[pt] Atualmente, diversas áreas de Computação Gráfica necessitam processar uma grande quantidade de dados. Para visualizar em tempo-real esses dados é necessário lidar com dois tipos de problema. O primeiro está relacionado com o pouco tempo destinado para realizar os cálculos no processo de síntese de imagem. O segundo problema surge da capacidade limitada de armazenamento dos dispositivos de alta velocidade, como memórias RAM e de textura. Para resolver o primeiro problema, este trabalho utilizou a técnica de multi-resolução para representar os dados gráficos. A representação em multi-resolução permite que a quantidade de dados processada durante a visualização seja praticamente constante. O segundo problema foi resolvido a partir de um sistema de gerenciamento de memória preditivo baseado no modelo de memória virtual. Este trabalho propõe uma arquitetura que permite que qualquer tipo de dispositivo de armazenamento seja inserido. Os dispositivos estão organizados em seqüência. O funcionamento do sistema consiste em reservar um espaço de memória em cada dispositivo e gerenciar esse espaço de forma otimizada. O sistema de predição tem a finalidade de carregar antecipadamente os dados que serão provavelmente utilizados pela aplicação num futuro próximo. Este trabalho propõe um algoritmo de predição adaptativo específico para o problema de visualização. Este algoritmo explora as informações sobre as variações dos parâmetros da câmera e as informações sobre a taxa de transferência de dados, que são usadas para decidir o que deve ser carregado. As informações dos parâmetros da câmera ajudam a determinar os dados que possivelmente serão utilizados pela aplicação. A informação da taxa de transmissão é utilizada para decidir qual o nível de resolução desses dados que devem ser carregados antecipadamente para os dispositivos de alta velocidade. O sistema de gerenciamento de memória preditivo foi testado em aplicações de visualização de imagens de satélite e panoramas virtuais,em tempo-real. / [en] Nowadays, many areas of computer graphics need to process a huge amount of data. In order to visualize the data in realtime time, it is necessary to solve two different problems. The first problem is the limited time available to perform rendering. The second one arises from the restricted capacity of storage high-speed memories, like RAM and texture memories. In order to solve the first problem, this work has used multi-resolution techniques. The multi-resolution representation allows the application to work with a constant amount of data during the rendering process. The second problem has been solved by a predictive management memory system based on the virtual memory model. This work proposes an architecture that allows any storage device to be incorporated in the system. Devices are organized sequentially. The heart of the system consists in allocating an area of memory for each device and managing this space optimally. The predictive system aims to load in advance. The data that will probably be used by the application in the near future. This work proposes a specific adaptative prediction algorithm for the visualization problem. This algorithm exploits the information about the camera parameter variations as well as the data transfer rate, in order to decide what should be loaded. The camera parameters are used to determine which data will possibly be used by the application. The transfer rate is used to decide which resolution level of the data should be loaded to the high- speed devices, in advance. The predictive memory management system has been tested for real-time visualization of satellite images and virtual panoramas.

