Orientador: Newton La Scala Junior / Coorientador: Glauco de Souza Rolim / Banca: Jansle Vieira Rocha / Banca: Alan Rodrigo Panosso / Resumo: O Brasil é um país que apresenta grande parte do seu território destinado a atividades agrícolas. Os cultivos de cana-de-açúcar, soja e milho têm grande importância na economia nacional. As práticas de manejo de cultivos e do solo estão intimamente ligadas à emissão de CO2 e a produtividade dos cultivos. Essas práticas também interferem no potencial de fotossíntese, que pode ser medido remotamente por satélites pela fluorescência da clorofila induzida pelo sol (SIF), e pelo Índice de Vegetação pela Diferença Normalizada (NDVI). O monitoramento do CO2 atmosférico via satélite vem sendo amplamente utilizado na compreensão dos fluxos de carbono. Neste estudo investigamos a variabilidade temporal do XCO2, o SIF e o NDVI em áreas com os principais cultivos do centro sul do Brasil, cana-de-açúcar, rotação soja-milho e pastagens. O estudo foi realizado em áreas correspondentes à três usos agrícolas, sendo cana-de-açúcar (Pradópolis - SP), rotação soja-milho (Santo Antônio do Paraíso - PR) e pastagens (Águas Claras - MS). As variáveis analisadas foram Temperatura do ar, Pluviosidade, NDVI, SIF e XCO2. Todos os dados foram ajustados em escalas mensais. A Temperatura do ar foi obtida através da plataforma NASAPOWER, a Pluviosidade através da plataforma NASA GIOVANNI, NDVI via SATveg EMBRAPA e SIF e XCO2 a partir da plataforma OCO-2 da NASA. O período de estudo foi de outubro/2014 a outubro/2016, totalizando uma série temporal de dois anos e todos os dados foram estratificados em escala... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Brazil's territory is covered, mostly, by agricultural activities. Sugarcane, soybean and corn cultivation and livestock plays a major role in the national economy. Management practices in the agricultural activity are correlated to CO2 emission. These practices can affect the crop biomass production and photosynthesis activity. Solar-induced Chlorophyll Fluorescence (SIF) can be used to estimate photosynthesis in cultivation areas. Normalized Difference Vegetation Index (NDVI) has been used to estimate biomass production, soil coverage and crops phenology. Recently, atmospheric CO2 monitored using remote sensing has been widely used in the understanding of carbon fluxes, correlating it to other parameter such as gross and net primary production or even the SIF. The aim of this study was to understand the temporal variability of XCO2, SIF and NDVI in areas under sugarcane cultivation, soybean-corn rotation and grassland to livestock production in the South-Central region of Brazil. This research was conducted in three localities, each one corresponding to one agricultural use: Pradópolis - SP (sugarcane cultivation), Santo Antônio do Paraíso (soybean-corn) and Águas Claras - MS (grassland and pasture to livestock). We analyzed temperature, pluviosity, NDVI, SIF and XCO2. Temperature and pluviosity data were obtained using the NASAPOWER and NASA GIOVANNI platform, respectively. NDVI was obtained in the SATveg project (EMBRAPA). SIF and XCO2 data were obtained in the OCO-2 prog... (Complete abstract click electronic access below) / Mestre

