Description: Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures.

Description: This brochure is designed for scientists and engineers of upcoming drilling projects and explains the key steps and important challenges in planning and executing continental scientific drilling.

Description: The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings.

Description: Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

Description: Newsletter 84 of February 11, 2021 Newsletter 85 of April 14, 2021 Newsletter 86 of June 16, 2021 Newsletter 87 of August 11, 2021 Newsletter 88 of October 13, 2021 Newsletter 89 of December 15, 2021

Description: Transnational access (TNA) allows cross‑border, short‑term and frequently free‑of‑charge access to world-class research facilities, to foster collaborations and exchanges of experience. Specifically, TNA aims to encourage open science and innovation and to increase the efficient and effective use of scientific infrastructure. Within EPOS, the European Plate Observing System, the Volcano Observatories and Multi‑scale Laboratories communities have offered TNA to their high‑quality research facilities through national and European funding. This experience has allowed the definition, design, and testing of procedures and activities needed to provide transnational access in the EPOS context. In this paper, the EPOS community describes the main objectives for the provision of transnational access in the EPOS framework, based on previous experiences. It includes practical procedures for managing transnational access from a legal, governance, and financial perspective, and proposes logistical and technical solutions to effectively execute transnational access activities. In addition, it provides an outlook on the inclusion of new thematic communities within the TNA framework, and addresses the challenges of providing market‑driven access to industry.

Description: The Pan-Eurasian Experiment (PEEX) Science Plan, released in 2015, addressed a need for a holistic system understanding and outlined the most urgent research needs for the rapidly changing Arctic-boreal region. Air quality in China, together with the long-range transport of atmospheric pollutants, was also indicated as one of the most crucial topics of the research agenda. These two geographical regions, the northern Eurasian Arctic-boreal region and China, especially the megacities in China, were identified as a “PEEX region”. It is also important to recognize that the PEEX geographical region is an area where science-based policy actions would have significant impacts on the global climate. This paper summarizes results obtained during the last 5 years in the northern Eurasian region, together with recent observations of the air quality in the urban environments in China, in the context of the PEEX programme. The main regions of interest are the Russian Arctic, northern Eurasian boreal forests (Siberia) and peatlands, and the megacities in China. We frame our analysis against research themes introduced in the PEEX Science Plan in 2015. We summarize recent progress towards an enhanced holistic understanding of the land–atmosphere–ocean systems feedbacks. We conclude that although the scientific knowledge in these regions has increased, the new results are in many cases insufficient, and there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures, especially the lack of coordinated, continuous and comprehensive in situ observations of the study region as well as integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, especially “the enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate climate change” and the “socio-economic development to tackle air quality issues”.

Description: The Global Geodetic Reference Frame (GGRF) plays a fundamental role in geodesy and related Positioning, Navigation, and Timing applications, and allows to quantify the Earth’s change in space and time. The ITRF and ICRF are the two most important components to realize GGRF, while the determination of these two reference frames relies on the combination of several space geodetic techniques, mainly, VLBI, SLR, GNSS, and DORIS. The combination is currently done on either the parameter level, or the normal equation level. However, the combination on the observation level, or the so-called integrated processing of multi-technique on the observation level, provides the results of best consistency, robustness, and accuracy. This thesis focuses on the investigation of the integrated processing of GNSS and VLBI on the observation level. The benefits of integrated processing are demonstrated in terms of TRF, CRF, and EOP, while the impact of global ties (EOP), tropospheric ties, and local ties are underlined. Several issues in integrated processing are addressed, including the systematic bias in ties (for instance, LOD and tropospheric ties), the relative weighting. An automatic reweighting strategy based on the normalized residuals is developed, which can properly handle the uncertainty of the ties without losing too much constraint. A software with state-of-the-art modules is the prerequisite to perform integrated processing. Based on the GNSS data processing software: Positioning And Navigation Data Analyst (PANDA), the VLBI and SLR modules are implemented in the common least-squares estimator. Therefore, the best consistency can be guaranteed. The software capability is demonstrated with the single-technique solutions. The station coordinate precision is at millimeter level for both GNSS and VLBI, while the EOP estimates are comparable to other Analysis Centers and the IERS products. It is also demonstrated that the SLR station coordinate precision is improved by 20% to 30% with additional GLONASS and GRACE satellites to contributing to the LAGEOS and ETALON constellation. Focusing on the tropospheric ties in GNSS and VLBI integrated processing, its contribution is demonstrated for the first time comprehensively. Applying tropospheric ties improves the VLBI station coordinate precision by 12% on the horizontal components and up to 30% on the vertical component. The network scale repeatability is reduced by up to 33%. The EOP estimates are also improved significantly, for instance, 10% to 30% for polar motion, and up to 10% for other components. Furthermore, applying the gradient ties in the VLBI intensive sessions reduces the systematic bias in UT1-UTC estimates. The consistent TRF, CRF, and EOP are achieved in the integrated VLBI and GNSS solution. Applying the global ties, tropospheric ties, and local ties stables the reference frame. The ERP estimates in the integrated solution are dominated by the GNSS technique, and the VLBI technique introduces additional 10% improvement on the y-pole component in terms of the day-boundary-discontinuity. The UT1-UTC and celestial pole offsets are also slightly improved in the integrated solution. It is also demonstrated that applying the LTs inappropriately distorts the network and introduces systematic biases to the ERP estimates, addressing the necessity of updating the local surveys. Moreover, the coordinates of AGN are also enhanced by up to 20% in the integrated solutions, especially the southern ones. This study reveals the importance of integrated processing of multi-technique on the observation level, as the best consistency can be achieved, and the applied ties improve the solutions significantly. It is strongly recommended that for the future realization of celestial and terrestrial reference frames, the concept of integrated processing on the observation level should be implemented, and all the possible ties should be applied, including the global ties (EOP), local ties, space ties, and tropospheric ties. Such kind of integrated solution of all the four techniques can provide robust estimates of the reference frames and EOP, with the advantage of each technique exploited to its full extend.

