terrestrial laser scanner (ground LiDAR) and UAS LiDAR for data acquisition. Based on the methods we construct multi-temporal high-resolution DTMs so as to access the activity and to monitoring the creeping landslides in Paolai village, southern Taiwan. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). Since 2015, more than 10 geospatial datasets have been produced for an area between 5-80 Km² with 8-12 cm spatial resolution. These datasets were then compared with the airborne LiDAR data to access the quality and interpretability of the data sets. Since 2017, we integrate UAS LiDAR to monitoring landslide area, and re-evaluate the data accuracy. Since 2018 we have integrate UAS LiDAR, terrestrial LiDAR, and photogrammetric point cloud for landslide study, to ensure no shadow effect of the dataset. The geomorphologic changes and landslide activities were quantified in Paolai area. The results of this study provide not only geoinfomatic datasets of the hazardous area, but also for essential geomorphologic information for other study, and for hazard mitigation and planning, as well.

How to cite: Chang, K.-J., Tseng, C.-M., Chang, H.-H., and Huang, M.-J.: Progressive landslide activity analysis and monitoring from Multi-temporal high-resolution geoinfomatic data sets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9090, https://doi.org/10.5194/egusphere-egu2020-9090, 2020.

Hyperspectral data acquired for different seasons provide the means to derive relevant plant biophysical properties during the growing season over agricultural areas, as well as determine soil properties, when the soils are exposed, e.g. during fallow or after harvesting. This combined information can give a detailed insight on the effect of soil degradation on vegetation growth and finally crop yield. In the Mediterranean region, land use practices for crop cultivation have a long history exploiting soils as a natural resource. The soils are an essential factor contributing to agricultural production of rainfed crops such as cereals, olive groves and vineyards. Inadequate land management is endangering soil quality and productivity, and in turn crop quality and productivity are affected. Therefore, the main objective of this work is to map crop stress related to soil degradation and land management practices within a Mediterranean environment focusing on hyperspectral data within the visible, near-infrared, and short-wave infrared as well as thermal infrared (0.4-12 µm) and test the transferability of the methods used to future hyperspectral space-borne sensors such as PRISMA, EnMAP, SHALOM, CHIME and SBG.

In this framework, CASI and AHS hyperspectral imagery have been obtained during the growing season within the Camarena agricultural area located in central Spain. The area is characterized by a Mediterranean climate, a gently undulating relief, evolved soils and traditional rainfed agriculture area. In this environment a combination of tillage erosion as a result of plowing practices, as well as water erosion, has led to the exposure of different soil horizons at the surface with contrasting soil properties. These surface properties have been previously characterized as erosion stages of the same cultivated area in a fallow state. Simultaneous to the airborne acquisitions, intensive field campaigns took place for the characterization of soil and crop variability. This included field spectroradiometry measurements of the different surface covers and vegetation parameters such as Leaf Area Index (LAI), leaf chlorophyll content, plant biomass and grain yield in locations with variable soil erosion and deposition stages from low to very high eroded soils. First results based on random forest modeling between the soil erosion stage mapping and the AHS/CASI remote sensing imagery of the growing season indicate a strong link between the soil conditions and the spectral properties of the crops. Furthermore, biophysical parameters derived from the imagery in the green season such as Leaf Area Index and Leaf Water Content correlated also well with the soil erosion stages. For selected test sites it could be shown that low crop yields are associated with 1) highly eroded areas, where exposure of the calcite rich bedrock can cause deficiency in nutrient uptake and 2) very sandy accumulation areas that are depleted in nutrients and have low potential for water retention. Whereas highest crop yields are associated with clay and iron rich, moderately to low eroded soils. This study integrates optical VNIR-SWIR-TIR spectral domain and present preliminary results that emphasize the strong influence of soil quality on crop stress and production.

How to cite: Milewski, R., Schmid, T., Escribano, P., Ben-Dor, E., Michavila, M. J., and Chabrillat, S.: Mapping of Crop Stress Related to Soil Degradation within Rainfed Mediterranean Agricultural Areas using Hyperspectral Optical and Thermal Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19342, https://doi.org/10.5194/egusphere-egu2020-19342, 2020.

In all kinds of natural disasters, earthquake is regarded as one of the greatest natural disaster in the world, and it seriously threats human's lives and properties. In the actual scene of earthquake disasters, the types of pre-earthquake satellite images available in the affected area are various, and they are from different sensors. However, the current researches on multi-source satellite image building recognition are not sufficient. In addition, when extracting building damage information, we can only determine whether the building is collapsed using the post-earthquake satellite images. Even the images have the sub-meter resolution, the identification of lightly damaged buildings is still a challenge. In order to solve the above problems, in this paper, we will use the post-earthquake UAV images and the pre-earthquake satellite images to extract the building damage information in rural areas of Sichuan, China. In particular, the main research contents of this paper are as follows:

• (1) According to the color feature of UAV images and the shape feature from point cloud data, we divide the building damage into four types: intact buildings, slightly damaged buildings, partially collapsed buildings and completely collapsed buildings, and give the rules of damage grades. In particular, the Chinese restaurant franchise model, which simultaneously fuses the color and shape features, is proposed to detect the earthquake-triggered roof-holes. Based on the roof-holes, the type of slightly damaged buildings is identificated.
• (2) At present, the model of building extraction from remote sensing images is suitable for an image, that is, for different images, the model needs to learn its model parameters again. In this paper, based on the generalized Chinese restaurant franchise (gCRF) model, we introduce the morphological profiles to propose the gCRF_MBI model. In the residential regions, the buildings are extracted by fusing the spatial information and the morphological profiles in the gCRF_MBI model.
• (3) The visual attention model selects the regions of interest from the complex scenes by simulating the visual attention mechanism of biological objects, which is similar to the extraction of residential regions from remote sensing images. In this paper, based on the basic principle of the spectral residual approach, we utilize the approach to extract the latent residential regions from remote sensing images, and we analyze the effects of different band combinations and different threshold methods on the extraction of residential regions.

