The chemical, mineralogical and textural investigation of drill cores demands objective and repeatable information unaffected by the human bias to be able to correlate significant features across drillcores. Imaging Laser induced Breakdown Spektroscopy (LIBS) can be applied to large scales at high spatial resolution in relatively short times to obtain detailed chemical, mineralogical and textural information with a minimum of sample preparation. The application of the Spectral Angle Mapper (SAM) algorithm for supervised classification of the LIBS hyperspectral data cubes provides a relatively fast, but easy to handle tool to visualize and quantify variations in the chemical, mineralogical composition of complex ores from the sub-millimetre to the metre scale. The information derived offers novel and barely investigated interpretation opportunities in a very detailed manner which directly can be used for exploration purposes. The investigated Merensky Reef is about 1 m thick. It consists of pegmatoidal pyroxenite framed by the lower and upper thin chromitite seams. The Merensky Reef is one major ore body out of three for platinum-group elements (PGE) within the Bushveld Igneous Complex which is the world’s largest known layered intrusion and largest PGE resource on Earth   Detailed LIBS-based imaging measurements with 200µm spotsize were accompanied by space-resolved reference measurements based on SEM/MLA (4µm) and µ-EDXRF (20µm), as well as bulk chemical analyses for multiple core slices. The SAM algorithm was applied for classification of hyperspectral LIBS images as being sensitive for differences in mineral chemistry. Focus was put on the pre-processing of LIBS spectra prior to SAM classification, on the development of the spectral library, and on the validation of the classified data. The SAM classification algorithm, which is solely based on ratios between spectral intensities, was found insensitive to normal shot-to-shot plasma variations and to chemically induced matrix effects. However, the algorithm may become inaccurate at low signal to noise ratios, at the border between different mineral grains (mixed spectra), or when classifying chemically similar phases such as pyrite and pyrrhotite. The extent of mixed spectra depends both on the size of the mineral grains as well as on the spot size of the LIBS laser. The SAM algorithm was successfully applied for classification of several base metal sulphides, rock-forming minerals, accessory minerals, as well as several mixed phases representing the main borders between different mineral grains. The obtained classified LIBS image images the spatial distribution of the different phases, which corresponds very well to the reference measurements based on highly space-resolved  EDXRF and SEM/MLA mineral distribution maps. The investigated core piece highlights the extremely heterogeneous distribution of e.g. the sulphide phases. The LIBS-SAM classification image was used to estimate metal concentrations based on point counting. The applicability has been explored for Cu, Ni, S, and Cr. This approach, when applied on sufficiently large surfaces, enables quantification of well-defined mineral phases, as well as the possible detection of trace elements (e.g. Pt, Pd) that occur in very small nuggets.

How to cite: Rammlmair, D. and Meima, J.: Quantitative mineralogy of chromite ore based on imaging Laser Induced Breakdown Spectroscopy and Spectral Angle Mapper Classification Algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19373, https://doi.org/10.5194/egusphere-egu2020-19373, 2020.

I will expose some possibilities regarding the use of metallic probes of different lengths in GPR and TDR prospecting. With regard to GPR, multi-length probes are dipole-like antennas whose length can be changed by means of switches. The switches can be implemented with PIN diodes, and can act as electronic “knifes”. Therefore, they allow to cut (switched off) or prolong (switched on) the branches of a couple of antennas, and this allows to have more couples of equivalent antennas making use of a unique physical couple of antennas. This allows to contain the size of the system. In particular, a reconfigurable prototypal stepped frequency GPR system was developed within the project AITECH (http://www.aitechnet.com/ibam.html)  and was tested in several cases histories  [1-3]. Within this reconfigurable GPR, it is also possible to reconfigure vs. the frequency the integration times of the harmonic tones constituting the radiated signal. This feature allows to reject external electromagnetic interferences without filtering the spectrum of the received signal [4] and without increasing the radiated power.

With regard to TDR measurements, a multi-length probe consists of a TDR device where the rods (in multi-wire version) or the length of internal and external conductor (in coaxial version) can be changed. This can be useful for the measurements of electromagnetic characteristics of a material under test (MUT), in particular its dielectric permittivity and magnetic permeability, both meant in general as complex quantities. Multi-length TDR measurements allow to acquire independent information on the MUT even at single frequency, and this can be of interest in the case of dispersive materials [5-6].

Acknowledgements

I collaborated with several colleagues about the above issues. To list of them would be long, so I will just mention their affiliations: Florence Engineering srl, University of Florence, IDSGeoradar srl, 3d-radar Ltd, Institute for Archaeological and Monumental Heritage IBAM-CNR, University of Bari, University of Malta. Finally, a particular mention is deserved for the Cost Action TU1208.

References

[1] R. Persico, M. Ciminale, L. Matera, A new reconfigurable stepped frequency GPR system, possibilities and issues; applications to two different Cultural Heritage Resources, Near Surface Geophysics, 12, 793-801, 2014.

[2] L. Matera, M. Noviello, M. Ciminale, R. Persico, Integration of multisensor data: an experiment in the archaeological park of Egnazia (Apulia, Southern Italy), Near Surface Geophysics, 13, 613-621, 2015.

[3] R. Persico, S. D'Amico, L. Matera, E. Colica, C. De, Giorgio, A. Alescio, C. Sammut and P. Galea, P. (2019), GPR Investigations at St John's Co‐Cathedral in Valletta, Near Surface Geophysics, 17, 213-229, 2019.

[4] R. Persico, D. Dei, F. Parrini, L. Matera, Mitigation of narrow band interferences by means of a reconfigurable stepped frequency GPR system, Radio Science, 51, 2016.

[5] R. Persico, M. Pieraccini, Measurement of dielectric and magnetic properties of Materials by means of a TDR probe, Near Surface Geophysics, 16,1-9, 2018.

[6] R. Persico, I. Farhat, L. Farrugia, S. d’Amico, C. Sammut, An innovative use of TDR probes: First numerical validations with a coaxial cable, Journal of Environmental & Engineering Geophysics, 23, 437-442, 2018.

How to cite: Persico, R.: Multi-length probes in GPR and TDR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7426, https://doi.org/10.5194/egusphere-egu2020-7426, 2020.

Assessment of a power lines condition is an important task for all countries. It includes GPS mapping of the: 1) wire breaks; 2) places of the increased current leakage, for example corona detection; 3) degree of wires sagging between power line towers; 4) location and conditions of the power line towers; 5) vegetation encroachment along a power line corridor.  Considering that power line currents, including leakage currents, create strong magnetic field, use of magnetometers in the range from DC to sound frequencies for the solution of tasks 1-4 is highly prospective. At the same time, it is possible the control of the vegetation critical proximity to a power line adjacent zone by the increased leakage current (threats of the increased leakage current or breakdown to a tree crown). Thus the task 5 also can be solved.

The goal of the present report is to introduce the new design of miniature low-weight three-component sensor for measurement of alternative vector magnetic field onboard UAV – induction magnetometer (IM) - with autonomous system including two-component tiltmeter and GPS antenna inside in order to obtain precise measurement timing, UAV coordinates and altitude during movement. These data are stored in the SD memory card. Construction details, tests results and technical specifications of this IM for are presented.

How to cite: Korepanov, V., Pronenko, V., Dudkin, F., and Prystai, A.: Induction Magnetometer transported by UAV for power lines monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7227, https://doi.org/10.5194/egusphere-egu2020-7227, 2020.

Submarine groundwater discharge (SGD) is any direct flow of fluid across the seafloor, which forms bubbly or leaky springs and seeps from the intertidal zone to the deep sea. SGDs can significantly alter physico-chemical conditions of seepage zones. Identifying and mapping SGD is crucial to further recognize its influence in both marine and terrestrial ecosystems. However, mapping this phenomenon has been a continuing challenge, mainly due to the difficulty in its detection and quantification. This study explores the capability and applicability of an inexpensive, commercially available, recreational-grade combination of depth meter and side scan sonar system to image different types and identify point sources of coastal SGDs. Standard and systematic methodologies for efficient imaging and processing were established. The utility of the recreational-grade system was assessed and validated using a research-grade side scan sonar. SCUBA diving and CTD casting were conducted for ground-truthing and further characterization. Lower frequency sonars (83/200 kHz) showed more distinct acoustic signatures of discrete and dispersed bubbly SGDs, than the higher frequency system (455 kHz and 780 kHz research-grade unit). Sonar images showed that SGD plumes can be indicated by near seafloor to midwater cloud-like features. Spring-type SGDs tend to form cloud features with a funnel-shaped morphology. In sites where SGDs are dispersed, the acoustic signature is a curtain-like cloud, with higher bubble density in the upper water column. This is consistent with diver-based observation of increasing bubble sizes (<1 mm to ~30 mm) from point source to water surface. CTD casts indicate that the SGDs have recirculated seawater, with increasing temperature and salinity with depth. In the assessment of system and data processing requirements, and costing, a recreational-grade unit provides a good alternative for coastal SGD works.

How to cite: Gabuyo, M. R., Siringan, F., Sarmiento, K. J., and Flores, P. C.: Cost-effective recreational-grade single beam echosounder with side scan sonar system in imaging bubbly coastal submarine groundwater discharge, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-407, https://doi.org/10.5194/egusphere-egu2020-407, 2020.

Absorption muon imaging is a technique that can measure density variations underground down to a few hundred meters. Then, it can prove to be useful in a mining environment: to help assess the ore bodies volumes and/or to monitor the underground for natural hazards that can happen at the surface because of the mining exploitation. Here we report the result of an experiment designed to test the capabilities and resolution power of a cosmic muon measurement in a mining environment compared to other standard geophysics tools: gravimetry and seismic studies. It consists of three independent measurement of a subset of the decommissioned iron mine of May-sur-Orne (France). The first one was made using a 50x50cm² micromegas based muon telescope installed at the deepest non-submerged level (50m underground) during 3 months. The second one is a gravimetry survey of the surface area inside the muon telescope acceptance cone. And the third one is a study of refracted and reflected seismic waves along a single line above the muon telescope location. The investigation volume was chosen because of the presence of surface risks (neighborhood), the unknown of some uncharted volume and the presence of an ore storage volume of several meter cubed that was used during the mine exploitation and which filling state is unknown.

The data analysis showed that while muon tomography is able to detect the negative density anomaly of the storage volume, the gravimetry measurement is not sensible to it. However, the seismic study was able to detect the volume as well and its location and extension is compatible with the muon measurement.

How to cite: Bouteille, S., Bitri, A., Dietz, M., and Gance, J.: Benchmark of multiple geophysics tools to study the voids in the upper levels of a decommissioned iron mine, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21715, https://doi.org/10.5194/egusphere-egu2020-21715, 2020.

The low cost seismograph under study consists of low cost open source hardware and software microprocessor boards (Arduino Uno R3  and Raspberry Pi 3 B+ ), customized low noise design signal amplifiers, low power dissipation sophisticated power supply, two (2) kinds of earth ground shaking sensors a) Ceramic Accelerometer with cutoff frequency fc=0.15Hz and b) moving coil geophone with cutoff frequency fc=4.5Hz. The signals from the two sensors are amplified independently, while an active second order low-pass anti-alias filter and an 8th order active low-pass anti-alias filter have been used. Finally, a low-cost microprocessor board is responsible for digitizing the analog data from the amplified signal of the sensors with a frequency sampling rate of 345Hz. The aim of the present work is to design and test a systemic protocol in order to evaluate the performance of the proposed low cost seismograph for monitoring local to regional seismicity and micro seismicity. The proposed low-cost system was installed in an area of high seismic activity (Lefkada Island – Village Evgiros) and the recordings are transmitted to the database continuously from the day of its installation up today. Thus have create an amount of data for more than 280 days and all of those data have been stored to our database. Collocated with a high resolution 24 bits digitizer equipped with a broad band seismometer give us the opportunity to compare the recordings. To this end, a testing list of 15 local events has been created with different epicenters and magnitudes. For each event the recording signals have been analyzed in terms of a) power spectrum analysis, b) estimation of first arrival times of both P and S waves, c) signal amplitudes and d) earthquake duration. The choice of those specific measures was done in order to evaluate the performance of the low-cost seismograph in terms of certain seismic parameters such as magnitude, epicenter and source properties. Initial results in terms of the proposed protocol are also presented showing an adequate performance of the propose low cost seismograph.

Keywords:
low-cost instruments, Ionian islands, performance protocol

Acknowledgements
«Telemachus – Innovative Seismic Risk Management Operational System of the Ionian Islands» which is part of the Operational Program «Ionian Islands 2014-2020» and is co-financed by the European Regional Development Fund (ERDF) (National Strategic Reference Framework - NSRF 2014-20).

How to cite: Avlonitis, M., Krokidis, S., Vlachos, I., Karakostas, V., and Kostoglou, A.: Building a perfomance protocol of a low cost seismograph, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18524, https://doi.org/10.5194/egusphere-egu2020-18524, 2020.

As the community further expands their scope of study, pushing into different sub-disciplines and evermore challenging environments, the need for dynamic and highly adaptable systems grows. One of the challenges for instrument pool managers is finding a system that can cater for a wide range of possible use scenarios.

This is where traditional broadband, force-feedback sensors meet their limitations: with constrained frequency responses and sensitivities, they tend to target very narrow applications offering limited flexibility. When managing a pool of instruments, this translates into increasing pressure to acquire multiple units within different instrument ranges to meet the requirements for each specific application. This in turn leads to complex pool maintenance and may require operators to use unfamiliar instruments if their first choice is being used owing to a reduced number of instruments for each application within the pool.

Güralp’s 35 years’ experience in working with major national instrument pools revealed the necessity to develop flexible, easy-to use systems that could fit a wider scope of applications. This has led to a new, highly versatile smart sensor that supports extensive user configuration and ultra-wide tilt ranges.

The new sensor has a configurable long period corner allowing for rapid deployment in a range of environments: the 1s mode ensures the sensor settles quickly for rapid response purposes, and the 120s mode is ideally suited for long period observation.

The group of products that use this technology deliver high sensor reliability, sophisticated tools for ease of instrument and data management as well as industry standard data formats. The sensors have been integrated into various instruments: the Certimus for surface and shallow burial, the Radian for deeper postholes and boreholes, and the Fortimus for strong-motion applications.  The same philosophy also brought about Aquarius, an Ocean Bottom Seismometer that utilises the same sensor technology for the benefit of OBS pools.

This family of just four instruments covers the vast majority of seismic monitoring requirements. They represent Güralp’s solution to make instrument pool management easier and more affordable.

How to cite: Mohr, S., Balon, M., Filippi, S., Watkiss, N., and Hill, P.: Simplifying Instrument Pools: The Next Generation Family of Smart Instrumentation., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19522, https://doi.org/10.5194/egusphere-egu2020-19522, 2020.

The European Cooperation in Science and Technology (COST) has a very important role in fostering the establishment of scientific excellence in many fields such as: Geoscience, Planetary and Environment. Over the years, COST Actions have contributed to European competitiveness through their many contributions to standardisation bodies, the small to medium enterprises originating from COST networks and the transfer of results to the European industry.

A series of COST Actions in the field of Meteorology developed global data transfer standards on the basis of infra-networks in collaboration with the World Meteorological Organization advantaging the competitiveness of the industrial participation. Such achievements include harmonisation of UV-index, developing operational programmes, services, networks and phenological responses to climate on a Pan-European Scale and were recognised by the Intergovernmental Panel on Climate Change.  European Centre for Medium-Range Weather Forecasts (ECMWF) is another good example as a result of an Action through its evolution to become an independent intergovernmental organisation with its own structure and headquarters supported by 34 states.

