Le rayonnement solaire est la variable la plus importante pour le calcul du bilan thermique du bâtiment. Son calcul requiert des relations géométriques pour la composante directe et un modèle de ciel pour distribuer le rayonnement diffus sur la voûte céleste. Des modèles développés pour des collecteurs solaires sont utilisés pour calculer le rayonnement solaire atteignant l'enveloppe du bâtiment. Des outils calculent le rayonnement en adaptant des modèles de ciel développés pour l'éclairage naturel. Bien que ces modèles de ciel, avec des genèses différents, servent à calculer le rayonnement solaire, il convient de préciser quel est le plus adapté pour travailler en milieu urbain.En nous appuyant sur une étude des données météorologiques, des modèles de ciel et des techniques numériques, on a mis en place un code susceptible de calculer le rayonnement direct (soleil) et diffus (ciel) et leur interaction avec la géométrie urbaine. La nouveauté réside dans l'évaluation du rayonnement solaire en utilisant un modèle de ciel isotrope et deux anisotropes. L’interaction entre ces modèles et la géométrie urbaine est mise en évidence avec une série d’exemples géométriques progressivement plus complexes. Des méthodes pour tuiler la voûte céleste sont présentées. Les différences entre le rayonnement calculé avec les modèles anisotropes (le modèle de source ponctuelle et le modèle tout temps de Perez) qui sont peu importantes dans une scène dégagée, deviennent significatives dans une scène urbaine. Des contributions ont également été apportées à la mise en place d’une station météorologique ainsi que des procédures pour l’analyse statistique des données et leur contrôle de qualité. / Solar irradiation is the most important parameter for building thermal simulation. Its calculation requires geometrical relationships for the direct radiation from the Sun and a sky model to distribute the radiance over the sky vault. Sky models developed for solar collectors are used to calculate the building’s solar irradiation availability. Some software calculates building’s irradiation by adapting sky models for lighting simulations. These models allow to compute solar irradiation, but the selection of the most suitable model for urban applications has not been defined clearly enough. We developed a code, based on the study of numerical methods, sky models and the necessary meteorological data. It calculates the solar irradiation availability in the urban context. The novelty lies in its capacity to evaluate the solar irradiation from the Sun and the sky by using three sky models: one isotropic and two anisotropic. The interaction between each sky model and the urban context is made clear in a series of progressively more complex geometric examples. Procedures to partition the sky vault are presented.Differences between the predicted irradiance by the anisotropic models (Perez punctual source and Perez All-Weather) are classified as small and large in unobstructed and obstructed scenes respectively. Contributions have also been made to set up a meteorological station. Statistical analyses as well as quality control procedures of meteorological data were also implemented.

Made available in DSpace on 2017-07-10T19:24:17Z (GMT). No. of bitstreams: 1 DENISE_M_GR_ZEGOZEWSKI.pdf: 8188144 bytes, checksum: 045f54782a1ea2161edf5aa7046a8c1c (MD5) Previous issue date: 2016-02-03 / Estimates of agricultural production are greatly important especially in economy field. However, they depend on area knowledge and cropping yield. Thus, this study aimed to propose a methodology to estimate the areas cropped with soybeans and corn in Paraná State according to multi-temporal EVI/MODIS vegetation index images for 2010/2011, 2011/2012 and 2012/2013 crop years. In addition, there was a research with spatial autocorrelation soybean yield in Paraná, with EVI vegetation index and meteorological variables in a decennial scale and estimate yield using CAR, SAR and GWR models. In Paraná State, there is a drawback to map soybeans crop since corn sowing period is very close to the first one. Therefore, images from the maximum and minimum vegetative vigour were drawn of each studied crop for mapping soybean and corn crops in order to obtain both cropping areas. Although, for the separation, Spectro Angle Mapper algorithm (SAM) was applied by one of the studied crops, while mapping was obtained by multiplying the other bands. Thus, for spatial statistics application of mapped data, the average EV profile of each municipality was extracted as well as for each multi-temporal image, in order to change them into a decennial scale. According to the spatial statistics of such areas, the descriptive analysis of univariate spatial autocorrelation (global and