The boundaries of the cities are not immutable, they can be changed. With the development of the economies and societies, the population and pollution of cities are increasing. Some urban areas are expanding with more population or other dynamics of urbanization, while other urban areas are reducing with the changing of the dynamics. Therefore, detecting urban areas or delineating the boundaries of the cities is one of the most important steps for urban studies, which is closely related to human settlements and human activities. Remote sensing data (RS) is widely used to monitor and detect land use and land cover on the surface of the earth. But the extraction of urban areas from the ordinary RS data is not easy work. The Operational Linescan System (OLS) is the sensors of the Defense Meteorological Satellite Program (DMSP). The nighttime lights from the DMSP/OLS provide worldwide remotely sensed data to analyze long-term light emissions which are closely related to human activities. But the nighttime lights imagery data contains inherent errors. Therefore, the approaches to calibrate the data and extract the urban areas from the data are complicated. The long-term objective of this thesis is to delineate the boundaries of the natural cities of the continental United States of America (USA) from 1992 to 2010 of nightlight imagery data with all the different satellites. In this thesis, the coefficients for the intercalibration of the nightlight imagery data have been calculated based on the method developed by Elvidge, et al. (2009), but the coefficients are new and available. The approach used to determine the most appropriate threshold value is very important to eliminate the possible data error. The method to offset this possible error and delineate the boundaries of the cities from nightlight imagery data is the head/tail breaks classification, which is proposed by Jiang (2012b). The head/tail breaks classification is also useful for finding the ht-index of the extracted natural cities which is developed by Jiang and Yin (2013). The ht-index is an indicator of the underlying hierarchy of the data. The results of this study can be divided into two categories. In the first, the achieved coefficients for the intercalibration of nightlight images of the continental USA are shown in a table, and the achieved data of the urban areas are stored in a data archive. In the second, the different threshold values of the uncalibrated images and the individual threshold value of the calibrated images are shown in tables, and the results of the head/tail breaks classification and power law test are also drawn. The results show that the acquired natural cities obey the power law distribution. And the results also confirm that the head/tail breaks classification is available for finding a suitable threshold value for the nightlight imagery data. Key words: cities’ boundaries; DMSP/OLS; head/tail breaks classification; nighttime lights; power law; urban areas

In recent decades, the night lights data of the Earth’s surface derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been used to detect the human settlements and human activities, because the DMSP/OLS data is able to supply the information about the urban areas  and non-urban areas on the Earth which means it is more suitable for urban studies than usual satellite imagery data.   The urban development is closed linked to the human society development. Therefore, studies of urban development will help people to understand how the urban changed and predict the urban change. The aim of this study was to detect Beijing’s urban development from 1992 to 2012, and find the contributions to the urban sprawl from socio-economic factors. Based on this objective, the main dataset used in this thesis was night lights images derived from the DMSP/OLS which was detected from  1992 to 2012. Due to the lacking of on-board calibration on OLS, and the over-glow of the lights resources, the information about the night lights cannot be extracted directly. Before any process, the night lights images should be calibrated. There is a method to calibrate the night light images which is called intercalibration. It is a second order regression model based method to find the related digital number values. Therefore, intercalibration was employed, and the threshold values were determined to extract urban areas in this study. Threshold value is useful for diffusing the over-glow effect, and finding the urban areas from the DMSP/OLS data. The methods to determine the threshold value in this thesis are empirical threshold method, sudden jump detection method, statistic data comparison method and k-mean clustering method. In addition, 13 socio-economic factors which included gross domestic product, urban population, permanent population, total energy consumption and so on were used to build the regression model. The contributions from these factors to the sum of the Beijing’s lights were found based on modeling.   The results of this thesis are positive. The intercalibration was successful and all the DMSP/OLS data used in this study were calibrated. And then, the appropriate threshold values to extract the urban areas were figured out. The achieved urban areas were compared to the satellite images and the result showed that the urban areas were useful. During the time certain factors used in this study, such as mobile phone users, possession of civil vehicles, GDP, three positively highest contributed to urban development were close to 23%, 8% and 9%, respectively.

