The objective of this dissertation is to investigate all the necessary components in spectral mixture analysis (SMA) for hyperspectral imagery under an unsupervised circumstance. When SMA is linear, referred to as linear spectral mixture analysis (LSMA), these components include estimation of the number of endmembers, extraction of endmember signatures, and calculation of endmember abundances that can automatically satisfy the sum-to-one and non-negativity constraints. A simple approach for nonlinear spectral mixture analysis (NLSMA) is also investigated. After SMA is completed, a color display is generated to present endmember distribution in the image scene. It is expected that this research will result in an analytic system that can yield optimal or suboptimal SMA output without prior information. Specifically, the uniqueness in each component is described as follow. 1)A new signal subspace-based approach is developed to determine the number of endmembers with relatively robust performance and the least parameter requirement. 2)The best implementation strategy is determined for endmember extraction algorithms using simplex volume maximization and pixel spectral similarity; and algorithm with the special consideration for anomalous pixels is developed to improve the quality of extracted endmembers. 3)A new linear mixture model (LMM) is deployed where the number of endmembers and their types can be changed from pixel to pixel such that the resulting endmember abundances are sum-to-one and nonnegative as required; and fast algorithms are developed to search for a sub-optimal endmember set for each pixel. 4)A simple approach for NLSMA based on LMM is investigated and an automated approach is developed to determine either linear or nonlinear mixing is actually experienced. 5)A color display strategy is developed to present SMA results with high class/endmember separability.

Obtaining up-to-date information concernmg the environment at reasonable costs is a challenge faced by many institutions today. Satellite images meet both demands and thus present a very attractive source of information. The following thesis deals with the comparison of satellite images and a vector based land use data base of the City of Vienna. The satellite data is transformed using the spectral mixture analysis, which allows an investigation at a sub-pixel level. The results of the transformation are used to determine how suitable this spectral mixture analysis is to distinguish different land use classes in an urban area. In a next step the results of the spectral mixture analysis of two different images (recorded in 1986 and 1991) are used to undertake a change detection. The aim is to show those areas, where building activities have taken place. This information may aid the update of data bases, by limiting a detailed examination of an area to those areas, which show up as changes in the change detection. The proposed method is a fast and inexpensive way of analysing large areas and highlighting those areas where changes have taken place. lt is not limited to urban areas but may easily be adapted for different environments. (author's abstract) / Series: Research Reports of the Institute for Economic Geography and GIScience

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

O objetivo desta pesquisa é analisar, em um período de 30 anos, a expansão urbana do Município de São Paulo e dos municípios da Região Metropolitana de São Paulo (RMSP) a partir do uso de imagens orbitais obtidas pela série Landsat nos anos de 1986, 1989, 2000, 2006, 2010 e 2015, aplicando o Modelo Linear de Mistura Espectral (MLME) com uso de membros de referência globais. Os dados obtidos com o uso do MLME foram associados às informações de produção habitacional formal e informal, considerando os empreendimentos imobiliários verticais, as favelas e os loteamentos irregulares (desde 1985 até o ano de 2015), possibilitando a compreensão do comportamento espacial da produção habitacional dessa região ao longo do período analisado. Para cada cena Landsat, foram calculadas as imagens relacionadas à fração Substrato (alto albedo), à fração Vegetação (biomassa) e à fração Sombra (baixo albedo). Durante o desenvolvimento do trabalho, é proposta uma metodologia de modelagem das mudanças do uso e ocupação do solo associados à produção habitacional, partindo da correlação temporal entre as frações. Cada imagem fração foi agregada em três níveis de escala: a escala da própria imagem (pixel de 30m), a escala intraurbana (setores censitários