local) of each ten-day variable was used based on the soybean cycle. A bivariate autocorrelation analysis between soybean yield and the studied varieties were also performed. Finalizing the methodology, variables with the highest significant level by stepwise method were selected and SAR, CAR and GWR models were generated to estimate soybean yield. As results, regarding mappings, the following answers for soybean were found out: r = 0.95 and r = 0.99, and while for corn, the answers were: r = 0.72 and r = 0.95 for 2012/2013 and 2013/2014 crop years in relation to the official data from SEAB. So, it has been proved some great efficiency of this methodology to separate and identify crops. When the descriptive statistics of municipalities for each variable was carried out, it was found out that some regions began an early sowing in relation to other ones in Paraná by the decennial vegetation index. The ten-day scale was also possible to be identified according to the climatic factors that caused soybean yield damage. Based on the analysis of spatial autocorrelation, the greatest similarities occurred in 2011/2012 crop year, the one affected by the weather change, whose yields were similar in the municipalities of Paraná State. For spatial modelling, it was observed that selection of decennial variables was different for each studied crop year, and the best model selected by the validation. And GWR was chosen as the best model by the AIC, BIC and adjusted R² validation criteria. The residuals were randomly distributed throughout all the State, so that spatial autocorrelation could be eliminated. / As estimativas das produções agrícolas têm grande importância, principalmente, no âmbito econômico. No entanto, elas são dependentes do conhecimento da área de cultivo e da produtividade da cultura. Desta forma, este trabalho teve por objetivo propor uma metodologia para estimar as áreas cultivadas com soja e milho em escala municipal no Estado do Paraná a partir de imagens multi-temporais do índice de vegetação EVI/MODIS, para os anos-safras 2010/2011, 2011/2012 e 2012/2013. Além disto, trabalhar com a autocorrelação espacial da produtividade da soja nesse Estado, com o índice de vegetação EVI e variáveis agrometeorológicas em escala decendial bem como estimar a produtividade a partir dos modelos CAR, SAR e GWR. No Paraná, há o inconveniente para mapear a soja devido à proximidade de datas de semeadura do milho. Assim, para o mapeamento da soja e do milho, utilizaram-se imagens englobando o período de máximo e mínimo vigor vegetativo de cada cultura, para se obter a área cultivada das duas. Para a separação, utilizou-se o algoritmo Spectro Angle Mapper (SAM) para uma das culturas e obteve-se o mapeamento da outra pela multiplicação de bandas. Para aplicação da estatística espacial dos dados mapeados, extraiu-se o perfil médio do EVI de cada município e para cada imagem multi-temporal para transformá-los em escala decendial. De acordo com a estatística espacial de áreas, utilizou-se a análise descritiva, de autocorrelação espacial univariada (global e local) de cada variável decendial com foco no ciclo da soja. Também realizou-se a análise de autocorrelação bivariada entre a produtividade da soja com as variáveis em estudo. Finalizando a metodologia, selecionaram-se as variáveis com maior índice de significância pelo método de stepwise e, em seguida, foram gerados os modelos estimados (SAR, CAR e GWR) da produtividade da soja. Como resultados, foram encontradas as seguintes respostas para os mapeamentos da soja r= 0,95 e 0,99, e para o milho de r = 0,72 e r= 0,95 para os anos-safras 2012/2013 e 2013/2014 em relação aos dados oficiais da SEAB. Logo, comprovou-se a grande eficiência da metodologia para separação e identificação das culturas. Quando realizada a estatística descritiva dos municípios para cada variável, verificaram-se regiões que iniciam as semeaduras antecipadas em relação a outras regiões do Estado pelos decêndios do índice de vegetação. Foi também possível identificar os decêndios em que os fatores climáticos causaram danos à produtividade da soja. Na análise da autocorrelação espacial, as maiores similaridades ocorreram no ano-safra 2011/2012, ano afetado pela variação climática, cujas produtividades foram semelhantes nos municípios do Paraná. Para a modelagem espacial, verificou-se que a seleção das variáveis decêndiais foi diferente para cada ano-safra estudado, e o GWR foi escolhido como melhor modelo pelos critérios de validação, AIC, BIC e R² ajustado. Foram encontrados resíduos distribuídos aleatoriamente por todo o Estado, para que assim se eliminasse a autocorrelação espacial

