Imaging spectrometry data or hyperspectral imagery acquired using airborne systems have been used in the geologic community since the early 1980&rsquo / s and represent a mature technology. The solar spectral range 0.4&ndash / 2.5 &amp / #956 / m provides abundant information about hydroxyl-bearing minerals, sulfates and carbonates common to many geologic units and hydrothermal alteration assemblages. Satellite based Hyperion image data is used to implement and test hyperspectral processing techniques to identify alteration minerals and associate the results with the geological setting. Sisorta gold prospect is characterized by porphyry related epithermal and mesothermal alteration zones that are mapped through field studies. Image specific corrections are applied to obtain error free image data. Extensive field mapping and spectroscopic survey are used to identify nine endmembers from the image. Partial unmixing techniques are applied and used to assess the endmembers. Finally the spectral correlation mapper is used to map the endmembers which are kaolinite, dickite, halloysite, illite, montmorillonite and alunite as clay group and hematite, goethite and jarosite as the iron oxide group. The clays and iron oxides are mapped with approximately eighty percent accuracy. The study introduces an image specific algorithm for alteration minerals identification and discusses the outcomes within the geological perspective.

ARAÚJO, Efraim Martins. Utilização do sensoriamento remoto para detecção de diferentes alvos no entorno de reservatórios no semiárido. 2017. 159 f. Tese (Doutorado em Engenharia Agrícola)-Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Aline Mendes (alinemendes.ufc@gmail.com) on 2017-05-30T20:44:38Z No. of bitstreams: 1 2017_tese_emaraujo.pdf: 10444465 bytes, checksum: 54198305b9d650c104ee92d5588717ee (MD5) / Approved for entry into archive by Aline Mendes (alinemendes.ufc@gmail.com) on 2017-05-30T21:13:40Z (GMT) No. of bitstreams: 1 2017_tese_emaraujo.pdf: 10444465 bytes, checksum: 54198305b9d650c104ee92d5588717ee (MD5) / Made available in DSpace on 2017-05-30T21:13:40Z (GMT). No. of bitstreams: 1 2017_tese_emaraujo.pdf: 10444465 bytes, checksum: 54198305b9d650c104ee92d5588717ee (MD5) Previous issue date: 2017 / The main goal of this work is to evaluate the potential of discrimination for soil use and occupation in the surroundings of reservoirs located in the semi-arid region, using spectral information obtained by remote sensor considering multispectral and hyperspectral satellites images. The satellite images selected for the survey are Landsat 8 and Hyperion images. The research evaluated and compared the performance of different techniques for image classification applied to multispectral (Landsat 8) and hyperspectral (Hyperion) sensors aiming the detection and delineation of the land uses around the reservoirs Paus Brancos, Nova Vida and Marengo, located in the 25 de Maio settlement, Madalena – CE, belongin the hydrographic basin of the Banabuiú reservoir. The classes identified based on surveys conducted in 2014 and 2015 campaigns around the reservoirs were: water (water bodies), macrophytes, exposed soil, native vegetation, agriculture, sparse vegetation and fload plaind crop, in addition to cloud and shadow targets. Different techniques for image processing are tested and compared, such as NDVI (Vegetation Index by Normative Difference), non-supervised classifier (ISODATA) and supervised classifiers (Maximum Likelihood, K-Nearest Neighbours - KNN, Minimal Distance and Random Forest). For processing hyperspectral images, we use SVM (Support Vector Machine) classifier, which provides to analyze all the 155 radiometrically calibrated bands of the Hyperion sensor, assigning them weights in the classification process. According to the results provided by SVM classifier, RGB compositions of the 10 best ranked bands are evaluated aiming the identification of the best successful combination for delineating classes in the surroundings of the three studied reservoirs (bands R – 51, G – 161, B – 19). The analysis of NDVI multispectral images behaved inaccurate for delineating classes, mainly considering targets with similar spectral response, such as some kinds of vegetation. Meanwhile, the unsupervised classification proved to be deficient, not being able to discriminate water bodies from cloud shadow, even after applying contrast enhancing techniques within the Matlab computing program environment. The spectral and temporal analysis of soil use reflectance allowed to identify the spectral behavior of the nine classes considered in this study and also the spectral bands with the highest potential for discriminating the referred classes. Indeed, even within these optimal bands, some targets present similar spectral behaviors, difficulting their discrimination. On the other hand, the supervised classification applied to Landsat 8 and Hyperion images achieved to be