This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\''s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\''s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.

This work is motivated by the question: how much complexity is appropriate for a cloud parameterization used in general circulation models (GCM). To approach this question, cloud parameterizations across the complexity range are explored using general circulation models and theoretical Monte-Carlo simulations. Their results are compared with high-resolution satellite observations and simulations that resolve the GCM subgrid-scale variability explicitly. A process-orientated evaluation is facilitated by GCM forecast simulations which reproduce the synoptic state. For this purpose novel methods were develop to a) conceptually relate the underlying saturation deficit probability density function (PDF) with its saturated cloudy part, b) analytically compute the vertical integrated liquid water path (LWP) variability, c) diagnose the relevant PDF-moments from cloud parameterizations, d) derive high-resolution LWP from satellite observations and e) deduce the LWP statistics by aggregating the LWP onto boxes equivalent to the GCM grid size. On this basis, this work shows that it is possible to evaluate the sub-grid scale variability of cloud parameterizations in terms of cloud variables. Differences among the PDF types increase with complexity, in particular the more advanced cloud parameterizations can make use of their double Gaussian PDF in conditions, where cumulus convection forms a separate mode with respect to the remainder of the grid-box. Therefore, it is concluded that the difference between unimodal and bimodal PDFs is more important, than the shape within each mode. However, the simulations and their evaluation reveals that the advanced parameterizations do not take full advantage of their abilities and their statistical relationships are broadly similar to less complex PDF shapes, while the results from observations and cloud resolving simulations indicate even more complex distributions. Therefore, this work suggests that the use of less complex PDF shapes might yield a better trade-off. With increasing model resolution initial weaknesses of simpler, e.g. unimodal PDFs, will be diminished. While cloud schemes for coarse-resolved models need to parameterize multiple cloud regimes per grid-box, higher spatial resolution of future GCMs will separate them better, so that the unimodal approximation improves.

Results are presented from the 2021 test run of active fire detection using the Visual Infrared Imaging Radiometer Suite (VIIRS) instrument, that is currently onboard the polar satellites Suomi-NPP and NOAA-20. The test is performed by the Swedish Civil Contingencies Agency and the Swedish Meteorological and Hydrological Institute, in cooperation with local fire departments in Sweden. The aim of this report was to study the impact of clouds on the ability of active fire detection, as well as to identify objects that potentially can cause commission errors in the VIIRS 375 m active fire algorithm (false positive notifications). Also, the study aimed to investigate what may cause omission errors in the algorithm, and to show to what extent the detections can be used to represent the true time development of the wildfire front and burned area. Using a cloudmask and a cloudtype classification product from Nowcasting Satellite Application Facilities (NWC SAF), the impact of clouds was anlalyzed by comparing the cloud data with the obtained fire notifications from the satellites. Active fires and newly burned areas were also studied using Sentinel-2 imagery, specifically the False Color Urban and the Short Wave Infrared (SWIR) RGB composites, as well as images from the 842 nm band, making use of the relatively high spatial resolution as well as the spectral signatures of fire and newly burned vegetation. Detection of active fires occurred in both cloud free and completely cloud covered conditions. How-ever, roughly 70% of the detected vegetation fire pixels were obtained in conditions with 20% clouds or less. / I rapporten presenteras resultat från 2021 års test av satellitdetektering av skogs- och vegetationsbränder, ett test som genomförs av MSB och SMHI i samverkan med kommunala räddningstjänster. De två satelliter som ingår i testet (Suomi-NPP och NOAA-20) går i polära omloppsbanor och är utrustade med instrumentet Visible Infrared Imaging Radiometer Suite (VIIRS). Detta projekt syftade till att undersöka hur molnighet påverkar möjligheten till detektion med satellit, vilka objekt som potentiellt kan ge upphov till falska detektioner samt vad som kan orsaka uteblivna satellitdetektioner. Ytterligare ett mål med rapporten var att genom fallstudier av större bränder undersöka i vilken utsträckning satellitdetektionerna kan användas för att representera brandfrontens utveckling med tiden och brandens faktiska utbredning. Vid studierna av molnighet analyserades en molnmask och en molnklassificeringsprodukt från Nowcasting Satellite Application Facilities (NWC SAF). I utvärderingen användes även data från Sentinel-2 för att studera pågående bränder och avbränd yta, som syns tydligt i RGB-kompositerna False Color Urban och Short Wave Infrared (SWIR) och i 842 nm-bandet, tack vare den relativt höga bildupplösningen och den nyligen avbrända ytans spektralsignatur. Brand detekterades i både molnfria och helt molntäckta förhållanden. Drygt 70 % av detektionerna vid vegetationsbrand kom emellertid i förhållanden med 20 % moln eller mindre.