Made available in DSpace on 2017-05-12T14:47:34Z (GMT). No. of bitstreams: 1 DENISE_M_GR_ZEGOZEWSKI.pdf: 8188144 bytes, checksum: 045f54782a1ea2161edf5aa7046a8c1c (MD5) Previous issue date: 2016-02-03 / Estimates of agricultural production are greatly important especially in economy field. However, they depend on area knowledge and cropping yield. Thus, this study aimed to propose a methodology to estimate the areas cropped with soybeans and corn in Paraná State according to multi-temporal EVI/MODIS vegetation index images for 2010/2011, 2011/2012 and 2012/2013 crop years. In addition, there was a research with spatial autocorrelation soybean yield in Paraná, with EVI vegetation index and meteorological variables in a decennial scale and estimate yield using CAR, SAR and GWR models. In Paraná State, there is a drawback to map soybeans crop since corn sowing period is very close to the first one. Therefore, images from the maximum and minimum vegetative vigour were drawn of each studied crop for mapping soybean and corn crops in order to obtain both cropping areas. Although, for the separation, Spectro Angle Mapper algorithm (SAM) was applied by one of the studied crops, while mapping was obtained by multiplying the other bands. Thus, for spatial statistics application of mapped data, the average EV profile of each municipality was extracted as well as for each multi-temporal image, in order to change them into a decennial scale. According to the spatial statistics of such areas, the descriptive analysis of univariate spatial autocorrelation (global and local) of each ten-day variable was used based on the soybean cycle. A bivariate autocorrelation analysis between soybean yield and the studied varieties were also performed. Finalizing the methodology, variables with the highest significant level by stepwise method were selected and SAR, CAR and GWR models were generated to estimate soybean yield. As results, regarding mappings, the following answers for soybean were found out: r = 0.95 and r = 0.99, and while for corn, the answers were: r = 0.72 and r = 0.95 for 2012/2013 and 2013/2014 crop years in relation to the official data from SEAB. So, it has been proved some great efficiency of this methodology to separate and identify crops. When the descriptive statistics of municipalities for each variable was carried out, it was found out that some regions began an early sowing in relation to other ones in Paraná by the decennial vegetation index. The ten-day scale was also possible to be identified according to the climatic factors that caused soybean yield damage. Based on the analysis of spatial autocorrelation, the greatest similarities occurred in 2011/2012 crop year, the one affected by the weather change, whose yields were similar in the municipalities of Paraná State. For spatial modelling, it was observed that selection of decennial variables was different for each studied crop year, and the best model selected by the validation. And GWR was chosen as the best model by the AIC, BIC and adjusted R² validation criteria. The residuals were randomly distributed throughout all the State, so that spatial autocorrelation could be eliminated. / As estimativas das produções agrícolas têm grande importância, principalmente, no âmbito econômico. No entanto, elas são dependentes do conhecimento da área de cultivo e da produtividade da cultura. Desta forma, este trabalho teve por objetivo propor uma metodologia para estimar as áreas cultivadas com soja e milho em escala municipal no Estado do Paraná a partir de imagens multi-temporais do índice de vegetação EVI/MODIS, para os anos-safras 2010/2011, 2011/2012 e 2012/2013. Além disto, trabalhar com a autocorrelação espacial da produtividade da soja nesse Estado, com o índice de vegetação EVI e variáveis agrometeorológicas em escala decendial bem como estimar a produtividade a partir dos modelos CAR, SAR e GWR. No Paraná, há o inconveniente para mapear a soja devido à proximidade de datas de semeadura do milho. Assim, para o mapeamento da soja e do milho, utilizaram-se imagens englobando o período de máximo e mínimo vigor vegetativo de cada cultura, para se obter a área cultivada das duas. Para a separação, utilizou-se o algoritmo Spectro Angle Mapper (SAM) para uma das culturas e obteve-se o mapeamento da outra pela multiplicação de bandas. Para aplicação da estatística espacial dos dados mapeados, extraiu-se o perfil médio do EVI de cada município e para cada imagem multi-temporal para transformá-los em escala decendial. De acordo com a estatística espacial de áreas, utilizou-se a análise descritiva, de autocorrelação espacial univariada (global e local) de cada variável decendial com foco no ciclo da soja. Também realizou-se a análise de autocorrelação bivariada entre a produtividade da soja com as variáveis em estudo. Finalizando a metodologia, selecionaram-se