How to cite: Li, S. and Tang, H.: Extraction of Rural Building Damage due to Earthquake using Remote Sensing Imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21124, https://doi.org/10.5194/egusphere-egu2020-21124, 2020.

Taiwan is located at the northwest side of Pacific Ocean and also in the Circum-Pacific Seismic Belt, as a result, suffers from frequently typhoons and earthquakes. The plate collision creates steep mountains account for 70% area of Taiwan. Averaged annual rainfall is 3600 mm in Taiwan, whenever typhoon or weather front brings heavy rainfall addition with geological instability thus increase the landslide occurrence. Rainfall gauge stations are sparse in the mountainous region, and the interpolated rainfall are usually underestimated. The error sources include 1.) variation of raindrop size distribution which is rarely known and varies in time and space (James, 1979); 2.) radar beam attenuation, the rainfall estimation will be underestimated as distance from radar to gauges increase ( Joss, 1998); 3.) beam blockage by mountains, when the beam encounters terrain blocking, it will cause signal interference, which is known as ground clutter (Li and Chen, 2002). Therefore, it is necessary to overcome those issues while try to predict accurate rainfall via radar reflectivity in the mountain regions.

In this research, we use radar reflectivity combines ground rainfall gauges to compensate the forecasting rainfall. The first method uses the known radar echo intensity (Z) and the rainfall of ground stations (R) to calculate the A and b coefficients by genetic algorithm with the exponential relationship, Z=A*R^b, proposed by Marshall and Palmer. However, the results are unreasonable, the value of A is varying in 0.01-1000, mostly under 1, and the value of b is varying in 0.1-30. Hence, we decide to use another method. First, we assume that for a short distance (ex: 30 Km), the raindrop size correction factor is constant without attenuation and beam blockage. Second, we estimate the correction factor with the attenuation pattern with distance. Third, the beam blockage from mountains is then considered, and it also takes the first two corrections in consideration. The approach we used is artificial neural network (ANN) to compensate the estimated rainfall from real time radar reflectivity.

The purpose of this study is to estimate the accurate rainfall of potential landslide area hours ahead typhoon or weather front reaches, we use the historical route of radar echo to infer the path of movement of next hour. If the estimated rainfall exceeds landslide thresholds, the alarm system will be activated. With these efforts the estimated rainfall in the mountain region is improved 70% from tryout experiments. It is found that is correction for radar reflectivity is not an universal transformation, it is depended on the nature of water concentration and also the drop size within the weather front.

Key words: Radar Echo, Artificial Neural Network, Early Warning

How to cite: Li, P.-C. and Yu, T.-T.: Landslide Early Warning with Rainfall Data from Correcting Weather Radar Reflectivity Using Machine Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19265, https://doi.org/10.5194/egusphere-egu2020-19265, 2020.

Subsidence can be caused by multiple natural or anthropogenic factors. Natural factors account for compaction of recent sedimentary deposits, oxidation and shrinkage of organic soils. Anthropogenic factors include the pumping of groundwater for human use and the exploitation of hydrocarbon reservoir, both inland and off-shore. The area of Ravenna (Northern Italy) is affected by both anthropogenic and natural subsidence. Natural contribution is due to the compaction of the deposits of the Po plain, of approximately 2 mm/yr. This phenomenon has dramatically increased since the 1950s because of shallow groundwater pumping and deep gas production from several on-shore and off-shore reservoirs in the Upper Adriatic Sea basin.

In this work, we used SAR, GPS and levelling data to investigate the deformation detected at Lido Di Dante, located along the coastal area of Ravenna. This area is subject to gas pumping of the Angela-Angelina gas field, a gas reservoir exploited since 1973, with platform located very close to the coast, at approximately 2 km from the shoreline. We analysed SAR data from multiple missions from 1992 to 2018. In particular, the ESA’s archives were exploited considering ERS data (ascending and descending orbits, spanning 1992-2000), ENVISAT data (ascending and descending orbits, 2003-2010) and Sentinel-1 satellites (ascending and descending orbits, 2015-2018) and ASI’s images acquired by Cosmo-SkyMed (ascending orbit, 2011-2017). The GPS data are provided by Eni S.p.A. In particular, we consider the GPS ANGA, located offshore on the Angela-Angelina platform, and the GPS FIUN, located near Lido Di Dante. The levelling data are from Eni S.p.A. archives, span 1983-2017.

The subsidence detected by InSAR (Interferometric SAR) time series at Lido Di Dante from 1992 to 2018 is approximately 250 mm. The ERS time series show a change in the slope between 1997 and 1998, when the Angela-Angelina platform came into operation. There is a general correlation between gas extraction and surface deformation, indeed the subsidence increases when the gas production increases. Therefore, to better analyze the correlation between gas extraction and observed deformation, the exploited reservoir is modelled as a closing crack (dislocation tensile fault), whose contraction rate is constrained by data inversions. The results indicate that the subsidence in the area of Lido di Dante is the sum of natural contribution due to soil compaction and of hydrocarbon extraction activities during the periods of massive extraction.

