Tesi sul tema "Spaceborne lida"

En France, la méthode de l'Inventaire Forestier National (IFN) répond à des besoins de politique publique aux échelles nationales et régionales. Sur des plus petits territoires, la précision est souvent insuffisante pour répondre aux besoins des activités de gestion. Les méthodes IFM peuvent répondre à ce besoin en combinant des données d'inventaire et des données de télédétection. La thèse vise à améliorer la précision de l'IFN à des échelles subrégionales à locales en intégrant les données du système lidar spatial GEDI dans des approches multisources.Cependant, cette intégration se heurte à un verrou majeur, lié à l'absence de correspondance spatiale entre les échantillons sur le terrain (placettes d'inventaire) et les empreintes GEDI. Par ailleurs, les données GEDI sont mal géoréférencées, ce qui complexifie leur intégration dans certaines approches d'IFM. Cette thèse se concentre sur ces problématiques et est divisée en trois parties principales.Premièrement, une méthode d'amélioration du géoréférencement de GEDI a été développée en se basant uniquement sur un modèle numérique de terrain (MNT) de référence à haute résolution spatiale. Cette méthode compare, pour une série de positions autour de la localisation indiquée dans les produits GEDI, les élévations du terrain des empreintes GEDI avec celles du MNT de référence, générant une carte d'écarts en fonction des décalages en X et Y. En utilisant un algorithme d'accumulation de flux sur cette carte, une position améliorée qui minimise l'écart avec le MNT est proposée pour chaque empreinte GEDI.Ensuite, deux approches d'utilisation des données GEDI avec les données de l'IFN ont été élaborées. Les zones d'étude se situent dans les Vosges et utilisent environ 500 placettes IFN et plus de 100,000 empreintes GEDI. La première approche est une approche d'échantillonnage double pour la stratification (2SS), reposant sur des variables communes entre GEDI et IFN, sans nécessiter de coïncidence spatiale entre les deux sources de données. Les approches 2SS reposent généralement sur des échantillons de données probabilistes, ce qui n'est a priori pas le cas de l'échantillonnage de GEDI. Ainsi, une analyse préliminaire a été nécessaire pour comprendre les caractéristiques spécifiques de l'échantillon des mesures GEDI. La pertinence de la variable commune choisie, la hauteur maximale des arbres, a également été vérifiée. Par rapport aux estimations basées uniquement sur les données IFN, l'approche 2SS a amélioré la variance des estimations de volume de 56%.La deuxième approche utilise un lien entre données GEDI et données IFN établi indirectement en utilisant les images Sentinel-2 et Sentinel-1, avec la méthode des k-plus proches voisins (kNN) combinée avec du bagging (bootstrap aggregation). Il s'agit de propager l'information des placettes terrain au niveau des empreintes GEDI pour densifier les placettes IFN en tirant parti des mesures de structure forestière GEDI, bien corrélées aux attributs forestiers d'intérêt (ex. le volume de bois). Tout d'abord, en utilisant un kNN-bagging, on cherche pour chaque placette IFN les empreintes GEDI ayant les caractéristiques les plus proches de celles du point IFN pour des variables de lien Sentinel, complétées ou non avec une variable de lien supplémentaire de hauteur. On estime ainsi l'ensemble des variables GEDI pour chaque placette IFN. Ensuite, un modèle de régression est établi par kNN-bagging pour estimer le volume de bois à partir des variables GEDI les mieux prédites à l'étape précédente et les variables Sentinel. Le volume est estimé au niveau de toutes les empreintes GEDI. La stratégie complétée par une variable de lien de hauteur a atteint un coefficient de détermination de 58%. Par la suite, sur la base du réseau dense de placettes avec volume ainsi obtenu, des méthodes standards d'estimation sur de petites surfaces (small area estimation) ou de cartographie haute résolution, pourront être implémentés