as variáveis com maior índice de significância pelo método de stepwise e, em seguida, foram gerados os modelos estimados (SAR, CAR e GWR) da produtividade da soja. Como resultados, foram encontradas as seguintes respostas para os mapeamentos da soja r= 0,95 e 0,99, e para o milho de r = 0,72 e r= 0,95 para os anos-safras 2012/2013 e 2013/2014 em relação aos dados oficiais da SEAB. Logo, comprovou-se a grande eficiência da metodologia para separação e identificação das culturas. Quando realizada a estatística descritiva dos municípios para cada variável, verificaram-se regiões que iniciam as semeaduras antecipadas em relação a outras regiões do Estado pelos decêndios do índice de vegetação. Foi também possível identificar os decêndios em que os fatores climáticos causaram danos à produtividade da soja. Na análise da autocorrelação espacial, as maiores similaridades ocorreram no ano-safra 2011/2012, ano afetado pela variação climática, cujas produtividades foram semelhantes nos municípios do Paraná. Para a modelagem espacial, verificou-se que a seleção das variáveis decêndiais foi diferente para cada ano-safra estudado, e o GWR foi escolhido como melhor modelo pelos critérios de validação, AIC, BIC e R² ajustado. Foram encontrados resíduos distribuídos aleatoriamente por todo o Estado, para que assim se eliminasse a autocorrelação espacial

In Brazil, the lack of meteorological data is an issue in order to apply a bioclimatic approach for building design. As one of the consequences, designers frequently apply general climate data from one region to another rather than specific data for design strategies purposes. However, this general climate data not necessarily represent accurately microclimatic conditions for different cities even when they are part of the same region. Facing this, this work discusses design strategies for bioclimatic housing in Maceió, Palmeira dos Índios and Pão de Açúcar, located in different geographical mesoregions in Alagoas, in order to adapt to local climate, to thermal comfort, energy efficiency and sustainability of the living space. The methodological steps were: choice of cities, field research, meteorological data evaluation and buildings design recommendations discussion. Meteorological data for approximately ten years was used, obtained from INMET (National Institute of Meteorology) and processed by statistical analysis and the methods of Test Reference Year (TRY) and Project Typical Day. The results showed that there are differences between the cities, especially about annual temperature range, precipitation and direction of winds. The main bioclimatic strategies recommended were: shading, ventilation and protection from rain for Maceió; shading, ventilation, protection from rain and high-mass in daily use rooms for Palmeira dos Índios and shading, ventilation, high-mass in daily use rooms and evaporative cooling for Pão de Açúcar. Besides that, inconsistencies between the recommendations made by the NBR 15220-3 for cities and the data analyzed were found. / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / No Brasil, a escassez de dados meteorológicos ainda é um desafio e em conseqüência disto, muitas vezes, o clima é desconsiderado ou adotado de maneira generalista na prática arquitetônica. Diante disto, o presente trabalho discute estratégias bioclimáticas para habitação nas cidades de Maceió, Palmeira dos Índios e Pão de Açúcar, localizadas nas diferentes Mesorregiões Geográficas do Estado de Alagoas, de forma que se adaptem ao clima local, visando conforto térmico, eficiência energética e sustentabilidade do espaço habitado. Os procedimentos metodológicos adotados foram: escolha das cidades; pesquisa de campo; tratamento e análise de dados meteorológicos e discussão das recomendações de projeto. Foram utilizados dados meteorológicos de um período de aproximadamente dez anos, obtidos junto ao INMET (Instituo Nacional de Meteorologia), tratados através de análise estatística e das metodologias de Ano Climático de Referência e Dia Típico de Projeto. Os resultados mostraram que, existem diferenças climáticas entre as cidades, em especial quanto à amplitude térmica anual, precipitação e direção dos ventos. As principais estratégias bioclimáticas recomendadas para as cidades foram: sombreamento, ventilação e proteção contra as chuvas para Maceió; sombreamento, ventilação, proteção contra as chuvas e massa térmica para resfriamento em ambientes de uso diurno para Palmeira dos Índios e sombreamento, ventilação, massa térmica para resfriamento em ambientes de uso diurno e resfriamento evaporativo para Pão de Açúcar. Além disto, foram identificadas contradições entre as recomendações feitas pela NBR 15.220-3 para as cidades e os dados analisados.