e distritos) e a escala regional (limites da RMSP) o que possibilitou, na escala intraurbana, a associação das informações extraídas do MLME aos dados de produção habitacional e aos censos demográficos de 1991, 2000 e 2010. Os resultados do modelo demonstram que as áreas de produção habitacional verticalizada, no período analisado, são detectadas pelo modelo como uma correlação negativa entre as frações substrato e sombra, indicando que a produção de sombra dos empreendimentos é acompanhada de uma diminuição da detecção da fração substrato, enquanto nas áreas de habitação precárias (favelas e loteamentos irregulares), a mudança detectada pelo modelo é de correlação negativa entre as frações vegetação e substrato e também entre as frações sombra e substrato, sendo indicativo do aumento do substrato para ambos os casos, ao longo do tempo. Os resultados também demonstram alta fidelidade posicional e possibilidade de aplicação geral do modelo proposto para outras categorias de uso do solo, indicando frentes para generalização da técnica aqui proposta. / The objective of this research is to analyze, in a period of 30 years, the urban sprawl of Sao Paulo city and the municipalities of São Paulo Metropolitan Region (SPMR) using orbital images obtained by Landsat series in 1986, 1989, 2000, 2006, 2010, and 2015, applying the Linear Spectral Mixture Model (LSMM) and the use of global reference endmembers. The data obtained using the LSMM were associated to information on formal and informal housing production, considering the vertical real estate development, the slums and the informal neighborhoods (from 1985 to 2015), making possible to understand the housing production in the region in the time period analyzed. For each Landsat scene, image fractions were calculated in relation to the Substrate fraction (high albedo), Vegetation Fraction (biomass) and Shadow fraction (low albedo). In the development of the work, is proposed a methodology to modeling the changes in land use/land cover associated with the housing production through the temporal correlation between the fractions. Each image fraction was aggregated at three levels of scale: the scale of the image itself (30m pixel), the intra urban scale (census tracts and districts) and regional scale (limits of the SPMR) allowing, in the intra urban range, the association of information extracted from the LSMM, the housing production and the census demographic data from 1991, 2000 and 2010. The model results show that the vertical housing production areas, in the analyzed period, are detected by a negative correlation between substrate and shadow fractions, showing that the shadow increase in these areas are accompanied by a decrease in the substrate fraction detection, while in areas of precarious housing (slums and informal neighborhoods) the model detects a negative correlation between vegetation ad substrate fractions and also between the substrate and shadow fractions, indicating the increase of substrate for both cases over time. The results also show high positional fidelity and possibility of a general application of the proposed model for other categories of land use/land cover changes, showing the possibility of generalization of the technique proposed here.

A urbanização modifica a superfície, promovendo a troca da cobertura natural por materiais de construção. As áreas urbanas, além do solo impermeável, têm a presença de edifícios, que alteram a rugosidade da superfície, a velocidade e a direção dos ventos e provocam o sombreamento da superfície, bloqueando a incidência da energia solar. Assim, analisar a geometria de ocupação da cidade é importante para o entendimento do clima urbano e para o planejamento da cidade. O sensoriamento remoto é uma importante forma de obtenção de informações das áreas urbanas, contudo é preciso considerar a heterogeneidade deste ambiente e a mistura espectral existente nos dados satelitais. Dessa forma, o modelo linear de mistura espectral apresenta-se como importante método de extração de informações dos ambientes urbanos. O objetivo deste trabalho é identificar áreas com padrão horizontal e com padrão vertical de ocupação urbana, em Porto Alegre, e relacionar essa característica geométrica com valores de temperatura de superfície. Para tanto, imagens do sensor TM do satélite Landsat 5, adquiridas entre 1984 e 2009 foram utilizadas, bem como dados censitários, dados meteorológicos e modelos gerados por varredura laser. A partir das imagens TM foram geradas três imagens fração: solo, sombra