The thesis focuses on the contribution of spaceborne lidar to the development of Multisource Forest Inventory (MFI) methods. In France, the National Forest Inventory (NFI) method addresses the requirements of public policies at regional and national levels. However, on smaller territories, precision is often insufficient to meet the needs of management activities. MFI methods better address these needs by combining inventory data with remote sensing data. This thesis aims to improve NFI accuracy at sub-regional to local scales by integrating data from the spaceborne lidar GEDI into multisource approaches.Unfortunately, this integration is complicated due to the lack of spatial correspondence between field samples (inventory plots) and GEDI footprints. Additionally, GEDI data are poorly georeferenced, making them difficult to integrate into certain MFI approaches. This thesis focuses on these issues and is divided into three main parts.As a first step, a method for improving GEDI georeferencing, based on a high-resolution reference digital elevation model (DEM) was developed. This method compares, for a series of positions around the location indicated in the GEDI products, the ground elevations of the GEDI footprints with those of the reference DEM, generating an error map according to X and Y offsets. Using a flow accumulation algorithm on this error map, an improved position minimizing the distance from the DEM is proposed for each GEDI footprint.Next, two approaches for using GEDI data with NFI data were developed. The study sites are located in the Vosges and use ∼ 500 IFN plots and over 100,000 GEDI footprints.The first approach is a double sampling for stratification (2SS) approach, based on common variables between GEDI and NFI, without requiring spatial correspondence of the two data sources. 2SS approaches are generally based on probabilistic data samples, which is not a priori the case for GEDI's sampling pattern. Thus, a preliminary analysis was required to understand the characteristics of the spatial distribution of the GEDI sample. The relevance of the chosen common variable, i.e. the maximum tree height, was also verified. Compared with estimates based only on NFI data, the 2SS approach improved the variance of growing stock volume estimates by up to 56%.The second approach is based on a link between GEDI data and NFI data, established indirectly by using spatially exhaustive data sources, the Sentinel-2 and Sentinel-1 images. To establish the model linking the different data sources, we chose to use the k-nearest neighbor (kNN) method combined with bagging (bootstrap aggregation). The aim is to propagate information from field plots to GEDI footprints in order to "densify" NFI plots by taking advantage of GEDI forest structure measurements, which are well correlated with the forest attributes of interest (e.g. growing stock volume). First, for each NFI plot, we looked for the GEDI footprints with the characteristics of the Sentinel link variables, supplemented or not with a height link variable, that are closest to those of the NFI point. Using a kNN-bagging approach, the set of GEDI variables is therefore estimated for each NFI plot. Next, a regression model is established by kNN-bagging to estimate the volume using the best predicted GEDI variables from the previous step and the Sentinel variables. The volume is estimated at the level of all GEDI footprints. The strategy supplemented by a height link variable performed best and reached a coefficient of determination of 58%. Subsequently, using the resulting dense sample of volume plots, standard methods for small area estimation (scale of the municipality or district) or high-resolution volume mapping can be implemented

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LIDAR has widely been used to create very accurate 3-D models for use in a wide range of commercial, governmental and nonprofit applications. This thesis identifies how recent advancements in Nd:YAG fiber lasers and InGaAs GmAPDs could be applied to space-borne missions, enabling low-cost solutions that fulfill NASA’s ICESat-2 and United States Geological Survey (USGS) objectives. An analysis of launch vehicles, standard spacecraft buses and payload technologies identified three potential low-cost solutions: one hosted aboard Iridium and two onboard a BCP2000 commercial bus. These systems were evaluated using NASA’s mass-based and aperture-based cost models to provide a rough estimate of cost versus NASA’s CALIPSO, ICESat-1 and ICESat-2 missions. Preliminary analysis shows a potential for these new technologies to outperform any previous space-based LIDAR mission. At $55M, the Iridium-hosted solution is 1/16th the cost of ICESat-2 at roughly one-third its capability. Two other solutions were estimated at $216.6M and $370.586M and provided over 3X and 10X the estimated capability of ICESat-2, respectively. Both systems are anticipated to fulfill NASA’s ice sheet and vegetation objectives while delivering a return on investment of roughly $1B per year based on USGS’s analysis of advanced 3-D data for the United States.