Submitted by (ana.araujo@ufrpe.br) on 2016-06-28T14:02:55Z No. of bitstreams: 1 Damocles Aurelio Nascimento da Silva.pdf: 1947231 bytes, checksum: 50646fd00ad98b9c11daeb3638122230 (MD5) / Made available in DSpace on 2016-06-28T14:02:55Z (GMT). No. of bitstreams: 1 Damocles Aurelio Nascimento da Silva.pdf: 1947231 bytes, checksum: 50646fd00ad98b9c11daeb3638122230 (MD5) Previous issue date: 2014-09-24 / Many aspects of the weather cycle could be described by time series data. Meteorologists often use time series data to assess climate conditions and forecasts. Such models are generally continuous models. The interest was to analyze discrete weather data with the INAR (1) model, using classical and Bayesian approach to parameter estimation. The proposal is to analyze the data series using mixed models with Bayesian approach. Thus, this work is described a sequence of procedures for estimating parameters of autoregressive models of order p = 1, for integer values INAR(1), by classical inference via maximum likelihood estimator and Bayesian inference via simulation Monte Carlo Markov Chain (MCMC). Two alternatives are considered for the a priori density of the model parameters. For the former case is adopted a density non-priori information. For the second, we adopt a density combined beta-gamma. A posteriori analysis is performed by algorithms of MCMC simulation. Also evaluates the prediction of new values of the series number of days with precipitation. The period of analysis comprised 30=11= 1993 to 29=02=2012 and obtained estimates of the period of 31=03=2012 to 28=02=2013. One INAR (1) model of classical parameter estimation and four models INAR (1) Bayesian estimation for the parameters were used. The choice of the most appropriate model the Akaike information criterion (AIC) was used. The analysis of forecast errors was an instrument used to determine which model is best suited to the data. We conclude that the use of MCMC simulation makes the process more exible Bayesian inference and can be extended to larger problems. Mixed models showed better performance than the classical model, mixed among the more robust results than had been the model INAR (1) Poisson-Normal using a priori Beta. Soon we propose the use of thinning operation in mixed models. / Muitos aspectos do ciclo meteorol ógico poderiam ser descrito por dados de s éries temporais. Os meteorologistas costumam usar dados de s éries temporais para avaliar as condições clim áticas e previsões. Esse modelos em geral são modelos contínuos. O interesse foi analisar dados meteorol ógicos discretos com o modelo INAR(1), atrav és de abordagem cl ássica e bayesiana na estima ção dos parâmetros. A proposta é analisar os dados da s érie utilizando modelos mistos com abordagem bayesiana. Sendo assim, neste trabalho é descrito uma sequência de procedimentos para estimar parâmetros de modelos autoregressivos de ordem p = 1, para valores inteiros INAR(1), por meio de inferência cl ássica via estimador de m áxima verossimilhan ça e inferência bayesiana via simula ção de Monte Carlo em Cadeias de Markov (MCMC). Duas alternativas são consideradas para a densidade a priori dos parâmetros do modelo. Para o primeiro caso, adota-se uma densidade a priori não informativa. Para o segundo, adota-se uma densidade conjugada beta-gama. A an álise a posteriori é efetuada por meio de algoritmos de simula ção MCMC. Avalia-se tamb ém a previsão de novos valores da s érie n úmero de dias com precipita ção. O per íodo de an álise compreendeu 30=11=1993 a 29=02=2012 e obteve previsões do per íodo de 31=03=2012 a 28=02=2013. Foram utilizados um modelo INAR(1) de estima ção cl ássica dos parâmetros e quatro modelos INAR(1) de estima ção bayesiana para os parâmetros. A escolha do modelo mais adequado foi utilizado o crit ério de informa ção de Akaike (AIC). A an álise dos erros de previsão foi um instrumento utilizado para verifi car qual modelo se adequou melhor aos dados. Conclui-se que o uso de simula ção MCMC torna o processo de inferência bayesiana mais flex ível, podendo ser estendido para problemas de dimensão maior. Os modelos mistos apresentaram melhores desempenho do que o modelo cl ássico, dentre os mistos o que teve resultados mais robustos foi o Modelo INAR(1) Poisson Normal utilizando a priori Beta. Logo propomos o uso da opera ção thinning em modelos mistos.