The key findings of COST networks not only contribute to the atmospheric drivers on the impacts of the global change but also increase the understanding of the function of marine ecosystems and its response to climate change. A number of Actions in the field of marine science have developed observing system to integrate the dynamic response of sea-level variations to combine effects of various natural drivers into multi-criteria tools by bringing together oceanographers and meteorologists. These developments urged for an integrated implementation of technology in sea-level monitoring, and for further international agreements on data storage and exchange.

A wide range of disciplines, evaluating the complex interactions between the oceans and the global change, geosciences, natural resources management, environmental monitoring, biogeochemical cycles,  ecology, hydrology, natural disasters, water cycle have well undertaken through COST Action networks. The results were published in high impact journals, guidelines were represented in position papers leading to new research projects on a global scale.  Participation in COST leads to significant results and follow-up in terms of number of proposals submitted for collaborative research in Horizon 2020, with a striking success rate of 33% (the Horizon 2020 average is at 12.2%). By enabling researchers and innovators from all career levels to network, COST connects complementary funding schemes, facilitating the entry of promising young talents into these schemes.

COST is committed to reinforcing its role as the leading networking instrument in the European Research Area (ERA), while creating even higher tangible impact on society.

How to cite: Karaca, D.: Creating Impact through COST Action networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22484, https://doi.org/10.5194/egusphere-egu2020-22484, 2020.

About 6 million years ago, the Mediterranean basin was the focus of one of the most extraordinary events in the recent geological history of the Earth: the so-called Messinian Salinity Crisis (MSC). MEDSALT aims to create a new flexible scientific network addressing the causes, timing, emplacement mechanisms, and consequences – at local and planetary scale – of the largest and most recent ‘salt giant’ on Earth: the Mediterranean Salt Giant (MSG). The MSG is a 1.5 km thick salt layer that was deposited on the bottom of deep Mediterranean basins about 5.5 million years ago, in late Miocene (Messinian), and is preserved beneath the deep ocean floor today. The origin of the Mediterranean salt giant is linked the Messinian Salinity Crisis. Research on the MSC has initiated one of the longest-living scientific controversies in Earth Science. Pioneering scientific drilling in 1970 induced some researchers to publish the theory of the ‘desiccation’ of the Mediterranean during the Messinian. In their view the Mediterranean Sea level dropped by 1–2 km and the basin was transformed into a huge hot, dry salt lake as a consequence of the tectonically-driven closure of the Atlantic gateway at the present-day Gibraltar strait.

This interpretation was successful not only among the scientific community, but also in public opinion. On one hand, the progress of scientific research provided additional evidence for the desiccation theory. On the other hand, researchers questioned the theory, providing alternative interpretations of the geological data, and theoretical arguments supporting a model of salt deposition from a deep brine, assuming a very limited sea level change. Controversial views also exist on the mechanisms that ended the MSC: was it a catastrophic flood of Atlantic waters from the re-opening of the Atlantic gateway, or a slow mixing with brackish water from the Black Sea area first before the re-establishment of the normal marine connection with the Atlantic Ocean? In order to trigger progress on the understanding of the MSC, a widespread international scientific community has promoted the largest coordinated research on the MSC since its discovery, clustered around scientific drilling. COST (European Cooperation in Science and Technology) was identified as the most appropriate tool, as COST Actions provide tools for networking, training, mobility and dissemination. The network has further promoted one Marie Skłodowska-Curie European Training Network (SALTGIANT) offering 15 PhD fellowships across Europe. New contacts have been activated with a variety of stakeholders, including governmental administrations, non-governmental organizations, the industry and, indirectly, society at large, demonstrating the importance that science and society renew a relationship of trust and confidence. In all, 200 scientists are working together – across disciplines such as geophysics, geology, biology, microbiology, and also social sciences – towards a common scientific goal: uncovering the Mediterranean salt giant.

How to cite: Camerlenghi, A. and Aloisi, V.: Uncovering the Mediterranean Salt Giant (MEDSALT) – Scientific Networking as Incubator of Cross- disciplinary Research in Earth Sciences, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3657, https://doi.org/10.5194/egusphere-egu2020-3657, 2020.

The European Cooperation in Science and Technology (COST) promoted and funded the Action ES1404 called “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction,” or “HarmoSnow” (2014-2018). With 29 European COST countries and the international partners Andorra and Taiwan (https://www.cost.eu/actions/ES1404/) HarmoSnow coordinated efforts towards harmonized snow data processing and handling practices by promoting new observing strategies. The vision was to connect different communities, facilitating data transfer, upgrading and enlarging knowledge through networking and linking them to activities in international agencies and global networks (www.harmosnow.eu).

The main aim of HarmoSnow was to enhance the capability of the research community and operational services to provide and exploit quality-assured and comparable observation data on the variability of the state and extent of snow. The overall objectives were (1) Establish a European-wide science network on snow measurements for their optimum use with interactions across disciplines and expertise, (2) Assess and harmonise practices, standards and retrieval algorithms applied to snow measurements, (3) Develop a rationale and long term strategy for snow measurements and their dissemination, (4) Advance snow data assimilation in NWP and hydrological models, (5) Establish a validation strategy for climate, NWP and hydrological models against snow observations and foster its implementation, (6) Training of a new generation of scientists on snow science and measuring techniques with a broader and more holistic perspective.

We will present outcomes from the HarmoSnow activities and discuss how to move forward.

How to cite: Arslan, A. N.: EU COST Action ES1404-HarmoSnow, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12910, https://doi.org/10.5194/egusphere-egu2020-12910, 2020.

Forests provide essential economic, social, cultural and environmental services. To be able to maintain the provision of these services, sustainable forest management (SFM) is a vital obligation. The maintenance of biodiversity, ranging from gene to ecosystem levels, is essential for functions and associated services, and it is one of the most important criterion for assessing sustainability in the Pan-European region. 
Currently, the majority of SFM Criteria and Indicators focuses on attributes relative to tree species or to the whole forest. With reference to biodiversity conservation, this means that the collected information cannot fully assess whether forests are being managed sustainably. To understand the drivers of forest biodiversity and drive sustainable management, several taxonomic groups should be investigated, since they may respond differently to the same environmental pressures. However, up to now, broad multi-taxonomic analyses were mainly performed through reviews and meta-analyses which limit our holistic understanding on the effects of forest management on different facets of biodiversity. Recently, several research institutions took up the challenge of multi-taxonomic field sampling. These local efforts, however, have limited extrapolation power to infer trends at the European scale. It is high time to share, standardize and use existing multi-taxon data through a common platform to inform sound management and political decisions. Biodiversity indicators have also some potential to be used in evaluation of impact of forest management on soils and surface waters in terms of naturalness, degradation and reclamation.
We present the COST Action CA18207 “Biodiversity of Temperate forest Taxa Orienting Management Sustainability by Unifying Perspectives” (Bottoms-Up). It will gather the most comprehensive knowledge of European multitaxonomic forest biodiversity through the synergy of research groups that collected data locally in more than 2200 sampling units across approximately 300 sites covering nine different European forest types. For each sampling unit, information will be available on at least three taxonomic groups (vascular plants, fungi, lichens, birds and saproxylic beetles being the most represented) and on live stand structure and deadwood. Multi-taxon biodiversity will be associated with: (i) information on forest management based on observational studies at the coarse scale, and (ii) structural data deriving from forest manipulation experiments at the fine scale. 

Specific objectives are:
• Developing a standardized platform of multi-taxon data;
• Establishing a network of forest sites with baseline information for future monitoring;
• Designing shared protocols for multi-taxon sampling;
• Assessing the relationships between multi-taxon biodiversity, structure and management;
• Creating a coordinated network of forest manipulation experiments;
• Evaluating indicators and thresholds of sustainability directly tested on biodiversity;
• Developing management guidelines defining sustainable management to be applied in forest certification and within protected areas.

The Action involves about 80 researchers and stakeholders from 29 countries and represents an outstanding opportunity to develop a strong network of collaboration for standardized broad-scale multitaxon studies in Europe.

Keywords:  Multi-taxon, Pan-European region, Sustainable Forest Management. 

How to cite: Sarginci, M., Ódor, P., Doerfler, I., Nagel, T., Paillet, Y., Sitzia, T., Tinya, F., Avdagić, A., and Ballweg, J. and the COST Action CA18207: BOTTOMS-UP: Biodiversity of Temperate Forest Taxa to Orient Management Sustainability by Unifying Perspectives, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10625, https://doi.org/10.5194/egusphere-egu2020-10625, 2020.

Biodiversity of ecosystems is an important driver for the supply of ecosystem services to people. Soils often have a larger biodiversity per unit surface area than what can be observed aboveground. Here, we present what is to our knowledge, the most extensive literature-based key-word assessment of the existing information about the relationships between belowground biodiversity and ecosystem services in European forests. The belowground diversity of plant roots, fungi, prokaryota, soil fauna, and protists was evaluated in relation to the supply of Provisioning, Regulating, Cultural, and Supporting Services. The soil biota were divided into 14 subgroups and the ecosystem services into 37 separate services. Out of the 518 possible combinations of biotic groups and ecosystem services, no published study was found for 374 combinations (72%). Of the remaining 144 combinations (28%) where relationships were found, the large majority (87%) showed a positive relationship between biodiversity of a belowground biotic group and an associated ecosystem service. We concluded that (1) soil biodiversity is generally positively related to ecosystem services in European forests; (2) the links between soil biodiversity and Cultural or Supporting services are better documented than those relating to Provisioning and Regulating services; (3) there is a huge knowledge gap for most possible combinations of soil biota and ecosystem services regarding how a more biodiverse soil biota is associated with a given ecosystem service. Given the drastically increasing societal demand for knowledge of the role of biodiversity in the functioning of ecosystems and the supply of ecosystem services, we strongly encourage the scientific community to conduct well-designed studies incorporating the belowground diversity and the functions and services associated with this diversity.

How to cite: Godbold, D., Bakker, M., Brunner, I., and Lukac, M.: BioLink: Linking belowground biodiversity and ecosystem function in European forests, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13066, https://doi.org/10.5194/egusphere-egu2020-13066, 2020.

Although the marginal seas represent only 7% of the total ocean area, the CO₂ fluxes are intensive and important for the carbon budget, exposing to an intense process of anthropogenic ocean acidification (OA). A decline in pH, especially in the estuarine waters, results also from the eutrophication-induced acidification. The Adriatic Sea is currently a CO₂ sink with an annual flux of approximately -1.2 to -3 mol C m^-2 yr^-1 which is twice as low compared to the net sink rates in the NW Mediterranean (-4 to -5 mol C m^-2 yr^-1). Based on the comparison of two winter cruises carried out in in the 25-year interval between 1983 and 2008, acidification rate of 0.003 pH_T units yr⁻¹ was estimated in the northern Adriatic which is similar to the Mediterranean open waters (with recent estimations of −0.0028 ± 0.0003 units pH_T yr⁻¹) and the surface coastal waters (-0.003 ± 0.001 and -0.0044 ± 0.00006 pH_T units yr⁻¹). The computed Revelle factor for the Adriatic Sea, with the value of about 10, indicates that the buffer capacity is rather high and that the waters should not be particularly exposed to acidification. Total alkalinity (TA) in the Adriatic (2.6-2.7 mM) is in the upper range of TA measured in the Mediterranean Sea because riverine inputs transport carbonates dissolved from the Alpine dolomites and karstic watersheds. The Adriatic Sea is the second sub-basin (319 Gmol yr^-1), following the Aegean Sea (which receives the TA contribution from the Black Sea), that contribute to the riverine TA discharges into the Mediterranean Sea. About 60% of the TA inflow into the Adriatic Sea is attributed to the Po river discharge with TA of ~3 mM and TA decreases with increasing salinity. Saturation state indicates that the waters of the Adriatic are supersaturated with respect to calcite (Ω_Ca) and aragonite (Ω_Ar) throughout the year. However, saturation states are considerably lower in the bottom water layers, due to the prevalence of benthic remineralization processes in the stratification period. The seasonal changes of the chemical and environmental conditions and relatively small size of the Adriatic Sea area the microbial community composition, function (growth, enzymatic activity) and carbon and nitrogen biogeochemical cycles. Significant effects on calcifying organisms and phytoplankton are expected while the effects of possible OA on microbially-driven processes are not known yet.

How to cite: Turk, V., Bednarsek, N., Faganeli, J., Gasparovic, B., Giani, M., Guerra, R., Kovac, N., Malej, A., Krajnc, B., Melaku Canu, D., and Ogrinc, N.: Carbonate System and Acidification of the Adriatic Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10977, https://doi.org/10.5194/egusphere-egu2020-10977, 2020.

The COST Action 18110 Underground4value (http://underground4value.eu) aims to advance knowledge on how to guarantee continuity of use and significance of underground historic fabric. It is collecting information, experiences and knowhow to base the development of research and training. The Action focusses on underground regeneration, revitalisation of the public realm and skills development for people concerned with underground heritage.

This contribution centres the attention of the Working Group on Planning Approaches. It also looks at the role of local authorities, as enablers and facilitators, in coordination, use  and management of underground built heritage. In this framework underground built heritage is considered as a social resource with integrated programmes of physical, economic and social measures, backed by strategic stakeholder dialogue.

On the one hand, this contribution discusses the structure and goals of the WG, as it pays attention to the necessary complementarities between functional approaches – at the level of regions and city – and social and cultural approaches involving citizens’ engagement and empowerment – at the local level. This WG aims to provide a reflection on sustainable approaches to preserve the underground built heritage and, at the same time, to unfold the case by case approach for potential use of underground space. On the other hand, to achieve its objectives the WG on Planning Approaches is setting together potentials and constraints in the efforts to make better use of underground heritage. This contribution, therefore, sheds lights on the preliminary results of the WG. It is centred on the learned lessons, challenges and barriers - from a planning science perspective - that experts met in their efforts to tackle Underground Built Heritage. Achieving this goal makes the call for an educational paradigm shift - as the Action is not only interested in compiling the results, rather on experiences that can be analysed and learned. This requires a dynamic understanding of knowledge, abilities and skills, towards creating more effective coalitions of ‘actors’ within localities, by developing structures, which encourage long term collaborative relationships. Enabled by the gained knowledge, the WG will define the best tailored ways to forward this knowledge for planners and decision-makers.

How to cite: Smaniotto Costa, C., Ruchinskaya, T., and Lalenis, K.: Perspectives for Planning Approaches in Promoting Underground Built Heritage - COST Action Underground4value, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22143, https://doi.org/10.5194/egusphere-egu2020-22143, 2020.

The dry equatorial forests in the north-western coast of Peru suffer from acute water stress and man-driven deforestation. Recent estimates indicate that the forests have reduced to approximately 10% of their original size. There are local reforestation efforts currently underway, for example to prevent the extinction of native species such as the Peruvian Plantcutter songbird (Phytotoma raimondii). However, irrigation needed to support such efforts is severely challenged by the issues of water scarcity in the region. These adverse effects are also being experienced at a local level by the nearby rural community in Lobitos.

The groundwater resources in Lobitos could potentially offer a solution to the above issues for the local community. However, a scientifically informed and sustainable method of mapping and utilising this resource is needed. To provide supporting evidence in this effort, an extensive ground penetrating radar survey was conducted using a commercial 80 MHz impulse radar to characterise the near subsurface within a 90 hectare plot called Ecológica Tallán, which is part of a natural dry water channel in Quebrada Montes and declared as an important conservation area for the district.

Through a pilot study between Lancaster University, EcoSwell Peru and University of Glasgow, we report on initial results from our ground penetrating radar survey to provide a better understanding of the subsurface characteristics in Quebrada Montes, Lobitos.