Description: Der Globale Geodätische Referenzrahmen (Global Geodetic Reference Frame, GGRF) spielt eine fundamentale Rolle in der Geodäsie und den damit verbundenen Positionierungs-, Navigations- und Zeitmessungsanwendungen (Positioning, Navigation, and Timing, PNT) und ermöglicht die Quantifizierung der Veränderung der Erde in Raum und Zeit. Der ITRF und der ICRF sind die beiden wichtigsten Komponenten zur Realisierung des GGRF, wobei die Bestimmung dieser beiden Referenzrahmen auf der Kombination verschiedener raumgeodätischer Techniken beruht, hauptsächlich VLBI, SLR, GNSS und DORIS. Die Kombination wird derzeit entweder auf der Parameterebene oder auf der Normalgleichungsebene durchgeführt. Die Kombination auf der Beobachtungsebene oder die sogenannte integrierte Daten-Verarbeitung von Multi-Techniken auf der Beobachtungsebene, bietet jedoch eine Lösung mit der besten Konsistenz, Robustheit und Genauigkeit. Diese Arbeit konzentriert sich auf die Untersuchung der integrierten Daten-Verarbeitung von GNSS und VLBI auf der Beobachtungsebene. Die Vorteile der integrierten Lösung werden in Bezug auf TRF, CRF, und EOP aufgezeigt, während die Auswirkungen von „Global Ties (EOP), Tropospheric Ties, and Local Ties“ hervorgehoben werden. Einige Punkte der integrierten Verarbeitung werden in dieser Arbeit untersucht, einschließlich der systematischen Abweichungen von „Ties“ (z.B. LOD und Tropospheric Ties), der relativen Gewichtung usw. Anhand der normalisierten Residuen wird eine automatische Umgewichtungsstrategie entwickelt, mit der die Unsicherheit der „Ties“ angemessen behandelt werden kann, ohne dass zu viel Einschränkung dabei verloren geht. Eine Software mit modernsten Modulen ist die Voraussetzung für die integrierte Daten Verarbeitung. Basierend auf der GNSS-Datenverarbeitungssoftware Paket: Positioning And Navigation Data Analyst (PANDA) werden die Module VLBI und SLR in demselben Least-Squares-Estimator wie GNSS implementiert, damit kann man die beste Konsistenz in der Datenverarbeitung erreichen. In dieser Arbeit wird die Leistungsfähigkeit der Software mit den Ein-Technik-Lösungen demonstriert. Die Genauigkeit der Stationskoordinaten liegt sowohl für GNSS als auch für VLBI im Millimeterbereich, und die geschätzten EOP-Parameter sind auch mit der anderer Analysezentren und den IERS-Produkten vergleichbar. Es wird auch gezeigt, dass die Koordinatengenauigkeit der SLR-Station um 20-30% verbessert wird, wenn zusätzliche GLONASS- und GRACE-Satelliten zur LAGEOS und ETALON-Konstellation beitragen. Mit dem Schwerpunkt auf den „Tropospheric Ties“ in der integrierten GNSS- und VLBI- Daten Verarbeitung wird ihr Beitrag zum ersten Mal umfassend dargestellt. Die Anwendung der „Tropospheric Ties“ verbessert die Genauigkeit der VLBI-Koordinaten um 12% bei der horizontalen Komponente und bis zu 30% bei der vertikalen Komponente. Die Genauigkeit im Netzwerkmaßstab wird um bis zu 33% verbessert. Auch die EOP-Bestimmungen werden deutlich verbessert, z.B. um 10-30% bei polaren Bewegungen und bis zu 10% bei anderen Komponenten. Darüber hinaus reduziert die Einführung der „Gradient Ties“ in der VLBI-Intensivsession die systematische Abweichung in den dUT1-Bestimmungen. Die konsistente TRF, CRF, und EOP werden bei der integrierten VLBI- und GNSS-Lösung erreicht. Die Anwendung der „Global Ties, Tropospheric Ties and Local Ties“ stabilisiert die Bestimmungen des Referenzrahmens. Die ERP-Bestimmungen in der integrierten Lösung werden von der GNSS-Technik dominiert, und die VLBI-Technik bringt eine zusätzliche Verbesserung um 10% auf die Tagesgrenzen-Diskontinuität (day-boundary-discontinuity, DBD) für die y-Pol-Komponente. Die dUT1- und CPO werden in der integrierten Lösung ebenfalls leicht verbessert. Es wird auch gezeigt, dass eine ungeeignete Anwendung der LTs das Netzwerk verzerrt und systematische Abweichungen in die ERP-Bestimmungen einführt, wodurch die Notwendigkeit einer Aktualisierung der lokalen Tie Messungen deutlich wird. Darüber hinaus werden die Koordinaten der AGN in den integrierten Lösungen um bis zu 20% verbessert, insbesondere im Süden. Diese Arbeit zeigt die Bedeutung der integrierten Daten Verarbeitung von Multi-Technik auf der Beobachtungsebene, da die beste Konsistenz erreicht werden kann und die angewandten „Ties“ die Lösungen erheblich verbessern. Es wird nachdrücklich empfohlen, für die zukünftige Realisierung von himmelsfesten und erdfesten Referenzrahmen das Konzept der integrierten Verarbeitung auf Beobachtungsebene durchzuführen und alle möglichen „Ties“ anzuwenden, einschließlich der „Global Ties (EOP), Local Ties, Space Ties, and Tropospheric Ties“. Eine solche integrierte Lösung aller vier Techniken kann die robusten Bestimmungen der Referenzrahmen und der EOP liefern, wobei die Vorteile jeder Technik voll ausgeschöpft werden.