e vegetação. A fração solo foi utilizada na identificação de áreas de ocupação horizontal e de expansão urbana, e a fração sombra foi utilizada na identificação de áreas verticalizadas. Utilizando as mesmas imagens, obtiveram-se os valores de temperatura de superfície. As áreas com ocupação horizontal, caracterizadas por moradias em casas, apresentaram baixos valores de sombra e altos valores de solo. As áreas verticalizadas apresentaram altos valores de sombra e baixos valores de solo. Os resultados extraídos das imagens fração têm similaridade com dados de artigos científicos e com os dados da varredura laser. A temperatura de superfície, em Porto Alegre, mostrou forte correlação com dados meteorológicos, e se caracteriza por valores mais altos nas áreas urbanizadas e mais baixos onde a ocupação é rarefeita. Nas áreas urbanizadas, maiores valores de temperatura de superfície são encontrados nas regiões com padrão de ocupação horizontal, enquanto os menores valores são encontrados nas regiões verticalizadas. A metodologia escolhida gerou resultados compatíveis com outros dados de uso e ocupação do solo, provenientes de diferentes fontes, e contribui com características da área urbana e do clima urbano da cidade de Porto Alegre, informações essas escassas nos principais bancos de dados acadêmicos. / The urbanization modifies the landscape, promoting changes from natural to man-made environment. Besides the impermeable soil, the urban areas have a lot of buildings, that changes the surface roughness, the wind speed and direction and also are responsible for shading the surface, blocking the incidence of solar energy. Analysing the city occupation geometry is important to understanding of the urban climate behaviour, and naturally the city planning. Remote sensing is a very important tool to get information about the urban areas, but is necessary to consider the heterogeneity of this environment and the existing spectral mixing in satellite data. Based on this, the linear model of spectral mixing can be classified as an important method of information extraction from urban environments. The goal of this research is to identify areas with horizontal and vertical patterns of urban occupation in the city of Porto Alegre – Brazil and relate this geometric characteristic with values of surface temperature. Therefore, images of the TM sensor of the Landsat 5 satellite were used (during the 1984-2009 period) and also the census data, meteorological data and models generated by laser scanning. Three fraction images were generated based on TM images: soil, shade and vegetation. The soil fraction was used for the identification of the areas with horizontal occupation and urban expansion, and the shadow fraction was used to identify verticalized areas. Based on the same images the surface temperature was obtained. Areas with horizontal occupation, mostly represented by houses, presented low shading values and high soil values. On the other hand, verticalized areas presented high shading values and low soil values. These results, obtained from the images fraction, are similar with the results from scientific papers and data from laser scanning. In Porto Alegre, the surface temperature indicated strong correlation with meteorological data, and was characterized by higher values in urbanized areas and lower values where the occupation is least intense. In urban areas, higher values of temperature are found in areas with horizontal occupation pattern, while the lowest values are found in verticalized regions. Furthermore, it is possible to suggest that the chosen methodology lead to conclusions that are consistent with other data of land use and occupation from different sources. Contributing with some information about characteristics of the urban area and urban climate of the city of Porto Alegre, which are usually not well documented in academic databases.