How to cite: Lok, L. B., Almendrades, D., Alderson, M., Pizarro, A., Bustamante, A., and Shi, J.: Ground penetrating radar investigations in Quebrada Montes, Lobitos, Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13645, https://doi.org/10.5194/egusphere-egu2020-13645, 2020.

Long Integration Dual Inlet (LIDI) is an established technology which enabled improved accuracy and precision of Δ47 analysis from carbonate samples by utilizing sequential measurement of the full sample and reference, rather than alternating between sample and reference on shorter time periods, as it is done in the classical Dual Inlet method. As of today, there are two key challenges that were limiting further improvements to Δ47 determination: the IRMS must be in a stable temperature environment during long measurement of sample and reference gas, and the crimping of the sample and reference capillaries must be precisely matched, otherwise the produced data will be inaccurate and have reduced precision.

Here we present the improvements made on the sample gas measurement and data evaluation, which we define as LIDI 2.

By applying the LIDI 2 method, sample bracketing is possible following a four-step approach, resulting in fully corrected temperature drift (i.e. eliminated from the data), decreasing the standard deviation by factor of 2. This is a substantial improvement for acquiring clumped isotope data as reaching a very stable temperature of ±0.1°C/h is a challenge for most laboratories.

Alongside eliminating variation in the Δ47 data caused by unstable laboratory air temperature, LIDI 2 also improves the overlap of sample and reference gas signals due to non-perfect crimping of the capillaries. The crimping procedure is laborious and rarely delivers perfect results. Additionally, the pressure adjustment before reference measurement must ensure there is no significant offset between sample and reference intensities. LIDI 2 delivers perfect sample versus reference intensity matching, which results in significantly higher precision on each sample gas analyzed. Standard error of a single sample measurement is improved by up to factor of 2.

The LIDI 2 method delivers improved accuracy and precision on Δ47 measurement from small Carbonate samples, which in combination with the latest advancements in inert capillaries coating and automated contaminant trapping contributes to enhanced clumped isotopes data quality.

How to cite: Mandic, M., Tuthorn, M., Stoebener, N., Radke, J., and Schwieters, J.: LIDI 2 – New Evaluation Strategy for Accurate and Precise Clumped Isotope CO2 Analysis on Carbonate Samples, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3052, https://doi.org/10.5194/egusphere-egu2020-3052, 2020.

Today, photos play an important role in geoscience and public discussion. When photographical techniques developed during the second half of the 19th century, it took several decades uuntil high mountain areas and specific features could be captured with this technique, as a follow upt o traditional paintings and drawings. In European geography, Friedrich Simony developed the idea of tackling geomorphological processes by time lapse photography. Contemporary literature shows that his technique of combining photography with empirical data and theories was convincing, and that he established a new style of scientific discussion. Still, the comparison of historical with contemporary photography offers scientific insights and information which is not covered by any other type of empirical evidence as measurements, maps or descriptions. For example not only extent, but also firn and debris cover of glaciers, information on type and extent of vegetation,  the width and style of roads, details of infrastructure and cultural practices can be tackled from early photographs.  Several archives do allow not only acess to photographic documents, but also to metadata. Interdisciplinary effort has to be taken to further analyse this wealth of information.

How to cite: Fischer, A.: The role of repeat photography in establishing theories of transition in high mountain environments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7070, https://doi.org/10.5194/egusphere-egu2020-7070, 2020.

Most large-format analogue aerial photography, especially in the form of roll negatives, is now held in centralised archives, but much still exists elsewhere, notably in the form of positive prints. Whilst many large archiving bodies have been and continue to digitise their holdings of such aerial photography using professional photogrammetric scanners, they are often prohibitive (on the grounds of cost and/or logistics) for use with low-volume, dispersed collections. Therefore, alternative methods are sought, which are presented here. Such alternatives can be subject to relatively poor geometric accuracy, making photogrammetric processing problematic. Here the results of photogrammetric processing with prints digitised using alternative methods are compared and contrasted with digitised roll negatives of the same frames. The quality of resulting elevation data are assessed against reference elevations, using a test site with topography which has remained stable over decades.

How to cite: Ford, A. and Papworth, H.: Digitising archive large-format analogue aerial photography with alternative methods; Implications for photogrammetric processing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20294, https://doi.org/10.5194/egusphere-egu2020-20294, 2020.

Mountain regions are disproportionately affected by global warming and changing precipitation conditions. Especially the strong variations within high mountain ranges at the local scale require additional sources in order to quantify changes within this challenging environment. With the emergence of alpine tourism, terrestrial photographs became available by the end of 1800, predating aerial imagery for the selected study areas by 50 years. Due to the earlier availability and oblique acquisition geometry these images are a promising source for quantifying changes within mountainous regions at the local scale. Within the research project SEHAG, methods to process these images and to analyse their potential to quantify and describe environmental changes are developed and applied to study areas in Austria and Italy.

One of the prerequisites for the estimation of changes based on terrestrial imagery is the calculation of the corresponding object point for each pixel in a global coordinate system resulting in a georeferenced orthorectified image. This can be achieved by intersecting the ray defined by the projection center of the camera and each pixel with a digital terrain model, a process known as monoplotting.

So far 1000 terrestrial images with unknown interior and exterior orientation have been collected from various archives for the selected study areas Kaunertal, Horlachtal (both Tyrol, Austria) and Martelltal (South Tyrol, Italy). In order to estimate all camera parameters a 3D viewer for the selection of ground control points has been developed and implemented. The estimation of the exterior and interior orientation is done in OrientAL. 

Preliminary results for selected images show, that especially the developed 3D viewer is an important improvement for the selection of well distributed ground control points and the accurate estimation of the exterior and interior orientation. Monoplotting depends on a digital terrain model, which cannot be computed from the terrestrial images alone due to missing overlap and different acquisitions times. Hence, the combination with historical digital terrain models derived from aerial imagery is necessary to minimize errors introduced due to changes in topography until today. While the large amount of terrestrial images with their oblique acquisition geometries can be exploited to fill occluded areas by combining the results from multiple images, the partly missing or inaccurate temporal information poses another limitation.

With this large image collection, for the first time, we are able to evaluate the use of historical oblique terrestrial photographs for change detection in a systematic manner. This will promote knowledge about challenges, limitations and the achievable accuracy of monoplotting within mountainous regions. The work is part of the SEHAG project (project number I 4062) funded by the Austrian Science Fund (FWF).

How to cite: Flöry, S., Ressl, C., Puercher, G., Pfeifer, N., Hollaus, M., Bayr, A., and Karel, W.: Development of a 3D Viewer for georeferencing and monoplotting of historical terrestrial images. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22327, https://doi.org/10.5194/egusphere-egu2020-22327, 2020.

Improving our understanding of the processes governing mass loss from the cryosphere is inhibited by a lack of data at high spatial and temporal resolution. Satellite sensors can provide regional to global scale coverage of glacier processes but fail to resolve processes that occur rapidly, for example glacier calving. To observe these processes, the glaciology community must invest in new techniques that can monitor these processes adequately and fill this major research gap. Here, we will discuss the implementation of an exciting new radar system that is capable of imaging glacial terrain at a high angular resolution and during most weather conditions. The system, named AVTIS2, operates at 94 GHz (~3 mm) and offers a compromise between imaging resolution and penetration through atmospheric obscurants. AVTIS2 scans mechanically across a scene of interest in defined increments of azimuth and elevation angles and generates a 3D data cube of backscattered power. We use a point to maximum power criterion to generate point clouds and construct Digital Elevation Models (DEMs) of the terrain. Because AVTIS2 is a real aperture radar it does not require the phase stability of interferometric radars and can acquire DEMs irrespective of local environmental conditions. In this work, we have used the AVTIS2 radar to map Rhône Glacier in the Swiss Alps, representing the first ever time a millimetre wave radar has been used in this way. To improve our understanding of the performance of AVTIS2 for mapping glaciers, we have characterised the scattering properties of glacial ice at 94 GHz by calculating its Radar Cross Section (RCS). This is key to understanding the performance of AVTIS2 for mapping glaciers. This study represents the first investigation into the reflectivity of ice at millimetre wavelengths and the utility of millimetre wave radar as a surveying tool. We will report on the future application of this instrument in glaciological studies and the unique perspective it can offer.

How to cite: Harcourt, W. D., Macfarlane, D. G., Robertson, D. A., Rea, B., and Spagnolo, M.: Observing the cryosphere with millimetre wave radar: The case study of Rhône Glacier, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-595, https://doi.org/10.5194/egusphere-egu2020-595, 2020.

Unoccupied Aerial Systems (UAS) are ideal tools for responding to nuclear incidents where large outdoor areas have become contaminated with a radiological hazard. They are advantageous because rapid response radiation surveys can be conducted while the human operator remains at a safe distance and avoids direct contamination of the platform. During fieldwork within the Chernobyl Exclusion Zone (Ukraine), an airborne platform was equipped with a GNSS enabled gamma spectrometer and used to survey an area surrounding a known highly contaminated building (a ‘hot-spot’), resulting in a radiation intensity map. The detected radiation pattern, however, was ‘blurred’ since the intensity recorded at any point counted nadir emissions, but also emissions from all sources within line-of-sight; The ‘hot-spot’ had an influence far outside its ground footprint. Methods exist to correct for errors introduced by varying terrain altitude, however, they do not remove the unwanted blurring. Hence, small point sources appear as broad regions of contamination which is entirely an artefact of the measurement process. The effect is further accentuated with increasing height above ground hence understanding and correcting for this phenomenon is particularly relevant to data collected using UAS. Here, we present a novel algorithm to refine the detected pattern to more accurately recover the ground-truth.

A forward model of the system is created which describes the relationship between the unknown ground-truth and the aerial measurements. Gamma ray emissions from a point source obey the inverse square law of spatial dilution and have an exponential attenuation in air. To model both effects, geometric information of the scene is required and is provided by the geotagged spectrometer data and photogrammetrically processed DEMs of the surveyed terrain. The resulting model is hyper-cube of linear equations, where every aerial measurement point is assumed to be influenced by every ground sample point. By finding the inverse solution of this system, the ground-truth radiation pattern is estimated in more detail. The Kaczmarz method is advantageous because a large system of equations can be broken down into smaller sub-routines and solved iteratively. A caveat is that the solution might settle to false positive. The refinement algorithm will be presented with simulated results, controlled laboratory experiments using robotic arms and sealed radioactive sources, and finally applied to a real-world data set collected in the Chernobyl Exclusion Zone.

How to cite: Wood, K., Connor, D., Groen, S., Smith, D., White, S., Martin, P., Verbelen, Y., Holland, E., Richardson, T., and Scott, T.: UAS radiation hot-spot detection and refinement, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1744, https://doi.org/10.5194/egusphere-egu2020-1744, 2020.

Steep volcano flanks are geomorphological systems highly responsive to both exogenous dynamics and endogenous forcing. While the external (gravitational) processes lead to a shift of material from steeper slopes to areas with lower gradients (erosion of loose deposits, rockfall of lavas/welded material), magmatic and tectonic activity can have either a constructional (accumulation) or a destructive effect (triggering moderate- to large-scale mass-wasting). Remotely sensed data have often been used to map areas affected by lithological and morphological changes, i.e. to identify areas impacted by eruptive and post-eruptive (landslides or floods) phenomena, as well as to quantify topographic changes.

In this work, the geomorphological evolution of the Sciara del Fuoco (SdF) depression on the Island of Stromboli (Italy) between July 2010 and October 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLEIADES-1 tri-stereo images and from LiDAR acquisitions allowed the topographic changes estimation. Data comprised also high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images allowing to map areas affected by major lithological and morphological changes. SdF was selected being the optimal test-site for monitoring the effect of volcanic eruption on steep-slope volcano flank, since: i) it is affected by persistent volcanic activity, ii) it is prone to mass-wasting phenomena, and iii) it is one of the best studied and, among all, monitored volcano on Earth, providing exceptional validation data and ground-truth constrains.

During the analysed period, the volcano experienced two eruptions (summer 2014 and summer 2019), with the emplacement of two lava flow fields on the SdF. Before the 2014 effusion and in between the two eruptions, geomorphological changes consisted of volcanoclastic sedimentation and some overflows outside the crater. The effusive (and partially explosive) activity produced larger topographic changes, related to the emplacement of the two lava flow fields and to the accumulation of a volcaniclastic wedge on the SdF. This work shows that, at Stromboli, the emplacements of lava flow fields were preceded and accompanied by the accumulation of volcanoclastic wedges on the SdF. The quantification of these volcanoclastic wedges is relevant because they are composed of the same material that was involved in the 30 December 2002 tsunamigenic landslide, besides being located in the same area.

PLEIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared, providing a first indication on the differences between two largely used methods for modelling topography. Although there are small artefacts in smaller ridges and valleys, there is still a clear consistency between the two DEMs for the main valleys and ridges. This analysis can be used by the volcanological community and the civil protection authorities in case of a cost-benefit analysis for planning the best method for updating topography and quantify morphological changes of an active volcano.

How to cite: Di Traglia, F., Fornaciai, A., Favalli, M., Nolesini, T., and Casagli, N.: Geomorphological response to volcanic activity at Stromboli volcano using multi-platform remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3134, https://doi.org/10.5194/egusphere-egu2020-3134, 2020.

Although the observed global climate change has particularly affected high-alpine regions and these geosystems seem to react very sensitively to changes in external forcing, there is a lack of understanding about the effect of a changing climate upon high-alpine landscapes at the timescale of decades.  In the case of rock glaciers, which are common features in high alpine periglacial landscapes, numerous studies suggest a general acceleration of rock glacier displacement rates accompanied by surface lowering. This behaviour has been attributed to the rising permafrost temperature, induced by atmospheric warming and regulated by thermo-hydrological processes. On the other hand, decoupled kinematics of nearby rock glaciers under the same climatic forcing have also been proven. This is attributed to different local topo-climatic conditions and genesis of the investigated rock glaciers.  To contribute to the understanding of multi-decadal rock glacier response to climate change, we investigate the morphodynamic changes for selected rock glaciers in the Upper Kauner Valley in the Ötztaler Alps, Austria, a catchment comprising numerous rock glaciers of different size, genesis, elevation, aspect and activity status. This is done for multiple time slices between 1953 and 2017. These changes are analysed with respect to rock glacier characteristics and changes in the meteorological forcing. This work is part of the interdisciplinary and multi-university research project SEHAG (Sensitivity of high alpine geosystems to climate change since 1850), which aims to investigate changes in different processes of alpine geosystems and their interaction.

In order to investigate morphodynamics of the rock glaciers, we use digital photogrammetry to generate orthophotos and digital elevation models from historical aerial images (available since 1953). Additionally, we use digital elevation models generated from three airborne LiDAR surveys within the period 2006-2017. While the diachronic analysis of digital elevation models predominantly addresses vertical surface changes, image correlation of multitemporal digital orthophotos yields information on rates of horizontal displacement. The results for the individual rock glaciers are compared to each other and to meteorological data of nearby weather stations to analyse the response of rock glaciers with different characteristics to changing climate forcing. 

How to cite: Fleischer, F., Haas, F., Heckmann, T., Altmann, M., Piermattei, L., Rom, J., and Becht, M.: Multi-decadal (1953 – 2017) response of rock glacier morphodynamics to climate change in the Kauner Valley in the Ötztaler Alps, Austria based on historical aerial images and airborne LiDAR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8231, https://doi.org/10.5194/egusphere-egu2020-8231, 2020.

The knowledge of past fluctuations of glaciers is the key for understanding their dynamics and their climate-related evolution. Glacier mass balance and length changes are the two metrics normally used for reconstructing past fluctuations series of glaciers. However, length change measurements series are often discontinuous and require validation, whereas mass balance measurements are available for only a few glaciers worldwide and only for the latest decades.