In order to better discriminate the factors affecting subsidence we build a Finite Element Model, by means of the software Comsol Multiphysics. The geometry of reservoir has been deduced by literature, while the pressure inside the reservoir is modulated by the GPS signals at ANGA between 1998 and 2018. The results show that the contraction of reservoir due to gas pumping produces measurable deformation along the coastline. The vertical and horizontal cumulative displacements between 1998 and 2018 reach the maximum values of 28 cm and 15-20 cm, respectively.

How to cite: Secreti, V., Trasatti, E., Polcari, M., Albano, M., Anderlini, L., Serpelloni, E., and Pezzo, G.: Natural and anthropogenic origin of subsidence of the Northern Adriatic coast (Italy) from satellite data and modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9903, https://doi.org/10.5194/egusphere-egu2020-9903, 2020.

Desertification is a process characterized by the degradation and drying of soils in arid, semiarid and subhumid regions that results from a combination of climatic factors and human activities. This process influences the productivity potential of the soils, impacting the populations residing in the affected areas, and may cause long-term economic problems and impacts on human health, such as hunger and food insecurity. The aim of this paper is to present a geospatial model for mapping desertification risk areas in northeastern Brazil. The test area for the model was located in the Brazilian semiarid climatic region in the state of Ceará. In this area, the dry season lasts for 7 to 8 months, and the original vegetation belongs to the Caatinga biome. The model was based on algebraic operations between maps of environmental variables, performed in a geographic information system, and based on equations obtained through logistic regression analysis. First, 300 points were mapped in the centroids of desertification polygons (D), and 300 points were mapped in areas where no desertification processes (ND) had occurred. All points were selected by visual interpretation of Sentinel-2A multispectral images. Then, 500 m radius buffers were mapped around the centroids of the D and ND areas, and the mean values of the following environmental variables were extracted within these buffers: the average annual rainfall (RAIN), altitude (ELV), vegetation index dry season (VID), wet season vegetation index (VIM), dry season soil temperature (LTD), and wet season soil temperature (LTM). The mean values ​​of the RAIN, ELV, VID, VIM, LTM and LTD variables for the D and ND areas were entered in the MedCalc software for logistic regression analysis. The p probability map of desertification occurrence was constructed in ArcGIS Pro using equations for which the parameters were obtained with the logistic regression analysis. The results showed that the variables RAIN, ELV, VID and LTD (p <0.0001) contributed significantly to the occurrence of desertification areas. The value obtained for the area under the ROC curve (AUC) parameter was 0.757, and the percentage of cases correctly classified by the model was 70.17%. In the next step of this research, this model will be tested on a larger area of 72,000 km² that is located in the Jaguaribe River basin, northeastern Brazil.

How to cite: César Ferreira, M., Monteiro Navarro de Oliveira, M., and Carneiro Valente, D.: A geospatial model for mapping desertification risk areas in the Caatinga biome, a semiarid region of Brazil, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12126, https://doi.org/10.5194/egusphere-egu2020-12126, 2020.

Hydrothermal alteration can progressively weaken volcanic flanks, leading to collapses and mass flows with potential hazards affecting communities and infrastructure many kilometres from the collapse source. Through a combination of geomagnetic and hyperspectral remote sensing, with field and laboratory measurements, we have developed an approach to assess and forecast these catastrophic hazards. Inversion of aerial geo-magnetic data is used to identify the subsurface structure and volume of weak (nominally altered) and strong (nominally unaltered) portions of the volcanic edifice of Mt. Ruapehu, New Zealand. Airborne hyperspectral imagery is used to classify the surface expression of hydrothermal alteration, which is combined with laboratory geotechnical measurements of field samples to estimate the strength of identified features. This data is essential to reducing the uncertainty in identifying flank collapse source areas through three-dimensional limit equilibrium modelling.

However, the range of potential collapse volumes, locations and triggering mechanisms still presents significant difficulties in forecasting the potential impacts of slope failures. Numerical mass flow models can be used to simulate debris avalanches, but it is infeasible to simulate all potential collapse scenarios to estimate the hazard. To ease the computational burden, we have developed a methodology that uses a reduced subset of potential slope failures through dimensional reduction and space-filling sampling techniques. Using debris avalanche simulations of this subset, a comprehensive mapping of debris flow impacts across the entire input space can be developed using statistical techniques. This mapping provides an efficient mechanism for understanding flank collapse hazards across a large spectrum of potential scenarios. This presentation will outline our framework for assessing and forecasting debris avalanche hazards through the integration of remote sensing surveys with geotechnical measurements.

How to cite: Mead, S., Kereszturi, G., Miller, C., and Schaefer, L.: Building a model of debris avalanche hazard using geophysical remote sensing data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12431, https://doi.org/10.5194/egusphere-egu2020-12431, 2020.

New remote sensing systems offer an increased spatiotemporal resolution and accuracy. These systems  increase the chance of snow- and cloud-free multispectral images to detect and monitor landslides for early warning issues. Various studies showed the applicability of multispectral remote sensing systems for landslide detection and monitoring. However, a systemic evaluation of the remote sensing systems especially in respect to early warning is still missing. In this study we present a new conceptional approach to evaluate the capability of different systems for early warning issues based on a well suited case study located in the Hohe Tauern Range, Austria.     