succeed in the delineation of either distinct (water, soil and vegetation) and similar (macrophytes, fload plaind crop, native vegetation, agriculture and sparse vegetation) targets. It should be emphasized that the performance results of the classifiers applied to the Hyperion images are generally superior to those obtained respectively by the same classifiers over the Landsat 8 images. This can be explained by the higher spectral resolution of the first sensor, which increases the potential for delineating targets with similar spectral response. Concerning the supervised classifiers, in the stage of performance test, it was observed that KNN method is more accurate than the others for Landsat 8 images, with a maximum Kappa coefficient equal to 0.68. Meanwhile, for Hyperion images, the Maximum Likelihood method achieves the highest performance result, with a maximum Kappa coefficient equal to 0,78. Additionally, a sensitivity analysis of the supervised classification applied to Landsat 8 and Hyperion images is performed regarding the number of samples per class randomly collected for training. It is clearly observed that the randomness concerning training stage allows finding subsets of samples which increase the performance results. For the evaluation of the supervised maximum likelihood classification method, Landsat 8 (24/08/2015) and Hyperion (285/08/2015) images are considered for the computing tests. The training data were collected through a research technical visit in November, 2015, around São Nicolau reservoir, also located in the 25 de Maio settlement, while the data for performance evaluation (validation) were extracted from the image generated through the overflight performed by an Unmanned Aerial Vehicle (UAV), in the same period in the Paus Brancos reservoir. The obtained results demonstrate the robustness for that classifier when applied to Hyperion image, with a Kappa of 0.83. Concerning Landsat 8 image, the computed Kappa is 0.49, which can be explained by the corresponding lower spectral resolution. Two other applications of the Maximum Likelihood classifier for Landsat 8 and Hyperion images were performed. In the first one, the accuracy of each classifier for detecting reservoirs contours was tested. In some of these reservoirs, that task is made difficult by the presence of macrophytes in the hydraulic basin. For this analysis, the intersection area between the scenes of the Landsat 8 and Hyperion sensors, which cover the area of 25 de Maio Settlement, was used, totalizing 48 reservoirs. The results showed that the classifier generally underestimates the reservoir areas, reaching 73% and 51% of the reference value in the Landsat 8 and Hyperion images, respectively. Finally, an application of the supervised Maximum Likelihood classifier was performed using Hyperion images for the detection of land uses in the surroundings of reservoirs of other regions of the State of Ceará. In the analysis of the available data, it is possible to identify a reservoir located in the municipality of Lavras da Mangabeira, displayed in the Hyperion image (26/09/2010), with low cloud cover, near the image of Google Earth (08/07/2009), also used for validation purposes. The results of the application indicate accurate performance for the classifier associated with the RGB composition selected for the Hyperion image (bands R - 51, G - 161, B - 19) concerning the detection of the uses around this reservoir, the resultant Kappa coefficient is 0.90. On the other hand, the availability of Hyperion sensor data in applications for the State of Ceará is very restricted, which makes difficult to develop continuous researches using hyperspectral images. / O objetivo deste trabalho é avaliar o potencial de discriminação dos uso e ocupação do solo no entorno de reservatórios localizados na região semiárida, mediante informações espectrais obtidas por sensor remoto com imagens de satélites multiespectrais e hiperespectrais. As imagens de satélites selecionadas para a realização da pesquisa foram imagens Landsat 8 e Hyperion. A pesquisa analisou o desempenho de diferentes técnicas de classificação de imagens aplicadas a sensores multiespectrais (Landsat 8) e hiperespectrais (Hyperion) para detecção e diferenciação das classes do solo no entorno dos reservatórios Paus Brancos, Nova Vida e Marengo, situados no Assentamento 25 de Maio, localizados no município de Madalena – CE, pertencentes a bacia hidrográfica do reservatório Banabuiú. As classes identificadas com base em levantamentos em campanhas realizadas em 2014 e 2015 no entorno dos reservatórios são: água (corpos hídricos), macrófitas, solo exposto, vegetação nativa, agricultura, vegetação rala e vazante, além dos alvos nuvem e sombra de nuvem. Testaram-se na pesquisa diferentes técnicas de processamento de imagens, tais como NDVI (Índice de Vegetação por Diferença Normatizada), classificador não supervisionado (ISODATA) e supervisionados (Máxima