In the context of glacier reconstructions, other sources of information such as historical-archival, glacio-archaeological and geomorphological data are of critical importance, because they enable the completion and validation of direct measurement series and their extension into the past, providing spatial and temporal constraints.

A unique source of unexploited historical information dating back to the First World War (WWI, 1915-1918) exists for many glaciers in the Eastern Italian Alps. This information mainly consists of old photos, which however are spread over a multitude of sources, often difficult to access, and in many cases not yet digitized.

We propose a workflow that enables extracting quantitative information from terrestrial photographs taken during the WWI period, aimed at the reconstruction of glacier area, volume and firn lines by means of the monoplotting technique. This method relies on the availability of high-resolution digital elevation models, which became recently available over wide areas thanks to LiDAR and aerial photogrammetry. This work presents the methods applied, and the results obtained, on several case studies in the Adamello-Presanella, Ortles-Cevedale, Dolomites, and Julian Alps.    

How to cite: Carturan, L., Bondesan, A., Carton, A., Cazorzi, F., Cucchiaro, S., De Marco, J., and Piermattei, L.: Use of WWI photos for quantitative reconstructions of glaciers along the Italian-Austrian front, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22512, https://doi.org/10.5194/egusphere-egu2020-22512, 2020.

The mountain cryosphere has been disproportionally affected by climate warming and changing precipitation conditions since the 19^th century. This has caused intense and multiple reactions in high mountain hydrosphere, lithosphere, reliefsphere, biosphere and pedosphere. Although there is general knowledge on climate-related changes of glaciers, little is known about the high-resoluted temporal and spatial development of glaciers in the last century. These knowledge gaps further implicate limitations by simulating past and future development of the mountain cryosphere difficult, as important calibration and validation data are missing.

Topographic maps contain important information, as they are among the most reliable area-wide representations of past landscape for the time before airborne data acquisition. Thus, they offer the opportunity to extract former glacier extents and to close the information gap between the LIA extent, reconstructed from moraine extent, and aerial derived glacier information in the recent past.

However, as maps represent entities of a real world generalized depending e.g. on the intension of mapping, we consider map uncertainties as a crucial aspect for the reconstruction of glaciers from historical data.

In order to assess the accuracy of glacier area and front position from topographic maps, we reconstruct glacier extents under consideration of a comprehensive systematic examination of the uncertainty with regard to position, time and attribute. For this purpose, we use information of 12 topographic maps with a scale of 1:75,000 or larger from Kaunertal, covering a time span of 139 years (1871 – 2010) and analyse the accuracy of the maps focusing on production-related and transformation-oriented uncertainty.

The comparison between the glacier changes, derived from the maps and the original data (if available) as well as those measured in situ, shows that topographic maps are a reliable data source for the reconstruction of glacier front variations and provide vital key information when studying long time series.

How to cite: Knoflach, B., Geitner, C., and Stötter, J.: Topographic maps – an important data source for investigating long-term glacier variations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22543, https://doi.org/10.5194/egusphere-egu2020-22543, 2020.

Since the end of the Little Ice Age, alpine geosystems have been subject to changes due to the effects of ongoing climate change, affecting e.g. the cryosphere, topography, surface materials and morphodynamics, landcover, landuse, and other anthropogenic factors. Our work forms part of the SEHAG project that investigates the sensitivity of alpine geosystems to climate change during that period.

In order to identify and assess such changes, we aim at generating multi-temporal geomorphological maps of three alpine catchments (Upper Kaunertal, Horlachtal, Austria; Val Martello, Italy). In contrast to “traditional” geomorphological maps, we do not use areal, linear and point symbols to represent landforms, their properties, and geomorphic processes. Our approach is entirely based on non-overlapping polygon features that represent a landform- and process-centered subdivision of the catchment. This enables the analysis of the resulting map in a GIS framework with respect to the type, size and other properties of landforms. Most importantly, it allows for the assessment of their spatial configuration (adjacency, topology) within the catchment in terms of toposequences and sediment cascades.

Mapping is based on photogrammetric products of aerial photos, that are orthophotos, digital elevation models (DEMs) and derivatives of the latter. Furthermore, DEMs can be used for the orientation of historical terrestrial photographs, making them an additional mapping basis through monoplotting. Depending on the availability of imagery (area-wide aerial images dating back to the mid of the 20^th century; local terrestrial photos starting from the second half of the 19^th century), an area-wide geomorphological map representing the present state of the system forms the basis of our investigations. Historical images are then used to “update” the map back into the past wherever differences to the temporally subsequent situation are conspicuous. This especially regards the massive decline of glaciers, but also the build-up and depletion of storage landforms, the development of lakes, and changes in the channel network.

At a later stage, the maps will be used for a network-based, multitemporal assessment of sediment connectivity. Nodes represent landforms contained in the geomorphological map(s), and all kinds of evidence (visible features indicating sediment transfer between adjacent landforms, measurements based on DEMs of difference, connectivity indices) will be used to establish edges that represent (potential) sediment transfer by geomorphic processes. As the configuration of system components and/or the activity of processes changes between maps of subsequent epochs, these changes will affect connectivity measures of the corresponding network model.

How to cite: Heckmann, T., Piermattei, L., Rom, J., Altmann, M., Fleischer, F., Haas, F., and Becht, M.: Multi-temporal geomorphological maps based on historical aerial images for the investigation of geomorphic changes in an Alpine catchment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22605, https://doi.org/10.5194/egusphere-egu2020-22605, 2020.

The project “Between Land and Sea” was initiated by OREA (Institute for Oriental and European Archaeology) and is an interdisciplinary approach to combine the geology, geomorphology, and paleo-environment of the Chekka region (Lebanon) to investigate the ancient history and its archaeological remains. In the course of the project, the first ever scientific LiDAR (Light Detection and Ranging) data acquisition in Lebanon was conducted in autumn 2018 and a high-resolution DEM (Digital Elevation Model) was calculated. However, this model represents the recent topographical situation, which has changed drastically not only in the archaeologically relevant period up to 5000 years before today but also during the last decades.

To be able to qualitatively and quantitatively record geomorphological processes in the study area and thus understand long-term natural and anthropogenic landscape changes, the recent elevation model is compared with a historical model. The historical elevation model was derived on the base of aerial images of a French overflight from 1962. With the help of SfM (Structure from Motion/Agisoft Metashape) in combination with referencing methods (e.g. ICP), this historical model can be adapted to the LiDAR model. Quantitative analyses of selected areas provide information about surface changes over the last 56 years. But these results give also ideas about landscape evolution over longer time periods. Besides the natural changes, the historical model also reveals major anthropogenic changes since 1962 and shows that possible archaeologically relevant sites have been lost as a result of extensive overprinting due to the construction of buildings, infrastructure, industrial mining or agricultural use.

Our promising results show, that the implementation of historical terrain models based on aerial photographs can lead to a better understanding of the natural landscape development as well as anthropogenic induced changes and thus can also provide important additional information for archeological surveys.

How to cite: Rom, J., Haas, F., Stark, M., Poschenrieder, A., Kopetzky, K., and Genz, H.: Between Land and Sea: An analysis of the landscape changes in the Chekka region (Lebanon) based on airborne LiDAR data and historical aerial images from 1962, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22519, https://doi.org/10.5194/egusphere-egu2020-22519, 2020.

After World War II, aerial photography i.e. vertical or oblique high-resolution aerial images spread rapidly into civil research sectors, such as landscape studies, geologic maps, natural sciences, archaeology, and more. Applying photogrammetric techniques, two or more overlapping historical aerial images can be used to generate an orthophoto and a 3D point cloud, wherefrom a digital elevation model can be derived for the respective epoch. Combining results from different epochs, morphological processes and elevation changes of the surface caused by anthropogenic and natural factors can be assessed. Despite the unequalled potential of such data, their use is not yet fully exploited. Indeed, there is a lack of clear processing workflows applying either traditional photogrammetric techniques or structure from motion (SfM) with camera self-calibration. In fact, on the one hand, many SfM and multi-view stereo software do not deal with scanned images. On the other hand, traditional photogrammetric approaches require information such as a camera calibration protocol with fiducial mark positions. Furthermore, the quality of the generated products is strongly affected by the quality of the scanned images, in terms of the conservation of the original film, scanner resolution, and acquisition parameters like image overlap and flying height.

To process a large dataset of historical images, an approach based on multi-epoch bundle adjustment has been suggested recently.  The idea is to jointly orient the images of all epochs of a historical image dataset. This recent approach relies on the robustness of the scale-invariant feature transform (SIFT) algorithm to automatically detect common features between images of the time series located in stable areas. However, this approach cannot be applied to process digital images of alpine environments, characterized by continuous changes also of small magnitude that might be challenging to automatically identify in image space. In this respect, our method implemented in OrientAL, a software developed by TU Wien, identifies stable areas in object space across the entire time series. After the joint orientation of the multi-epoch aerial images, dense image matching is performed independently for each epoch. We tested our method on an image block over the alpine catchment Kaunertal (Austria), captured at nine different epochs with a time span of fifty years. Our method definitely speeds up the process of image orientation of the entire data set, since stable areas do not need to be masked manually in each image. Furthermore, we could improve the orientation of images from epochs with poor overlap. To estimate the improvements obtained with our methods in terms of time and accuracy of the image orientation, we compare our results with photogrammetric and commercial SfM software and we analyse the accuracy of tie points with respect to a reference Lidar point cloud. The work is part of the SEHAG project (project number I 4062) funded by the DFG and FWF.

How to cite: Ressl, C., Karel, W., Piermattei, L., Puercher, G., Hollaus, M., and Pfeifer, N.: Multi-epoch bundle block adjustment for processing large dataset of historical aerial images , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22544, https://doi.org/10.5194/egusphere-egu2020-22544, 2020.

The state-of-the-art in surveying of open surface the last few decades is based on Point Cloud processing and interpretation. Lately, similar technology tends to be used for indoor surveying as well. One of the extreme applications is the use of the exact same technology in underground karstic cavities, evolving the methodology used in cave mapping. Geometric and morphometric analysis of the caves or any containing components (speleothems) include various techniques aiming at quantifying their dimensions in order to determine the characteristics and consequently the relationship between the cavity morphology and the surrounding structural, lithological and hydrogeological properties. The purpose of this research is to combine high resolution topographic data acquired with different instruments for both the underground morphology of a karstic cave (Koutouki, Peania, Greece) and the open-air surface above it. The described methodology is based on photogrammetric processing of Unmanned Aerial System image data and the extraction of a point cloud recorded with the use of a handheld laser scanning system. The latter resulted a 3D model of the cave and led to the production of a digital relief for the roof of the cave, which in turn was combined with the digital terrain model of the open-air surface above the cave. The final product is a high-resolution information layer with measurements of the rock thickness between the roof of the underground karstic structure and the open-surface topography with high accuracy.

How to cite: Konsolaki, A. and Vassilakis, E.: Karst Topography Analysis Based on Multi-sensor (UAS & LiDAR) Data Acquisition, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2928, https://doi.org/10.5194/egusphere-egu2020-2928, 2020.

A rock wall failure occurred along a major highway in south-eastern Norway, shutting down two lanes of traffic for an extended period of time while the road authority inspected and repaired the wall. It was desired to have a high-resolution digital surface model along a 215-m long section of the 34-m tall vertical rock wall that included the failure zone.

A Structure-from-Motion (SfM)-based methodology was selected to achieve the desired resolution on the rock wall face, as well as below the foot and above the head of the wall. Due to the proximity of the wall face to the remaining open lanes of traffic, it was not possible to survey the face of the wall using a remotely piloted aircraft system (RPAS). Therefore, a combined platform photogrammetric surveying technique was employed to ensure optimal photographic coverage and to generate the best possible model. Ground control points (GCP) were distributed and surveyed along the bottom and top of the wall and an RPAS was flown manually over the head of the wall to capture downward facing (nadir) images. A lift crane was also employed to capture images from elevations varying between 20–30 meters with a standoff distance of 15 meters from the wall. Finally, ground-based images were captured using a camera equipped with real-time GNSS from the top of the opposite rock wall (across the highway) with standoff distance of approximately 65 meters.

In total, over 800 images were ingested into a commercial SfM software package. The bundle adjustments were assisted by the GNSS-equipped camera locations and the surveyed GCP were imported to georeference the resulting model. The dense point cloud product was exported to a separate meshing software package for comparison with a second dense surface model that was derived from pre-existing images of the as-built condition of same rock wall face (prior to failure). By subtracting the post-failure model from the pre-failure model, a volume estimate of the material, that was mobilized during the failure, was determined.

The utility of the multi-platform survey technique was demonstrated. The combination of aerial and ground-based photographic surveying techniques provided optimal photographic coverage of the entire length of the rock wall to successfully derive high-resolution surface models and volume estimates.

Keywords: Structure-from-Motion, photogrammetry, digital surface model, natural hazards, ground control.

How to cite: Smebye, H., Salazar, S., and Lysdahl, A.: Combined aerial and ground-based Structure-from-Motion modelling for a vertical rock wall face to estimate volume of failure , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20471, https://doi.org/10.5194/egusphere-egu2020-20471, 2020.

Forests and woodlands offer many benefits to people. They can provide timber and food, store carbon to help deal with the effects of climate change, decrease flooding and soil erosion, and provide recreation for people and habitat for a multitude of species we care to conserve. Scottish forests cover roughly 19% of the country. The Scottish government has the ambition to add several thousand hectares a year over the next decades, to support the rural economy, the environment, and communities. It is important that a substantial proportion of the expansion is made up by native trees and shrub species due to better habitat for wildlife.

These challenges were explored with a case study of virtual forest landscape from Cairngorms National Park (CNP) which was used to test preferences for scenarios of future woodland expansion. Spatial Multi-criteria Analysis (sMCA) has been applied to decide where to plant new forests and woodlands, recognizing a range of land-use objectives while acknowledging concerns about possible conflicts with other uses of the land. The tools used in the development and implementation of the 3D model were PC and Mobile based, and enable the incorporation of interactive functionality for manipulating features. Model inputs comprise 5m DTM, 25cm Aerial Imagery, 3D Tree Species, GIS layers of Current Forest and Woodland Expansion inside CNP. Afforestation animation has been attached in Google My Maps. This is through setting different keyframes by storyboard camera path animation around the area of CNP. Stereo panorama has been applied to selection of woodland expansion scenarios (e.g. Broadleaved potential corridors, Conifer potential corridors), which is viewed with mobile technology and Virtual Reality (VR) equipment.

The 3D model with simulation of woodland expansion was used at the event of 2019 Royal Highland Show and European Forest Institute Annual Conference 2019. Audience feedback suggested the enhancement of user interaction through VR has potential implications for the planning of future woodland to increase the effectiveness of their use and contribution to wider sustainable ecosystems.

How to cite: Wang, C., Gimona, A., Compagnucci, A. B., and Jiang, Y.: Use of Digital and 3D Visualisation Technology in Planning for Woodland Expansion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1243, https://doi.org/10.5194/egusphere-egu2020-1243, 2020.

With more and more data being gathered from environmental sensor networks, the importance of automated quality-control (QC) routines to provide usable data in near-real time is becoming increasingly apparent. Machine-learning (ML) algorithms exhibit a high potential to this respect as they are able to exploit the spatio-temporal relation of multiple sensors to identify anomalies while allowing for non-linear functional relations in the data. In this study, we evaluate the potential of ML for automated QC on two spatio-temporal datasets at different spatial scales: One is a dataset of atmospheric variables at 53 stations across Northern Germany. The second dataset contains timeseries of soil moisture and temperature at 40 sensors at a small-scale measurement plot.