A urbanização modifica a superfície, promovendo a troca da cobertura natural por materiais de construção. As áreas urbanas, além do solo impermeável, têm a presença de edifícios, que alteram a rugosidade da superfície, a velocidade e a direção dos ventos e provocam o sombreamento da superfície, bloqueando a incidência da energia solar. Assim, analisar a geometria de ocupação da cidade é importante para o entendimento do clima urbano e para o planejamento da cidade. O sensoriamento remoto é uma importante forma de obtenção de informações das áreas urbanas, contudo é preciso considerar a heterogeneidade deste ambiente e a mistura espectral existente nos dados satelitais. Dessa forma, o modelo linear de mistura espectral apresenta-se como importante método de extração de informações dos ambientes urbanos. O objetivo deste trabalho é identificar áreas com padrão horizontal e com padrão vertical de ocupação urbana, em Porto Alegre, e relacionar essa característica geométrica com valores de temperatura de superfície. Para tanto, imagens do sensor TM do satélite Landsat 5, adquiridas entre 1984 e 2009 foram utilizadas, bem como dados censitários, dados meteorológicos e modelos gerados por varredura laser. A partir das imagens TM foram geradas três imagens fração: solo, sombra e vegetação. A fração solo foi utilizada na identificação de áreas de ocupação horizontal e de expansão urbana, e a fração sombra foi utilizada na identificação de áreas verticalizadas. Utilizando as mesmas imagens, obtiveram-se os valores de temperatura de superfície. As áreas com ocupação horizontal, caracterizadas por moradias em casas, apresentaram baixos valores de sombra e altos valores de solo. As áreas verticalizadas apresentaram altos valores de sombra e baixos valores de solo. Os resultados extraídos das imagens fração têm similaridade com dados de artigos científicos e com os dados da varredura laser. A temperatura de superfície, em Porto Alegre, mostrou forte correlação com dados meteorológicos, e se caracteriza por valores mais altos nas áreas urbanizadas e mais baixos onde a ocupação é rarefeita. Nas áreas urbanizadas, maiores valores de temperatura de superfície são encontrados nas regiões com padrão de ocupação horizontal, enquanto os menores valores são encontrados nas regiões verticalizadas. A metodologia escolhida gerou resultados compatíveis com outros dados de uso e ocupação do solo, provenientes de diferentes fontes, e contribui com características da área urbana e do clima urbano da cidade de Porto Alegre, informações essas escassas nos principais bancos de dados acadêmicos. / The urbanization modifies the landscape, promoting changes from natural to man-made environment. Besides the impermeable soil, the urban areas have a lot of buildings, that changes the surface roughness, the wind speed and direction and also are responsible for shading the surface, blocking the incidence of solar energy. Analysing the city occupation geometry is important to understanding of the urban climate behaviour, and naturally the city planning. Remote sensing is a very important tool to get information about the urban areas, but is necessary to consider the heterogeneity of this environment and the existing spectral mixing in satellite data. Based on this, the linear model of spectral mixing can be classified as an important method of information extraction from urban environments. The goal of this research is to identify areas with horizontal and vertical patterns of urban occupation in the city of Porto Alegre – Brazil and relate this geometric characteristic with values of surface temperature. Therefore, images of the TM sensor of the Landsat 5 satellite were used (during the 1984-2009 period) and also the census data, meteorological data and models generated by laser scanning. Three fraction images were generated based on TM images: soil, shade and vegetation. The soil fraction was used for the identification of the areas with horizontal occupation and urban expansion, and the shadow fraction was used to identify verticalized areas. Based on the same images the surface temperature was obtained. Areas with horizontal occupation, mostly represented by houses, presented low shading values and high soil values. On the other hand, verticalized areas presented high shading values and low soil values. These results, obtained from the images fraction, are similar with the results from scientific papers and data from laser scanning. In Porto Alegre, the surface temperature indicated strong correlation with meteorological data, and was characterized by higher values in urbanized areas and lower values where the occupation is least intense. In urban areas, higher values of temperature are found in areas with horizontal occupation pattern, while the lowest values are found in verticalized regions. Furthermore, it is possible to suggest that the chosen methodology lead to conclusions that are consistent with other data of land use and occupation from different sources. Contributing with some information about characteristics of the urban area and urban climate of the city of Porto Alegre, which are usually not well documented in academic databases.