L'informatique en nuage (Cloud Computing) est un modèle à grande échelle et en évolution continue, permettant le provisionnement et l'utilisation des ressources informatiques à la demande, selon un modèle rentable de facturation à l'usage "pay-as-you-go". Ce nouveau paradigme a rapidement révolutionné l'industrie IT et a permis de nouvelles tendances en matière de prestation de services informatiques, y compris l'externalisation des infrastructures IT vers des prestataires tiers spécialisés. Cependant, la nature multi-utilisateur des plateformes d'hébergement, ainsi que la complexité des demandes, soulèvent plusieurs défis liés à la gestion des ressources Cloud. Malgré l'attention croissante portée à ce sujet, la plupart des efforts ont été axés sur des solutions centrées utilisateur, et malheureusement beaucoup moins sur les difficultés rencontrées par les fournisseurs pour maximiser leurs bénéfices. Dans ce contexte, la fédération de Cloud a été récemment proposée comme une solution clé pour répondre à l'augmentation et la fluctuation des charges de travail. Les fournisseurs ayant des besoins complémentaires en ressources au fil du temps, peuvent collaborer et partager leurs infrastructures respectives via l'externalisation ("Outsourcing") pour mieux satisfaire les demandes et exigences des utilisateurs. Cette thèse aborde le problème d'optimisation du profit via la fédération et l'allocation optimale des ressources parmi plusieurs fournisseurs d'infrastructures Cloud. L'étude examine les principaux défis et opportunités liés à la maximisation des revenus dans une fédération de Clouds, et définit des stratégies efficaces pour diriger les fournisseurs dans leurs décisions de coopération. Le but est de fournir des algorithmes qui automatisent la sélection du plan d'allocation le plus rentable, qui satisfait à la fois la demande des utilisateurs et les exigences de mise en réseau. Nous visons des modèles d'allocation génériques et robustes qui répondent aux nouvelles tendances Cloud, et de traiter les requêtes simples ainsi que complexes nécessitant le provisionnement de services composites avec différentes ressources distribuées et connectées. Conformément aux objectifs de la thèse, nous avons mené une étude approfondie des travaux antérieurs traitant la problématique de provisionnement des ressources d'infrastructure dans les environnements Cloud. L'analyse a porté notamment sur les modèles d'allocation ayant pour objectif la maximisation de profit et les lacunes et défis associés dans les fédérations de Clouds. Dans un deuxième temps, nous avons proposé un programme linéaire en nombre entiers (ILP), pour aider les fournisseurs de services dans leurs décisions de coopération via des actions optimales d'externalisation (outsourcing), d'internalisation (insourcing) et d'allocation en local. Ces différentes décisions d'allocation sont traitées conjointement dans une formule d'optimisation globale qui partitionne les graphes de requêtes entre les membres de la fédération, tout en satisfaisant les exigences de communication entre les services élémentaires. En plus de la topologie des graphes de ressources, ce partitionnement prend en compte les prix dynamiques et les quotas proposés par les membres de la fédération ainsi que les coûts d'hébergement des ressources et de leur mise en réseau. Enfin, nous avons proposé un algorithme heuristique pour améliorer les temps de convergence avec les instances de problèmes à grande échelle. L'approche proposée utilise un algorithme de "clustering" basé sur les arbres de Gomory-Hu pour le partitionnement des graphes et une stratégie de meilleur ajustement (Best-Fit matching) pour l'allocation et le placement des sous-graphes résultants. L'utilisation conjointe de ces deux techniques permet de capturer l'essence du problème d'optimisation et de respecter les différents objectifs fixés, tout en améliorant le temps de convergence vers les solutions quasi-optimales de plusieurs ordres de grandeur / Cloud computing is a steadily maturing large-scale model for providing on-demand IT resources on a pay-as-you-go basis. This emerging paradigm has rapidly revolutionized the IT industry and enabled new service delivery trends, including