Structural organization refers to the particular ordering of scatterers. Probing structural organization by imaging polarized spectral scatter provides insight into the composition of a medium, and can aid in remote sensing, the identification of tissue pathologies, and material characterization and differentiation. The vector nature of polarized light enables it to interact with optical anisotropies within a medium, while the spectral aspect of polarization is sensitive to small-scale structure. However, many polarization studies have limitations, as they provide qualitative image analysis, incomplete anisotropy information, or both. The ability to image the effects of anisotropy and small-scale structure at multiple wavelengths is key for parameterizing structural organization. The Stokes/Mueller formalism is a framework that quantifies a medium's complete spectral polarization response, and allows for the parameterization of structural organization. Additionally, advances in liquid crystal (LC) technology have resulted in new polarimetric devices. These computer-controlled devices impart spectral polarization effects on the millisecond timescale with no mechanically moving hardware, providing the ability for making rapid polarimetric measurements. This dissertation describes a methodology for revealing structural organization by exploiting the Stokes/Mueller formalism and by utilizing measurements from a spectral imaging polarimeter constructed from variable retardance LC devices, such as liquid crystal variable retarders (LCVRs) and a liquid crystal tunable filter (LCTF). The methodology includes developing the system, the Stokes/Mueller model, and all of the procedures, calibrations, and data interpretation. Developing the system also consists of component and system calibration, a system sensitivity and performance analysis, and finally test measurements for system validation. The final validation measurement is made on a mineral sample for inferring structural organization.

Agricultural drought is a creeping disaster that overshadows the vegetative cover in general and cropland specifically in Iraq, a country that was well known for its agricultural production and fertile soil. In the recent years, the arable lands in Iraq experienced increasing land degradation that led to desertification, economic losses, food insecurity, and deteriorating environment. Remote sensing is employed in this study and four different indices are utilized, each of which is derived from MODIS satellite mission products. Agricultural drought maps are produced from 2003 to 2015 after masking the vegetation cover. Year 2008 was found the most severe drought year during the study period, where drought covered 37% of the vegetated land. This part of the study demonstrated the capability of remote sensing in fulfilling the need of an early warning system for agricultural drought over such a data-scarce region. This study also aims to monitor hydrological drought. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) is the hydrological drought indicator, that is used to calculate the deficit. Severity of drought events are calculated by integrating monthly water deficit over the drought period. In addition, drought recovery time is assessed depending on the estimated deficit. Major drought events are classified into several levels of severity by applying a drought monograph approach. The results demonstrated that GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information for decision makers which can be utilized in developing drought adaptation and mitigation strategies.

This thesis deals with the possibility of using webcams as a source of spatial data for snow occurrence. The aim of this study is to propose a suitable method of snow cover detection from web camera images. From a sample of 6 webcams of the Czech Hydrometeorological Institute (CHMI) the snow cover is detected by pixel classification methods. The effect of training file size on the accuracy of classification is examined and the overall accuracy achieved by the SVM method was shown to be 97.46%. This study also aims to propose a system for determining the proportion of snow-covered areas. The algorithm consists of several sub-steps: filtering and registration of images, detection of snow, introduction of a coordinate system, calculation of the size of the surveyed area and the proportion of snow-covered area. The designed model can be used for automatic processing of images for various webcams. The melting curves of the snow cover are generated from the obtained daily values of the snow covered area. The results are validated using data from selected CHMI stations. The proposed and parameterized model confirms the possibility of successful use of webcams as a complement to ground measurement of meteorological stations and for the validation of remote sensing products.