The transformation of solar energy into electricity is starting to impact to overall worldwide energy production mix. Photovoltaic-generated electricity can play a significant role in minimizing the use of non-renewable energy sources. Sunlight consists of three main components: global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI). Typically, these components are measured using specialized instruments in order to study solar radiation at any location. However, these measurements are not always available, especially in the case of the DNI and DHI components of sunlight. Consequently, many models have been developed to estimate these components from available GHI data. These models have their own merits. For this thesis, solar radiation data collected at four locations have been analyzed. The data come from Al-Hanakiyah (Saudi Arabia), Boulder (U.S.), Ma’an (Jordan), and Ottawa (Canada). The BRL, Reindl*, DISC, and Perez models have been used to estimate DNI and DHI data from the experimentally measured GHI data. The findings show that the Reindl* and Perez model outcomes offered similar accuracy of computing DNI and DHI values when comparing with detailed experimental data for Al-Hanakiyah and Ma’an. For Boulder, the Perez and BRL models have similar estimation abilities of DHI values and the DISC and Perez models are better estimators of DNI. The Reindl* model performs better when modeling DHI and DNI for Ottawa data. The BRL and DISC models show similar metrics error analyses, except in the case of the Ma’an location where the BRL model shows high error metrics values in terms of MAE, RMSE, and standard deviation (σ). The Boulder and Ottawa locations datasets were not complete and affected the outcomes with regards to the model performance metrics. Moreover, the metrics show very high, unreasonable values in terms of RMSE and σ. It is advised that a global model be developed by collecting data from many locations as a way to help minimize the error between the actual and modeled values since the current models have their own limitations. Availability of multi-year data, parameters such as albedo and aerosols, and one minute to hourly time steps data could help minimize the error between measured and modeled data. In addition to having accurate data, analysis of spectral data is important to evaluate their impact on solar technologies.

The thesis generally describes matters of data warehouses and knowledge discovery in databases. Then it focuses on the meteorological databases and their problems. The practical part of thesis describes design methods for data mining project, NOAA Global Surface Summary of the Day (GSOD), which is then implemented in two different ways using the Pentaho tools. Finally, an evaluation and comparison of these two approaches.

Cette étude se propose d’explorer les potentialités des données de télédétection optique et radar libre d’accès pour l’évaluation et le suivi des écosystèmes forestiers tropicaux, secs ou humides. Différents sites tests situés dans ces écosystèmes forestiers tropicaux, ont été sélectionnés. Il s’agit : du parc national des Virunga en République Démocratique du Congo (RDC), de la réserve de biosphère de l’Oti-Keran-Mandourie (OKM) et de la réserve de faune de Togodo (RFT) au Togo, de la zone située autour du pont faisant la liaison entre la ville de Saint-Georges de l’Oyapock et la plaine du littorale de Kourou en Guyane française et de la province de la Monté Cristi en république Dominicaine. Différentes données ont été utilisés lors de cette étude : pour les images radar, des séries temporelles Sentinel-1, des mosaïques Alos-2 et, pour les images optiques, Sentinel-2 et Landsat-8. Des données exogènes comme les points GPS, modèles numériques de terrain et les cartes de référence. L’approche méthodologie utilisé est composée de prétraitement sur les images optiques et radar. Les approches spécifiques, variables selon le site d’étude, ont comporté : photo interprétation détaillée, la classification supervisée SVM, l’inventaire forestier et l’application des équations allométriques, une approche de détection des changements par décomposition en ondelettes, une de détection des changements automatiques par seuillage et la caractérisation de ces changements. Les principaux résultats sont les suivants : Site du PNVI : les cartes d’occupation du sol et les cartes binaires forêts, non-forêt de 1987, 1997, 2007 et 2017 sont réalisées sur le PNVI. Sur la période de 30 ans en utilisant les cartes binaires entre 1987 à 2017 le taux moyen annuel de déforestation est de 1,07%. Ce taux de déforestation élevé montre la pression croissante sur les ressources forestières dans le paysage des Virunga. Site de l’OKM et du RFT : une classification menée sur une combinaison d’images optiques et radar donne des performances légèrement meilleures