A urbanização modifica a superfície, promovendo a troca da cobertura natural por materiais de construção. As áreas urbanas, além do solo impermeável, têm a presença de edifícios, que alteram a rugosidade da superfície, a velocidade e a direção dos ventos e provocam o sombreamento da superfície, bloqueando a incidência da energia solar. Assim, analisar a geometria de ocupação da cidade é importante para o entendimento do clima urbano e para o planejamento da cidade. O sensoriamento remoto é uma importante forma de obtenção de informações das áreas urbanas, contudo é preciso considerar a heterogeneidade deste ambiente e a mistura espectral existente nos dados satelitais. Dessa forma, o modelo linear de mistura espectral apresenta-se como importante método de extração de informações dos ambientes urbanos. O objetivo deste trabalho é identificar áreas com padrão horizontal e com padrão vertical de ocupação urbana, em Porto Alegre, e relacionar essa característica geométrica com valores de temperatura de superfície. Para tanto, imagens do sensor TM do satélite Landsat 5, adquiridas entre 1984 e 2009 foram utilizadas, bem como dados censitários, dados meteorológicos e modelos gerados por varredura laser. A partir das imagens TM foram geradas três imagens fração: solo, sombra e vegetação. A fração solo foi utilizada na identificação de áreas de ocupação horizontal e de expansão urbana, e a fração sombra foi utilizada na identificação de áreas verticalizadas. Utilizando as mesmas imagens, obtiveram-se os valores de temperatura de superfície. As áreas com ocupação horizontal, caracterizadas por moradias em casas, apresentaram baixos valores de sombra e altos valores de solo. As áreas verticalizadas apresentaram altos valores de sombra e baixos valores de solo. Os resultados extraídos das imagens fração têm similaridade com dados de artigos científicos e com os dados da varredura laser. A temperatura de superfície, em Porto Alegre, mostrou forte correlação com dados meteorológicos, e se caracteriza por valores mais altos nas áreas urbanizadas e mais baixos onde a ocupação é rarefeita. Nas áreas urbanizadas, maiores valores de temperatura de superfície são encontrados nas regiões com padrão de ocupação horizontal, enquanto os menores valores são encontrados nas regiões verticalizadas. A metodologia escolhida gerou resultados compatíveis com outros dados de uso e ocupação do solo, provenientes de diferentes fontes, e contribui com características da área urbana e do clima urbano da cidade de Porto Alegre, informações essas escassas nos principais bancos de dados acadêmicos. / The urbanization modifies the landscape, promoting changes from natural to man-made environment. Besides the impermeable soil, the urban areas have a lot of buildings, that changes the surface roughness, the wind speed and direction and also are responsible for shading the surface, blocking the incidence of solar energy. Analysing the city occupation geometry is important to understanding of the urban climate behaviour, and naturally the city planning. Remote sensing is a very important tool to get information about the urban areas, but is necessary to consider the heterogeneity of this environment and the existing spectral mixing in satellite data. Based on this, the linear model of spectral mixing can be classified as an important method of information extraction from urban environments. The goal of this research is to identify areas with horizontal and vertical patterns of urban occupation in the city of Porto Alegre – Brazil and relate this geometric characteristic with values of surface temperature. Therefore, images of the TM sensor of the Landsat 5 satellite were used (during the 1984-2009 period) and also the census data, meteorological data and models generated by laser scanning. Three fraction images were generated based on TM images: soil, shade and vegetation. The soil fraction was used for the identification of the areas with horizontal occupation and urban expansion, and the shadow fraction was used to identify verticalized areas. Based on the same images the surface temperature was obtained. Areas with horizontal occupation, mostly represented by houses, presented low shading values and high soil values. On the other hand, verticalized areas presented high shading values and low soil values. These results, obtained from the images fraction, are similar with the results from scientific papers and data from laser scanning. In Porto Alegre, the surface temperature indicated strong correlation with meteorological data, and was characterized by higher values in urbanized areas and lower values where the occupation is least intense. In urban areas, higher values of temperature are found in areas with horizontal occupation pattern, while the lowest values are found in verticalized regions. Furthermore, it is possible to suggest that the chosen methodology lead to conclusions that are consistent with other data of land use and occupation from different sources. Contributing with some information about characteristics of the urban area and urban climate of the city of Porto Alegre, which are usually not well documented in academic databases.