The Sattelkar is a highly dynamic west-facing deglaciated high-alpine cirque in the Großvenedigergruppe, Austria. The abundant rock debris exhibits high movement rates and showed massively enhanced landslide activity after ongoing heavy precipitation in 08/2014, resulting in a 170.000 m³ debris flow event. We estimated time demands for three successive steps consisting of (i) image collection, (ii) processing with motion delineation and (iii) the final evaluation. Digital image correlation, an established tool in landslide remote-sensing research, was used to derive displacement patterns and assess the capabilities of the multispectral images in terms of spatiotemporal resolution and data quality. For our study we used Sentinel-2, RapidEye and PlanetScope images and compared their deduced motion patterns and rates to those from accurate UAV data as well as manually digitized boulder tracks (≥10 m in diameter).

Within a reasonable amount of processing time, some satellite data revealed similar clustered motions identifiable in the UAV images. However, our analysis also showed identification limitations due positional inaccuracy, image errors and spatiotemporal resolution of the data. On that account, certain processing steps reduce the forecasting window and as a result the lead time, i. e. the remaining time to take action. We postulate that remote sensing data has the ability to support landslide monitoring, but the pre-selection of usable and sound data is essential as it directly influences the forecasting window. Sound knowledge of its different application possibilities enhances overall steps of image collection, processing and final analysis. The critical selection of which data source is best can lead to faster response times for landslide events. This increases the forecasting window, hence the time to take action until a landslide occurs.

How to cite: Keuschnig, M., Hermle, D., and Krautblatter, M.: A conceptual approach on optimising lead time for the forecasting of landslides using remote sensing systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17267, https://doi.org/10.5194/egusphere-egu2020-17267, 2020.

Mass movement processes correspond to one of the most dangerous geological events, mainly where human settlements are present, due to their destructive power and unpredictable nature. Chilean Patagonia has experienced important mass removal events in recent years. In this work, we are seeking to detect trends in the occurrence of these events and the relationship with long-term and short-term dispositions driven mainly by hydrometeorological events and the geology of the study area.

In the Chilean Patagonia, the Chilean Geological Survey (Sernageomin) has detected more than 713 landslides events in the Chilean Northern Patagonia (~42.7ºS, ~72.4ºW)” alone, a small area compared to the Chilean Patagonia. However, there is a lack of understanding of the triggers and mechanisms that control such events, and further studies need to be carried in order to understand the evolution of these events, linkages to climate change or anthropogenic changes, and to understand where they potentially can affect village directly destroying houses and taking human lives.

In this study, we use remote sensing to detect mass removals, fieldwork data collection to understand the geological predisposition to enable mass removal, and the analysis of hydrometeorological information to statistically establish relationships between the events and the potential triggers. For the remote sensing, we use Google Engine to create an exhaustive dataset of mass removal of 35 years in the study area. We apply the Normalized Difference Vegetation Index (NDVI) and the Grain Size Index (GSI) in Landsat Imagery. We will use the Sernageomin dataset and fieldwork to validate the methodology. For the geology, we analyze the conditioning factors associated with the geomorphological, structural, and lithological characteristics of the area. Finally, we used ERA5 data to determine the relationship between climate and mass removal events, analyzing, for example, the total annual precipitation patterns (TP) and extreme indicators as the maximum number of consecutive dry days (CDD) as well as annual temperatures and heatwaves.

The results of this research sought to provide the foundations for a complete risk assessment in the Chilean Patagonia and to increase awareness and preparedness in the region.

How to cite: Somos-Valenzuela, M., Fustos-Toribio, I., Lizama-Montecinos, E., Morales-Vargas, B., and Manque-Roa, N.: Mass movement tendencies and interaction with climate change in the Northern Chilean Patagonia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12149, https://doi.org/10.5194/egusphere-egu2020-12149, 2020.

Space-borne hot-spot detection on the Earth surface is key to monitoring and studying volcanic activity, wildfires and anthropogenic heat sources from space. Lower intensity thermal emission hot-spots, which often represent the onset of volcanic eruptions and large wildfires, are difficult to detect. We are improving the MODVOLC algorithm, which monitors Earth’s surface for hot-spots by analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) data every 48 hours, to allow lower intensity thermal emission detection. Improving the existing MODVOLC algorithm for hot-spot detection from MODIS image data is not trivial. A new approach, which we refer it to as the Maximum Radiance Algorithm for MODIS, has been explored. The new approach requires a MODIS 4 µm and accompanying 12 µm global radiance time-series at ~1 km grid spacing. This reference data set describes the maximum radiance that has been measured from each square km of Earth’s surface over a ten year period (having first excluded high natural and anthropogenic heat sources from the time-series, using the existing MODVOLC approach). For each new geolocated MODIS image data, the observed radiance for each pixel is compared with this reference, and if its radiance exceeds the historical maximum, it can be considered a potential hot-spot. A dynamic tolerance is used to then confirm if the potential hot-spot is an actual hot-spot. We show that this new approach for hot-spot detection offers significant advantage over existing techniques for lower intensity thermal emission hot-spot detection during both day and nighttime conditions.

How to cite: Gabrieli, A., Wright, R., Garbeil, H., and Pilger, E.: Improving Earth hot-spot detection from MODIS data using MODVOLC algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12494, https://doi.org/10.5194/egusphere-egu2020-12494, 2020.