Esta tese propõe o uso da geoinformação a serviço da demografia tomando-se a distribuição da população na Amazônia Brasileira como objeto de estudo. Foram desenvolvidos métodos baseados em dados de sensoriamento remoto e técnicas de análise espacial para representar a população em superfícies de densidade. O objetivo geral foi verificar a utilidade dos dados de sensoriamento remoto para compor a base do processo de redistribuição da população, em comparação com métodos tradicionais. Especificamente, foram utilizadas imagens de luzes noturnas do sistema DMSP/OLS para a Amazônia Legal, e imagens dos sistemas CCD-CBERS1 e TM-Landsat, para a escala municipal. Inicialmente, um mosaico de imagens de luzes noturnas DMSP/OLS foi avaliado quanto à capacidade de detectar presença e atividade humana na região. As relações lineares entre pixels de luzes DMSP e população urbana, área urbanizada e consumo de energia elétrica obtidas, motivaram o desenvolvimento de um produto de luzes noturnas mais recente e adequado para estudos de distribuição de população. A análise deste mosaico indicou o potencial e as restrições da informação de luzes noturnas para estimar a evolução dos valores de população urbana. Um novo método para interpolar e redistribuir população utilizando informações de luzes noturnas, denominado DMSPop_M foi apresentado. A superfície de densidade populacional resultante mostrou-se uma opção intermediária entre as superfícies obtidas através das técnicas tradicionais para interpolar população, e a representação através dos setores censitários. Para a escala municipal, outro método multivariado para distribuir população foi desenvolvido para Marabá - PA. Dados provenientes da classificação digital de imagens CBERS e Landsat e outros dados geográficos foram selecionados como variáveis indicadoras da presença de população. Através de técnicas de pertinência e inferência Fuzzy a população dos setores censitários foi redistribuída em superfícies de densidade populacional. A superfície que melhor representou a distribuição populacional foi aquela obtida através de média simples das variáveis. O método desenvolvido poderá ser aplicado para modelos mais robustos em que as superfícies resultantes refletirão a relação entre variáveis proposta. Os dados de sensoriamento remoto foram fundamentais para incluir a heterogeneidade espacial nos métodos desenvolvidos. Finalmente, este trabalho contribui para que estudos de modelagem, e planejamento para a região Amazônica possam incluir adequadamente a dimensão humana no que se refere à distribuição e representação de sua densidade populacional. / This thesis presents the use of geoinformation as a valuable tool for demographic studies where the subject of interest is the population distribution over the Brazilian Amazon. Population density surfaces based on remote sensing data and spatial analysis techniques are developed as an alternative approach to represent population distribution. Remote sensing data is hypothesized as the basis for disaggregation methods to distribute population in regular cells. Specifically, imagery from DMSP/OLS night-time lights is used at global scale and from CCD-CBERS1 with TM-Landsat imagery at municipal scale. First, DMSP/OLS data is evaluated about the relations between night-time lights and human activities in Amazônia. Significative linear correlations between night-time lights and urban population, electrical power consumption and urban area are obtained. Thus, alternative techniques to generate a night-time lights mosaic are proposed and a new mosaic is generated to be used as a reference of urban population distribution. The analysis of this new DMSP mosaic reveals its potential and restrictions to estimate and to monitor the annual evolution of urban settlements. A new interpolation method to generate population density surface is developed using night-time lights data. The resultant surface, comparing to the usual interpolation methods for population is considered an intermediary option between the traditional techniques and the surface representing population data from a higher scale (the census sector). At municipal scale, a multivariate method to distribute population inside the municipal boundaries is developed for Marabá, state of Pará. CBERS and Landsat imagery and other geographical data are selected as indicator variables of human presence. These variables are converted to Fuzzy memberships and related to each other towards average and Fuzzy operators. The census sector population is redistributed in the population density surfaces. The best surface is obtained from simple average of the indicator variables. The proposed method can support different models and its population density will reproduce the consistence of them. Finally, this thesis contributes to represent the population distribution at global and municipal scale at the Brazilian Amazon. The methods and results obtained here will be helpful for any environmental modeling study in the Amazon region that cares for the local population that has been living there all along.