A textura e o conteúdo do carbono orgânico (CO) influenciam na resposta espectral dos solos. O estudo desses atributos é de grande importância para a preservação e o manejo adequado da terra na busca de uma agricultura sustentável. O uso de sensores de laboratório e satélites tem se mostrado como uma ferramenta no auxílio para o estudo destes, porém a análise dos atributos do solo com esses sensores tem focado principalmente nas regiões do espectro eletromagnético do visível (Vis), infravermelho próximo (NIR) e infravermelho de ondas curtas (SWIR), com poucos estudos no infravermelho médio (MIR). O objetivo deste trabalho foi identificar o padrão espectral do solo com diferentes granulometrias (areia e argila) e teores de CO utilizando sensores de laboratório e satélite na região do MIR, especificamente na faixa do infravermelho termal (TIR). O estudo teve uma avaliação qualitativa e quantitativa da argila, CO e das frações de areia (fina e grossa). A área de estudo está localizada na região de Piracicaba, São Paulo, Brasil. Foram coletadas 150 amostras de solo a uma profundidade de 0-20 cm. A textura do solo foi determinada pelo método da pipeta e a porcentagem de CO via combustão seca. Dados espectrais em refletância e emissividade (ε) foram adquiridos com o sensor Fourier Transform Infrared (FT-IR) Alpha (Bruker optics Corporation). Uma imagem \"ASTER_05\" foi adquirida em 15 de julho de 2017 em valores de ε. As amostras foram separadas por classes texturais e o comportamento espectral no TIR foi descrito. Os dados obtidos pelo sensor de laboratório foram reamostrados para as bandas do sensor de satélite. O comportamento entre os espectros de ambos sensores foi semelhante e teve correlação significativa com os atributos estudados, principalmente para areia. Para os modelos de regressão por mínimos quadrados parciais (PLSR), foram utilizadas seis estratégias (MIR, MIR_ASTER, ASTER, Termal, Termal IDC e MIR IDC) que consistiram no uso de todas as bandas de sensores, ou pela seleção das mesmas que apresentaram as correlações mais significativas com cada um dos atributos. Os modelos apresentaram um bom desempenho na predição de todos os atributos usando o MIR inteiro. No TIR, o modelo para areia total e para as frações fina e grossa foi bom. No caso dos modelos criados com os dados do sensor ASTER, não foram tão promissores quanto os de laboratório. O uso de bandas específicas ajudou a estimar alguns atributos no MIR e no TIR, aumentando o desempenho preditivo melhorando a validação dos modelos. Portanto, a discriminação dos atributos do solo com sensores de satélite pode ser melhorada com a identificação de bandas específicas, como observado nos resultados com sensores de laboratório. / Soil texture and organic carbon (OC) content influence its spectral response. The study of these attributes is relevant for the preservation and proper management of land in pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a useful tool for studying soil attributes, but their analysis with these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The objective of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range. This study had qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil. 150 soil samples were collected at a depth of 0-20 cm. Soil texture was determined by the pipette method and the percentage of OC via dry combustion. Reflectance and emissivity (ε) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image \"ASTER_05\" from July 15, 2017 was acquired with values of ε. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, Thermal, Thermal IDC and MIR IDC), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR, models for total sand content and for fine and coarse fractions were good. In the case of models created with ASTER sensor data, they were not as promising as those with laboratory data. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors.

Le traitement des images satellitaires est considéré comme l'un des domaines les plus intéressants dans les domaines de traitement d'images numériques. Les images satellitaires peuvent être dégradées pour plusieurs raisons, notamment les mouvements des satellites, les conditions météorologiques, la dispersion et d'autres facteurs. Plusieurs méthodes d'amélioration et de restauration des images satellitaires ont été étudiées et développées dans la littérature. Les travaux présentés dans cette thèse se concentrent sur la restauration des images satellitaires par des techniques de filtrage statistique non linéaire. Dans un premier temps, nous avons proposé une nouvelle méthode pour restaurer les images satellitaires en combinant les techniques de restauration aveugle et non aveugle. La raison de cette combinaison est d'exploiter les avantages de chaque technique utilisée. Dans un deuxième temps, de nouveaux algorithmes statistiques de restauration d'images basés sur les filtres non linéaires et l'estimation non paramétrique de densité multivariée ont été proposés. L'estimation non paramétrique de la densité à postériori est utilisée dans l'étape de ré-échantillonnage du filtre Bayésien bootstrap pour résoudre le problème de la perte de diversité dans le système de particules. Enfin, nous avons introduit une nouvelle méthode de la combinaison hybride pour la restauration des images basée sur la transformée en ondelettes discrète (TOD) et les algorithmes proposés à l'étape deux, et nos avons prouvé que les performances de la méthode combinée sont meilleures que les performances de l'approche TOD pour la réduction du bruit dans les images satellitaires dégradées. / Satellite image processing is considered one of the more interesting areas in the fields of digital image processing. Satellite images are subject to be degraded due to several reasons, satellite movements, weather, scattering, and other factors. Several methods for satellite image enhancement and restoration have been studied and developed in the literature. The work presented in this thesis, is focused on satellite image restoration by nonlinear statistical filtering techniques. At the first step, we proposed a novel method to restore satellite images using a combination between blind and non-blind restoration techniques. The reason for this combination is to exploit the advantages of each technique used. In the second step, novel statistical image restoration algorithms based on nonlinear filters and the nonparametric multivariate density estimation have been proposed. The nonparametric multivariate density estimation of posterior density is used in the resampling step of the Bayesian bootstrap filter to resolve the problem of loss of diversity among the particles. Finally, we have introduced a new hybrid combination method for image restoration based on the discrete wavelet transform (DWT) and the proposed algorithms in step two, and, we have proved that the performance of the combined method is better than the performance of the DWT approach in the reduction of noise in degraded satellite images.