The Fukushima Daiichi Nuclear Power Plant, Japan, underwent a series of sequential meltdowns in 2011 related to the magnitude 9.0 earthquake and tsunami of the same year - causing the world’s second ‘Level 7’ nuclear event after Chernobyl. Japan and the Tokyo Electrical Power Company (TEPCO) have been proactive in taking steps towards decommissioning the now hazardous site, with a clean-up timeline continuing work for another 30-40 years. However, this creates a need for long-term monitoring strategies that mitigate radiation hazards for the personnel involved with the decommissioning. Remote sensing can fill this emerging need, more specifically with Interferometric Synthetic Aperture Radar (InSAR).

InSAR can monitor ground and structure stability with millimetre scale accuracy, as well as create a historical baseline for past movement using data from ESA’s Sentinel-1 satellite mission. Here we show the applicability of InSAR monitoring across the Fukushima plant using Sentinel-1 data spanning October 2015 to October 2019. Our results clearly show an uplift signal of ~75 mm around the reactor, during the time period directly coinciding with the implementation of a perimeter ice wall which was constructed to mitigate groundwater leeching.

This study demonstrates the benefits of InSAR to monitor ground stability in near-real time, and across a wide area, without the need for direct interaction with such a hazardous site. Via this study, we have demonstrated that InSAR is a powerful technique for monitoring potential ground stability issues at highly hazardous sites, with applications for the engineering, oil and gas, and mining sectors.

How to cite: Bestard, J., Magnall, N., Holley, R., and Thomas, A.: InSAR as a tool to monitor nuclear decommissioning – a case study across the Fukushima Daiichi Nuclear Power Plant, Japan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16738, https://doi.org/10.5194/egusphere-egu2020-16738, 2020.

Multi-temporal SAR interferometry (MTInSAR) provides mean displacement maps and displacement time series over coherent objects on the Earth surface, allowing analysis of wide areas to identify ground deformations, and studying evolution of displacement phenomena over long time scales. MTInSAR techniques have proven very useful for detecting and monitoring also slope instabilities.

Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit times. The Italian X-Band COSMO-SkyMed constellation acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus  improving the monitoring of local scale events as well as the detection of non-linear displacements.  The recent Sentinel-1 C-band mission from the European Space Agency (ESA) provides a spatial resolution comparable to previous ESA SAR missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and thus the chances to catch pre-failure signals characterised by high rate and non-linear behaviour. Moreover, it allows collecting large data stacks in a short time periods, thus improving MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. 

In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. Montescaglioso in 2013) and more recently (e.g. Pomarico in 2019), causing extensive damage to houses, commercial buildings, and infrastructures.

SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using the SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to slope instabilities.  Mean velocity maps and displacement time series have been analysed looking, in particular, for non-linear trends that are possibly related to relevant ground instability episodes and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to known events occurred in the area of interest.

How to cite: Refice, A., Bovenga, F., Pasquariello, G., Argentiero, I., Spilotro, G., Nutricato, R., Nitti, D. O., and Chiaradia, M. T.: MTInSAR long-term monitoring of nonlinear slope instabilities on hilltop villages in Southern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18740, https://doi.org/10.5194/egusphere-egu2020-18740, 2020.

In 2016, Central Italy was hit by a months-lasting earthquake sequence that started off in August 24^th 2016 with a Mw 6.2 earthquake which provoked severe damage to the towns of Accumoli (RI) and Amatrice (RI). The following October 30^th 2016 earthquake (Mw 6.5), with epicenter in Norcia (PG) about 20 km NW of the first shock, triggered landslides in the area of Visso (MC), as reported by local newspapers.

The purpose of this work is to individuate the areas affected by such landslides using the radiance variation recorded by multispectral images acquired by Sentinel 2. The time series analysis of the images has been carried out in Google Earth Engine environment, that allows access to the entire suite of available images. Due to the steep terrain, the shadowing effect of the hills was taken into account and comparison of images have been made only for those taken in the same seasonal moment of different years, thus guaranteeing the same solar elevation.

It was found that the band of red was instrumental in identifying landslides along slopes made up of limestone, which is the typical outcrop of the area. Due to the extended time period between the images (July 2015 and July 2017), anthropogenic changes in land-use were present and had to be distinguished from landslides. A criterion involving the slope angle was developed, maintaining only the changes that had occurred on slopes steeper than 25°, since man-made interventions giving similar spectral response are hardly done in steep areas. The slope analysis and correlation study with the extension and location of landslides was carried out using a Geographic Information System. (ESRI ArcGIS 10.5) The total extent of the area affected by the surveyed landslides is very large, having  been estimated to be more than 200 000 m².

How to cite: Bartola, M., Braitenberg, C., and Bisci, C.: Landslides in Central Italy identified from Sentinel 2 multispectral imaging time series analysis with Google Earth Engine, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18214, https://doi.org/10.5194/egusphere-egu2020-18214, 2020.