Verossimilhança, K-Nearest Neighbours - KNN, Mínima Distância e Random Forest). Para processamento de imagens hiperespectrais utilizou-se, adicionalmente, o classificador SVM (Support Vector Machine), por permitir o processamento de todas as 155 bandas radiometricamente calibradas do sensor Hyperion, atribuindo-lhes pesos no processo de classificação. Testaram-se, então, composições RGB das 10 melhores bandas de acordo com o ranking resultante do classificador SVM, para identificação daquela com melhor desempenho na diferenciação das classes no entorno dos três reservatórios estudados (bandas R – 51, G – 161, B – 19). A análise de imagens multiespectrais do NDVI apresentou limitações na diferenciação de classes, sobretudo em alvos com resposta espectral similar como tipos de vegetação. Já a classificação não-supervisionada mostrou-se deficiente por não conseguir separar corpos hídricos de sombra de nuvem, mesmo após a aplicação de técnicas de realces implementados dentro do ambiente Matlab. A análise espectral e temporal da reflectância de classes permitiu identificar o comportamento espectral das nove classes analisadas neste estudo, indicando as faixas espectrais com maior potencial de diferenciação, embora se perceba que, mesmo nestas faixas, alguns alvos apresentam comportamento espectral similar, não sendo facilmente separados. A classificação supervisionada, por sua vez, destacou-se por conseguir separar tanto alvos distintos (água, solo e vegetação) como alvos semelhantes (macrófitas, vazante, vegetação nativa, agricultura e vegetação rala) quando aplicadas as imagens dos sensores Landsat 8 e Hyperion. Cabe destacar, entretanto, que o desempenho dos classificadores aplicados à imagem do sensor Hyperion foi, em geral, superior aos obtidos em imagem Landsat 8, o que pode ser explicado pela alta resolução espectral do primeiro, que facilita a diferenciação de alvos com reposta espectral similar. Na etapa de teste de desempenho dos classificadores supervisionados, observou-se que o método KNN foi superior aos demais no processamento de imagem Landsat 8, com coeficiente Kappa de 0,68. Já no caso do Hyperion, o método de Máxima Verossimilhança teve melhor desempenho com Kappa de 0,78. Adicionalmente, realizou-se uma análise de sensibilidade da classificação supervisionada aplicada a imagens Landsat 8 e Hyperion quanto ao número de amostras por classe usadas no treinamento, indicando que, em geral, o caráter aleatório de escolha das amostras potencializa o desempenho dos classificadores. Para validação do método de classificação supervisionada de Máxima Verossimilhança, utilizaram-se imagens Landsat 8 (24/08/2015) e Hyperion (28/08/2015). Os dados de treinamento do classificador foram coletados na campanha de novembro de 2015, no entorno do reservatório São Nicolau, também localizado no Assentamento 25 de Maio, enquanto que os dados de verificação do desempenho do método foram extraídos da imagem gerada no sobrevoo realizado, no mesmo período, no reservatório Paus Branco, usando um VANT (veículo aéreo não tripulado). Os resultados mostraram um excelente desempenho do classificador quando aplicado à imagem do sensor Hyperion, com Kappa de 0,83. Já a aplicação para a imagem do sensor Landsat 8 resultou em um Kappa de 0,49, o que pode ser explicado por sua baixa resolução espectral. Realizaram-se, ainda, duas aplicações do classificador supervisionado de Máxima Verossimilhança em imagens Landsat 8 e Hyperion para testar a eficiência do método. Na primeira, verificou-se a habilidade do classificador na detecção de contornos de reservatórios, em alguns dificultada pela presença de macrófitas na bacia hidráulica. Para isso, utilizou-se a área de interseção entre as cenas dos sensores Landsat 8 e Hyperion, que cobrem a área do Assentamento 25 de Maio, identificando 48 reservatórios. Os resultados mostraram que, em geral, o classificador subestima as áreas dos reservatórios, atingindo 73% e 51% do valor referência nas imagens Landsat 8 e Hyperion, respectivamente. Por fim, realizou-se uma aplicação do classificador supervisionado de Máxima Verossimilhança em imagens Hyperion para detecção de classes no entorno de reservatórios de outras regiões do Estado do Ceará. Na análise dos dados disponíveis, identificou-se um reservatório no município de Lavras da Mangabeira-CE, presente na imagem Hyperion (26/09/2010), com baixa cobertura de nuvens, em período próximo à imagem do google Earth (08/07/2009), usada para validação dos resultados. Os resultados da aplicação indicaram um bom desempenho do classificador associado à composição RGB da imagem Hyperion escolhida (bandas R – 51, G – 161, B – 19) na detecção das classes no entorno deste reservatório, produzindo um coeficiente Kappa de 0,90. Por outro lado, a disponibilidade de dados do sensor Hyperion em aplicações para o Estado do Ceará é bem restrita, o que dificulta o desenvolvimento de pesquisas continuadas usando imagens hiperespectrais.