Zum Aufbau eines flächendeckenden Monitors der Siedlungs- und Freiraumentwicklung in Deutschland werden geeignete Geodaten benötigt. Ausgehend von den raum- und umweltplanerischen Zielstellungen eines Flächenmonitorings in Verbindung mit dem Anliegen der laufenden Raumbeobachtung ergeben sich dafür grundlegende Anforderungen. Verfügbare Datenquellen werden im Hinblick auf ihre potenzielle Eignung vorgestellt und verglichen: das Amtliche Topographisch-Kartographische Informationssystem ATKIS (insbesondere das Basis-DLM), das Amtliche Liegenschaftskataster-Informationssystem ALKIS (basierend auf der automatisierten Liegenschaftskarte ALK und dem Liegenschaftsbuch ALB) sowie klassifizierte Luft- und Satellitenbilddaten. Erkennbare Datenprobleme werden im Hinblick auf die Berechnung von Indikatoren diskutiert und mit Fallbeispielen illustriert. Außerdem wird eine Lösung für die administrative Bezugsgeometrie des Monitors vorgestellt.

Les Séries Temporelles d'Images Satellitaires (STIS), visant la même scène en évolution, sont très intéressantes parce qu'elles acquièrent conjointement des informations temporelles et spatiales. L'extraction de ces informations pour aider les experts dans l'interprétation des données satellitaires devient une nécessité impérieuse. Dans ce mémoire, nous exposons comment on peut adapter l'extraction de motifs séquentiels fréquents à ce contexte spatio-temporel dans le but d'identifier des ensembles de pixels connexes qui partagent la même évolution temporelle. La démarche originale est basée sur la conjonction de la contrainte de support avec différentes contraintes de connexité qui peuvent filtrer ou élaguer l'espace de recherche pour obtenir efficacement des motifs séquentiels fréquents groupés (MSFG) avec signification pour l'utilisateur. La méthode d'extraction proposée est non supervisée et basée sur le niveau pixel. Pour vérifier la généricité du concept de MSFG et la capacité de la méthode proposée d'offrir des résultats intéressants à partir des SITS, sont réalisées des expérimentations sur des données réelles optiques et radar. / The Satellite Image Time Series (SITS), aiming the same scene in evolution, are of high interest as they capture both spatial and temporal information. The extraction of this infor- mation to help the experts interpreting the satellite data becomes a stringent necessity. In this work, we expound how to adapt frequent sequential patterns extraction to this spatiotemporal context in order to identify sets of connected pixels sharing a same temporal evolution. The original approach is based on the conjunction of support constraint with different constraints based on pixel connectivity that can filter or prune the search space in order to efficiently ob- tain Grouped Frequent Sequential (GFS) patterns that are meaningful to the end user. The proposed extraction method is unsupervised and pixel level based. To verify the generality of GFS-pattern concept and the proposed method capability to offer interesting results from SITS, real data experiments on optical and radar data are presented.