Forests are considered important reservoirs of organic carbon and have been identified as essential in moderating climate change. Measuring the amount of carbon stored in forests helps improve our understanding of the carbon budget and help with climate change adaptation strategies. Therefore, effective and accurate methods in characterizing changing forest cover and biomass densities are needed. Both LiDAR (light detection and ranging) and radar (radio detection and ranging) technologies can contribute towards the study of forest biomass but one sensor alone cannot provide all the information necessary to monitor forests. Understanding and investigating synergies between different remotely sensed data sets provides new and innovative opportunities to monitor forests. The overall objective reported in this thesis is to demonstrate novel methods to integrate two remotely sensed data sets (i.e., radar and LiDAR) for the application of biomass estimation. This research was divided into two main questions: (1) can shorter wavelength radar variables provide improved biomass estimates when combined with LiDAR data; and (2) can the use of space-borne radar extend aboveground biomass estimates over a larger area using spatial modeling methods. In the first study, relationships between biomass and biomass components with LiDAR and radar data were examined through regression analyses to determine the best combined parameters to estimate biomass. Results indicated that integrating radar variables to a LiDAR-derived model of aboveground biomass helped explain an additional 17.9% of the variability in crown biomass. This corresponded in an improvement in crown biomass estimates of 10% RMSE. Furthermore, InSAR coherence magnitudes from C-band and L-band radars provided the best estimate of aboveground biomass using radar alone. In the second study, aboveground biomass transects derived from plot-based field data and LiDAR, and wall-to-wall radar were spatially integrated using three kriging techniques. The results indicated the importance of correlation between primary and secondary variables when using these kriging approaches. Also a 1000 m distance between biomass transects, was found to provide reasonable compromise between ease of use, accuracy, and cost of obtaining LiDAR data for the study area. Insights into other opportunities for further development in spatial modeling techniques are discussed.

Several studies suggest that deep convection that penetrates the tropical tropopause layer may influence the long-term trends in lower stratospheric water vapor. This thesis investigates the relationship between penetrating deep convection and lower stratospheric water vapor variability using historical infrared (IR) observations. However, since infrared observations do not directly resolve cloud vertical structure and cloud top height, and there has been some debate on their usefulness to characterize penetrating deep convective clouds, CloudSat/Calipso and Aqua MODIS observations are first combined to understand how to best interpret IR observations of penetrating tops. The major findings of the combined CloudSat/Calipso and Aqua MODIS analysis show that penetrating deep convection predominantly occur in the western tropical Pacific Ocean. This finding is consistent with IR studies but is in contrast to previous radar studies where penetrating deep convective clouds predominantly occur over land regions such as equatorial Africa. Estimates on the areal extent of penetrating deep convection show that when using IR observations with a horizontal resolution of 10 km, about two thirds of the events are large enough to be detected. Evaluation of two different IR detection schemes, which includes cold cloud features/pixels and positive brightness temperature differences (+BTD), show that neither schemes completely separate between penetrating deep convection and other types of high clouds. However, the predominant fraction of +BTD distributions and cold cloud features/pixels ≤ 210 K is due to the coldest and highest penetrating tops as inferred from collocated IR and radar/lidar observations. This result is in contrast to previous studies that suggest the majority of cold cloud features/pixels ≤ 210 K are cirrus/anvil cloud fractions that coexist with deep convective clouds. Observations also show that a sufficient fraction of penetrating deep convective cloud tops occur in the extratropics. This provides evidence that penetrating deep convection should be documented as a pathway of stratospheric-tropospheric exchange within the extratropical region. Since the cold cloud feature/pixel ≤ 210 K approach was found to be a sufficient method to detect penetrating deep convection it was used to develop a climatology of the coldest penetrating deep convective clouds from GridSat observations covering years 1998-2008. The highest frequencies of the coldest penetrating deep convective clouds consistently occur in the western-central Pacific and Indian Ocean. Monthly frequency anomalies in penetrating deep convection were evaluated against monthly anomalies in lower stratospheric water vapor at 82 mb and show higher correlations for the western-central Pacific regions in comparison to the tropics. At a lag of 3 months, the combined western-central Pacific had a small but significant anticorrelation, where the largest amount of variance explained by the combined western-central Pacific region was 8.25%. In conjunction with anomalies in the 82 mb water vapor mixing ratios, decreasing trends for the 1998-2008 period were also observed for tropics, the western Pacific and Indian Ocean. Although none of these trends were significant at the 95% confidence level, decreases in the frequency of penetrating deep convection over the 1998-2008 shows evidence that could explain in part some of the 82 mb lower stratospheric water vapor variability.