infrastructure externalization to large third-party providers. The Cloud multi-tenancy architecture raises several management challenges for all stakeholders. Despite the increasing attention on this topic, most efforts have been focused on user-centric solutions, and unfortunately much less on the difficulties encountered by Cloud providers in improving their business. In this context, Cloud Federation has been recently suggested as a key solution to the increasing and variable workloads. Providers having complementary resource requirements over time can collaborate and share their respective infrastructures, to dynamically adjust their hosting capacities in response to users' demands. However, joining a federation makes the resource allocation more complex, since providers have to also deal with cooperation decisions and workload distribution within the federation. This is of crucial importance for cloud providers from a profit standpoint and especially challenging in a federation involving multiple providers and distributed resources and applications. This thesis addresses profit optimization through federating and allocating resources amongst multiple infrastructure providers. The work investigates the key challenges and opportunities related to revenue maximization in Cloud federation, and defines efficient strategies to govern providers' cooperation decisions. The goal is to provide algorithms to automate the selection of cost-effective distributed allocation plans that simultaneously satisfy user demand and networking requirements. We seek generic and robust models able to meet the new trends in Cloud services and handle both simple and complex requests, ranging from standalone VMs to composite services requiring the provisioning of distributed and connected resources. In line with the thesis objectives, we first provide a survey of prior work on infrastructure resource provisioning in Cloud environments. The analysis mainly focuses on profit-driven allocation models in Cloud federations and the associated gaps and challenges with emphasis on pricing and networking issues. Then, we present a novel exact integer linear program (ILP), to assist IaaS providers in their cooperation decisions, through optimal "insourcing", "outsourcing" and local allocation operations. The different allocation decisions are treated jointly in a global optimization formulation that splits resource request graphs across federation members while satisfying communication requirements between request subsets. In addition to the request topology, this partitioning takes into account the dynamic prices and quotas proposed by federation members as well as the costs of resources and their networking. The algorithm performance evaluation and the identified benefits confirm the relevance of resource federation in improving providers' profits and shed light into the most favorable conditions to join or build a federation. Finally, a new topology-aware allocation heuristic is proposed to improve convergence times with large-scale problem instances. The proposed approach uses a Gomory-Hu tree based clustering algorithm for request graphs partitioning, and a Best-Fit matching strategy for subgraphs placement and allocation. Combining both techniques captures the essence of the optimization problem and meets the objectives, while speeding up convergence to near-optimal solutions by several orders of magnitude

The paper describes a theoretical base and realization of realistic volumetric clouds visualization in an environment of real-time simulator. The first part is concerned with a meteorological background of this problem. I show international classification of ten basic cloud types including a short description and cases of occurence. The following part is concerned with an interaction between cloudiness and simulation core, which is based on the fact, that each cloud acts as a mechanical or an electromagnetic obstacle. This should be considered on some way in simulation. The next part describes technologies and practical implementations of visualization and evaluates their characteristics. Finally, the last chapter describes my implementation and tries to outline project advancement.

