Automatisierte Erkennung und Kartierung von Lawinenablagerungen mit optischen Fernerkundungsdaten

Lawinen bedrohen Gebäude sowie Verkehrsinfrastruktur im Alpenraum. Sie fordern in der Schweiz mehr Todesopfer als jeder andere Typ von Naturkatastrophen. Deshalb sind rasch verfügbare und präzise Informationen über die Lage und Reichweite von Lawinenereignissen wichtig für die Lawinenwarnung und die Entscheidungsfindung bezüglich der Sperrung von Strassen, Bergbahnen und Skipisten. Für die Evaluation der Gefahrenprognose, für die Erstellung von Kataster und Gefahrenkarten sowie für die Kalibrierung und Evaluation von Lawinenmodellen sind sie ebenfalls von grosser Bedeutung. Heute werden diese Informationen vorwiegend von Beobachtern vor Ort erhoben. Aufgrund der eingeschränkten Zugänglichkeit hochalpiner Gebiete im Winter kann aber nur ein Bruchteil aller Lawinenereignisse erfasst werden. Insbesondere kleinere bis mittlere Lawinenereignisse in abgelegenen Gebieten werden nur sporadisch kartiert. Aber gerade dieser Lawinentyp fordert die meisten Todesopfer unter der steigenden Zahl von Wintersportlern, die sich abseits der markierten Pisten bewegen. Fernerkundungssensoren können auch über schwer zugänglichem Gebiet grossflächig Daten erheben und sind deshalb ein potentielles Werkzeug, das zur Schliessung dieser Informationslücke beitragen kann. In dieser Arbeit wird systematisch untersucht, inwiefern Lawinenkegel mit räumlich hochauflösenden optischen Fernerkundungsdaten erkannt und kartiert werden können. Anhand von Feld-Spektroradiometermessungen von neun Lawinenkegeln wird analysiert, ob allgemeingültige, substantielle spektrale Unterschiede zwischen Lawinenkegel und der angrenzenden, ungestörten Schneedecke bestehen. Obwohl interessante Absorptionsfeatures im nahen Infrarotbereich des elektromagnetischen Spektrums identifiziert werden können, sind die Unterschiede kaum ausgeprägt genug, um sie mit flugzeug- oder satellitengestützten Sensoren zu erfassen. Das direktionale Reflexionsverhalten der rauen Oberfläche eines Lawinenkegels verhält sich konträr zum Reflexionsverhalten der ungestörten Schneedecke. Anhand von Daten des Luftbildscanners ADS40, aufgenommen aus unterschiedlichen Blickwinkeln, kann gezeigt werden, dass dieser Unterschied im Reflexionsverhalten der zwei Schneeoberflächentypen mit grosser Wahrscheinlichkeit genutzt werden kann, um Lawinenkegel zu detektieren. Allerdings reicht der in dieser Untersuchung verfügbare Blickwinkelunterschied von 16° nicht aus, um Lawinenkegel allein auf Basis der direktionalen Unterschiede mit genügender Genauigkeit zu kartieren. Die Texturen von Lawinenkegeln und der ungestörten Schneedecke unterscheiden sich deutlich. Eine grobe Unterscheidung ist bereits von blossem Auge möglich. Die Statistik zweiter Ordnung, welche die räumliche Verteilung von Intensitätswerten berücksichtigt, kann Texturmerkmale in digitalen Bilddaten quantitativ erfassen. Dies ist die Voraussetzung für eine automatisierte Erkennung spezifischer Texturen. Anhand von RC30 Luftbildern, aufgenommen während des Lawinenwinters 1999, werden in der Literatur beschriebene Texturmasse auf ihre Eignung für die Unterscheidung zwischen Lawinenkegel und ungestörter Schneedecke getestet. Dabei werden die massgebenden Parameter systematisch variiert, um die optimalen Einstellungen zu identifizieren. Das Texturmass Entropy erweist sich als stabilster Indikator für die Differenzierung zwischen rauen und glatten Schneeoberflächen. Weil aber auch weitere raue Schneeoberflächen, wie vom Wind modellierte Schneedecken oder künstlich angehäufter Schnee an Rändern von Skipisten, vergleichbare Texturwerte wie Lawinenkegel zeigen, reichen Texturparameter alleine nicht aus, um Lawinenkegel eindeutig zu identifizieren. Basierend auf den Erkenntnissen aus den vorangegangenen Untersuchungen wird eine Prozessierungskette entwickelt, welche spektrale und direktionale Parameter mit Texturparametern und Informationen aus Hilfsdatensätzen kombiniert. Diese Prozessierungskette wird anhand von Daten des Luftbildscanners ADS40 im Raum Davos evaluiert und verbessert. Dabei werden 94% der in drei Testgebieten vorhandenen Lawinenkegel vom Algorithmus korrekt erkannt. Auch kleinere Kegel mit einer Fläche von weniger als 2000 m2 und Kegel in Schattenhängen werden korrekt erfasst. Dieses Ergebnis zeigt das grosse Potential des entwickelten Ansatzes für die automatisierte Erkennung und Kartierung von Lawinenkegeln. Die Verfügbarkeit geeigneter Daten ist aber aufgrund der nach intensiven Schneefällen häufigen noch vorhandenen Bewölkung eingeschränkt. Zudem treten vereinzelt Fehlklassifikationen auf. Dies sind hauptsächlich vom Wind modellierte Schneedecken, künstlich angehäufter Schnee und von spärlicher Vegetation durchsetzte Flächen. Trotz diesen Einschränkungen kann der in dieser Arbeit entwickelte Ansatz in Zukunft zur Schliessung substanzieller Datenlücken beitragen. Besonders in Gebirgen von