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86. / Only in 2011 were acquired over 1.000TB of new digital image registers arising from orbital remote sensing. This range of data, which has a geometric progression increasing, is added annually to an extraordinary and incredible mass of data from existing satellite images of Earth's surface (acquired since the 70s of last century). This massive amount of raw data requires computational tools which allow the automatic recognition of image patterns desired to allow the extraction of geographical objects and targets of interest more quickly and concisely. The proposal for such recognition to be performed automatically through Spectral Analysis and Computational Intelligence integration, based on knowledge acquired by image experts, was implemented as an integrator based on Computational Neural Networks (via Kohonens Self-Organizing Feature Maps - SOM) and Fuzzy Logic (through Mamdani) techniques. These techniques were applied to the spectral signatures pattern formed by the quantization levels or gray levels of the corresponding pattern in each spectral band of each pattern of interest, so that the pattern classification will depend, in an inseparable manner, of the spectral signatures correlation of the six bands of the sensor, like the work of image experts. Bands 1 to 5 and 7 of the Landsat-5 satellite were used for the determination of five classes / targets of interest in cover and land occupation, in three test areas located in the State of Rio de Janeiro (Guaratiba, Mangaratiba and Magé) in this integration with comparison of results with those derived from the interpretation of the imaging expert, which was corroborated by checking the ground truth. There was also a results comparison obtained with two commercial computer systems (IDRISI Taiga and ENVI 4.8) with the integrator, regarding the quality of classification (Kappa) and response time. The integrator, with hybrid classifications (supervised and unsupervised) in its implementation, proved to be effective in multispectral automatic (unsupervised) pattern recognition and in learning of these patterns, because as the input of a new test area occurs, the lower became the process of learning, which achieve a final average accuracy o f 87%, compared to the experts classifications. Its efficacy was also demonstrated compared to systems tested, with average Kappa of 0.86.

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86. / Only in 2011 were acquired over 1.000TB of new digital image registers arising from orbital remote sensing. This range of data, which has a geometric progression increasing, is added annually to an extraordinary and incredible mass of data from existing satellite images of Earth's surface (acquired since the 70s of last century). This massive amount of raw data requires computational tools which allow the automatic recognition of image patterns desired to allow the extraction of geographical objects and targets of interest more quickly and concisely. The proposal for such recognition to be performed automatically through Spectral Analysis and Computational Intelligence integration, based on knowledge acquired by image experts, was implemented as an integrator based on Computational Neural Networks (via Kohonens Self-Organizing Feature Maps - SOM) and Fuzzy Logic (through Mamdani) techniques. These techniques were applied to the spectral signatures pattern formed by the quantization levels or gray levels of the corresponding pattern in each spectral band of each pattern of interest, so that the pattern classification will depend, in an inseparable manner, of the spectral signatures correlation of the six bands of the sensor, like the work of image experts. Bands 1 to 5 and 7 of the Landsat-5 satellite were used for the determination of five classes / targets of interest in cover and land occupation, in three test areas located in the State of Rio de Janeiro (Guaratiba, Mangaratiba and Magé) in this integration with comparison of results with those derived from the interpretation of the imaging expert, which was corroborated by checking the ground truth. There was also a results comparison obtained with two commercial computer systems (IDRISI Taiga and ENVI 4.8) with the integrator, regarding the quality of classification (Kappa) and response time. The integrator, with hybrid classifications (supervised and unsupervised) in its implementation, proved to be effective in multispectral automatic (unsupervised) pattern recognition and in learning of these patterns, because as the input of a new test area occurs, the lower became the process of learning, which achieve a final average accuracy o f 87%, compared to the experts classifications. Its efficacy was also demonstrated compared to systems tested, with average Kappa of 0.86.