ARAÚJO, E. M. Detecção de diferentes alvos no entorno de reservatórios no semiárido através do uso de sensoriamento remoto. 2017. 161 f. Tese (Doutorado em Engenharia Agrícola)- Universidade Federal do Ceará, Fortaleza, 2016. / Submitted by Weslayne Nunes de Sales (weslaynesales@ufc.br) on 2017-06-21T11:25:55Z No. of bitstreams: 1 2017_tese_emaraujo.pdf: 9310147 bytes, checksum: d2e4fbc1a2d900355b4d2243ab7adb84 (MD5) / Approved for entry into archive by Weslayne Nunes de Sales (weslaynesales@ufc.br) on 2017-06-21T11:27:46Z (GMT) No. of bitstreams: 1 2017_tese_emaraujo.pdf: 9310147 bytes, checksum: d2e4fbc1a2d900355b4d2243ab7adb84 (MD5) / Made available in DSpace on 2017-06-21T11:27:46Z (GMT). No. of bitstreams: 1 2017_tese_emaraujo.pdf: 9310147 bytes, checksum: d2e4fbc1a2d900355b4d2243ab7adb84 (MD5) Previous issue date: 2017 / The main goal of this work is to evaluate the potential of discrimination for soil use and occupation in the surroundings of reservoirs located in the semi-arid region, using spectral information obtained by remote sensor considering multispectral and hyperspectral satellites images. The satellite images selected for the survey are Landsat 8 and Hyperion images. The research evaluated and compared the performance of different techniques for image classification applied to multispectral (Landsat 8) and hyperspectral (Hyperion) sensors aiming the detection and delineation of the land uses around the reservoirs Paus Brancos, Nova Vida and Marengo, located in the 25 de Maio settlement, Madalena – CE, belongin the hydrographic basin of the Banabuiú reservoir. The classes identified based on surveys conducted in 2014 and 2015 campaigns around the reservoirs were: water (water bodies), macrophytes, exposed soil, native vegetation, agriculture, sparse vegetation and fload plaind crop, in addition to cloud and shadow targets. Different techniques for image processing are tested and compared, such as NDVI (Vegetation Index by Normative Difference), non-supervised classifier (ISODATA) and supervised classifiers (Maximum Likelihood, K-Nearest Neighbours - KNN, Minimal Distance and Random Forest). For processing hyperspectral images, we use SVM (Support Vector Machine) classifier, which provides to analyze all the 155 radiometrically calibrated bands of the Hyperion sensor, assigning them weights in the classification process. According to the results provided by SVM classifier, RGB compositions of the 10 best ranked bands are evaluated aiming the identification of the best successful combination for delineating classes in the surroundings of the three studied reservoirs (bands R – 51, G – 161, B – 19). The analysis of NDVI multispectral images behaved inaccurate for delineating classes, mainly considering targets with similar spectral response, such as some kinds of vegetation. Meanwhile, the unsupervised classification proved to be deficient, not being able to discriminate water bodies from cloud shadow, even after applying contrast enhancing techniques within the Matlab computing program environment. The spectral and temporal analysis of soil use reflectance allowed to identify the spectral behavior of the nine classes considered in this study and also the spectral bands with the highest potential for discriminating the referred classes. Indeed, even within these optimal bands, some targets present similar spectral behaviors, difficulting their discrimination. On the other hand, the supervised classification applied to Landsat 8 and Hyperion images achieved to be succeed in the delineation of either distinct (water, soil and vegetation) and similar (macrophytes, fload plaind crop, native vegetation, agriculture and sparse vegetation) targets. It should be emphasized that the performance results of the classifiers applied to the Hyperion images are generally superior to those obtained respectively by the same classifiers over the Landsat 8 images. This can be explained by the higher spectral resolution of the first sensor, which increases the potential for delineating targets with similar spectral response. Concerning the supervised classifiers, in the stage of performance test, it was observed that KNN method is more accurate than the others for Landsat 8 images, with a maximum Kappa coefficient equal to 0.68. Meanwhile, for Hyperion images, the Maximum Likelihood method achieves the highest performance result, with a maximum Kappa coefficient equal to 0,78. Additionally, a sensitivity analysis of the supervised classification applied to Landsat 8 and Hyperion images is performed regarding the number of samples per class randomly collected for training. It is clearly observed that the randomness concerning training stage allows finding subsets of samples which increase the performance results. For the evaluation of the supervised maximum likelihood classification method, Landsat 8 (24/08/2015) and Hyperion (285/08/2015) images are considered for the computing tests. The training data were collected through a research technical visit in November, 2015, around São Nicolau reservoir, also located in the 25 de Maio settlement, while the data for performance evaluation (validation) were extracted from the image