que des classifications menées sur des images optiques et radar considérées séparément. Les cartes d’occupation du sol issues de ces classifications ont servis de base pour l’estimation de stocks de carbone à travers l’évaluation des ressources forestières. Sur le site de Saint-Georges de l’Oyapock, l’analyse temporelle menée à partir de décompositions en ondelettes, a permis de détecter trois grands types de changements dus à : la déforestation anthropique, les évolutions saisonnières et les évolutions agricoles. Sur le site de la province de Monté Cristi en République dominicaine, l’analyse conjointe d’images radar et optiques a permis de proposer une cartographie comportant 18 classes d’occupation du sol contrôlées sur le terrain avec une précision globale de plus de 90 %. Le suivi historique des forêts montre une régression de la couverture forestière. Parallèlement, nous observons une régression de la surface des mangroves entre 2015 et 2018.Cette étude a mis en évidence l’immense potentialité des données de télédétection optique et radar dans la caractérisation, la cartographie et le suivi des strates d’occupation des sols dans les écosystèmes tropicaux dans différentes régions du monde et en fonction des conditions saisonniers. Si chaque type de données de télédétection possède ces qualités et capacités discriminatoire, cette étude a montré que l’utilisation conjointe et combinée de deux types de données permet d’augmenté significativement la caractérisation et la discrimination des classes d’occupation des sols et ainsi augmente les chances de fiabilité des actions à mener / This study aims to explore the potential of optical remote sensing and free access radar data for the assessment and monitoring of tropical, dry or wet forest ecosystems. Different test sites located in these tropical forest ecosystems have been selected. These are: the Virunga National Park in the Democratic Republic of Congo (DRC), the Oti-Keran-Mandourie Biosphere Reserve (OKM) and Togodo Wildlife Reserve (RFT) in Togo, the area around the bridge linking the city of Saint-Georges de l'Oyapock and the plain of the Kourou coast in French Guiana and the province of Monté Cristi in the Dominican Republic. Different data were used in this study: for radar images, Sentinel-1 time series, Alos-2 mosaics and, for optical images, Sentinel-2 and Landsat-8. Exogenous data such as GPS points, digital terrain models and reference maps. The methodology approach used consists of pretreatment on optical and radar images. Specific approaches, varying by study site, included: photo detailed interpretation, supervised SVM classification, forest inventory and application of allometric equations, a wavelet decomposition detection approach, a detection approach automatic changes by thresholding and the characterization of these changes. The main results are:PNVI site: land cover maps and forest, non-forest binary maps of 1987, 1997, 2007 and 2017 are produced on the PNVI. Over the 30-year period using the binary maps between 1987 and 2017 the average annual rate of deforestation is 1.07%. This high deforestation rate shows the increasing pressure on forest resources in the Virunga landscape. OKM and RFT site: a classification carried out on a combination of optical and radar images gives slightly better performances than classifications carried out on optical and radar images considered separately. The land cover maps from these classifications were used as a basis for estimating carbon stocks through forest resource assessment. At the Saint-Georges de l'Oyapock site, temporal analysis using wavelet decompositions revealed three main types of changes due to anthropogenic deforestation, seasonal changes and agricultural changes. On the site of the Monté Cristi province in the Dominican Republic, the joint analysis of radar and optical images made it possible to propose a cartography comprising 18 field-controlled land cover classes with an overall accuracy of more than 90%. Historical forest monitoring shows a decline in forest cover. At the same time, we observe a regression of the surface of mangroves between 2015 and 2018.This study has highlighted the immense potential of optical and radar remote sensing data in the characterization, mapping and monitoring of land use layers in tropical ecosystems in different regions of the world and according to seasonal conditions. While each type of remote sensing data has these discriminatory qualities and capabilities, this study has shown that the joint and combined use of two types of data significantly increases the characterization and discrimination of land-use classes and thus increases the chances of reliability of the actions to be carried out