Entwicklungsländern, in denen noch kaum verlässliche Informationen über Lawinenniedergänge existieren, können damit wertvolle Informationen für die Gefahrenkartierung und die Siedlungsplanung gewonnen werden. Summary Snow-avalanches kill more people in Switzerland than any other natural hazard and threaten buildings and traffic infrastructure. Rapidly available and accurate information about the location and extent of avalanche events is important for avalanche forecasting, safety assessments for roads and ski resorts, verification of warning products as well as for hazard mapping and avalanche model calibration/validation. Today, isolated observations from individual experts in the field provide information with limited coverage. Only a fraction of all avalanche events can be recorded due to restricted accessibility of many alpine terrain sections during winter season. Information on small to medium size avalanche events within remote regions is collected only sporadically. However, these avalanches notably claim most casualties within the raising number of people pursing off-slope activities. Remote sensing instruments are able to acquire wide-area datasets even over poorly accessible regions. Therefore they are promising tools to close the above- mentioned information gap. This research systematically investigates the potential of spatially high resolved remote sensing instruments for the detection and mapping of snow-avalanche deposits. Fieldspectroradiometer data of nine avalanche deposits are analysed to identify universally valid and significant spectral offsets between avalanche deposits and the adjacent undisturbed snow cover. Promising absorption features are found in the near-infrared region of the electromagnetic spectrum. Nevertheless, the differences are unlikely to be distinct enough for a detection using air- or spaceborne remote sensing instruments. The directional reflection of rough avalanche deposit surfaces is contrary to the directional reflection of smooth undisturbed snow covers. The potential of multriangular remote sensing data for the detection and mapping of avalanche deposits is demonstrated using multiangular data acquired by the airborne scanner ADS40. However, the difference between observation angles (16°) proves to be insufficient for accurate avalanche detection solely on the base of directional properties. Therefore, auxiliary data has to be utilised. The texture of avalanche deposits and undisturbed snow cover can already be distinguished by the naked eye. Using second-order statistics, comprising the spatial distribution of the variation in pixel brightness, textural characteristics in digital image data can be quantified. This is a prerequisite for an automated detection of particular textures. Different established texture measures are tested for their discriminating potential of avalanche deposits and undisturbed snow cover using RC30 aerial images of avalanche deposits acquired within the avalanche winter 1999 in Switzerland. The control parameters such as the size of the filter box are systematically varied to find the ideal settings. The texture measure Entropy is identified as the most distinct and stable indicator to distinguish between rough and smooth snow surfaces. But avalanche deposits are not the only rough snow surfaces within the Alpine winter landscape. For example wind modeled snow surfaces or artificially piled snow at the edge of roads and ski slopes show texture characteristics similar to avalanche deposits. Consequently, a classification approach using texture information only is not sufficient for an accurate identification of avalanche deposits. Based on the findings described above, we develop an avalanche detection and mapping processing chain, combining spectral, directional and textural parameters with auxiliary datasets. The processing chain is tested and improved using data acquired by the airborne scanner ADS40 over the region of Davos, Switzerland. The accuracy assessment, based on ground reference data within three test sites, shows that 94% of all existing avalanche deposits are identified. Even small scale deposits (area < 2000 m2) and deposits within shadowed areas are detected correctly. These results demonstrate the big potential of the proposed approach for automated detection and mapping of avalanche deposits. Yet, cloud cover constrains the availability of appropriate optical remote sensing data after heavy snowfall while wind modeled snow surfaces, artificially piled snow and sparsely vegetated snow surfaces cause sporadic misclassifications. Despite these constraints, the approach developed within this research shows a big potential to fill existing gaps in avalanche information. Especially within alpine areas of developing countries with almost no reliable information on past avalanche events, such an approach may be used to acquire valuable data for hazard mapping and settlement planning