generated through the overflight performed by an Unmanned Aerial Vehicle (UAV), in the same period in the Paus Brancos reservoir. The obtained results demonstrate the robustness for that classifier when applied to Hyperion image, with a Kappa of 0.83. Concerning Landsat 8 image, the computed Kappa is 0.49, which can be explained by the corresponding lower spectral resolution. Two other applications of the Maximum Likelihood classifier for Landsat 8 and Hyperion images were performed. In the first one, the accuracy of each classifier for detecting reservoirs contours was tested. In some of these reservoirs, that task is made difficult by the presence of macrophytes in the hydraulic basin. For this analysis, the intersection area between the scenes of the Landsat 8 and Hyperion sensors, which cover the area of 25 de Maio Settlement, was used, totalizing 48 reservoirs. The results showed that the classifier generally underestimates the reservoir areas, reaching 73% and 51% of the reference value in the Landsat 8 and Hyperion images, respectively. Finally, an application of the supervised Maximum Likelihood classifier was performed using Hyperion images for the detection of land uses in the surroundings of reservoirs of other regions of the State of Ceará. In the analysis of the available data, it is possible to identify a reservoir located in the municipality of Lavras da Mangabeira, displayed in the Hyperion image (26/09/2010), with low cloud cover, near the image of Google Earth (08/07/2009), also used for validation purposes. The results of the application indicate accurate performance for the classifier associated with the RGB composition selected for the Hyperion image (bands R - 51, G - 161, B - 19) concerning the detection of the uses around this reservoir, the resultant Kappa coefficient is 0.90. On the other hand, the availability of Hyperion sensor data in applications for the State of Ceará is very restricted, which makes difficult to develop continuous researches using hyperspectral images. / O objetivo deste trabalho é avaliar o potencial de discriminação dos uso e ocupação do solo no entorno de reservatórios localizados na região semiárida, mediante informações espectrais obtidas por sensor remoto com imagens de satélites multiespectrais e hiperespectrais. As imagens de satélites selecionadas para a realização da pesquisa foram imagens Landsat 8 e Hyperion. A pesquisa analisou o desempenho de diferentes técnicas de classificação de imagens aplicadas a sensores multiespectrais (Landsat 8) e hiperespectrais (Hyperion) para detecção e diferenciação das classes do solo no entorno dos reservatórios Paus Brancos, Nova Vida e Marengo, situados no Assentamento 25 de Maio, localizados no município de Madalena – CE, pertencentes a bacia hidrográfica do reservatório Banabuiú. As classes identificadas com base em levantamentos em campanhas realizadas em 2014 e 2015 no entorno dos reservatórios são: água (corpos hídricos), macrófitas, solo exposto, vegetação nativa, agricultura, vegetação rala e vazante, além dos alvos nuvem e sombra de nuvem. Testaram-se na pesquisa diferentes técnicas de processamento de imagens, tais como NDVI (Índice de Vegetação por Diferença Normatizada), classificador não supervisionado (ISODATA) e supervisionados (Máxima Verossimilhança, K-Nearest Neighbours - KNN, Mínima Distância e Random Forest). Para processamento de imagens hiperespectrais utilizou-se, adicionalmente, o classificador SVM (Support Vector Machine), por permitir o processamento de todas as 155 bandas radiometricamente calibradas do sensor Hyperion, atribuindo-lhes pesos no processo de classificação. Testaram-se, então, composições RGB das 10 melhores bandas de acordo com o ranking resultante do classificador SVM, para identificação daquela com melhor desempenho na diferenciação das classes no entorno dos três reservatórios estudados (bandas R – 51, G – 161, B – 19). A análise de imagens multiespectrais do NDVI apresentou limitações na diferenciação de classes, sobretudo em alvos com resposta espectral similar como tipos de vegetação. Já a classificação não-supervisionada mostrou-se deficiente por não conseguir separar corpos hídricos de sombra de nuvem, mesmo após a aplicação de técnicas de realces implementados dentro do ambiente Matlab. A análise espectral e temporal da reflectância de classes permitiu identificar o comportamento espectral das nove classes analisadas neste estudo, indicando as faixas espectrais com maior potencial de diferenciação, embora se perceba que, mesmo nestas faixas, alguns alvos apresentam comportamento espectral similar, não sendo facilmente separados. A classificação supervisionada, por sua vez, destacou-se por conseguir separar tanto alvos distintos (água, solo e vegetação) como alvos semelhantes (macrófitas, vazante, vegetação nativa, agricultura e vegetação rala) quando aplicadas as imagens dos sensores Landsat 8 e Hyperion. Cabe destacar, entretanto, que o desempenho dos classificadores aplicados à imagem do sensor Hyperion foi, em geral, superior aos obtidos em imagem Landsat 8, o que pode ser explicado pela alta resolução espectral do primeiro, que facilita a diferenciação de alvos com reposta