Description: Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

Description: Although the Csingle bondH chains of petroleum derivatives display unique absorption features in the short-wave infrared (SWIR), it is a challenge to identify plastics on terrestrial surfaces. The diverse reflectance spectra caused by chemically varying polymer types and their different kinds of brightness and transparencies, which are, moreover, influenced further by the respective surface backgrounds. This paper investigates the capability of WorldView-3 (WV-3) satellite data, characterized by a high spatial resolution and equipped with eight distinct and relatively narrow SWIR bands suitable for global monitoring of different types of plastic materials. To meet the objective, hyperspectral measurements and simulations were conducted in the laboratory and by aircraft campaigns, based on the JPL-ECOSTRESS, USGS, and inhouse hyperspectral libraries, all of which are convolved to the spectral response functions of the WV-3 system. Experiments further supported the analyses wherein different plastic materials were placed on different backgrounds, and scaled percentages of plastics per pixel were modeled to determine the minimum detectable fractions. To determine the detectability of plastics with various chemical and physical properties and different fractions against diverse backgrounds, a knowledge-based classifier was developed, the routines of which are based on diagnostic spectral features in the SWIR range. The classifier shows outstanding results on various background scenarios for lab experimental imagery as well as for airborne data and it is further able to mask non-plastic materials. Three clusters of plastic materials can clearly be identified, based on spectra and imagery: The first cluster identifies aliphatic compounds, comprising polyethylene (PE), polyvinylchloride (PVC), ethylene vinyl acetate copolymer (EVAC), polypropylene (PP), polyoxymethylene (POM), polymethyl methacrylate (PMMA), and polyamide (PA). The second and third clusters are diagnostic for aromatic hydrocarbons, including polyethylene terephthalate (PET), polystyrene (PS), polycarbonate (PC), and styrene-acrylonitrile (SAN), respectively separated from polybutylene adipate terephthalate (PBAT), acrylonitrile butadiene styrene (ABS), and polyurethane (PU). The robustness of the classifier is examined on the basis of simulated spectra derived from our HySimCaR model, which has been developed in-house. The model simulates radiation transfer by using virtual 3D scenarios and ray tracing, hence, enables the analysis of the influence of various factors, such as material brightness, transparency, and fractional coverage as well as different background materials. We validated our results by laboratory and simulated datasets and by tests using airborne data recorded at four distinct sites with different surface characteristics. The results of the classifier were further compared to results produced by another signature-based method, the spectral angle mapper (SAM) and a commonly used technique, the maximum likelihood estimation (MLE). Finally, we applied and successfully tested the classifier on WV-3 imagery of sites known for a high abundance of plastics in Almeria (Spain), Cairo (Egypt), and Accra, (Ghana, West Africa). Both airborne and WV-3 data were atmospherically corrected and transferred to “at-surface reflectances”. The results prove the combination of WV-3 data and the newly designed classifier to be an efficient and reliable approach to globally monitor and identify three clusters of plastic materials at various fractions on different backgrounds.