Persistently active volcanoes such as Etna, in southern Italy, are subject to frequent morphological and structural changes, especially at the summit. In recent decades, in particular, Etna has shown an evident increase in both summit and flank eruptive activity. This caused a striking transformation of the morphologies of its summit craters, which increased in number and size, also causing the formation of new small eruptive vents, fumarolic fields, fractures and crater collapses. Sometimes these morpho-structural modifications of the top of the volcano have been so rapid that they have not been all recorded accurately, or they have occurred in sequences so rapid as to overlap the effects of the eruptions, making some transitional events between an eruption and the next one. Eruptive activity during the period considered occurred mostly at the summit craters of Etna (May 2016: Voragine; February-April 2017: New Southeast Crater and fissures on its slopes; August and November-December 2018: New Southeast Crater). This was interrupted by the brief fissure eruption on the upper southeast flank of the volcano on 24-27 December 2018; renewed eruptions occurred at the New Southeast Crater and fissures on its flanks in May-July 2019. Finally, in September 2019, eruptive activity shifted to the Northeast Crater and Voragine, the latter feeding intermittent lava flows into the adjacent Bocca Nuova crater. In cases like this, satellite observations can complete terrestrial monitoring systems, providing a useful contribution of knowledge and detail of the eruptive activity and morpho-structural transformations of greater significance. In this study, we analysed the Mt. Etna activity using data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2 and Landsat 8 satellites, processed by means of the recently proposed NHI (Normalized Hotspot Indices) algorithm. The latter allowed us to identify thermal anomalies associated to main effusive and explosive activities as well as to the smaller eruptive events, revealing in some cases thermal phenomena several days in advance that can be interpreted as potential precursors. In addition, NHI also showed a fair sensitivity in grasping the incipient fracturing of the Etna summit area, an important phenomenon in the life of this volcano due to its close correlation with the slow lateral collapses that characterize its flanks, and which in turn can trigger lateral eruptions that are potentially dangerous for the Etnean populations.

How to cite: Marchese, F., Neri, M., Behncke, B., and Genzano, N.: Main morpho-structural changes and eruptions of Etna in 2016-2019 captured by satellite observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13909, https://doi.org/10.5194/egusphere-egu2020-13909, 2020.

Riverbank collapses frequently occur in the lower reaches of the Yellow River, China, which result in a great loss of farmland and significant hydro-morphological evolution in the channel. A combination of field investigation and remote sensing analysis was conducted to understand the current status of riverbank collapse in the Shandong Reaches of the lower Yellow River. The results show that the planar failure and upward-concave collapse were the main types of river failures in these reaches. Taking the Jiyang section as an example, the average lateral dynamic displacements in the Jiyang section were 2.8 and 11.4 m, the retreat areas were 248.8 and 835.0 m² and the maximum lateral dynamic displacement were 7.4 and 26.0 m during the periods 3/31/2016-4/18/2017 and 04/18/2017-5/10/2018, respectively. Factors such as the soil properties, upstream river-control works, and channel bends may change the probability of downstream riverbank collapse. Building materials that are effective, low-cost and environmental friendly, and easy to use, are anticipated in the river management projects to protect the riverbanks and improve the ecological environment in the study area.

How to cite: Gao, L. and Xu, X.: Collapses on the riverbank: what happened to the Lower Yellow River?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1764, https://doi.org/10.5194/egusphere-egu2020-1764, 2020.

Measuring the sources of carbon dioxide is of high interest in order to know the distribution of this greenhouse gas and quantify the natural/anthropogenic emissions. The aim of the present study is to understand the capability of the absorption band at 4.8 µm to detect and measure the CO₂ emissions from different HTEs (High Temperature Events) like degassing plumes from active volcanic sources, fires and industrial emissions. The performance of this channel was investigated by using the MODTRAN (MODerate resolution atmospheric TRANsmission) radiative transfer model. Simulations of the TOA (Top Of Atmosphere) radiance have been performed by using real input data to reproduce realistic scenarios on a volcanic high elevation point source (>2 km). The sensitivity of the channel has been analysed varying CO₂ concentrations (in the range 0-1000 ppm) and surface temperatures from standard (300 K) to high temperature (1000 K). Moreover, typical response functions of imaging sensors carried on aircraft and operating in the Middle Wave InfraRed (MWIR) spectral region were used: the channel width values of 0.15 µm and 0.30 µm were tested. Simulations provide results about the sensitivity necessary to appreciate carbon dioxide concentration changes considering a target variation of 10 ppm in the gas column concentration. The results show the strong dependence of at-sensor radiance on the surface temperature: radiances sharply increase, from 1 Wm^-2sr^-1µm^-1 (in the standard condition) to >1200 Wm^-2sr^-1µm^-1 (in the warmest case). The highest sensitivity has been obtained considering the channel width equal to 0.15 µm with noise equivalent delta temperature (NEDT) values in the range from 0.045 to 0.560 K at surface temperatures ranging from 300 to 1000 K. Furthermore, data acquired by the multispectral MASTER (Modis ASTER) airborne simulator on Kilauea volcano (Hawaii), during the January/February 2018 campaign, were considered. The aim is to estimate lava flow/lake temperatures and to test the channel at 4.8 µm for retrieving CO₂ emissions on volcanic craters.

How to cite: Romaniello, V., Spinetti, C., Silvestri, M., and Buongiorno, M. F.: Sensitivity studies of the 4.8 micron carbon dioxide absorption band for high temperature events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4757, https://doi.org/10.5194/egusphere-egu2020-4757, 2020.