Furthermore, we investigate strategies to tackle three challenges that are commonly present when applying ML for QC: 1) As sensors might drop out, the ML models have to be designed to be robust against missing values in the input data. We address this by comparing different data imputation methods, coupled with a binary representation of whether a value is missing or not. 2) Quality flags that mark erroneous data points to serve as ground truth for model training might not be available. And 3) There is no guarantee that the system under study is stationary, which might render the outputs of a trained model useless in the future. To address 2) and 3), we frame the problem both as a supervised and unsupervised learning problem. Here, the use of unsupervised ML-models can be beneficial as they do not require ground truth data and can thus be retrained more easily should the system be subject to significant changes. In this presentation, we discuss the performance, advantages and drawbacks of the proposed strategies to tackle the aforementioned challenges. Thus, we provide a starting point for researchers in the largely untouched field of ML application for automated quality control of environmental sensor data.

How to cite: Schmidt, L., Mollenhauer, H., Rebmann, C., Schäfer, D., Claussnitzer, A., Schartner, T., and Bumberger, J.: On the potential and challenges of using machine-learning for automated quality control of environmental sensor data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20777, https://doi.org/10.5194/egusphere-egu2020-20777, 2020.

How to cite: Pravia-Sarabia, E., Gómez-Navarro, J. J., Montávez, J. P., and Jiménez-Guerrero, P.: A new tracking algorithm for cyclones with tropical characteristics in the Mediterranean basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9590, https://doi.org/10.5194/egusphere-egu2020-9590, 2020.

            Fuzzy modelling of multisource geoscience data and its implications to mineral prospectivity mapping is drawing wide attention of the mineral exploration sector. Mineral deposits are basically end products resulted from optimal combination of certain metallogenetically favourable earth processes which leave their imprints in the associated geological entities at a range of scales that can be interpreted from their direct or indirect manifestation in several geospatial datasets. Therefore, in geologically potential yet under-explored or greenfield areas with no or very few discovered mineral deposits, the qualitative knowledge on the spatial relationship between mineralisation of interest and geoscience data could be an important guide to delineate exploration targets. In such a case, fuzzy set theory aided with Geographic Information System (GIS) is preferred as an effective mechanism for the transformation of subjective knowledge into quantitative information that further helps in modelling of earth science data.

            The Archaean to Paleo-proterozoic Sonakhan Greenstone Belt (SGB) located in the north-eastern fringe of the Baster Craton in central India is considered as a potential geological terrane for mesothermal gold mineralisation based on its geological and geochemical similarities with other mineralised greenstone belts. In this case study, a part of SGB has been taken as a target area that exposes sequence of metamorphosed mafic to ultramafics rocks and associated metasedimentary units in a gneissic country and younger granites. Since the study area represents a less explored terrane in terms of mineralisation, the objective of this research is to generate gold prospectivity maps using fuzzy logic modelling. A total of 17 multiclass evidential maps were generated using four independent geoscience datasets viz. geological, geochemical, geophysical, and remote sensing. The sources of data include existing databases from the Geological Survey of India (GSI) and published work along with the newly produced exploratory data in this research. Fuzzy membership values (0-1) were assigned to each class of evidential maps based on subjective judgement. The fuzzyfied evidential maps were then combined using fuzzy operators (AND, OR, SUM, PRODUCT, and GAMMA) through a series of logical steps i.e. the fuzzy inference network. Two different fuzzy inference networks were created using several combinations of fuzzy operations and accordingly, two prospectivity maps resulted which were classified as very high, high, moderate, low, very low favourable zones. To further enhance the result, the two maps were intersected to produce the final gold prospectivity map in support of targeting gold exploration in the region. A part of the study area, that is the Baghmara gold block, that was already identified as a gold enriched block based on traditional exploration works, coincides with the very high to high favourable zones predicted in the final map and this ensures the reliability of the gold prospectivity map and the efficiency of the adopted fuzzy logic approach in delineating promising targets for exploration.

How to cite: Behera, S. and Panigrahi, M. K.: Application of various fuzzy inference networks to integrate mineral exploration datasets: Implication for gold prospectivity mapping in Sonakhan greenstone belt, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7576, https://doi.org/10.5194/egusphere-egu2020-7576, 2020.

This study reveals the use of high resolution images collected by small Unmanned Aerial Vehicle (UAV) and Digital Image Processing (DIP) from Structure from Motion (SfM) technique applied to the prospection and geometric characterization of fossil tanks in the Guanambi region, located at Bahia state, Brazil. Geologically, the region is located in the Guanambi Batolith, composed of granites, migmatites and orthognaisses. In the research region for example, there was the Lagoa das Abelhas fossiliferous tank, which was previously excavated and in which bone fragments of various pleistocene mammal taxa, such as those of the order Xenarthra, were found, represented by sloths, glyptodonts and armadillos. Considering that there are no records of an effective scientific method to identify these features, the main objective of this work is to map the distribution of fossiliferous tanks excavated as well as those with prospective potential, and to estimate the geometries that they present through the use of the high resolution DIP. The Phantom 4 Advanced equipped with RGB 1’’ CMOS effective 20 M sensor were the UAV model type and camera used for conducting the flight plan. The Pix4D Capture was the tablet/smartphone application used for conducting the flight operation and image collection in an area with ​​80 ha. After this step, the images were submitted as DIP routines using the SfM technique from the Agisoft Metashape software, version 1.5.1. The DIP is divided into stages like point cloud calculation, 3D models generation from mesh and texture procedures, digital elevation model (DEM) and orthomosaic. With the integration of images (DEM and orthomosaic) it was possible to identify and delineate a total of 14 targets through geometric information such as surface area, length, width, depth and internal format. The configuration in relation to soil type, vegetation and rock outcrops was the same around the Lagoa das Abelhas fossil tank. After that, the team came back to fieldwork and found fossil fragments of three out of fourteen targets. Thus, this study could show the potential of using UAV to cover large areas directed to the prospecting part of fossiliferous tanks with good flight autonomy, low cost and fast data analysis. Some of the 11 targets can be prospected because they have a high prospective potential due to their similarity to past prospects which became sites for future paleontological prospection.

How to cite: Magalhães, J., Pereira, G., Leão, A., and Scherer, C.: UAV-based digital image processing applied to the fossiliferous tanks prospection: insights at Guanambi region, northeast Brazil, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21002, https://doi.org/10.5194/egusphere-egu2020-21002, 2020.

Earthquakes have disastrously impacted communities by destructing the buildings and infrastructure and creating substantial setbacks in the socio-economic development of a region in addition to the huge human loss. They are inevitable and considered extremely difficult to predict. Earthquake prediction research is being carried out for more than 100 years with no well acknowledged model achieved till date. However, the analysis of past seismic stress history of an active fault may help in understanding the stress build up and the local breaking points of the faults. Yet, analysing and interpreting the abundant seismological dataset is most time consuming and is a herculean task.

The possibilities to solve big data, complex problems with Deep Learning are undeniable, however, it’s usage in Seismology is still in its early stage. The implementation of Deep Learning algorithms has the potential to decipher the complex patterns and hidden information in past stress history that is nearly impossible for scientists. The careful implementation of various Deep Learning algorithms in the exponentially growing seismic data can significantly improve the Early Warning System. In the present study, we train a time efficient machine/deep learning algorithm to self-learn and decode the intricate stress accumulation and release pattern, to estimate the probability of local breakdowns of the fault.

The study region for the present research is Indonesia, which under the influence of the Eurasian, Indo-Australian, Philippine and Pacific plates, immensely suffers due to high seismic activity. The principal contributor in the seismicity of the region is Java-Sunda Trench which lies in the Pacific Ring of Fire (PROF). Owing to the high frequency of earthquakes striking every year from different epicentres, the region provides a huge database. The earthquakes triggering in the region from 1970-2018 is downloaded from the International Seismological Centre website (http://www.isc.ac.uk). These earthquake data comprised of ~270,000 events with the information of Latitude, Longitude, Time of the event and focal depth. To respect the bias which is unavoidable due to the change of the quality of the sensors and the data over the decades, the data is divided into subsets. We considered both small and large magnitude earthquakes along the subduction line to generate a localized time series of stress release to understand the seismic history of the region. By using different neural network models such as one dimensional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), an optimized Deep Learning algorithm is trained to understand the intricate pattern associated with the seismic stress release in region. This specialized model is expected to empower seismologist by providing a time saving, automated process for the identification of the zone of failures.

How to cite: Srivastava, N., Zhou, K., Steinheimer, J., Faber, J., and Stoecker, H.: Pattern recognition of Seismic Activity in Indonesia through Deep Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1538, https://doi.org/10.5194/egusphere-egu2020-1538, 2020.

The aim of this work is to study the subsurface structures in the west Beni Suef area of the Western Desert in Egypt and to determine their effects on surface geologic structures. A detailed land magnetic survey has been carried out for the total component of the geomagnetic field using two proton magnetometers. The necessary corrections concerning daily variation, the regional gradient and time variations have been applied. Then, the total magnetic intensity anomaly map (TMI) has been constructed and transformed to the reduced to the pole magnetic map (RTP). The reduction-to-pole magnetic and Bouguer anomaly maps were used to obtain regional extensions of this subsurface structure. Regional–residual separation is carried out using the power spectrum. Also, Edge detection techniques are applied to delineate the structure and hidden anomalies. Data analysis was performed using trend analysis, Euler deconvolution, the results indicate that the area is affected by tectonic forces in the N-S, NW-SE, NE-SW and E-W trends, which are correlated with the directions of surface geologic lineaments. In addition, depths to the basement rocks have been estimated using spectral analysis technique. The computed depths have been used to construct the basement relief map which resulted from gravity and magnetic data. They show that the depth to the basement rocks ranges from 2.3 km to 4.7 km.

KEYWORDS
Land magnetic, Gravity, Euler deconvolution, Edge detection and Spectral analysis.

How to cite: Khalil, A., El Emam, A., Abdel Hafeez, T., Saleh, H., and Mohamed, W.: Integrated Geophysical study on the subsurface structural characterization of West Beni Suef area, Western Desert, Egypt, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-148, https://doi.org/10.5194/egusphere-egu2020-148, 2020.

Rhourde Chegga field is located in the north of Hassi Messaoud giant field, Algeria. The main hydrocarbon-bearing reservoir in Rhourde Chegga field is the lower Triassic Argilo-Gréseux reservoir. The Triassic sand is deposited as fluvial channels and overbank sands with a thickness ranging from 15 to 20 m, lying unconformably on the Paleozoic formations. Lateral and vertical distribution of the sand bodies makes their mapping very difficult and, sometimes, even impossible with conventional seismic interpretation. 

To better define drilling targets within the Triassic sand in the Rhourde Chegga field, 3D stratigraphic seismic attribute analysis was performed along the reservoir level, using PSTM and mid angle stack seismic data. By combining various attributes (RMS amplitude, half energy, variance, etc.), the channelized feature has been clearly imaged and delineated on the horizon slices and the volume extraction. The relationship between the combined seismic attributes and reservoir properties at well locations showed a good correlation.

Based on this study, about ten produced wells have been successfully drilled, confirming the efficiency of seismic attribute analysis to predicted channel body geometry.

Keywords: Channel, Attributes, Amplitude, Fluvial reservoir.

How to cite: Medjdouba, N., benaissa, Z., and boudella, A.: Seismic attribute mapping of a fluvial reservoir in Rhourde Chegga field (Hassi Messaoud, Algeria), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4260, https://doi.org/10.5194/egusphere-egu2020-4260, 2020.

Geochemical anomalies are an important indicator in prospecting. In particular, geochemical anomalies of Cu play a very important role in geological prospecting of minerals. Geochemical anomalies of Cu are mainly related to mafic-ultramafic rocks and porphyry bodies, which are also associated with ore-forming elements of the Co-Zn-Cr-Ni-Cu combination. The conventional technique of geochemical prospecting involves superimposition of element symbols (Au, Fe, Cu, Al, Ca, etc.) on the geological map of an area by analysing geochemical anomalies using geochemical data. However, this technique is not suitable for regions where geochemical anomaly data are limited. The development of hyperspectral remote sensing has enabled the mapping of spectral features related to characteristic absorption bands of elements in minerals at high spatial resolution, providing a means for precise and detailed reconstructions of geochemical anomalies facies (surface). Compared to conventional techniques for identifying elements, reflectance spectroscopy offers a rapid, inexpensive, and non-destructive tool for determining the mineralogy of rock and soil samples. Hyperspectral remote sensing also provides data for prospecting in areas without sufficient geochemical data, and thus is of vital significance in prospecting for ores in such regions. However, approaches for remotely sensing elements are still lacking, particularly for element content. In this study, a level analysis of Cu content via spectral indices in the northwestern Junggar region, Xinjiang, was conducted. Based on four levels (0–100 ppm, 100–1000 ppm, 1000–10000 ppm, and >10000 ppm) of Cu content and corresponding spectral reflectance, simple and useful spectral indices for estimating Cu content at different levels were explored. The best wavelength domains for a given type of index were determined from four types of spectral indices by screening all combinations using correlation analysis. The coefficient of determination (R2) for Cu was calculated for all indices derived from the spectra of rock samples and was found to range from 0.02–0.75. With sensitive wavelengths and a significant correlation coefficient (R2 = 0.63, P < 0.005), the Normalized Difference (ND)-type index was the most sensitive to Cu content exceeding 10000 ppm. Although the ND-type index has a few limitations, it is a useful, simple, and robust indicator for determining Cu at high concentrations. With the advent of new platforms and satellites in the future, such relationships with other elements are required to enable the widespread use of this index in broad-scale surveys of mineral elements in the field.

How to cite: Shanshan, W. and Kefa, Z.: Study on the Geochemical Anomaly of Copper Element Based on Hyperspectral Indices, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6433, https://doi.org/10.5194/egusphere-egu2020-6433, 2020.

An accurate estimation of electric power production (EPP) is a crucial first step to design a floating photovoltaics (PV) project. This study estimates the EPP of a floating PV system and validates the results by comparing with the actual EPP observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPPs. Three-year average observed EPPs (2012, 2013, and 2015) were used as reference values for the validation. The results showed the seasonal EPPs were the highest in spring and the lowest in winter. The monthly estimated EPPs were lower than the monthly observed EPPs. These results are ascribed to the fact the SAM was unable to consider the natural cooling effect of the water environment on the PV module. The error results showed it was possible to estimate the monthly EPPs with an error of less than 15% simply by simulation. However, it may possible to estimate the monthly EPPs with an error of approximately 9% when considering empirical results: The floating PV efficiency was approximately 1.1 times (110%) the overland PV efficiency. This indicates that the approach of using empirical results can provide reliable monthly estimation of EPP in feasibility assessment stage of floating PV projects.

How to cite: Suh, J., Kim, S., and Choi, Y.: Estimation and validation of electric power output from a fixed-type floating photovoltaic system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6503, https://doi.org/10.5194/egusphere-egu2020-6503, 2020.