The analysis of heterogeneous, complex data sets has become important in many scientific domains. With the help of scientific visualization, researchers can be supported in exploring their research results. One domain, where researchers have to deal with spatio-temporal data from different sources including simulation, observation and time-independent data, is meteorology. In this thesis, a concept and workflow for the 3D visualization of meteorological data was developed in cooperation with domain experts. Three case studies have been conducted based on the developed concept. In addition, the concept has been enhanced based on the experiences gained from the case studies. In contrast to existing all-in-one software applications, the proposed workflow employs a combination of existing software applications and their extensions to make a variety of already implemented visualization algorithms available. The workflow provides methods for data integration and for abstraction of the data as well as for generating representations of the variables of interest. Solutions for visualizing sets of variables, comparing results of multiple simulation runs and results of simulations based on different models are presented. The concept includes the presentation of the visualization scenes in virtual reality environments for a more comprehensible display of multifaceted data. To enable the user to navigate within the scenes, some interaction functionality was provided to control time, camera, and display of objects. The proposed methods have been selected with respect to the requirements defined in cooperation with the domain experts and have been verified with user tests. The developed visualization methods are used to analyze and present recent research results as well as for educational purposes. As the proposed approach uses generally applicable concepts, it can also be applied for the analysis of scientific data from other disciplines. / In nahezu allen Wissenschaftsdisziplinen steigt der Umfang erhobener Daten. Diese sind oftmals heterogen und besitzen eine komplexe Struktur, was ihre Analyse zu einer Herausforderung macht. Die wissenschaftliche Visualisierung bietet hier Möglichkeiten, Wissenschaftler bei der Untersuchung ihrer Forschungsergebnisse zu unterstützen. Eine der Disziplinen, in denen räumlich-zeitliche Daten aus verschiedenen Quellen inklusive Simulations- und Observationsdaten eine Rolle spielen, ist die Meteorologie. In dieser Arbeit wurde in Zusammenarbeit mit Experten der Meteorologie ein Konzept und ein Workflow für die 3D-Visualisierung meteorologischer Daten entwickelt. Dabei wurden drei Fallstudien erarbeitet, die zum einen auf dem erstellten Konzept beruhen und zum anderen durch die während der Fallstudie gesammelten Erfahrungen das Konzept erweiterten. Der Workflow besteht aus einer Kombination existierender Software sowie Erweiterungen dieser. Damit wurden Funktionen zur Verfügung gestellt, die bei anderen Lösungsansätzen in diesem Bereich, die oft nur eine geringere Anzahl an Funktionalität bieten, nicht zur Verfügung stehen. Der Workflow beinhaltet Methoden zur Datenintegration sowie für die Abstraktion und Darstellung der Daten. Es wurden Lösungen für die Visualisierung einer Vielzahl an Variablen sowie zur vergleichenden Darstellung verschiedener Simulationsläufe und Simulationen verschiedener Modelle präsentiert. Die generierten Visualisierungsszenen wurden mit Hilfe von 3D-Geräten, beispielsweise eine Virtual-Reality-Umgebung, dargestellt. Die stereoskopische Projektion bietet dabei die Möglichkeit, diese komplexen Daten mit verbessertem räumlichem Eindruck darzustellen. Um dem Nutzer eine umfassende Analyse der Daten zu ermöglichen, wurden eine Reihe von Funktionen zur Interaktion zur Verfügung gestellt, um beispielsweise Zeit, Kamera und die Anzeige von 3D-Objekten zu steuern. Das Konzept und der Workflow wurden entsprechend der Anforderungen entwickelt, die zusammen mit Fachexperten definiert wurden. Des Weiteren wurden die Anwendungen in verschiedenen Entwicklungsstadien durch Nutzer getestet und deren Feedback in die Entwicklung einbezogen. Die Ergebnisse der Fallstudien wurden von den Wissenschaftlern benutzt, um ihre Daten zu analysieren, sowie diese zu präsentieren und in der Lehre einzusetzen. Da der vorgeschlagene Workflow allgemein anwendbare Konzepte beinhaltet, kann dieser auch für die Analyse wissenschaftlicher Daten anderer Disziplinen verwendet werden.

The present thesis deals with the process of automation of geodetic measurements using electronic geodetic instruments, e.g. electronic levels, electronic total stations, as well as other measuring instruments and devices generally used for geodetic measurements, including meteorological sensors. The goal is to design and create a platform for automated measurement system with integrated data collection, their storage and analysis with the help of remote internet access and allowing to export data in various formats.