O objetivo desta pesquisa é analisar, em um período de 30 anos, a expansão urbana do Município de São Paulo e dos municípios da Região Metropolitana de São Paulo (RMSP) a partir do uso de imagens orbitais obtidas pela série Landsat nos anos de 1986, 1989, 2000, 2006, 2010 e 2015, aplicando o Modelo Linear de Mistura Espectral (MLME) com uso de membros de referência globais. Os dados obtidos com o uso do MLME foram associados às informações de produção habitacional formal e informal, considerando os empreendimentos imobiliários verticais, as favelas e os loteamentos irregulares (desde 1985 até o ano de 2015), possibilitando a compreensão do comportamento espacial da produção habitacional dessa região ao longo do período analisado. Para cada cena Landsat, foram calculadas as imagens relacionadas à fração Substrato (alto albedo), à fração Vegetação (biomassa) e à fração Sombra (baixo albedo). Durante o desenvolvimento do trabalho, é proposta uma metodologia de modelagem das mudanças do uso e ocupação do solo associados à produção habitacional, partindo da correlação temporal entre as frações. Cada imagem fração foi agregada em três níveis de escala: a escala da própria imagem (pixel de 30m), a escala intraurbana (setores censitários e distritos) e a escala regional (limites da RMSP) o que possibilitou, na escala intraurbana, a associação das informações extraídas do MLME aos dados de produção habitacional e aos censos demográficos de 1991, 2000 e 2010. Os resultados do modelo demonstram que as áreas de produção habitacional verticalizada, no período analisado, são detectadas pelo modelo como uma correlação negativa entre as frações substrato e sombra, indicando que a produção de sombra dos empreendimentos é acompanhada de uma diminuição da detecção da fração substrato, enquanto nas áreas de habitação precárias (favelas e loteamentos irregulares), a mudança detectada pelo modelo é de correlação negativa entre as frações vegetação e substrato e também entre as frações sombra e substrato, sendo indicativo do aumento do substrato para ambos os casos, ao longo do tempo. Os resultados também demonstram alta fidelidade posicional e possibilidade de aplicação geral do modelo proposto para outras categorias de uso do solo, indicando frentes para generalização da técnica aqui proposta. / The objective of this research is to analyze, in a period of 30 years, the urban sprawl of Sao Paulo city and the municipalities of São Paulo Metropolitan Region (SPMR) using orbital images obtained by Landsat series in 1986, 1989, 2000, 2006, 2010, and 2015, applying the Linear Spectral Mixture Model (LSMM) and the use of global reference endmembers. The data obtained using the LSMM were associated to information on formal and informal housing production, considering the vertical real estate development, the slums and the informal neighborhoods (from 1985 to 2015), making possible to understand the housing production in the region in the time period analyzed. For each Landsat scene, image fractions were calculated in relation to the Substrate fraction (high albedo), Vegetation Fraction (biomass) and Shadow fraction (low albedo). In the development of the work, is proposed a methodology to modeling the changes in land use/land cover associated with the housing production through the temporal correlation between the fractions. Each image fraction was aggregated at three levels of scale: the scale of the image itself (30m pixel), the intra urban scale (census tracts and districts) and regional scale (limits of the SPMR) allowing, in the intra urban range, the association of information extracted from the LSMM, the housing production and the census demographic data from 1991, 2000 and 2010. The model results show that the vertical housing production areas, in the analyzed period, are detected by a negative correlation between substrate and shadow fractions, showing that the shadow increase in these areas are accompanied by a decrease in the substrate fraction detection, while in areas of precarious housing (slums and informal neighborhoods) the model detects a negative correlation between vegetation ad substrate fractions and also between the substrate and shadow fractions, indicating the increase of substrate for both cases over time. The results also show high positional fidelity and possibility of a general application of the proposed model for other categories of land use/land cover changes, showing the possibility of generalization of the technique proposed here.