Resumo: A agricultura de precisão dispõe de tecnologias que auxiliam a produção agrícola, permitindo o monitoramento do desenvolvimento das culturas. Assim, os objetivos deste trabalho foram: (i) avaliar o efeito de dois preparos de solo sobre características agronômicas da cultura do amendoim; (ii) comparar os índices de vegetação (NDVI e IRVI) por meio de monitoramento temporal, e sua correlação com variáveis agronômicas em dois preparos de solo; (iii) avaliar o comportamento da variabilidade espacial dos atributos do solo e de parâmetros de colheita e correlaciona-los com o índice de vegetação NDVI. O experimento foi realizado na safra 2017/2018 em área agrícola do município de Jaboticabal, estado de São Paulo, Brasil, localizada próximo as coordenadas geográficas 21°14’S e 48°16’O, com altitude média de 615 m. Foram realizados dois preparos de solo, o conservacionista e o convencional. O delineamento experimental foi baseado nas premissas básicas do Controle Estatístico de Qualidade – CEQ, contendo 60 pontos amostrais com GRID de 15 x 15 m. As avaliações ocorreram aos 30, 45, 80, 90 e 110 dias após a semeadura (DAS) por meio de coletas a campo e sensoriamento proximal utilizando sensor GreenSeeker. A partir da malha georreferenciada, foram estudados os atributos do solo (areia, argila, resistência mecânica do solo a penetração na linha e entrelinha de semeadura) e parâmetros relacionados com a colheita do amendoim como perdas totais e produtividade. As análises estatísticas utili... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Precision agriculture has technologies that help agricultural production, allowing the monitoring of crop development. Thus, the objectives of this work were: (i) to evaluate the effect of two soil tillage on peanut crop agronomic characteristics; (ii) compare vegetation indices (NDVI and IRVI) by temporal monitoring and their correlation with agronomic variables in two soil tillage; (iii) evaluate the behavior of spatial variability of soil attributes and harvesting parameters and correlate them with the NDVI vegetation index. The experiment was carried out in the 2017/2018 crop in an agricultural area of Jaboticabal, São Paulo State, Brazil, located near the geographic coordinates 21°14'S and 48° 6'O, with an average altitude of 615 m. Two soil tillage were done, the conservationist and the conventional one. The experimental design was based on the basic assumptions of the Statistical Quality Control - CEQ, containing 60 sampling points with 15 x 15 m GRID. Evaluations took place at 30, 45, 80, 90 and 110 days after sowing (DAS) by field collections and proximal sensing using GreenSeeker sensor. From the georeferenced mesh, the soil attributes (sand, clay, soil mechanical resistance to penetration in the row and sowing row) and parameters related to peanut harvest as total losses and yield were studied. The statistical analyzes used were descriptive statistics, Pearson correlation, individual control charts for process monitoring and geostatistics in the generation of spati... (Complete abstract click electronic access below) / Doutor

Orientador: Carlos Eduardo Angeli Furlani / Resumo: A agricultura de precisão dispõe de tecnologias que auxiliam a produção agrícola, permitindo o monitoramento do desenvolvimento das culturas. Assim, os objetivos deste trabalho foram: (i) avaliar o efeito de dois preparos de solo sobre características agronômicas da cultura do amendoim; (ii) comparar os índices de vegetação (NDVI e IRVI) por meio de monitoramento temporal, e sua correlação com variáveis agronômicas em dois preparos de solo; (iii) avaliar o comportamento da variabilidade espacial dos atributos do solo e de parâmetros de colheita e correlaciona-los com o índice de vegetação NDVI. O experimento foi realizado na safra 2017/2018 em área agrícola do município de Jaboticabal, estado de São Paulo, Brasil, localizada próximo as coordenadas geográficas 21°14’S e 48°16’O, com altitude média de 615 m. Foram realizados dois preparos de solo, o conservacionista e o convencional. O delineamento experimental foi baseado nas premissas básicas do Controle Estatístico de Qualidade – CEQ, contendo 60 pontos amostrais com GRID de 15 x 15 m. As avaliações ocorreram aos 30, 45, 80, 90 e 110 dias após a semeadura (DAS) por meio de coletas a campo e sensoriamento proximal utilizando sensor GreenSeeker. A partir da malha georreferenciada, foram estudados os atributos do solo (areia, argila, resistência mecânica do solo a penetração na linha e entrelinha de semeadura) e parâmetros relacionados com a colheita do amendoim como perdas totais e produtividade. As análises estatísticas utili... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Precision agriculture has technologies that help agricultural production, allowing the monitoring of crop development. Thus, the objectives of this work were: (i) to evaluate the effect of two soil tillage on peanut crop agronomic characteristics; (ii) compare vegetation indices (NDVI and IRVI) by temporal monitoring and their correlation with agronomic variables in two soil tillage; (iii) evaluate the behavior of spatial variability of soil attributes and harvesting parameters and correlate them with the NDVI vegetation index. The experiment was carried out in the 2017/2018 crop in an agricultural area of Jaboticabal, São Paulo State, Brazil, located near the geographic coordinates 21°14'S and 48° 6'O, with an average altitude of 615 m. Two soil tillage were done, the conservationist and the conventional one. The experimental design was based on the basic assumptions of the Statistical Quality Control - CEQ, containing 60 sampling points with 15 x 15 m GRID. Evaluations took place at 30, 45, 80, 90 and 110 days after sowing (DAS) by field collections and proximal sensing using GreenSeeker sensor. From the georeferenced mesh, the soil attributes (sand, clay, soil mechanical resistance to penetration in the row and sowing row) and parameters related to peanut harvest as total losses and yield were studied. The statistical analyzes used were descriptive statistics, Pearson correlation, individual control charts for process monitoring and geostatistics in the generation of spati... (Complete abstract click electronic access below) / Doutor