Zc3h13/Flacc is required for adenosine methylation by bridging the mRNA binding factor Rbm15/Spenito to the m6A machinery component Wtap/Fl(2)d

N6-methyladenosine (m6A) is the most abundant mRNA modification in eukaryotes, playing crucial roles in multiple biological processes. m6A is catalyzed by the activity of Mettl3, which depends on additional proteins whose precise functions remain poorly understood. Here we identified Flacc/Zc3h13 as a novel interactor of m6A methyltransferase complex components in Drosophila and mouse. Like other components of this complex, Flacc controls m6A levels and is involved in sexdetermination in Drosophila. We demonstrate that Flacc promotes m6A deposition by bridging Fl(2)d to the mRNA binding factor Nito. Altogether, our work advances our molecular understanding of conservation and regulation of the m6A machinery

Rapid mapping with remote sensing data during flooding 2005 in Switzerland by object-based methods: a case study

Rapid mapping and monitoring with remote sensing techniques is an important source of information for decision-makers faced with large scaled disasters. In August 2005 several regions in central Switzerland were affected by severe flooding. The “National Emergency Operations Centre” (NEOC) of Switzerland invoked the International Charter on “Space and Major Disasters”, requesting support by remote-sensing data for disaster-management. In this paper, a SPOT-5 and a RADARSAT-1 satellite-scene acquired a few days after the flood-peak are described and processed to map the affected area with object-based methods. The main difficulties with optical images are the small extent of the Swiss landscape, the large height-differences in the relief, shadows, clouds and snow covered areas. Comparing different approaches, it was found that beside the NIR and SWIR band the application of a DEM and a VISNIR water index showed the most promising results for a fast discrimination of the affected areas. The object-based classification is mainly dependent on threshold-values and Boolean operators. As investigations show, the spatial resolution of the acquired radar dataset in this case is not sufficient to map the affected areas. The results of the classification based on SPOT-5 scenes are up-to-date maps of flooded areas. We show that maps for decision-makers can be produced using auxiliary topographical and land use data. Aerial infrared images acquired from SWISSTOPO after the flooding were used to test the accuracy of the result. Procedures for rapid mapping applications in future disaster cases are discussed

Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas

Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km²), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.ISSN:1994-0416ISSN:1994-042

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.ISSN:1994-0416ISSN:1994-042

C-H and Si-H Activation Reactions at Ru/Ga Complexes: A Combined Experimental and Theoretical Case Study on the Ru-Ga Bond

International audienceTreatment of  [Ru(COD)(MeAllyl) 2 ] and [Ru(COD)(COT)]  with GaCp* under hydrogenolytic conditions leads to reactive intermediates which activate Si-H or C-H bonds, respectively. The product complexes [Ru(GaCp*) 3 (SiEt 3 )H 3 ] ( 1 ) and [Ru(GaCp*) 3 (C 7 H 7 )H 3 ] ( 2 ) are formed with HSiEt 3 or with toluene as the solvent, respectively. While  1  was isolated and fully characterized by NMR, MS, IR and SC-XRD,  2  was too labile to be isolated and was observed and characterized  in-situ  by using mass spectrometry, including labelling experiments for the unambiguous assignment of the elemental composition. The structural assignment was confirmed by DFT computations. The relative energies of the four isomers possible upon toluene activation at the  ortho -,  meta -,  para - and CH 3 -positions have been determined and point  to aromatic C-H activation. The Ru-Ga bond was analyzed by EDA and QTAIM and compared to the Ru-P bond in the analogue phosphine compound. Bonding analyses indicate that the Ru-GaCp* bond is weaker than the Ru-PR 3  bond

Measurement of electrons from semileptonic heavy-flavour hadron decays at midrapidity in pp and Pb–Pb collisions at √sNN = 5.02 TeV

The differential invariant yield as a function of transverse momentum (pT) of electrons from semileptonic heavy-flavour hadron decays was measured at midrapidity in central (0–10%), semi-central (30–50%) and peripheral (60–80%) lead–lead (Pb–Pb) collisions at √sNN = 5.02 TeV in the pT intervals 0.5–26 GeV/c (0–10% and 30–50%) and 0.5–10 GeV/c (60–80%). The production cross section in proton–proton (pp) collisions at √s = 5.02 TeV was measured as well in 0.5 < pT < 10 GeV/c and it lies close to the upper band of perturbative QCD calculation uncertainties up to pT = 5 GeV/c and close to the mean value for larger pT. The modification of the electron yield with respect to what is expected for an incoherent superposition of nucleon–nucleon collisions is evaluated by measuring the nuclear modification factor RAA. The measurement of the RAA in different centrality classes allows in-medium energy loss of charm and beauty quarks to be investigated. The RAA shows a suppression with respect to unity at intermediate pT, which increases while moving towards more central collisions. Moreover, the measured RAA is sensitive to the modification of the parton distribution functions (PDF) in nuclei, like nuclear shadowing, which causes a suppression of the heavy-quark production at low pT in heavy-ion collisions at LHC

The CMS Barrel Calorimeter Response to Particle Beams from 2 to 350 GeV/c

The response of the CMS barrel calorimeter (electromagnetic plus hadronic) to hadrons, electrons and muons over a wide momentum range from 2 to 350 GeV/c has been measured. To our knowledge, this is the widest range of momenta in which any calorimeter system has been studied. These tests, carried out at the H2 beam-line at CERN, provide a wealth of information, especially at low energies. The analysis of the differences in calorimeter response to charged pions, kaons, protons and antiprotons and a detailed discussion of the underlying phenomena are presented. We also show techniques that apply corrections to the signals from the considerably different electromagnetic (EB) and hadronic (HB) barrel calorimeters in reconstructing the energies of hadrons. Above 5 GeV/c, these corrections improve the energy resolution of the combined system where the stochastic term equals 84.7$\pm$1.6$\%$ and the constant term is 7.4$\pm$0.8$\%$. The corrected mean response remains constant within 1.3$\%$ rms