Ce travail porte sur la modélisation pour la simulation et la prédiction desparamètres de performance des photodiodes à avalanche polarisée en mode Geiger en technologieCMOS, ou SPADs CMOS, pour Lidars spatiaux. Ce travail de thèse vise à développerune méthodologie basée sur : des modèles de la physique du semi-conducteur, des mesuresfournissant des informations sur le procédé technologique visé et des outils commerciaux desimulation. Ceci, dans le but de simuler les paramètres de performance des SPADs en serapprochant autant que possible de la réalité du procédé technologique afin d’améliorer lesprédictions. Des SPADs ont été conçues et caractérisées de manière à acquérir les paramètresde performances et les confronter aux résultats de simulation pour valider notre approche.De plus, la conception des SPADs s’est faite en regard des spécifications Lidar du CNESet d’Airbus Defence and Space en vue d’obtenir des structures permettant d’améliorer nosconnaissances en matière de : compréhension des mécanismes physiques, conception et méthodede caractérisation des SPADs CMOS. Ceci, dans l’intention d’étudier la possibilitéd’intégrer ces détecteurs dans leurs futurs systèmes Lidars spatiaux
This work focuses on modelling for simulation and prediction purposes ofCMOS SPADs performance parameters used in spaceborne Lidars. The innovative side ofthis work lies in a new methodology based on physical models for semiconductor devices,measurements performed on the targeted CMOS process and commercial simulation tools topredict CMOS SPADs performances. This method allows to get as close as possible to theprocess reality and to improve predictions. A set of SPAD has been designed and fabricated,and is used for measurements and model validation. SPAD design has been done with respectto CNES and Airbus Defence Space Lidar specification, in order to produce devices that willimprove our knowledge in terms of understanding of the involved physical mechanisms, SPADsdesign and test method, for a possible integration within their future spaceborne Lidars

The main goal of this PhD thesis is the development and performance assessment of innovative techniques for the autonomous navigation of aerospace platforms by exploiting data acquired by electro-optical sensors. Specifically, the attention is focused on active LIDAR systems since they globally provide a higher degree of autonomy with respect to passive sensors. Two different areas of research are addressed, namely the autonomous relative navigation of multi-satellite systems and the autonomous navigation of Unmanned Aerial Vehicles. The global aim is to provide solutions able to improve estimation accuracy, computational load, and overall robustness and reliability with respect to the techniques available in the literature. In the space field, missions like on-orbit servicing and active debris removal require a chaser satellite to perform autonomous orbital maneuvers in close-proximity of an uncooperative space target. In this context, a complete pose determination architecture is here proposed, which relies exclusively on three-dimensional measurements (point clouds) provided by a LIDAR system as well as on the knowledge of the target geometry. Customized solutions are envisaged at each step of the pose determination process (acquisition, tracking, refinement) to ensure adequate accuracy level while simultaneously limiting the computational load with respect to other approaches available in the literature. Specific strategies are also foreseen to ensure process robustness by autonomously detecting algorithms' failures. Performance analysis is realized by means of a simulation environment which is conceived to realistically reproduce LIDAR operation, target geometry, and multi-satellite relative dynamics in close-proximity. An innovative method to design trajectories for target monitoring, which are reliable for on-orbit servicing and active debris removal applications since they satisfy both safety and observation requirements, is also presented. On the other hand, the problem of localization and mapping of Unmanned Aerial Vehicles is also tackled since it is of utmost importance to provide autonomous safe navigation capabilities in mission scenarios which foresee flights in complex environments, such as GPS denied or challenging. Specifically, original solutions are proposed for the localization and mapping steps based on the integration of LIDAR and inertial data. Also in this case, particular attention is focused on computational load and robustness issues. Algorithms' performance is evaluated through off-line simulations carried out on the basis of experimental data gathered by means of a purposely conceived setup within an indoor test scenario.