Dans le contexte de la vision par ordinateur cette thèse étudie le problème d’appariement d’images dans le cadre de la télédétection pour la géologie. Plus précisément, nous disposons dans ce travail de deux images de la même scène géographique, mais acquises à partir de deux points de vue différents et éventuellement à un autre moment. La tâche d’appariement est d'associer à chaque pixel de la première image un pixel de la seconde image.Bien que ce problème soit relativement facile pour les êtres humains, il reste difficile à résoudre par un ordinateur. De nombreuses approches pour traiter cette tâche ont été proposées. Les techniques les plus prometteuses formulent la tâche comme un problème d'optimisation numérique. Malheureusement, le nombre d'inconnues ainsi que la nature de la fonction à optimiser rendent ce problème extrêmement difficile à résoudre. Cette thèse étudie deux approches avec un schéma multi-échelle pour résoudre le problème numérique sous-jacent / This thesis studies the computer vision problem of image registration in the context of geological remote sensing surveys. More precisely we dispose in this work of two images picturing the same geographical scene but acquired from two different view points and possibly at a different time. The task of registration is to associate to each pixel of the first image its counterpart in the second image.While this problem is relatively easy for human-beings, it remains an open problem to solve it with a computer. Numerous approaches to address this task have been proposed. The most promising techniques formulate the task as a numerical optimization problem. Unfortunately, the number of unknowns along with the nature of the objective function make the optimization problem extremely difficult to solve. This thesis investigates two approaches along with a coarsening scheme to solve the underlying numerical problem

This dissertation investigates the role of the seasonal wetlands in the political economy and subsistence strategies of the ancient Maya of Chunchucmil, Yucatan, Mexico. A combination of pedestrian surveys and remote-sensing tasks were performed in order to better understand the settlement patterns and potential communication routes in and through the wetlands between Chunchucmil and the Gulf of Mexico. These western wetlands had been proposed as the principal avenue for interregional trade between coastal merchants and inland consumers, yet were thought to be uninhabited and uncultivable. Following the survey tasks outlined in this dissertation, these wetlands were found to contain an abundance of archaeological settlements and features indicating habitation, utilization, and trade throughout this diverse ecological zone The remote-sensing platforms utilized in this study include both multispectral (Landsat) and synthetic aperture radar (AirSAR), combined with additional remotely sensed resources. One of the goals of this survey was to test the capabilities of these two sensors for the direct detection of archaeological features from air and space. The results indicate that Landsat can be highly successful at detecting site location and measuring site size under certain environmental conditions. The Airborne Synthetic Aperture Radar proved to be adept at detecting large mounded architecture within the Yucatecan karstic plain, but its further utility is hampered by limitations of resolution, scale, and land cover One of the salient features of the landscape west of Chunchucmil is a network of stone pathways called andadores. These avenues through the wetlands outline a dendritic network of communication, trade, and extraction routes. The following dissertation places this network and its associated settlements (from suburban centers to diminutive camps) within their regional context, examining the roles they may have played in supporting a large mercantile economy centered at the site of Chunchucmil / acase@tulane.edu