espectral similar. Na etapa de teste de desempenho dos classificadores supervisionados, observou-se que o método KNN foi superior aos demais no processamento de imagem Landsat 8, com coeficiente Kappa de 0,68. Já no caso do Hyperion, o método de Máxima Verossimilhança teve melhor desempenho com Kappa de 0,78. Adicionalmente, realizou-se uma análise de sensibilidade da classificação supervisionada aplicada a imagens Landsat 8 e Hyperion quanto ao número de amostras por classe usadas no treinamento, indicando que, em geral, o caráter aleatório de escolha das amostras potencializa o desempenho dos classificadores. Para validação do método de classificação supervisionada de Máxima Verossimilhança, utilizaram-se imagens Landsat 8 (24/08/2015) e Hyperion (28/08/2015). Os dados de treinamento do classificador foram coletados na campanha de novembro de 2015, no entorno do reservatório São Nicolau, também localizado no Assentamento 25 de Maio, enquanto que os dados de verificação do desempenho do método foram extraídos da imagem gerada no sobrevoo realizado, no mesmo período, no reservatório Paus Branco, usando um VANT (veículo aéreo não tripulado). Os resultados mostraram um excelente desempenho do classificador quando aplicado à imagem do sensor Hyperion, com Kappa de 0,83. Já a aplicação para a imagem do sensor Landsat 8 resultou em um Kappa de 0,49, o que pode ser explicado por sua baixa resolução espectral. Realizaram-se, ainda, duas aplicações do classificador supervisionado de Máxima Verossimilhança em imagens Landsat 8 e Hyperion para testar a eficiência do método. Na primeira, verificou-se a habilidade do classificador na detecção de contornos de reservatórios, em alguns dificultada pela presença de macrófitas na bacia hidráulica. Para isso, utilizou-se a área de interseção entre as cenas dos sensores Landsat 8 e Hyperion, que cobrem a área do Assentamento 25 de Maio, identificando 48 reservatórios. Os resultados mostraram que, em geral, o classificador subestima as áreas dos reservatórios, atingindo 73% e 51% do valor referência nas imagens Landsat 8 e Hyperion, respectivamente. Por fim, realizou-se uma aplicação do classificador supervisionado de Máxima Verossimilhança em imagens Hyperion para detecção de classes no entorno de reservatórios de outras regiões do Estado do Ceará. Na análise dos dados disponíveis, identificou-se um reservatório no município de Lavras da Mangabeira-CE, presente na imagem Hyperion (26/09/2010), com baixa cobertura de nuvens, em período próximo à imagem do google Earth (08/07/2009), usada para validação dos resultados. Os resultados da aplicação indicaram um bom desempenho do classificador associado à composição RGB da imagem Hyperion escolhida (bandas R – 51, G – 161, B – 19) na detecção das classes no entorno deste reservatório, produzindo um coeficiente Kappa de 0,90. Por outro lado, a disponibilidade de dados do sensor Hyperion em aplicações para o Estado do Ceará é bem restrita, o que dificulta o desenvolvimento de pesquisas continuadas usando imagens hiperespectrais.

The goal of this thesis is to evaluate the implementation of new information system in a selected corporation. Minor goal is to evaluate the outsourcing solutions related to the information system in the same company. The thesis includes detailed description of the impact of implementation and the current state of business taking into consideration the information system and its limits. Additional insights are provided by explaining the relationship between the corporation and the outsourcing companies chosen. This is evaluated and with the support of internal expertise and statistics, is concluded, that the implementation was successful. Added value of this thesis is an insight in the process of implementation of a complex information system in a worldwide corporation.

The four novels Hyperion, The Fall ofHyperion, Endymionand The Rise of Endymionconstitute the Hyperion Cantosby the American science fiction writer Dan Simmons. Thisgalactic-empire,epic,science fictionnarrative containsa plethora ofliterary references. The dominant part comes from the nineteenth-century Romantic poet John Keats. The inclusion of passages from his poetry and letters is pursuedin my analysis.EmployingLubomír Doležel’scategorizations of intertextuality—“transposition,” “expansion,” and “displacement”—I seek to show how Keats’s writings and his persona constitute a privilegedintertext inSimmons’s tetralogyand I show its function.Simmons constructs subsidiary plots, some of which are drivenby Keats’s most well-known poetry. In consequence, some of the subplotscan be regarded as rewrites of Keats’s works.Although quotations of poetry have a tendency to direct the reader’s attention away from the main plot,slowing down the narrative,such passages in the narrativesevokeKeats’s philosophy of empathy, beauty andlove,which is fundamental for his humanism.ForKeats, the poet is a humanist, giving solace to mankind through his poetry. I argue that the complex intertextual relationships with regards toKeats’s poetryand biographyshow the way Simmons expresses humanism as a belief in man’s dignity and worth, and uses it as the basis for his epic narrative.