Aerosol particles are an important component of the atmosphere. Most of the aerosol mass suspended in the atmosphere resides within the PBL, which is the atmospheric layer directly above the ground. Atmospheric aerosols affect air quality and, in turn, human and ecosystem well-being (WHO, 2013a), and they also play an important role in the Earth’s climate system (IPCC, 2013). In fact, PM (Particles Mater) pollution is probably the most urgent issue in air quality regulation worldwide, and at the same time it represents one of the biggest sources of uncertainty in current climate simulations. Therefore, vertically resolved measurements of physical and optical properties of aerosol particles are of great interest, and height-resolved observations of these parameters can only be carried out with lidar techniques. The lidar technique has proved to be effective to measure the vertical profile of aerosol optical properties with high vertical and temporal resolution. Spaceborne lidars are capable of mapping vertical distributions of aerosol over globe spatial regions in a short amount of time. For existing spaceborne lidars, such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), an assumption of aerosol extinction-to-backscatter ratio is needed to retrieve aerosol optical properties. To measure the vertical profile of aerosol extinction without assumptions of the aerosol extinction-to-backscatter ratio, High Spectral Resolution Lidar (HSRL) technique has been employed due to the advantage of day and night measurements compared to Raman lidar. A spectral analysis system developed for a spaceborne HSRL has been implemented and is presented in this thesis. The spectral analysis system is based on the combination of an interference filter, a planar Fabry-Perot interferometer (PFPI) background filter, and a confocal Fabry-Perot interferometer (CFPI) high spectral resolution filter. By the comparison of a molecular absorption filter and three kinds of interferometer filters, the CFPI has been adopted for the high spectral resolution filter of the spaceborne HSRL. Unlike gas absorption cells, CFPI has no problem of leak and vapor formation, which would make the gas absorption cells useless. Compared to other interferometers, the CFPI can provide high spectral resolution and a large étendue simultaneously, and is much less sensitive to alignment errors and vibrations. A frequency-locking subsystem is used to lock the center transmission wavelength of the spectral analysis system with the wavelength of the emitted laser. The developed two-stage frequency-locking technique is a novel technique and can be used whether the locking laser is a pulsed laser or a continuous laser. The frequency-locking subsystem of the spectral analysis system has been designed and realized. The tests show that it is a robust apparatus, with very good stability. The parameter requirements of the spectral analysis system have been obtained by a simulation of the spaceborne HSRL. All the components of the spectral analysis system have been designed and realized. After being assembled, the spectral analysis system has been tested by using a narrow linewith continuous laser. The test results show that transmission and reflection at the wavelength of 532nm are close to the theoretical value, when the central wavelength of the spectral analysis system is locked to the wavelength of the laser source by the developed frequency-locking system. The error sources that affect the accuracy of aerosol optical properties are analyzed. The results show that the detector noise is the dominant source of error. Further analysis also shows that the relative error of the retrieved aerosol and molecular signals are more sensitive to the error of the CFPI peak transmission than to the error of the CFPI bandwidth.

In the context of growing concerns regarding global climatic change, developing methods to assess the carbon storage of various ecosystems has become important. This research attempts to develop low or no cost methods to estimate carbon stock in forests using satellite-based data. More specifically, this research explores the utility of spaceborne Light Detection and Ranging (LiDAR) data for forest canopy height and aboveground biomass estimation. High-resolution (sub meter) airborne LiDAR data were collected and validated for a 75 000 ha area near Clearwater, British Columbia. Airborne LiDAR has been widely demonstrated to yield accurate aboveground biomass estimates. 110 temporally coincident Geospatial Laser Altimeter System (GLAS) waveforms from the study site were used in this research. First, I demonstrate that airborne LiDAR can be manipulated to represent waveform curves with a high degree of similarity to GLAS waveform curves. Based on the relationship between the GLAS and simulated waveforms I am able to visualize the ground contribution to GLAS waveforms. Second, I calculate a suite of novel GLAS waveform metrics and develop models of terrain relief, canopy height, and terrain adjusted canopy height. These models compare favourably to other GLAS studies (terrain relief R2=0.76, canopy height R2= 0.75-0.88) and indicate that terrain relief should be included in GLAS derived canopy height models. Third, I attempt to extrapolate the spatially discrete GLAS estimates to spatially continuous estimates using Landsat TM data. Landsat data have been used extensively for AGBM estimation, although they are known to have limitations for studies in high biomass or structurally complex forests. I develop models to predict GLAS AGBM estimates from Landsat bands and indices (R2=0.6). I then use an airborne LiDAR derived AGBM map to generate a map of over and under prediction of AGBM, and evaluate the success of the model in areas of differing forest species and structure. I conclude that GLAS data is appropriate for AGBM estimation in forests over a wide range of biomass values, but that GLAS and Landsat integration for AGBM estimation should only be conducted in forests with less than approximately 120 Mg/ha of AGBM, 60 years of age, or 60% canopy cover.