Esta tese propõe o uso da geoinformação a serviço da demografia tomando-se a distribuição da população na Amazônia Brasileira como objeto de estudo. Foram desenvolvidos métodos baseados em dados de sensoriamento remoto e técnicas de análise espacial para representar a população em superfícies de densidade. O objetivo geral foi verificar a utilidade dos dados de sensoriamento remoto para compor a base do processo de redistribuição da população, em comparação com métodos tradicionais. Especificamente, foram utilizadas imagens de luzes noturnas do sistema DMSP/OLS para a Amazônia Legal, e imagens dos sistemas CCD-CBERS1 e TM-Landsat, para a escala municipal. Inicialmente, um mosaico de imagens de luzes noturnas DMSP/OLS foi avaliado quanto à capacidade de detectar presença e atividade humana na região. As relações lineares entre pixels de luzes DMSP e população urbana, área urbanizada e consumo de energia elétrica obtidas, motivaram o desenvolvimento de um produto de luzes noturnas mais recente e adequado para estudos de distribuição de população. A análise deste mosaico indicou o potencial e as restrições da informação de luzes noturnas para estimar a evolução dos valores de população urbana. Um novo método para interpolar e redistribuir população utilizando informações de luzes noturnas, denominado DMSPop_M foi apresentado. A superfície de densidade populacional resultante mostrou-se uma opção intermediária entre as superfícies obtidas através das técnicas tradicionais para interpolar população, e a representação através dos setores censitários. Para a escala municipal, outro método multivariado para distribuir população foi desenvolvido para Marabá - PA. Dados provenientes da classificação digital de imagens CBERS e Landsat e outros dados geográficos foram selecionados como variáveis indicadoras da presença de população. Através de técnicas de pertinência e inferência Fuzzy a população dos setores censitários foi redistribuída em superfícies de densidade populacional. A superfície que melhor representou a distribuição populacional foi aquela obtida através de média simples das variáveis. O método desenvolvido poderá ser aplicado para modelos mais robustos em que as superfícies resultantes refletirão a relação entre variáveis proposta. Os dados de sensoriamento remoto foram fundamentais para incluir a heterogeneidade espacial nos métodos desenvolvidos. Finalmente, este trabalho contribui para que estudos de modelagem, e planejamento para a região Amazônica possam incluir adequadamente a dimensão humana no que se refere à distribuição e representação de sua densidade populacional. / This thesis presents the use of geoinformation as a valuable tool for demographic studies where the subject of interest is the population distribution over the Brazilian Amazon. Population density surfaces based on remote sensing data and spatial analysis techniques are developed as an alternative approach to represent population distribution. Remote sensing data is hypothesized as the basis for disaggregation methods to distribute population in regular cells. Specifically, imagery from DMSP/OLS night-time lights is used at global scale and from CCD-CBERS1 with TM-Landsat imagery at municipal scale. First, DMSP/OLS data is evaluated about the relations between night-time lights and human activities in Amazônia. Significative linear correlations between night-time lights and urban population, electrical power consumption and urban area are obtained. Thus, alternative techniques to generate a night-time lights mosaic are proposed and a new mosaic is generated to be used as a reference of urban population distribution. The analysis of this new DMSP mosaic reveals its potential and restrictions to estimate and to monitor the annual evolution of urban settlements. A new interpolation method to generate population density surface is developed using night-time lights data. The resultant surface, comparing to the usual interpolation methods for population is considered an intermediary option between the traditional techniques and the surface representing population data from a higher scale (the census sector). At municipal scale, a multivariate method to distribute population inside the municipal boundaries is developed for Marabá, state of Pará. CBERS and Landsat imagery and other geographical data are selected as indicator variables of human presence. These variables are converted to Fuzzy memberships and related to each other towards average and Fuzzy operators. The census sector population is redistributed in the population density surfaces. The best surface is obtained from simple average of the indicator variables. The proposed method can support different models and its population density will reproduce the consistence of them. Finally, this thesis contributes to represent the population distribution at global and municipal scale at the Brazilian Amazon. The methods and results obtained here will be helpful for any environmental modeling study in the Amazon region that cares for the local population that has been living there all along.