RSTVOLC is a multi-temporal algorithm developed for detecting volcanic hotspots that was successfully used to monitor active volcanoes located in different geographic areas exploiting both polar and geostationary satellite data. The algorithm runs operationally at the Institute of Methodologies for Environmental Analysis (IMAA) to monitor Italian volcanoes in near-real time by means of Advanced Very-High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this study, we assess the possible RSTVOLC implementation on data from the Sea and Land Surface Temperature Radiometer (SLSTR). The latter is a new generation sensor flying onboard the ESA (European Space Agency) Sentinel-3 mission, offering some spectral channels in the infrared bands particularly suited to identify high temperature surfaces such as lava flows. Here, we verify the RSTVOLC implementation on SLSTR data despite the absence of a multiannual time series of satellite records, by using synthetic spectral reference fields. Results achieved by investigating recent eruptions of Mt. Etna and Stromboli (Italy) volcanoes are presented and discussed.

How to cite: Falconieri, A., Marchese, F., Mazzeo, G., Pergola, N., and Tramutoli, V.: Assessing the RST_VOLC algorithm implementation on infrared Sentinel 3 SLSTR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2701, https://doi.org/10.5194/egusphere-egu2020-2701, 2020.

The Pearl River Basin (PRB), as one of the most prosperous and densely populated areas in China, is a flood-prone area in which huge casualties and big economic losses constantly happen. Therefore, it is of great importance for the study on the characteristics of flood hazards and spatiotemporal trends in the PRB. Based on Google Earth Engine, this study combined 913-phase Modis 8-Day composite (MOD09Q1.006) images with 30-meters SRTM DEM to monitor flood dynamics in the PRB from 2000 to 2019 using an integrated threshold method. The approach synthesized several key factors, including spectrum characters of water body, cloud and the slope (slope<1º) information derived from SRTM DEM. Moreover, Sentinel-1 images were used to validate the accuracy of flood inundation maps. The results indicated that, from 2000 to 2019, the flood inundation area in PRB expanded significantly, especially in the Pearl River Delta region. With the development of urbanization, the expansion of impervious surfaces would probably increase the probability of flood hazard.

How to cite: Junliang, Q., Xiankun, Y., and Tarolli, P.: Spatiotemporal trends in flood hazards using MODIS time-series images in the Pearl River Basin (China), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2939, https://doi.org/10.5194/egusphere-egu2020-2939, 2020.

River erosion and landslides are linked geomorphic processes that shape landscapes representing natural hazards for human settlements, infrastructure, and heritage. Remote Sensing & GIS methods, and Earth Observation data allow us to study these geomorphic processes to asses their interactions and evolution. We present a study case of a representative landslide triggered by river incision and its evolution in the last 50 years. Aerial imagery and photogrammetry are used to asses the initial state of the hillslope, while LiDAR and SfM high-resolution DEMs allow us to characterize the evolution mechanism and geomorphic changes between 2012 and 2019. SAR interferometry results correlate well with the geomorphic change detection data. The river is incising through meander migration, its right bank being developed in the landslide basal part. The continuous erosion of the basal part of the landslide maintains an active landslide process, with a slow-moving rate, intensified mainly by rainfall. The landslide is a translational slide with scarp slumps. Crucial information about the gravitational mechanism is shown by the SAR and change detection data: crown extension, scarp cracking, scarp slumping, translational flow, allowing us to sketch up a pattern of river-landslide interaction that can be used to asses the hazard, vulnerability, and risk for the river-induced landslides from Northeastern Romania.

How to cite: Niculiță, M., Mărgărint, M. C., Ciotină, C., Necula, N., Văculișteanu, G., and Stoilov-Linu, V.: River-landslide erosion interaction assessed through LiDAR and UAV SfM high-resolution DEMs, SAR and photogrammetry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5451, https://doi.org/10.5194/egusphere-egu2020-5451, 2020.

A flash-flood event hit in the 9th October 2018 the northeastern part of Mallorca Island, causing 13 casualties. As global change may exacerbate devastating flash floods, comprehensive analyses of catastrophic events are crucial to support effective prevention and mitigation measures. Field-based, remote-sense and modelling techniques were used to evaluate rainfall-runoff processes at catchment scale linked to hydrological modelling. Continuous streamflow monitoring data revealed a peak discharge 442 m³ s⁻¹ with an unprecedented runoff response (lag time, 15’). This very flashy behaviour triggered the natural disaster as a combination of heavy rainfall (246 mm in 10 h), karstic features and land cover disturbances in the Begura de Saumà River catchment (i.e., 23 km²). Topography-based connectivity index and geomorphic change detection were used as a rapid post-catastrophe decision-making tool, playing a key role during the rescue searching tasks. These hydrogeomorphological precision techniques were also applied in combination with Copernicus EMS and ground-based damage assessment illustrating with high accuracy the damage driving factors in the village of Sant Llorenç des Cardassar.  The incorporation of hydrogeomorphological precision tools during Emergency post-catastrophe operational has been revealed as a powerful tool. Then, the simple application of a geomorphometric index from easy-access LiDAR-based topographic data resulted in a rapid identification of deposition zones in the different compartments of a catchment helping in the search and rescue of missing persons. In addition, the evaluation of landforms signature by using UAVs effectively quantified the sediment deposits generated by the flash-flood and/or mobilised by the Emergency operational during the rescue searching tasks.