Arid lands cover approximately 30% of the earth’s surface. Due to the broadness, remoteness, and harsh condition of these lands, land condition assessment and monitoring using ground-based techniques appear to be limited. Remote sensing imagery with its broad areal coverage, repeatability, cost and time-effectiveness has been suggested and used as an alternative approach for more than three decades. This thesis evaluated the potential of different remote sensing techniques for assessing and monitoring land condition of southern arid lands of South Australia. There were four specific objectives: 1) to evaluate vegetation indices derived from multispectral satellite imagery for prediction of vegetation cover; 2) to compare vegetation indices and field measurements for detecting vegetation changes and assessing land condition; 3) to examine the potential of hyperspectral imagery for discriminating vegetation components that are important in land management using unmixing techniques; and 4) to test whether spatial heterogeneity in land surface reflectance can provide additional information about land condition and effects of management on land condition. The study focused on Kingoonya and Gawler Soil Conservation Districts that were dominated by chenopod shrublands and low open woodlands over sand plains and dunes. The area has been grazed predominately by sheep for more than 100 years and land degradation or desertification due to overgrazing is evident in some parts of the region, especially around stock watering points. Grazing is the most important factor that influences land condition. Four full scenes of Landsat TM and ETM+ multispectral and Hyperion hyperspectral data were acquired over the study area. The imagery was acquired in dry seasons to highlight perennial vegetation cover that has an important role in land condition assessment and monitoring. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were compared with vegetation cover estimates at monitoring points made by state government agency staff during the first Pastoral Lease assessments in 1991. To examine the performance of vegetation indices, they were tested at two scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, selected Stress Related Vegetation Indices using red, nearinfrared and mid-infrared bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was predicted best within land systems (R2=0.88), while combined vegetation, plant litter and soil cryptogam crust cover was predicted best at landscape scale (R2=0.39). The results of applying one of the stress related vegetation indices (STVI-4) to 1991 TM and 2002 ETM+ Landsat imagery to detect vegetation changes and to 2005 Landsat TM imagery to discriminate Land Condition Index (LCI) classes showed that it is an appropriate vegetation index for both identifying trends in vegetation cover and assessing land condition. STVI-4 highlighted increases and decreases in vegetation in different parts of the study area. The vegetation change image provided useful information about changes in vegetation cover resulting from variations in climate and alterations in land management. STVI-4 was able to differentiate all three LCI classes (poor, fair and good condition) in low open woodlands with 95% confidence level. In chenopod shrubland and Mount Eba country only poor and good conditions were separable spectrally. The application of spectral mixture analysis to Hyperion hyperspectral imagery yielded five distinct end-members: two associated with vegetation cover and the remaining three associated with different soils, surface gravel and stone. The specific identity of the image end-members was determined by comparing their mean spectra with field reflectance spectra collected with an Analytical Spectral Devices (ASD) Field Spec Pro spectrometer. One vegetation end-member correlated significantly with cottonbush vegetation cover (R2=0.89), distributed as patches throughout the study area. The second vegetation end-member appeared to map green and grey-green perennial shrubs (e.g. Mulga) and correlated significantly with total vegetation cover (R2=0.68). The soil and surface gravel and stone end-members that mapped sand plains, sand dunes, and surface gravel and stone did not show significant correlations with the field estimates of these soil surface components. I examined the potential of a spatial heterogeneity index, the Moving Standard Deviation Index (MSDI), around stock watering points and nearby ungrazed reference sites. One of the major indirect effects of watering points in a grazed landscape is the development around them of a zone of extreme degradation called a piosphere. MSDI was applied to Landsat red band for detection and assessment of these zones. Results showed watering points had significantly higher MSDI values than non-degraded reference areas. Comparison of two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Perpendicular Distance vegetation index (PD54), which were used as reference indices, showed that the PD54 was more sensitive than NDVI for assessing land condition in this perennial-dominated arid environment. Piospheres were found to be more spatially heterogeneous in land surface reflectance. They had higher MSDI values compared to non-degraded areas, and spatial heterogeneity decreased with increasing distance from water points. The study has demonstrated overall that image-based indices derived from Landsat multispectral and Hyperion hyperspectral imagery can be used with field methods to assess and monitor vegetation cover (and consequently land condition) of southern arid lands of South Australia in a quick and efficient way. Relationships between vegetation indices, end-members and field measurements can be used to estimate vegetation cover and monitor its variation with time in broad areas where field-based methods are not effective. Multispectral vegetation indices can be used to assess and discriminate ground-based land condition classes. The sandy-loam end-member extracted from Hyperion imagery has high potential for monitoring sand dunes and their movement over time. The MSDI showed that spatial heterogeneity in land surface reflectance can be used as a good indicator of land degradation. It differentiated degraded from nondegraded areas successfully and detected grazing gradients slightly better than widely used vegetation indices. Results suggest further research using these remote sensing techniques is warranted for arid land condition assessment and monitoring in South Australia. / http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1295218 / Thesis (Ph.D.) -- School of Earth and Environmental Science, 2007

Thesis (M.S.)--Indiana University, 2008. / Title from screen (viewed on June 3, 2009). Department of Earth Science, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Lin Li, Pierre Jacinthe, Gabriel M. Filippelli. Includes vita. Includes bibliographical references (leaves 78-81).