Medidas complementares ao monitoramento in situ da qualidade da água podem ser obtidas por meio de sensoriamento remoto, sendo clorofila-a e sólidos suspensos alguns dos parâmetros que podem ser estimados. Este trabalho teve como objetivo explorar técnicas de processamento de imagens, análises estatísticas e de inteligência artificial com o objetivo de predizer e modelar as concentrações de clorofila-a e sólidos suspensos totais na Represa do Lobo/SP. Metodologicamente, foram realizadas coletas em campo, em três diferentes datas, para amostragem de água e posterior análise laboratorial. Os resultados limnológicos foram analisados, modelados e comparados com imagens processadas do satélite Sentinel-2. Análises de regressão e redes neurais artificiais (RNA) foram exploradas para gerar modelos de predição para a área de estudo. Os resultados indicam que métodos de regressão podem não ser adequados para capturar as relações lineares e/ou não-lineares entre os compostos de interesse e as respostas espectrais da água recebidas pelo satélite, indicando a capacidade das redes neurais em modelar relações mais complexas. Através da integração da resposta que o sensor MSI do satélite Sentinel-2 coletou nas regiões do visível ao infravermelho médio e de RNAs foi possível modelar a concentração de clorofila-a, com valores de R² superiores a 0,65 e de RMSE inferiores a 2,5 μg/L, e gerar mapas que permitam seu monitoramento temporal e análise espacial na área de estudo. Os resultados para SST não foram satisfatórios devido à complexidade óptica do ambiente analisado, bem como as baixas concentrações de SST na represa. Portanto, a integração de dados de sensoriamento remoto no mapeamento de corpos d\'água com a aplicação de redes neurais na análise de dados é uma abordagem promissora para prever clorofila-a e sólidos suspensos, bem como suas variações temporais e espaciais. / Complementary measures to in situ monitoring of water quality can be obtained through remote sensing, with chlorophyll-a and suspended solids being some of the parameters that can be estimated. The objective of this work was to explore techniques for image processing, statistical analysis and artificial intelligence with the objective of predicting and modeling the concentrations of chlorophyll-a and total suspended solids in the Lobo Reservoir/SP. Methodologically, field samples were collected in three different dates for water sampling and laboratory analysis. The limnological results were analyzed, modeled and compared with processed images of the Sentinel-2 satellite. Regression analysis and artificial neural networks (ANNs) were explored to generate prediction models for the study area. The results indicate that regression methods may not be adequate to capture linear and/or nonlinear relationships between the compounds of interest and the spectral responses of water received by the satellite, indicating the ability of neural networks to model more complex relationships. Through of the integration of response wich the MSI sensor of Sentinel satellite collected in the visible and near-infrared regions and of the ANN analysis was possible modeling the chlorophyll-a concentration, wich R² values highers of 0.65 and RMSE less 2.5, and create predict maps wich allow your temporal monitoring and spatial analysis in the study area. The TSS results were unsatisfactory because of the optic complexity of analysed ambient, as well as your small TSS concentrations in the Lobo Reservoir. Therefore, the integration of remote sensing data in the mapping of water bodies with the application of neural networks in the data analysis is a promising approach to predict chlorophyll-a and suspended solids as well as their temporal and spatial variations.

Since the texture of surfaces plays a key role in the shaping of many environmental processes, high resolution measurements are important to study these phenomena. Specifically, 3-D point cloud data is desirable to document river shape and evolution, surface roughness, and erosion-sedimentation processes. The best method of obtaining these measurements is using a terrestrial laser scanner. However, these are too expensive for limited-use experiments. Therefore, I developed a simple, affordable, and robust system used to acquire high resolution data relating to hydraulic and fluvial environments.