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Ein großes Anwendungsgebiet des Flugzeuglaserscannings ist in Bereichen der Forstwirtschaft und der Forstwissenschaft zu finden. Die Daten dienen flächendeckend zur Ableitung von digitalen Gelände- und Kronenmodellen, aus denen sich die Baumhöhe ableiten lässt. Aufgrund der Aufnahmerichtung aus der Luft lassen sich spezielle bodennahe Baumparameter wie Stammdurchmesser und Kronenansatzhöhe nur durch Modelle schätzen. Der Einsatz terrestrischer Laserscanner bietet auf Grund der hochauflösenden Datenakquisition eine gute Ergänzung zu den Flugzeuglaserscannerdaten. Inventurrelevante Baumparameter wie Brusthöhendurchmesser und Baumhöhe lassen sich ableiten und eine Verdichtung von digitalen Geländemodellen durch die terrestrisch erfassten Daten vornehmen. Aufgrund der dichten, dreidimensionalen Punktwolken ist ein hoher Dokumentationswert gegeben und eine Automatisierung der Ableitung der Geometrieparameter realisierbar. Um den vorhandenen Holzvorrat zu kontrollieren und zu bewirtschaften, werden in periodischen Zeitabständen Forstinventuren auf Stichprobenbasis durchgeführt. Geometrische Baumparameter, wie Baumhöhe, Baumposition und Brusthöhendurchmesser, werden gemessen und dokumentiert. Diese herkömmliche Erfassung ist durch einen hohen Arbeits- und Zeitaufwand gekennzeichnet. Aus diesem Grund wurden im Rahmen dieser Arbeit Algorithmen entwickelt, die eine automatische Ableitung der geometrischen Baumparameter aus terrestrischen Laserscannerpunktwolken ermöglichen. Die Daten haben neben der berührungslosen und lichtunabhängigen Datenaufnahme den Vorteil einer objektiven und schnellen Parameterbestimmung. Letztendlich wurden die Algorithmen in einem Programm zusammengefasst, das neben der Baumdetektion eine Bestimmung der wichtigsten Parameter in einem Schritt realisiert. An Datensätzen von drei verschiedenen Studiengebieten werden die Algorithmen getestet und anhand manuell gewonnener Baumparameter validiert. Aufgrund der natürlich gewachsenen Vegetationsstruktur sind bei Aufnahmen von einem Standpunkt gerade im Kronenraum Abschattungen vorhanden. Durch geeignete Scankonfigurationen können diese Abschattungen minimiert, allerdings nicht vollständig umgangen werden. Zusätzlich ist der Prozess der Registrierung gerade im Wald mit einem zeitlichen Aufwand verbunden. Die größte Schwierigkeit besteht in der effizienten Verteilung der Verknüpfungspunkte bei dichter Bodenvegetation. Deshalb wird ein Ansatz vorgestellt, der eine Registrierung über die berechneten Mittelpunkte der Brusthöhendurchmesser durchführt. Diese Methode verzichtet auf künstliche Verknüpfungspunkte und setzt Mittelpunkte von identischen Stammabschnitten in beiden Datensätzen voraus. Dennoch ist die größte Unsicherheit in der Z-Komponente der Translation zu finden. Eine Methode unter Verwendung der Lage der Baumachsen sowie mit einem identischen Verknüpfungspunkt führt zu besseren Ergebnissen, da die Datensätze an dem homologen Punkt fixiert werden. Anhand eines Studiengebietes werden die Methoden mit den herkömmlichen Registrierungsverfahren über homologe Punkte verglichen und analysiert. Eine Georeferenzierung von terrestrischen Laserscannerpunktwolken von Waldbeständen ist aufgrund der Signalabschattung der Satellitenpositionierungssysteme nur bedingt und mit geringer Genauigkeit möglich. Deshalb wurde ein Ansatz entwickelt, um Flugzeuglaserscannerdaten mit terrestrischen Punktwolken allein über die Kenntnis der Baumposition und des vorliegenden digitalen Geländemodells zu verknüpfen und zusätzlich das Problem der Georeferenzierung zu lösen. Dass ein terrestrischer Laserscanner nicht nur für Forstinventuren gewinnbringend eingesetzt werden kann, wird anhand von drei verschiedenen Beispielen beleuchtet. Neben der Ableitung von statischen Verformungsstrukturen an Einzelbäumen werden beispielsweise auch die Daten zur Bestimmung von Vegetationsmodellen auf Basis von Gitterstrukturen (Voxel) zur Simulation von turbulenten Strömungen in und über Waldbeständen eingesetzt. Das aus Laserscannerdaten abgeleitete Höhenbild einer Rinde führt unter Verwendung von Bildverarbeitungsmethoden (Texturanalyse) zur Klassifizierung der Baumart. Mit dem terrestrischen Laserscanning ist ein interessantes Werkzeug für den Einsatz im Forst