Dans leur présentation traditionnelle, les systèmes à mémoire distribuée virtuellement partagée (MVP, en anglais DSM) permettent à des processus de partager un espace d'adressage commun selon un modèle de cohérence fixé : cohérence séquentielle, à la libération, etc. Les pro- cessus peuvent habituellement être distribués sur des noeuds physiquement distincts et leurs in- teractions par la mémoire commune sont implémentées (de manière transparente) par la MVP, en utilisant une bibliothèque de communication. Dans la plupart de travaux dans ce domaine, il est sous-entendu que la MVP et l'architecture sous-jacente sont données. Le programmeur doit alors adapter son application à ce cadre fixe, afin d'obtenir une exécution efficace. Cette approche impose des limitations statiques et ne permet pas de comparer des approches alternatives. La contribution de cette thèse consiste à proposer une plate-forme générique d'implémentation et d'expérimentation appelée DSM-PM2, qui permet de développer et d'optimiser conjointement les applications distribuées et le(s) protocole(s) de cohérence de la MVP sous-jacente. Cette plate-forme, implémentée entièrement au niveau logiciel, est portable sur plusieurs architectures de grappes hautes performances. Elle fournit les briques de bases nécessaires pour implémenter et évaluer une large classe de protocoles de cohérence multithreads dans un cadre unifié. Trois mo- dèles de cohérence sont actuellement supportés : la cohérence séquentielle, la cohérence à la libéra- tion et la cohérence Java. Plusieurs études de performance ont été effectuées à l'aide d'applications multithreads pour l'ensemble des protocoles proposés, sur différentes plates-formes. DSM-PM a été validé par son utilisation en tant que cible d'un système de compilation Java pour des grappes appelé Hyperion.

Dan Simmons's far-future science fiction epic Hyperion Cantos, in which seven disparate individuals become enmeshed in a convoluted plot to enslave humanity, provides extensive support for British theologian John Hick's theory of transcendental pluralism. Using the central figures of the Shrike, a mysterious killing machine, and the Technocore, a collective of autonomous artificial intelligences, Simmons demonstrates Hick's postulation that all major Western religions actually focus on the same divine being (God) by creating a negative divine being, akin to Satan, to which characters of various religions react in similar ways. Simmons's pilgrims each represent a particular spiritual outlook, from specific organized religions to less-defined positions such as secularism and agnosticism, but each pilgrim's tale contributes to the evidence of transcendental pluralism. This thesis explores each characters' experiences as they relate to the Shrike, the Technocore, and, ultimately the theory of transcendental pluralism. / by Zachary Stewart. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

This thesis describes the research undertaken for the degree of Doctor of Philosophy, testing the hypothesis that spectrometer data can be used to establish usable relationships for prediction of pasture quality attributes. The research data consisted of reflectance measurements of various temperate pasture types recorded at four different times (years 2000 to 2002), recorded by three hyperspectral sensors, the in situ ASD, the airborne HyMap and the satellite-borne Hyperion. Corresponding ground-based pasture samples were analysed for content of chlorophyll, water, crude protein, digestibility, lignin and cellulose at three study sites in rural Victoria, Australia. This context was used to evaluate effects of sensor differences, data processing and enhancement, analytical methods and sample variability on the predictive capacity of derived prediction models. Although hyperspectral data analysis is being applied in many areas very few studies on temperate pastures have been conducted and hardly any encompass the variability and heterogeneity of these southern Australian examples. The research into the relationship between the spectrometer data and pasture quality attribute assays was designed using knowledge gained from assessment of other hyperspectral remote sensing and near-infrared spectroscopy research, including bio-chemical and physical properties of pastures, as well as practical issues of the grazing industries and carbon cycling/modelling. Processing and enhancement of the spectral data followed methods used by other hyperspectral researchers with modifications deemed essential to produce better relationships with pasture assay data. As many different methods are in use for the analysis of hyperspectral data several alternative approaches were investigated and evaluated to determine reliability, robustness and suitability for retrieval of temperate pasture quality attributes. The analyses employed included stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR). The research showed that the spectral research data had a higher potential to be used for prediction of crude protein and digestibility than for the plant fibres lignin and cellulose. Spectral transformation such as continuum removal and derivatives enhanced the results. By using a modified approach based on sample subsets identified by a matrix of subjective bio-physical and ancillary data parameters, the performance of the models were enhanced. Prediction models from PLSR developed on ASD in situ spectral data, HyMap airborne imagery and Hyperion and corresponding pasture assays showed potential for predicting the two important pasture quality attributes crude protein and digestibility in hyperspectral imagery at a few quantised levels corresponding to levels currently used in commercial feed testing. It was concluded that imaging spectrometry has potential to offer synoptic, simultaneous and spatially continuous information valuable to feed based enterprises in temperate Victoria. The thesis provide a significant contribution to the field of hyperspectral remote sensing and good guidance for future hyperspectral researchers embarking on similar tasks. As the research is based on temperate pastures in Victoria, Australia, which are dominated by northern hemisphere species, the findings should be applicable to analysis of temperate pastures elsewhere, for example in Western Australia, New Zealand, South Africa, North America, Europe and northern Asia (China).