Dans cette thèse, nous combinons les outils de la théorie des probabilités et de la géométrie stochastique pour proposer de nouvelles solutions au problème de la détection et le suivi d'objets multiples dans des séquences d'images haute résolution. Nous créons un cadre fondé sur des modèles de processus ponctuels marqués spatio-temporels pour détecter et suivre conjointement plusieurs objets dans des séquences d'images. Nous proposons l'utilisation de formes paramétriques simples pour décrire l'apparition de ces objets. Nous construisons de nouveaux modèles fondés sur des énergies dédiées constituées de plusieurs termes qui tiennent compte à la fois l'attache aux données et les contraintes physiques telles que la dynamique de l'objet, la persistance de la trajectoire et de l'exclusion mutuelle. Nous construisons un schéma d'optimisation approprié qui nous permet de trouver des minima locaux de l'énergie hautement non-convexe proposée qui soient proche de l'optimum global. Comme la simulation de ces modèles requiert un coût de calcul élevé, nous portons notre attention sur les dernières mises en oeuvre de techniques de filtrage pour le suivi d'objets multiples, qui sont connues pour être moins coûteuses en calcul. Nous proposons un échantillonneur hybride combinant le filtre de Kalman avec l'échantillonneur MCMC à sauts réversibles. Des techniques de calcul de haute performance sont également utilisées pour augmenter l'efficacité de calcul de notre méthode. Nous fournissons une analyse en profondeur du cadre proposé sur la base de plusieurs métriques classiques de suivi d'objets et de l'efficacité de calcul. / In this thesis, we combine the methods from probability theory and stochastic geometry to put forward new solutions to the multiple object detection and tracking problem in high resolution remotely sensed image sequences. We create a framework based on spatio-temporal marked point process models to jointly detect and track multiple objects in image sequences. We propose the use of simple parametric shapes to describe the appearance of these objects. We build new, dedicated energy based models consisting of several terms that take into account both the image evidence and physical constraints such as object dynamics, track persistence and mutual exclusion. We construct a suitable optimization scheme that allows us to find strong local minima of the proposed highly non-convex energy. As the simulation of such models comes with a high computational cost, we turn our attention to the recent filter implementations for multiple object tracking, which are known to be less computationally expensive. We propose a hybrid sampler by combining the Kalman filter with the standard Reversible Jump MCMC. High performance computing techniques are also used to increase the computational efficiency of our method. We provide an in-depth analysis of the proposed framework based on standard multiple object tracking metrics and computational efficiency.

Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage für eine genaue Langzeitüberwachung und Kartierung der Erdoberfläche und Atmosphäre. Unterschiedliche Sensoreigenschaften verhindern jedoch oft eine synergetische Nutzung. Daher besteht ein dringender Bedarf heterogene Multisensordaten zu kombinieren und als geometrisch und spektral harmonisierte Zeitreihen nutzbar zu machen. Diese Dissertation liefert einen vorwiegend methodischen Beitrag und stellt zwei neu entwickelte Open-Source-Algorithmen zur Sensorfusion vor, die gründlich evaluiert, getestet und validiert werden. AROSICS, ein neuer Algorithmus zur Co-Registrierung und geometrischen Harmonisierung von Multisensor-Daten, ermöglicht eine robuste und automatische Erkennung und Korrektur von Lageverschiebungen und richtet die Daten an einem gemeinsamen Koordinatengitter aus. Der zweite Algorithmus, SpecHomo, wurde entwickelt, um unterschiedliche spektrale Sensorcharakteristika zu vereinheitlichen. Auf Basis von materialspezifischen Regressoren für verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die Abschätzung einseitig fehlender Spektralbänder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die Abschätzung von Brandschäden aus Landsat mittels synthetischer Red-Edge-Bänder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lässt. Die Ergebnisse zeigen die Effektivität der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung für nachfolgende Produkte. Synthetische Red-Edge-Bänder erwiesen sich als wertvoll bei der Abschätzung vegetationsbezogener Parameter wie z. B. Brandschweregraden. Zudem zeigt die Arbeit das große Potenzial zur genaueren Überwachung und Kartierung von sich schnell entwickelnden Umweltprozessen, das sich aus einer Sensorfusion ergibt. / Earth observation satellite data acquired in recent years and decades provide an ideal data basis for accurate long-term monitoring and mapping of the Earth's surface and atmosphere. However, the vast diversity of different sensor characteristics often prevents synergetic use. Hence, there is an urgent need to combine heterogeneous multi-sensor data to generate geometrically and spectrally harmonized time series of analysis-ready satellite data. This dissertation provides a mainly methodical contribution by presenting two newly developed, open-source algorithms for sensor fusion, which are both thoroughly evaluated as well as tested and validated in practical applications. AROSICS, a novel algorithm for multi-sensor image co-registration and geometric harmonization, provides a robust and automated detection and correction of positional shifts and aligns the data to a common coordinate grid. The second algorithm, SpecHomo, was developed to unify differing spectral sensor characteristics. It relies on separate material-specific regressors for different land cover classes enabling higher transformation accuracies and the estimation of unilaterally missing spectral bands. Based on these algorithms, a third study investigated the added value of synthesized red edge bands and the use of dense time series, enabled by sensor fusion, for the estimation of burn severity and mapping of fire damage from Landsat. The results illustrate the effectiveness of the developed algorithms to reduce multi-sensor, multi-temporal data inconsistencies and demonstrate the added value of geometric and spectral harmonization for subsequent products. Synthesized red edge information has proven valuable when retrieving vegetation-related parameters such as burn severity. Moreover, using sensor fusion for combining multi-sensor time series was shown to offer great potential for more accurate monitoring and mapping of quickly evolving environmental processes.