gegeben. Bestehende Konzepte der Forstinventur können erweiterte werden und es eröffnen sich neue Felder in forstwirtschaftlichen und forstwissenschaftlichen Anwendungen, wie beispielsweise die Nutzung eines Scanners auf einem Harvester während des Erntevorganges. Mit der stetigen Weiterentwicklung der Laserscannertechnik hinsichtlich Gewicht, Reichweite und Geschwindigkeit wird der Einsatz im Forst immer attraktiver. / An important application field of airborne laser scanning is forestry and the science of forestry. The captured data serve as an area-wide determination of digital terrain and canopy models, with a derived tree height. Due to the nadir recording direction, near-ground tree parameters, such as diameter at breast height (dbh) and crown base height, are predicted using forest models. High resolution terrestrial laser scanner data complements the airborne laser scanner data. Forest inventory parameters, such as dbh and tree height can be derived directly and digital terrain models are created. As a result of the dense three dimensional point clouds captured, a high level of detail exists, and a high degree of automation of the determination of the parameters is possible. To control and manage the existing stock of wood, forest inventories are carried out at periodic time intervals, on the base of sample plots. Geometric tree parameters, such as tree height, tree position and dbh are measured and documented. This conventional data acquisition is characterised by a large amount of work and time. Because of this, algorithms are developed to automatically determine geometric tree parameters from terrestrial laser scanner point clouds. The data acquisition enables an objective and fast determination of parameters, remotely, and independent of light conditions. Finally the majority of the algorithms are combined into a single program, allowing tree detection and the determination of relevant parameters in one step. Three different sample plots are used to test the algorithms. Manually measured tree parameters are also used to validate the algorithms. The natural vegetation structure causes occlusions inside the crown when scanning from one position. These scan shadows can be minimized, though not completely avoided, via an appropriate scan configuration. Additional the registration process in forest scenes is time-consuming. The largest problem is to find a suitable distribution of tie points when dense ground vegetation exists. Therefore an approach is introduced that allows data registration with the determined centre points of the dbh. The method removes the need for artificial tie points. However, the centre points of identical stem sections in both datasets are assumed. Nevertheless the biggest uncertainness is found in the Z co-ordinate of the translation. A method using the tree axes and one homologous tie point, which fixes the datasets, shows better results. The methods are compared and analysed with the traditional registration process with tie points, using a single study area. Georeferencing of terrestrial laser scanner data in forest stands is problematic, due to signal shadowing of global navigation satellite systems. Thus an approach was developed to register airborne and terrestrial laser scanner data, taking the tree positions and the available digital terrain model. With the help of three examples the benefits of applying laser scanning to forest applications is shown. Besides the derivation of static deformation structures of single trees, the data is used to determine vegetation models on the basis of a grid structure (voxel space) for simulation of turbulent flows in and over forest stands. In addition, the derived height image of tree bark using image processing methods (texture analysis) can be used to classify the tree species. Terrestrial laser scanning is a valuable tool for forest applications. Existing inventory concepts can be enlarged, and new fields in forestry and the science of forestry are established, e. g. the application of scanners on a harvester. Terrestrial laser scanners are becoming increasingly important for forestry applications, caused by continuous technological enhancements that reduce the weight, whilst increasing the range and the data rate.

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