Combining multi-source data can improve the accuracy and the spatial resolution of the three-dimensional (3-D) displacements field. How to effectively integrate multi-source data to obtain high-precision and high spatial resolution 3-D displacements field is worthy of further study. The stochastic model and fusion model of integrating multi-source data affect the accuracy of data fusion. In this paper, based on the least squares method, the effects of different stochastic models and data fusion models on the 3-D displacements field’s accuracy are studied. The optimal method for estimating large-scale 3-D displacements field from integrated InSAR, leveling and GPS measurements is obtained. Then we realize the integrating InSAR, leveling and GPS measurements to obtain the high-precision 3-D displacements velocity field in Tianjin(China) from 2016 to 2018. The results are validated with GPS measurements at 6 independent stations, with the root mean squares (RMS) residuals of the discrepancies being 2.39mm/yr、2.54mm/yr and 2.83mm/yr in eastern, northern and vertical directions, respectively. By comparing different stochastic models, the 3-D displacements field obtained from multi-source data is optimized by the variance component estimation-least squares method, which is better than weighted least squares (WLS) method. By comparing different data fusion models, the accuracy of the horizontal displacements velocity is better than that of interpolated GPS results. The horizontal displacements component has a great influence on the vertical displacements velocity accuracy in the process of acquiring the 3-D displacements velocity by integrating InSAR, GPS and leveling measurements. This study provides a reference method for integrating multi-source data to obtain 3-D displacements field. This method effectively utilizes the advantages of GPS, InSAR and leveling measurements, and extends the limitations of single technical in describing surface-time scale applications. The 3-D displacements information with a large spatial scale and high spatial resolution provide a reliable data basis for studying the crustal movement and its dynamic mechanism.

How to cite: Guo, N.: Influence of Different Data Fusion Methods on the Accuracy of Three-Dimensional Displacements Field , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6601, https://doi.org/10.5194/egusphere-egu2020-6601, 2020.

A 3D geological model was constructed for the Gråbo site to investigate its suitability for artificial groundwater infiltration, to provide drinking water. The modelling work was performed by the Geological Survey of Sweden (SGU) as part of ongoing groundwater investigations. The site is located close to the city of Gothenburg, in western Sweden. A relatively thick succession of coarse-grained glaciofluvial sediment is located at the site, which overlies a typically finer grained and more clay rich sequence. Previously, the site has been the target of several investigations, the most extensive of these was performed in 2006, where a range of geophysical (seismic refraction, ground penetrating radar and resistivity) and borehole measurements were conducted. Based on previous studies the upper course-grained layer has the best potential for infiltration. However, although these investigations improved the understanding of the site, significant uncertainty remained as to the geometry of the upper course grained layer away from borehole locations.

In order to improve the understanding of the site, additional data was collected in 2018 using a tTEM (towed transient electromagnetic) system developed by Aarhus university. The system is comprised of a transmitter and receiver coil, which are towed behind an ATV (all terrain vehicle). Using the tTEM data a 3D resistivity model of the subsurface was generated down to a depth of between 50 and 70 m at the Gråbo site. On comparison with the available borehole data, it was clear that the course-grained layer could be mapped with relatively high accuracy as a region of high resistivity. The tTEM data was combined with the pre-existing geophysical and borehole data to construct both a voxel and layer-based model of the site. These 3D models have subsequently been used as part of ongoing efforts to evaluate the suitability of the site for infiltration (for example, to decide the location of additional investigation boreholes and to provide input to hydrogeological modelling). In this study we present the tTEM data and the 3D geological model. Finally, we exemplify how the 3D model has been used in subsequent investigations and decision making.

How to cite: Sopher, D., Wendelin, E., Lång, L.-O., Öhman, J., and Lindhe, A.: 3D geological model of the Gråbo site from ground TEM measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7516, https://doi.org/10.5194/egusphere-egu2020-7516, 2020.

In this study, we proposed a method to utilize a multi-sensor Unmanned Aerial System (UAS) for exploration of hydrothermal alteration zones. This study selected an area (10m × 20m) composed mainly of the andesite and located on the coast, with wide outcrops and well-developed structural and mineralization elements. Multi-sensor (visible, multispectral, thermal, magnetic) data were acquired in the study area using UAS, and were studied using machine learning techniques. For utilizing the machine learning techniques, we applied the stratified random method to sample 1000 training data in the hydrothermal zone and 1000 training data in the non-hydrothermal zone identified through the field survey. The 2000 training data sets created for supervised learning were first classified into 1500 for training and 500 for testing. Then, 1500 for training were classified into 1200 for training and 300 for validation. The training and validation data for machine learning were generated in five sets to enable cross-validation. Five types of machine learning techniques were applied to the training data sets: k-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). As a result of integrated analysis of multi-sensor data using five types of machine learning techniques, RF and SVM techniques showed high classification accuracy of about 90%. Moreover, performing integrated analysis using multi-sensor data showed relatively higher classification accuracy in all five machine learning techniques than analyzing magnetic sensing data or single optical sensing data only.

How to cite: Choi, Y., Baek, J., Suh, J., and Kim, S.-M.: Application of multi-sensor unmanned aerial system for identification of hydrothermal alteration zones, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12546, https://doi.org/10.5194/egusphere-egu2020-12546, 2020.

The city of Gijón is located on the Cantabrian Coast (NW, Spain), and its subsurface is formed mainly by sand linked to an old estuarine mouth barrier (beach and dunes), sand bay and marshes. Under these sediments, there is a layer of clays related to the weathering of a Jurassic rock basement. This research addresses the setting of the estuary sediments in both the submerged area, located north of the city, and under the built-up area.

The seafloor morphology was investigated by means of a bathymetric survey with multi-beam echo sounder. A geophysical survey using high-resolution reflection seismic profiles allowed studying the thickness of the unconsolidated deposits that fill the bay of Gijón. Likewise, the distribution of coastal sediments under the city was reviewed from boreholes collected within a GIS-based geotechnical database.

The bathymetric reconstruction led to the identification of a paleo-valley supposedly excavated by the main river of the city, with N-S orientation that evolves to NNE-SSW towards the north. It shows a sandy bottom with a very low slope, a length of about 4 km and a width that ranges between 400 and 800 m. In this channel, the unconsolidated deposits reach a maximum thickness of around 15 m while at S, in the urban subsurface, the thickness exceeds 20 m locally. With these data, it was also possible to investigate the geometry of the bedrock under the sedimentary filling.

How to cite: Pando, L., Flor-Blanco, G., Rey Díaz de Rada, J., and García-Rodríguez, A.: Morphology and sedimentary filling of an ancient estuarine valley in an urban environment (Gijón, NW Spain), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14447, https://doi.org/10.5194/egusphere-egu2020-14447, 2020.

With the development of remote sensing technology, the copyright protection of remote sensing images has become an urgent problem to be solved. In this paper, a blind watermarking scheme based on invariant features is applied. In the embedding process, the stable image features are firstly extracted from the original host using block DCT, and the embedding positions are constructed adaptively according to feature processing theory. Then, the watermark is embedded into the low-frequency coefficients by modifying the DC coefficients. For watermark extraction, according to the invariant image features in each region, the watermark location and the watermark information can be extracted without the original host. Experimental results show that the proposed watermarking is not only invisible and robust against common image processing, such as noise addition, image filtering, and JPEG compression, but also robust against cropping attack.

How to cite: Ren, N. and Zhu, C.: Watermark-based Copyright Protection Using Invariant Features for Remote Sensing Images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21392, https://doi.org/10.5194/egusphere-egu2020-21392, 2020.

Evaluating digital soil mapping approaches to predict topsoil exchangeable calcium and magnesium in a sugarcane field of Australia

Maryem Arshad¹, Dongxue Zhao¹, Tibet Khongnawang¹ and John Triantafilis^1*

¹School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW, 2052, Australia

Corresponding

John Triantafilis, School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW, 2052, Australia

Email: j.triantafilis@unsw.edu.au

Abstract

Knowledge about spatial distribution of exchangeable (exch.) calcium (Ca) and magnesium (Mg) is needed to maintain sugarcane biomass in north Queensland, Australia. To create digital soil maps (DSM), herein, we evaluated three approaches, including; geostatistical (i.e. ordinary kriging [OK]), statistical and hybrid. We first determined the number of samples (10 – 120) required to compute variogram by calculating nugget to sill ratio (NSR) and sum of squared error (SSE). We then used this variogram with OK to predict topsoil (0 – 0.3 m) exch. Ca and Mg. For comparison, four statistical models, including; one linear regression (LR) and three machine learning (ML) models (i.e. Cubist, support vector machine [SVM] and random forest [RF]) were used. Doing so, usefulness of two digital data, including; gamma-ray (g-ray) and soil apparent electrical conductivity (EC_a), either individual or combined, was tested. Regression residuals (RR) were then added to find out improvement in prediction performance (i.e. Lin’s) and in hybrid approach. Influence of varying sample size (10 – 120) was also determined on all three DSM approaches. Comparisons were then drawn with a traditional soil type map and by calculating the mean square prediction error (MSPE). Finally, Digital soil maps (DSM) of exch. Ca and Mg were developed. Results showed that 50 samples were enough to compute a good variogram for exch. Ca (NSR = 11%, SSE = 0.39) and Mg (NSR = 33%, SSE = 0.005). Considering OK, exch. Ca and Mg were predicted with moderate agreement (Lin’s = 0.65 – 0.80). Comparing statistical models and to predict exch. Ca, RF (0.64) and SVM (0.63) outperformed Cubist and LR (0.60) while to predict exch. Mg, SVM (0.79), RF and Cubist (0.74) outperformed LR (0.62). Combined and individual g-ray data performed best and equally well. Hybrid models i.e. RK and CubistRR improved prediction of exch. Ca (0.76) and Mg (0.81) using individual g-ray and EC_a data, respectively. Considering sample size, OK and statistical models required 80 samples while hybrid models required only 30 samples to satisfactorily (Lin’s ≥ 0.70) predict exch. Ca and Mg. Comparisons based on MSPE showed that to predict exch. Ca, hybrid (RK = 1.44) was the best approach followed by geostatistical (OK = 1.94), statistical (Cubist = 2.15) and then traditional soil map (2.64). Same was the case for exch. Mg. DSM of predicted exch. Ca and Mg were consistent with contour plots of measured data. However, some poor predictions were apparent across field edges or areas where small scale variation in digital or soil data was prevalent.  

How to cite: Arshad, M.: Evaluating digital soil mapping approaches to predict topsoil exchangeable calcium and magnesium in a sugarcane field of Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-292, https://doi.org/10.5194/egusphere-egu2020-292, 2020.

The Dorog Basin is a morphologically unique region of the Transdanubian Mountains revealing the combined work of tectonic forces and erosion. Overprinted by the forms of fluvial erosion, numerous NW-SE striking half-graben and horst structures are present. The surface is dominantly covered by lose 1–15 m thick Quaternary sediments (aeolian loess, and siliciclastic alluvial and coluvial formations), while the lithified bedrock consists of Mesozoic carbonates, Paleogene limestones, marls and sandstones and limnic coal sequences. The rheological difference of the Quaternary and pre-Quaternary formations is so pronounced that the morphological characteristics of the outcrops also differ significantly. The area was in the focus of geologists for many decades, due to its Eocene coal beds, and a renewal of the geological map of the region is in progress. The current research aims to assist the mapping with multivariate methods based on geomorphological attributes, such as slope angle, aspect, profile curvature, height, and topographic wetness index. We perform a random forest classification (RFC) using these variables, to predict the outcrops of pre-Quaternary formations in the study area.

Random forest is a powerful tool for multivariate classification that uses several decision trees, each one with a prediction, where the most popular one will be the overall result [1]. The reason why it is getting popular in spatial predictions is the high accuracy to classify raster-type objects [2]. We used raster-type spatial data as subject of RFC predicting a result for each pixel. The geology of the study area was known from previous geological mapping [3]. Morphological information was derived from the MERIT DEM.

Our model used a raster with multiple bands containing geomorphological variables, and training data from the digitalized geological map. The number of random samples of data was 2500. After testing several combinations of the bands, and several spacing of the study areas, the best prediction has cca. 80% accuracy. Model validation is based on the calculation of rates of well predicted pixels in the same rasterized geological map that was used for training. Our aim was to use exact data, which is completely true for remotely sensed images, but not for geological maps. That means the accuracy still can be improved by field perception, or from borehole data.

References:

[1] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.

[2] Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.

[3] Gidai, L., Nagy, G., & Siposs, Z. (1981). Geological map of the Dorog Basin 1: 25 000. [in Hungarian] Geological Institute of Hungary, Budapest.

How to cite: Pogacsas, R. and Albert, G.: Predicting the outcrop of pre-Quaternary formations in the Dorog Basin (Hungary) using random forest classification, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7255, https://doi.org/10.5194/egusphere-egu2020-7255, 2020.

Apart high elevation and arid regions, bedrock is generally covered by unconsolidated materials that result from recent or actual bedrock weathering and fracturing, consequent transport along the hillslope mainly by un-channelized flux or gravity-dominated processes and deposition. These slope deposits (SD) are largely affected by shallow landslides triggered during intense rainfall events, hence mapping SD spatial distribution and properties is a challenging task to perform accurate regionalized analysis of landslide hazard.

Nevertheless, geological and geomorphological maps typically represent the spatial distribution of SD following a 0/1 approach, instead of attempting to describe, in a more realistic perspective, the spatial variation of SD depth, even though this latter is a fundamental input parameter for landslide hazard estimation by physically based models.

In this work we present two different approaches to assess SD depth at regional scale, coupling field SD depth measurements, statistical analysis and then topographic-based regionalization: unsupervised clustering and multilinear regression analysis. Geo-environmental variables such as geology, land use and morphometric parameters, have been considered. The unsupervised clustering analysis has been based on some morphometric variables (eg. flow accumulation, slope and hillslope curvatures), derived from a digital elevation model with cell size of 10 m. These variables allowed us to extract, for homogeneous regions obtained by considering bedrock lithology, a set of morphometric units where the distribution of SD depth was assessed. The same variables were processed in the multilinear regression analysis in order to obtain equations estimating the spatial distribution of SD depth. The results of this work were then compared each other, as well as to the outputs obtained by implementing other methods of SD depth estimation known in the literature. Finally, the proposed methods were applied to evaluate SD depth in a test area, where field survey measurements were used as a check to assess the prediction capability. The results are analyzed and discussed in order to identify best solutions to evaluate and represent SD depth at regional scale.

How to cite: Disperati, L., D'Addario, E., Papasidero, M. P., Pignatiello, M., Marzini, L., Amaddii, M., and Broda, N.: Assessment of slope deposits depth at regional scale by means of morphometric clustering and multi-linear regression: a comparison, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18101, https://doi.org/10.5194/egusphere-egu2020-18101, 2020.

The Arctic is warming twice as fast as the rest of the globe, causing changes to Arctic ecosystems. While wetlands in the Arctic provide many ecosystem services with both local and global importance, still more knowledge is needed on the location and state of Arctic wetlands to successfully focus adaptation and mitigation efforts. To understand the links between temperature changes and changes to Arctic wetlands, this study includes the use of spatial tools to map existing wetlands and model permafrost response to temperature changes, highlighting wetland areas with risks of future changes. Using available high-resolution wetland databases together with soil wetness and soil type data, a wetland map covering the Arctic was created. Based on existing relationships between climate and observed permafrost, future changes in permafrost were modeled using projected mean annual temperature from the HadGEM2-ES climate model outputs for the RCP2.6, 4.5 and 8.5 scenarios and for years 2050, 2075 and 2100. We found that the Arctic contains a large number of wetlands and a very significant number of these exist on permafrost. As substantial permafrost thaw is projected, the extent and properties of wetlands will shift, and local/regional increases or decreases in wetland extent will depend on variables such as soil type. These changes could lead to serious local consequences, such as threats to food and water security, changes in distribution and demographics of animal and plant species, and losses and disruptions of infrastructure. The findings of this study highlight vulnerable areas that need extra attention in terms of adaptation and mitigation efforts to limit the likely impacts of projected changes, given the current trends.

Keywords: Arctic wetland, spatial modeling, permafrost, climate change

How to cite: Jonsson, E., Ghajarnia, N., Hugelius, G., and Kalantari, Z.: Mapping of Arctic Wetlands with Threats of Future Permafrost Thaw, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18208, https://doi.org/10.5194/egusphere-egu2020-18208, 2020.