This work was supported by the research project CGL2017-88200-R “Functional hydrological and sediment connectivity at Mediterranean catchments: global change scenarios –MEDhyCON2” funded by the Spanish Ministry of Science, Innovation and Universities, the Spanish Agency of Research (AEI) and the European Regional Development Funds (ERDF)

How to cite: Estrany Bertos, J., Ruíz-Pérez, M., Mutzner, R., Nácher-Rodríguez, B., Tomàs-Burguera, M., García-Comendador, J., Peña, X., Calvo-Cases, A., and Vallés-Morán, F. J.: Application of precision technologies in geomorphology: analysis of the flash flood occurred in Sant Llorenç des Cardassar, Mallorca, October 2018, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18196, https://doi.org/10.5194/egusphere-egu2020-18196, 2020.

Soil mineralogy holds important information on the soil origin and development. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the longwave infrared (LWIR) region (8.0–12 μm range), whereas quartz is featureless in the optical region (0.4–2.5 μm range). A procedure for determining the soil surface mineralogy from hyperspectral LWIR data was used to assess the interaction with desert dust particles that accumulate on the soil surface during dust storms. Ground- and field-based hyperspectral LWIR images of different types of Israeli soils, before and after dispersion of desert dust-like material on the surface, were acquired with the Telops Hyper-Cam sensor, to calculate the surface emissivity spectra of soils, representing the surface mineralogy. Identifying mineral-related emissivity features and calculating their relative intensities, using two created indices―SQCMI (Soil Quartz Clay Mineral Index) and SCI (Soil Carbonate Index)―enabled determining the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant, and identifying changes in their abundance resulting from the dispersion of dust on the surface. The dust affected the mineral-related spectral features of the soil surface, depending on the mineral composition of the dust compared to soil surface mineralogy, and its amount. The ability to detect minor mineralogical changes on the soil surface using high spectral resolution LWIR data was demonstrated.

How to cite: Ben Dor, E., Notesko, G., and Weksler, S.: Application of hyperspectral remote sensing in the longwave infrared region technology for assessing the influence of settled desert dust particles on soil surface, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1821, https://doi.org/10.5194/egusphere-egu2020-1821, 2020.

During the field campaign held on June 2018 at Parco delle Biancane and Sasso Pisano areas, near Grosseto (Italy), we have measured the surface temperature using data acquired by different sensors at different spatial resolutions: Earth Observation (EO) data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Hyperspectral Thermal Emission Spectrometer (HyTES) airborne imaging data and thermal images acquired by the FlyBit drone. ECOSTRESS has five spectral bands in the range 8-12.5 μm and pixel size resolution (at nadir) of 69x38 m (2 pixels in cross track and 1 pixel in down track); HyTES is an airborne imaging spectrometer having 256 spectral channels in the range 7.5-12 μm and high spatial resolution (0.8 m for the June campaign); VUE PRO-R mounted on FlyBit drone acquires in the range 7.5-13.5 µm with a spatial resolution depending on the flight altitude (in this work the pixel size is about 0.25 m). In addition, the Sony Alpha 600 visible camera was mounted on the FlyBit drone to acquire a very high resolution optical images over the test site. Our goal is to test the possibility to integrate data at different observation scales and to use the proximal measurements to better understand the thermal structure of test sites, also related to the area morphology and to validate the methodology for estimating the surface temperature by using EO data.

How to cite: Silvestri, M., Marotta, E., Buongiorno, M. F., Hulley, G., Romaniello, V., Bellucci Sessa, E., Caputo, T., Belviso, P., Avvisati, G., Teggi, S., and Hook, S.: Multi-scale observation of surface temperature on Parco delle Biancane and Sasso Pisano (Italy) sites: from space to proximal measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4833, https://doi.org/10.5194/egusphere-egu2020-4833, 2020.

Environmental change is a trigger of land use change and possibly for migration in the eastern Hindu Kush mountains. Vegetation along the river valleys has undergone alterations by the impact of geomorphological processes and flood dynamics, but little research has been carried out to detect and map these changes. This study aims to close research gaps by detecting change within Landsat time series for the eastern Hindu Kush region.

The study area is approximately 25000 km² large and located in the highlands of northern Pakistan and eastern Afghanistan. It is part of upper Indus basin and is prone to natural hazards such as floods, glacial lake outbursts and landslides.

The opening of the United States Geological Survey (USGS) Landsat data archive in 2008 led to the development of several satellite image-based time series methods for change detection. Among them, Breaks For Additive Seasonal and Trend (BFAST) was developed in 2010 to detect changes in both trend and seasonal components of the time series. The BFAST tool iteratively decomposes the time series into trend, seasonal and remainder components. The changes in the trend component denote abrupt and gradual changes while changes in seasonal component represent phenological changes.  

In this study we use Landsat data in time series analysis to detect change by using BFAST. All available Surface reflectance derived data is accessed from the Landsat data archive of USGS (World Reference System-2, Path 151 and Row 35) for the years 1988 to 2019. Data is acquired from the corresponding scenes of Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). It is processed to Landsat Level-2 Surface Reflectance Product by USGS and therefore has already undergone geo-referencing, atmospheric correction and detection of clouds and shadow. Data have spatial and temporal resolutions of 30 m and 16 days respectively.

The BFAST approach was first tested on locations with a known history of change (e.g. floods) and then scaled up to the whole study area. The magnitude and timing of the change was detected and mapped for the study area. We expect that the findings of the research will benefit future local and regional risk studies.

How to cite: Khan, S. A., Sass, O., and Samimi, C.: Detecting change in Landsat time series with BFAST in the eastern Hindu Kush region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19630, https://doi.org/10.5194/egusphere-egu2020-19630, 2020.