Die zunehmende Verfügbarkeit von Satellitenfernerkundungs- und Wildtier-Telemetriedaten eröffnet neue Möglichkeiten für eine verbesserte Überwachung von Wildtierhabitaten durch Habitatmodelle, doch fehlt es häufig an geeigneten Ansätzen, um dieses Potenzial voll auszuschöpfen. Das übergeordnete Ziel dieser Arbeit bestand in der Konzipierung und Weiterentwicklung von Ansätzen zur Nutzung des Potenzials großer Satellitenbild- und Telemetriedatensätze in Habitatmodellen. Am Beispiel von drei großen Säugetierarten in Europa (Eurasischer Luchs, Rothirsch und Reh) wurden Ansätze entwickelt, um (1) Habitatmodelle mit dem umfangreichsten global und frei verfügbaren Satellitenbildarchiv der Landsat-Satelliten zu verknüpfen und (2) Wildtier-Telemetriedaten über Wildtierpopulationen hinweg in großflächigen Analysen der Habitateignung und -nutzung zu integrieren. Die Ergebnisse dieser Arbeit belegen das enorme Potenzial von Landsat-basierten Variablen als Prädiktoren in Habitatmodellen, die es ermöglichen von statischen Habitatbeschreibungen zu einem kontinuierlichen Monitoring von Habitatdynamiken über Raum und Zeit überzugehen. Die Ergebnisse meiner Forschung zeigen darüber hinaus, wie wichtig es ist, die Kontextabhängigkeit der Lebensraumnutzung von Wildtieren in Habitatmodellen zu berücksichtigen, insbesondere auch bei der Integration von Telemetriedatensätzen über Wildtierpopulationen hinweg. Die Ergebnisse dieser Dissertation liefern neue ökologische Erkenntnisse, welche zum Management und Schutz großer Säugetiere beitragen können. Darüber hinaus zeigt meine Forschung, dass eine bessere Integration von Satellitenbild- und Telemetriedaten eine neue Generation von Habitatmodellen möglich macht, welche genauere Analysen und ein besseres Verständnis von Lebensraumdynamiken erlaubt und so Bemühungen zum Schutz von Wildtieren unterstützen kann. / The growing availability of satellite remote sensing and animal tracking data opens new opportunities for an improved monitoring of wildlife habitats based on habitat models, yet suitable approaches for making full use of this potential are commonly lacking. The overarching goal of this thesis was to develop and advance approaches for harnessing the potential of big satellite image and animal tracking data in habitat models. Specifically, using three large mammal species in Europe as an example (Eurasian lynx, red deer, and roe deer), I developed approaches for (1) linking habitat models to the largest global and freely available satellite image record, the Landsat image archive, and (2) for integrating animal tracking datasets across wildlife populations in large-area assessments of habitat suitability and use. The results of this thesis demonstrate the enormous potential of Landsat-based variables as predictors in habitat models, allowing to move from static habitat descriptions to a continuous monitoring of habitat dynamics across space and time. In addition, my research underscores the importance of considering context-dependence in species’ habitat use in habitat models, particularly also when integrating tracking datasets across wildlife populations. The findings of this thesis provide novel ecological insights that help to inform the management and conservation of large mammals and more broadly, demonstrate that a better integration of satellite image and animal tracking data will allow for a new generation of habitat models improving our ability to monitor and understand habitat dynamics, thus supporting efforts to restore and protect wildlife across the globe.