Aerosols show type-specific characteristics, which depend on intensive aerosol optical and microphysical properties that influence the radiation processes in the atmosphere in several ways. There are still large uncertainties in the calculation of the aerosol direct radiative effect. The classification of aerosols and the characterization of the vertical aerosol distribution is needed in order to provide more accurate information for radiative-transfer simulations. In the framework of the present thesis, the vertical and spatial distribution as well as optical properties of atmospheric aerosols over the European continent were investigated based on lidar measurements. Possibilities for an aerosol classification or so-called aerosol typing were presented and major aerosol types were specified. Former studies about the classification of aerosols were summarized and representative values for aerosol-type-dependent parameters were given. Case studies were used to demonstrate how observations of the European lidar network EARLINET from 2008 until 2010 were analyzed for aerosol layers and how model simulations and auxiliary data including the assessment of meteorological conditions were applied to determine the origin of each single aerosol layer. Thus, aerosol-type dependent parameters were evaluated and a novel method for the typing of aerosols was developed, which can be used, e.g., within algorithms of satellite data retrievals. Additionally, conversion factors were determined, which are needed for the harmonization of satellite data of present and upcoming missions. Furthermore, findings of the aerosol typing based on EARLINET data were compared to results of the aerosol classification scheme for satellite-borne lidar measurements onboard CALIPSO. It could be shown that deficient classifications of the aerosol type emerged systematically within the automated CALIPSO algorithm. Those wrong classification leads to an underestimation of the single-scattering albedo and hence to an overestimation of the warming effect of the respective aerosol layer. This overestimated warming effect has to be kept in mind for simulations of the global aerosol radiative effect based on CALIPSO data.
Die Bestimmung des direkten Strahlungsantriebs von Aerosolen ist mit großen Unsicherheiten behaftet. Inwiefern Aerosole die Strahlungsprozesse in der Atmosphäre beeinflussen ist abhängig von ihren optischen und mikrophysikalischen Eigenschaften. Zur Optimierung von Strahlungstransfersimulationen werden daher ergänzende Informationen über typspezifische Aerosoleigenschaften sowie die vertikale Aerosolverteilung benötigt. Im Rahmen der vorliegenden Arbeit wurden anhand von Lidarmessungen die vertikale und räumliche Verteilung atmosphärischer Aerosole über Europa analysiert sowie deren optische Eigenschaften ermittelt. Einleitend werden Möglichkeiten der Aerosolklassifizierung erläutert und Aerosoltypen spezifiziert, die über Europa beobachtet werden können. Vorherige Studien zur Aerosolklassifizierung sind in einer Literaturübersicht zusammengefasst. Anhand von Fallstudien wurde zunächst die Analyse von Beobachtungen des europäischen Lidarnetzwerkes EARLINET von 2008 bis 2010 auf das Vorhandensein von Aerosolschichten verdeutlicht. Die Herkunft jeder einzelnen Aerosolschicht wurde anschließend unter Verwendung von Modellrechnungen sowie weiteren Informationen bestimmt und aerosoltypspezifische Kenngrößen berechnet. Mit Hilfe dieser Kenngrößen ist es möglich, den Typ des Aerosols abzuleiten. Daraus wurde eine neuartige Methode zur Typisierung von Aerosolen entwickelt, die z.B. in Algorithmen zur Verarbeitung von Satellitendaten verwendet werden kann. Zusätzlich wurden Umrechnungsfaktoren bestimmt, die zur Zusammenführung und zum Vergleich von Daten aktueller und zukünftiger Satellitenmissionen benötigt werden. Die Ergebnisse der Aerosoltypisierung auf Basis von EARLINET-Daten wurden anschließend mit Ergebnissen der automatischen Typisierung weltraumbasierter Lidarmessungen des CALIPSO-Satelliten verglichen. Es konnte gezeigt werden, dass innerhalb des CALIPSO-Algorithmus systematisch fehlerhafte Klassifizierungen des Aerosoltyps auftreten. Diese falsche Klassifizierung führt zu einer Unterschätzung der Einfachstreualbedo und zu einer Überschätzung der erwärmenden Wirkung der betreffenden Aerosolschicht. Die überschätzte Wärmewirkung hat wiederum fehlerhafte Ergebnisse bei Strahlungstransferrechnungen, die auf CALIPSO-Daten basieren, zur Folge.