Ecosystem engineers continuously shape and re-shape the spatial and temporal structure of the environment. Burrowing animals are an important group of ecosystem engineers, because of their ability to rework sediments and soils with consequences for e.g. soil formation and vegetation patterns. Simultaneous, burrowing animals depend on climate, local soil characteristics and vegetation. The endemic Giant Molerat (GMR) is a burrowing animal and important ecosystem engineer in the Bale Mountains. As part of the Bale Mountain Exile Hypothesis Project, the aim of this study is to investigate (1) the interlinkages between GMR, climate and vegetation patterns as well as (2) to upscale the influence of GMR on the vegetation pattern across the plateau with Sentinel satellite data. Field data comprise 47 paired plots of 5m x 5m with and without GMR activity. Additionally, 1.500 independent GMR burrow openings have been mapped. For investigating interlinkages, all parameters are first pre-analysed for correlations and their dependencies (1). In the following these results, the remote sensing data and the individual variables are implemented into the prediction model. To increase the accuracy, an error correction of the model is pursued. For this, the area is calculated into likelihoods of areas influenced by GMR, based on the vegetation survey pairs serving as training areas for the correction. The corrected results are used as final input model in a machine learning-based classification approach using Random Forest with forward-feature selection and leave-feature-out option (2). In the following the results of this ongoing upscaling approach used for the Sanetti Plateau, Ethiopia is presented.

How to cite: Wraase, L., Reuber, V., Kurth, P., Farwig, N., Miehe, G., Opgenoorth, L., Schabo, D., and Nauss, T.: Predicting the impact of Giant Molerat influenced vegetation on Sanetti Plateau, Bale Mountains, Ethiopia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18984, https://doi.org/10.5194/egusphere-egu2020-18984, 2020.

Shallow restrictive soil layer may enhance plant growth and development in dry years due to the creation of subsurface-minipond-like water storages in dry years and may hamper plant development in wet years due to overwetting. These effects may manifest itself differently under different nutrient management. The objective of this work was to see there exist spatial patterns that are temporally stable (do not change over several years), can explain substantial proportions of spatiotemporal variation of maize yield, and can be related to subsurface restrictive layer topography and fertilizer application. Empirical orthogonal functions (EOFs) were good candidates to express such patterns. Data were collected with yield monitors across maize fields with manure applications, uniform chemical fertilization, and precision farming-based chemical fertilizer application over the six-year period. The subsurface restrictive layer was found at depths from one to three meters using the ground penetration radar. Three EOFs explained around 60, 30 and 10 % of interannual yield variation, respectively. As evidenced by semivariograms, the spatial structure was well pronounced in EOFs at the manured field and to a lesser extent at the chemical fertilizer fields. Little difference was observed in cumulative probability distributions of the first EOF across fields with different fertilizer applications. The topography of the restrictive layer was analyzed to determine the subsurface preferential flow lateral flow pathways that could provide water accumulation in dry years and enhanced drainage in wet years. The first EOF on average increased as the distance to the subsurface flow pathways decreased both at the manure and uniform chemical fertilized field, but not at the precision fertilization field where unfavorable water availability conditions could be compensated by the improved fertilizer availability. Differences in the soil surface topography could be reflected by the second EOF. Overall, the temporal stability in crop yields reveals the topography of the shallow vadose zone boundary as the powerful control of yield variation in space and time.  

How to cite: Kim, S., Daughtry, C., Russ, A., and Pachepsky, Y.: Using empirical orthogonal functions to interpret the spatiotemporal variability of crop yields in presence of shallow restrictive soil layer, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11684, https://doi.org/10.5194/egusphere-egu2020-11684, 2020.

Air quality and traffic-related pollutants in urban areas are major concerns especially in meg-cities. Current Air Quality Monitoring Station (AQMS) cannot sufficiently reveal these pollution conditions with limited point measurements and limited information cannot supply adequate insight on personal exposure in a complex urban environment. Land Use Regression (LUR) model provided a feasible solution for estimating outdoor personal exposure by adding multiple data sources. However, fixed-site passive monitoring still lacks enough spatial coverage or spatial flexibility to estimate pollutant distribution at the fine-scale level.

A Mobile Air Sensor Network (MASEN) project was deployed in the Hong Kong area, with electrochemical gas sensors installed on the routine buses to capture on-road NO_x pollutant measurement, the data was collected by the integrated sensor system and transfer to the database for real-time visualization. Compared with previous mobile measurements used for LUR model building which limited to 1-2 routes, this measurement covered major roads in the Hong Kong area and get an overview of pollutant distribution at various ambient. Two main variables were introduced to improve the model performance: 1) Sky View Factor (SVF) which represented pollutant dispersion status were obtained from Google street view image, a deep learning model was used for scene parsing to recognized targets in this procedure, 2) a Real-time Traffic Congestion Index (RTCI) which represented traffic pollutants emission was obtained from Google map and merged with road network. A common LUR model will be built based on a distance-decay regression selection strategy for variables selection. Meanwhile, a spatial-temporal LUR model will be built which contained both diurnal variability and day-to-day variability. Finally, a high-resolution pollution map of the urban areas will illustrate NO₂ pollutant distribution.

In this work, we aimed at estimating traffic-related pollutants in a complex city environment and identifying hotspots at both spatial and temporal aspects. Meanwhile, the novel data source which closely associated with traffic-related pollutant emission also gives a better understanding of guidance on urban planning.

How to cite: Wei, P., Xing, Y., Sun, L., and Ning, Z.: Building spatial-temporal NO2 land use regression models in complex urban environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6478, https://doi.org/10.5194/egusphere-egu2020-6478, 2020.

Radon generation, migration and exhalation into the atmosphere are natural processes that can lead to infiltration of radon into indoor environments, thus constituting a health risk. Analyses and models of these three processes can be used to create different maps depicting the potential of geological radon sources (GRS), geogenic radon migration (GRM), and radon exhalation (REX). The latter includes the first two processes, and be used to identify areas with increased radon levels in buildings (Radon Prone or Priority Areas, RPA). Here, we limit our analysis to the first two processes, and propose a spatial technique to map the contribution of some geological factors to the potential radon risk or geogenic radon potential (GRP) at European scale. The GRP can be understood as a measure of susceptibility of a location or of an area to increased indoor radon concentration for geogenic reasons.

The problem of estimating GRP has been examined over several years, using different multivariate statistical and spatial techniques. A number of direct and indirect models have been developed in order to create GRP maps (i.e., susceptibility maps) of a certain region by using both deterministic and probabilistic models. Direct models can be ascribed to multivariate regression of some predictors, but this was possible only at local scale where the response variables (i.e. soil gas radon and thoron) are available. The indirect mapping method integrates many factors and criteria and weighs the importance of the factors, based on subjective decision-making rules according to the experience of the geoscientists involved, or on multivariate statistical analysis.

In this work, we first propose to construct/create a GRP map at European level by using a GIS-based (spatial) multicriteria decision analysis (SMCDA) to quantify the geogenic contribution to indoor radon; and then, to create a European map of geogenic radon priority areas. SMCDA involves combining and handling of different criteria that determine the presence of a RPA, then uses the Analytical Hierarchy Process (AHP) to assess their importance and derives the relative weights for factors and criteria; finally it determines the overall final scores. The GRS map was derived by using a new lithological classification of the International Geological Map of Europe. Lithologies were ranked according to the mean content of uranium, thorium and potassium associated with each lithology. The GRS map was then coupled with maps of other parameters that serve as proxies for permeability, such as available water capacity and the fine fraction of the soil, the fault density and the map of the karst areas. All these maps were standardised by using the max score function and weighted by using AHP. A variance-based sensitivity analysis was conducted to define the uncertainty of the final map. In the absence of direct soil gas measurements, the final map was validated by using the indoor radon values collected by the JRC in the framework of the European Atlas of Natural Radiation. The work is conducted as a task within the framework of the European Metro RADON project (http://metroradon.eu/).

How to cite: Ciotoli, G., Procesi, M., Finoia, M., Bossew, P., Cinelli, G., Tollefsen, T., Elìo, J., and Gruber, V.: Spatial Multicriteria Decision Analysis (SMCDA) for the construction of the European Geogenic Radon Migration map, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7350, https://doi.org/10.5194/egusphere-egu2020-7350, 2020.

How to cite: Schneider, C.: Making use of open geo-environmental and agricultural datasets to model NO3 pollution in groundwater bodies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22527, https://doi.org/10.5194/egusphere-egu2020-22527, 2020.

The Budyko curve is an effective tool for estimating how precipitation (P) partition into evapotranspiration (E) and streamflow (Q). Controlling the shape of the Budyko curve, the Budyko parameter represents the superimposed impact of various periodic factors (including climatic factors, catchment characteristics, teleconnection factors and anthropogenic activities) on the watershed water-energy balance dynamics, and such superimposed impact is not conducive to identifying the driving factors of the dynamic change of Budyko parameter at different time scales, and thus affect the parameter estimation. Here we obtain the dynamic change of Budyko parameter for the Wei River Basin (WRB)-a typical Loess Plateau region in China based on a 11-years moving window, and then adopt the Empirical Mode Decomposition (EMD) method to reveal the relationships between influencing factors and Budyko parameter series at multiple time scales by considering the interplay among different influencing factors. Results indicate that (1) Budyko parameter series are decomposed into 4-, 12-, 20-, exceeding 20-year time scale oscillations and a residual component with an significantly increasing trend in the upstream of the WRB (UWR) and the middle and lower reaches of the WRB (MDWR), a non-significantly decreasing trend in the Jing River Basin (JRB) and Beiluo River Basin (BLRB); (2) by analyzing the residual trend component, evaporation ratio (E/P), soil moisture (SM) and effective irrigated area (EIA) are found to induce the significant increase of parameter in the UWR, whereas that in the MDWR is dominated by baseflow (BF) and Niño 3.4; (3) parameter dynamics at the 4-year time scale is dominated by E/P, aridity index (E_P/P), BF and SM; BF, PDO and sunspots attribute to the dynamics at 12-year time scale; all the factors except BF and SM contribute to the dynamics at 20- or exceeding 20-year time scales. The results of this study will help identify the connection between watershed water-energy balance dynamics and changing environment at multiple time scales, and also be beneficial for guiding water resources management and ecological development planning on the Loess Plateau region.

How to cite: Huang, S., Zhao, J., and Ren, K.: Watershed water-energy balance dynamics and their association with diverse influencing factors at multiple time scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12162, https://doi.org/10.5194/egusphere-egu2020-12162, 2020.

Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and extreme events. Remotely sensed time-series can be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Thus, our primary goal was to study the impact of applying different remotely sensed time series on yield estimation in U.S. at the county and field scale. Furthermore, the impact of crop growth conditions on yield variability was assessed. For county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Whereas field-level analysis was carried out using NASA’s Harmonized Landsat Sentinel-2 (HLS) product. 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R² exceeding 0.8 when data from mid growing season were used. The results highlight the potential of yield estimation at different management scales.

How to cite: Ghazaryan, G., Skakun, S., König, S., Eyshi Rezaei, E., Siebert, S., and Dubovyk, O.: Crop Yield Estimation Using Multi-source Satellite Image Series and Deep Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13957, https://doi.org/10.5194/egusphere-egu2020-13957, 2020.

Climate change is an important factor in crop growth, and it is significant to understand the relationship between climate change and rice yield. This study used annual rice yield from the USDA(United States Department of Agriculture) for each of China’s 16 administrative regions from 1979 to 2009, as well as average climate data from July to August, which were meteorological observations collected from the CRU(Climate Research Unit). The relationship between selected rice yield and climate change was nonlinear and modelled using a deep neural network to train even rows and verify odd rows of data. This study is expected to contribute to better food self-sufficiency and forecast future grain yields in China.

How to cite: Cho, S.-B. and Lee, Y.-W.: Analysis of Rice Yield in CHINA by Climate Change using Deep Neural Network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15701, https://doi.org/10.5194/egusphere-egu2020-15701, 2020.

One of the consequences of global climate change is the more frequent occurrence of extreme weather conditions. Semi-arid regions are especially vulnerable since evapotranspiration significantly exceeds precipitation for most of the year and rainfall occurrence is dominantly sporadic and highly variable in amount and spatial extent. Consequently, these regions suffer from droughts of increasing duration and severity, occasionally interrupted by strong rainfall events generating high surface runoff and in part highly destructive floods. In semi-arid regions water retention capability is often further reduced by changes of the original vegetation cover due to conversion into farmland and intensification of land use. The result is widespread land degradation by a decrease in permanent vegetation cover and an increase in soil erosion. Under such conditions sustainable water resources management is of key importance, however, reliable long-term observations describing the water cycle and the resulting water budget are missing for many regions of the world. This situation requires new approaches in improving seasonal forecast for relevant water resources parameters as well as spatiotemporally explicit understanding the of influence of water and land use management on the long-term development of water availability and land surface conditions. 
The German collaborative research project ‘Seasonal water resources management in semi-arid regions: Transfer of regionalized global information to practice’ (SaWaM) aims at the development of methods allowing the use of global data for deriving information needed for regional water resources management in semi-arid regions by integrating meteorological, hydrological and ecosystem sciences and supported by satellite remote sensing analysis. The performance, practical applicability and transferability of the developed methods are assessed in several semi-arid regions including Brazil, Iran and Sudan. Here, we present our work on the analysis of the seasonal and long-term vegetation dynamics at different spatial and temporal scales using satellite time series data of different spatial and temporal resolution (MODIS and Sentinel-2).  Our goal is linking the derived vegetation dynamics to changes in meteorological conditions, water availability and land use. In this context we put emphasis on the spatiotemporal analysis of bioproductivity related to different land use types and climatic conditions to identify and characterize hotspots of water usage in form of irrigated agriculture as a basis for further evaluation of the underlying water management practices.
We perform time series analysis of satellite-derived vegetation indices (VI) using various statistical aggregates, such as maximum, mean and temporal duration related to variable time periods (hydrological year, dry and wet season, growing patterns) as well as additive time series decomposition. Thus, we analyze long-term trends, seasonal deviations from long-term average conditions, and break points in the time series related to land use and water management changes. Moreover, we compare the derived spatiotemporal VI dynamics against the dynamics of hydrometeorological conditions (e.g. precipitation, evapotranspiration, temperature) as well as land use patterns in order to evaluate the impact of hydrometeorological drought conditions on different land use types and water management practices.  In conclusion, we present prototypes for information products supporting decision making of the local experts in the target regions.

How to cite: Behling, R., Roessner, S., and Foerster, S.: Satellite time series analysis of vegetation dynamics for water resources management in semi-arid regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16520, https://doi.org/10.5194/egusphere-egu2020-16520, 2020.

Thanks to the geostationary meteorological satellites of METEOSAT (Europe), GOES (USA), and Himawari (Japan), the nowcast of convective systems (CS) can be performed in most of the world with a 5-15-minute observation time sampling and about 2.8-km spatial resolution (up to about 1-km for the new-generation satellites).

However, the CS forecast, including the prediction of their effects on the surface, is still a challenge due to the lack of high-resolution radar data and a deep understanding of this topic. Indeed, for now, most numerical weather prediction (NWP) models cannot deliver an accurate time and space estimation of surface wind patterns and wind gusts associated with the CS.

In the meantime, Synthetic Aperture Radar (SAR) and ASCAT (scatterometers) may be used for the detection of surface wind patterns potentially associated with the deep convective clouds that may be identified on METEOSAT images. Additionally, the intensity of wind patterns may be estimated from SAR and ASCAT data. Based on this result, Deep Learning (or Machine Learning) is proposed in an ongoing study for improving the predictions of wind gusts, based on the combination of several data sources such as SAR, ASCAT, METEOSAT. The obtained results of this step will be used to integrate into the current NWP models.

How to cite: La, T. V., Messager, C., Sahl, R., and Honnorat, M.: Artificial Intelligence for Improvement of Convective System Tracking and Its Surface Effect Prediction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17825, https://doi.org/10.5194/egusphere-egu2020-17825, 2020.

Semi-arid areas suffer from small amounts and a large variability in rainfall combined with an increasing risk of droughts under climate change. These long and short-term changes in water availability directly affecting regional livelihoods are depicted in the condition of the rather sparse vegetation. In this study, seasonal and long-term trends in indicators of the vegetation condition related to water availability and droughts (NDVI vs. fAPAR, NPP, soil water content, excess water) are identified from remote sensing data (MODIS) and a process-based dynamic vegetation model (LPJ-GUESS) for at least two semi-arid river basins. Identified trends of both methods are compared and evaluated based on the underlying processes and related to knowledge of past drought events. Finally, we answer the question, which methods and indicators are suitable to identify changes in the vegetation condition preceding a drought and during drought phases considering the methods and indicators as above plus simple precipitation-based drought indicators (e.g. standardized precipitation index, SPI) and enhanced drought indicators applying multiple indicators theirselves (e.g. combined drought indicator, CDI). The study is imbedded in the SaWaM project (Seasonal Water Management for semi-arid areas) and contributes to improved water management in the project regions by the integrated analysis of remote sensing and ecosystem modelling results that are made available to regional stakeholders tasked with water management in an online tool .

How to cite: Bayer, A., Mihalyfi-Dean, C., Behling, R., Lorenz, C., Foerster, S., Roessner, S., and Arneth, A.: Identification of droughts from monitored and modelled vegetation condition for improved water management in semi-arid areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18580, https://doi.org/10.5194/egusphere-egu2020-18580, 2020.

Droughts in Africa cause severe impacts such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards.  In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the continent of Africa. To facilitate the diagnosis of droughts of different duration, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement between the coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with average correlation coefficient (R) of 0.54 and 0.77, respectively – further suggests that SPEI-HR can provide valuable information to study drought-related ecological and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA), with link: http://dx.doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019). 

Peng, J.; Dadson, S.; Hirpa, F.; Dyer, E.; Lees, T.; Miralles, D.G.; Vicente-Serrano, S.M.V.-S.; Funk, C. (2019): High resolution Standardized Precipitation Evapotranspiration Index (SPEI) dataset for Africa. Centre for Environmental Data Analysis, 05 August 2019. doi:10.5285/bbdfd09a04304158b366777eba0d2aeb.

How to cite: Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., and Funk, C.: A pan-African high-resolution drought index dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18591, https://doi.org/10.5194/egusphere-egu2020-18591, 2020.

Understanding climate variability and change and its impacts on natural systems is becoming more and more important as changes in earth surface condition near surface air temperature and precipitation. Over Portugal, the observed warming trends have been found to be asymmetric with respect to seasonal and diurnal cycles, with greatest warming occurring for the minimum temperature and during winter and spring. These observed trends exert strong influences on agriculture systems, affecting production viability through changes in winter hardening, frost occurrence, growing season lengths and heat accumulation for ripening potential.

Remote sensing technology has been developing steadily and its products can provide many applications in agriculture, namely crop identification, crop growth monitoring and yield prediction. Recently the LSA SAF team set up a strategy to generate long term data records from Meteosat Second Generation satellite series (2004 to present), releasing Land Surface Temperature (LST), Reference Evapotranspiration (ETREF) and Vegetation parameters (FAPAR, LAI and FVC) using a stable set of input data and algorithm, which would be suitable for climate variability and change detection studies. On the other hand, a new product to characterize the ecosystem processes, the Gross Primary Production (GPP), is under production since 2018.

In this work we propose to computed Water Use Efficiency (WUE), as the ratio between Gross Primary Production (GPP) and Reference Evapotranspiration (ETREF), using LSA-SAF Products. WUE translates the exchanges of carbon and water gross fluxes, between natural ecosystem and the atmosphere, allowing to monitor the adaptability of the ecosystems to climate change. The role played by Evapotranspiration and Water Use Efficiency for different crops in Portugal is evaluated, namely on Wine Production for Douro Region. Results for 2018 and 2019 highlights the vulnerability of the different sectors of Douro Region to dry and wet conditions, namely helping to analyze the impact of droughts on Douro wine production.

Acknowledgements: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under projects CLMALERT (ERA4CS/0005/2016).

How to cite: Vaz, J., Gouveia, C. M., and Trigo, I. F.: The role of Evapotranspiration and Water Use Efficiency in Agriculture in Portugal , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19248, https://doi.org/10.5194/egusphere-egu2020-19248, 2020.

The aim of this study is the development of an operational Early Warning System (EWS) that will utilize new and enhanced satellite Earth Observation (EO) sensors with the purpose of forecasting and risk mapping the West Nile Virus (WNV) outbreaks. Satellite EO data were leveraged to estimate environmental variables that influence the transmission cycle of the pathogen that leads to WNV, a mosquito-borne disease (MBD). The system was trained with epidemiological and entomological data from the region of Central Macedonia, the most epidemic-prone region in Greece regarding the WNV. The satellite derived environmental parameters of the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Land Surface Temperature (LST), precipitation data as well as proximity to water bodies were coupled with meteorological data and were used as explanatory variables for the models. The management and analysis of the big satellite data was conducted with the Open Data Cube (ODC), providing an open and freely accessible exploitation architecture. Statistical and machine learning algorithms were used for short-term forecast, while dynamical models were utilized for the seasonal forecast.The system explores the analysis of big satellite data and proves its scalability by replicating the same models in different geographic regions; e.g the northeastern Italian region of Veneto. This EWS will be used as a tool for helping local decision-makers to improve health system responses, take preventive measures in order to curtail the spread of WNV in Europe and address the relevant priorities of the Sustainable Development Goals (SDGs) such as good health and well-being (SDG 3) and climate action (SDG 13).

How to cite: Parselia, E., Kontoes, C., Kioutsioukis, I., Mourelatos, S., Hadjichristodoulou, C., and Stilianakis, N. I.: Early Warning System for West Nile Virus outbreaks based on Satellite Earth Observation Data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20612, https://doi.org/10.5194/egusphere-egu2020-20612, 2020.

Water-driven soil erosion is the most widespread form of soil degradation worldwide, which threatens to the sustainability of agriculture. Climate change may aggravate the threat of erosion. On the basis of the Revised Universal Soil Loss Equation, combined with Geographic Information Systems (GIS), we assessed spatiotemporal variances in global water erosion risk trends during the period 1992–2015 using the linear regression model. The research objective was to explore the spatial pattern of global water erosion risk change in recent decades and to identify the driving factors. The results show that the global water erosion risk increased over 54% of the surface during 1992–2015, with an average rate of 0.17 t·ha^-1·yr^-2. The lands with significant increasing trends (p < 0.05) accounted for 12% of global lands, with an average rate of 0.27 t·ha^-1·yr^-2. In which, over 75% regions with significant increasing trends were croplands and forest lands in the cold climate zone as the rainfall intensity increased. However, the increasing rates of soil erosion risk on bare lands and croplands were extremely larger than that on lands with natural vegetation, which means that water erosion on natural lands had much lower sensitive to rainfall changes. These results suggest that improving vegetation conditions in the region with sensitive climate change could reduce the erosion threat.

How to cite: Xiong, M.: Global water soil erosion risk associated with climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20942, https://doi.org/10.5194/egusphere-egu2020-20942, 2020.

Understanding historical crop yield response to climate change is critical for projecting future climate change impacts on yields. Previous assessments rely on statistical or process-based crop models, but each has its own strength and weakness. A comprehensive comparison of climate impacts on yield between the two approaches allows for evaluation of the uncertainties in future yield projections. Here we assess the impacts of historical climate change on global maize yield for the period 1980-2010 using both statistical and process-based models, with a focus on comparing the performances between the two approaches. To allow for reasonable comparability, we develop an emulator which shares the same structure with the statistical model to mimic the behaviors of process-based models. Results show that the simulated maize yields in most of the top 10 producing countries are overestimated, when compared against FAO observations. Overall, GEPIC, EPIC-IIASA and EPIC-Boku show better performance than other models in reproducing the observed yield variations at the global scale. Climate variability explains 42.00% of yield variations in observation-based statistical model, while large discrepancy is found in crop models. Regionally, climate variability is associated with 55.0% and 52.20% of yield variations in Argentina and USA, respectively. Further analysis based on process-based model emulator shows that climate change has led to a yield loss by 1.51%-3.80% during the period 1980-1990, consistent with the estimations using the observation-based statistical model. As for the period 1991-2000, however, the observed yield loss induced by climate change is only captured by GEPIC and pDSSAT. In contrast to the observed positive climate impact for the period 2001-2010, CLM-Crop, EPIC-IIASA, GEPIC, pAPSIM, pDSSAT and PEGASUS simulated negative climate effects. The results point to the discrepancy between process-based and statistical crop models in simulating climate change impacts on maize yield, which depends on not only the regions, but also the specific time period. We suggest that more targeted efforts are required for constraining the uncertainties of both statistical and process-based crop models for future yield predictions. 

How to cite: Yin, X. and Leng, G.: Global contribution of climate variability and trends to maize yield change in observations and crop models during 1980-2010, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21042, https://doi.org/10.5194/egusphere-egu2020-21042, 2020.

Land surface temperature is crucial in many field of study, such as Earth's surface water cycle, energy balances, energy exchange of ecosystem and  global climate change. As the role of LST is important, it should be accurately obtained on a global scale. However, it is still difficult to calculate LST from satellite because there are constraints of Atmospheric correction, cloud effect, verification representatively. Therefore, the goal is to improve and optimize the accuracy of LST estimates in satellite-based measurement by mixing various data such as multi-spectral thermal infrared image, hyperspectral thermal infrared image, microwave satellite image, etc., or comparing and applying many LST calculation methods and algorithms.

How to cite: kim, S. and lee, Y.: Improvement of satellite-based land surface temperature estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21240, https://doi.org/10.5194/egusphere-egu2020-21240, 2020.

Recently in Korea, the fish catch of offshore waters recorded less than 1 million tons in 44 years due to climate change and drastic changes in the fishing environment. Therefore, it is essential to produce and provide accurate fishing forecast information, such as the location of fishing fields and the amount of fish production, that varies in time and space according to fishing conditions to enhance the competitiveness of the fishing industry. Since the factors affecting the fish catch have various and nonlinear relationships, so this study predicted the catch based on deep learning. The study was selected as the three major fish species of the Korean coast -- anchovy, mackerel and squid. The research area was selected as four fishing area. (One fishing area is 14 km * 14 km). In order to produce accurate forecasted fishing information, it is necessary to identify major marine weather and biological factors affecting the fish catch by fish species and artificial intelligence modeling using marine and weather satellite images. The satellite data used in the study are from the Korea Meteorological Administration (KMA). So far, research on the relationship between two or more factors and fish catches has been insufficient in the previous research, so this study may contribute to the prediction of fishing trends.

How to cite: yoon, Y. J. and lee, Y. W.: Deep learning-based prediction of fish catch for the offshore waters in south korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21243, https://doi.org/10.5194/egusphere-egu2020-21243, 2020.

Use of advanced health monitoring strategies for bridges is essential to provide a comprehensive condition assessment of these structures and ensure their structural integrity. To this purpose, new technologies have been applied in recent years for both static and dynamic assessments offering unprecedented opportunities within the context of risk management and structural analysis. Among these, areal deformation measurement techniques from ground-based synthetic aperture radar (GB-SAR) sensors were successfully applied for continuous monitoring of dynamic and static displacements of bridges [1] [2]. However, a main limitation for the ground-based microwave interferometry is that, as a linear measurement technique, it is difficult to pinpoint the damage location and obtain accurate displacement time-series for bridges [3]. Moreover, it is known that vertical displacements are usually more relevant than horizontal displacements in the dynamic monitoring of bridges, and the GB-SAR interferometry can only provide the line-of-sight (LOS) displacement of the monitored bridge [4].

In this research, we focus on remote monitoring of the dynamic displacement responses of bridges with a polarimetric GB-SAR system. To this purpose, various strategies were used to overcome the existing limitations of this technique. Results from the monitoring of a long-span metallic railway bridge and a reinforced concrete Shinkansen bridge are discussed.

The aim of this research is to provide more comprehensive and accurate information for bridge health monitoring using a polarimetric sensor. To this extent, a polarimetric analysis was performed to identify the reflection from the side surface of the bridges. In addition, the information about the polarisation orientation angle and the local incidence angle were processed under the acquisition geometry to calculate the radar look angle. Therefore, the bridge deformation fields in the vertical direction were easily converted using the slant range distances and the corresponding maximum transient vertical deformation was transformed through the LOS deformation while a train passing the bridge.

References

[1] Monserrat, O. et al., 2014. A review of ground-based SAR Interferometry for deformation measurement. ISPRS Journal of Photogrammetry and Remote Sensing, pp. 40–48.

[2] Pieraccini, M. et al., 2006. Dynamic monitoring of bridges using a high-speed coherent radar. IEEE Transaction Geoscience and Remote Sensing, pp. 3284–3288.

[3] Sato M., Zou L., Nico G., 2017. Monitoring of Infrastructure by GB-SAR. IEICE technical report, pp. 11-16.

[4] Sato M., Zou L., Nico G., Kikuta K., 2019. Displacement and Vibration Monitoring by GB-SAR. IEICE Transactions on Communications, pp.844-852.

How to cite: Zou, L., Sato, M., Tosti, F., and Alani, A. M.: Advanced Bridge Monitoring Strategies by Polarimetric GB-SAR, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11982, https://doi.org/10.5194/egusphere-egu2020-11982, 2020.

In order to plan infrastructure maintenance strategies, Non-Destructive Techniques (NDT) have been largely employed in recent years, achieving outstanding results in the identification of infrastructural deficiencies. Nevertheless, the extensive combination of different NDT that can cover various factors affecting infrastructure durability has not yet been thoroughly investigated.

This paper proposes a methodology for evaluating the resilience of infrastructures towards endogenous factors by combining different NDT outcomes. Machine Learning (ML) Regression algorithms have been used to predict the pavement surface roughness connected to a set of potential endogenous conditioning factors. The development, application, and comparison of two different regression algorithms, specifically Regression Tree (RT) and Random Forest (RF) have been carried out.

The study area involves 4 testing sites, both in the rural and urban context, for a total length of 11400 m. In addition to the International Roughness Index (IRI) calculated by profilometric measurements, a set of endogenous features of the infrastructure were collected by using NDT such as Falling Weight Deflectometer (FWD), and Ground Penetrating Radar (GPR). Moreover, a set of topographical data of roadside areas, information on properties of materials composing the subgrade and the pavement structure, traffic flow, rainfall, temperature, and age of infrastructure were gathered.

The database was randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation (CV), the models have been trained and validated. A set of three performance metrics, namely Correlation Coefficient (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MSE), has been used for the Goodness-of-Fit (GoF) assessment. Also, with the Test set, the Predictive Performance (PP) of the models has been evaluated.

Results indicate that the suggested methodology is satisfactory for supporting processes on planning road maintenance by National Road Authorities (NRA) and allows decision-makers to pursue better solutions.

How to cite: Fiorentini, N., Leandri, P., and Losa, M.: Evaluating Resilience of Infrastructures Towards Endogenous Events by Non-Destructive High-Performance Techniques and Machine Learning Regression Algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21183, https://doi.org/10.5194/egusphere-egu2020-21183, 2020.