Laboratory and image spectroscopy for mapping of selected rocks in peak areas of the Krkonoše Mountains Abstract This thesis deals with geological mapping of selected rocks in peak areas of the Krkonoše Mountains. Four areas of interest were situated in two parts of Krkonše Mountains - on the west side it is the area of Vysoké kolo and Harrachovy kameny and on the east side there is the area of Sněžka and the area of Kozí hřbety. The main data were acquired by the hyperspectral sensor APEX. Ground spectral measurments of selected rocks and block fields were executed and the laboratory spectral measurments of geological samples and lichens were executed. Practical part aims at classification of rocks and lichens in selected areas using four classification methods: SAM, SID, MESMA and LSU. The spectral library is one of the outputs of this thesis. This spectral library contains the spectra of pure rocks and lichens and mixtured spectra of rocks and lichens. The output of this thesis is the comparation of used classification methods, the analysis of spatial and geological accuracy and evaluation of lichens influence on the classification results, spectral library and maps of classified rocks occurrence. Keywords: classification, block fields, hyperspectral data, spectral mixture, lichens, The Krkonoše Mountains

A Geobotânica por sensoriamento remoto é uma técnica de obtenção de informações geológicas indiretas em ambientes cobertos por vegetação e apresenta grandes perspectivas por sua capacidade de otimizar trabalhos de campo e gerar possíveis alvos a serem examinados. O objetivo deste estudo foi realizar a discriminação espectral, em escala de folha e de copa, de espécies arbóreas neotropicais associadas localmente a diferentes formações e fácies geológicas, bem como defini-las remotamente como indicadoras geológicas na Estação Ecológica de Mogi-Guaçu, São Paulo, Brasil. Dados obtidos em 70 unidades amostrais, como texturais de solos e sedimentos, químicos de solos, de nível do freático, altitudinais (modelo digital de terreno),fitossociológicos e fisiológicos/estruturais de vegetação (índices hiperespectrais de vegetação), foram minerados e analisados através da técnica de quantização vetorial Self-Organizing Maps (SOM). Inga veraWilld. subsp. affinis (DC.) T.D. Penn (INVE) e Calophyllum brasiliense Cambess. (CABR) mostraram-se associadas à planície de inundação, incluindo meandros abandonados (Depósitos Aluvionares), com amplo domínio das frações argila e silte nos sedimentos. Qualea grandifloraMart. (QUGR) e Tabebuia ochracea(Cham.) Standl. (TAOC) foram identificadas apenas nas colinas e platôs da Formação Aquidauana,com altas porcentagens das frações areia fina, média e grossa, e escasso silte. Cedrela fissilisVell. (CEFI) e Zeyheria tuberculosa(Vell.) Bur. (ZETU) demonstraram estar associadas a uma fácies aflorante da Formação Aquidauana, com distinta presença das frações de areia grossa e muito grossa, além da baixa porcentagem das frações silte e areiafina. As cinco primeiras espécies tiveram dados bioquímicos e espectrais (400-2.500 nm, FieldSpec 3 Hi-Res) coletados, em escala de folha, tanto no período chuvoso quanto noseco. Seus espectros foram classificados através da técnica Multiple Endmember Spectral Mixture Analysis(MESMA). As espécies foram bem discriminadas em ambos os momentos sazonais, nessa escala de trabalho. Dentre os melhores resultados por intervalo espectral, as exatidões do produtor e do usuário não foram inferiores a 87,5%. Esse sucesso mostrou estar intimamente ligado à alta variabilidade bioquímica observada em suas folhas. As variações intra e interespecíficas em compostos bioquímicos puderam ser correlacionadas às suas variações espectrais. A discriminação espectral em escala de copa foi realizada com dois membros-finais (MF) via MESMA para CEFI, INVE e QUGR. Os pixels das imagenspré-processadas do sistema de sensores aeroportados ProSpecTIR-VS (530-2.532 nm, 1 m de resolução espacial) foram modelados por três MF: MF da classe de espécie-alvo, MF de outras classes de vegetação e sombra fotométrica. A falta de comissão espectrale a relativa baixa omissão espectral atingidas por QUGR na modelagem com dois MF, que incluiu outras classes de vegetação, refletiu em um mapeamento satisfatório de sua fração espectral. As tendências em distribuição dessa espécie indicaram claramente as colinas e platôs da Formação Aquidauana na área estudada. / Geobotany via remote sensing is a technique for obtaining indirect geological information in vegetated areas and presents great perspectives by its capability for field work optimization and target generation to be evaluated afterwards. The aim of this research was to perform the spectral discrimination of Neotropical tree species (at leaf and crown levels) which are locally associated to geological facies and formations in the Mogi-Guaçu Ecological Station, in southeastern Brazil. Data from 70 sampling units, such as soils and sediments texture, soils chemistry, groundwater level, elevation (digital terrain model), plant sociology and vegetation physiology/structure (hyperspectral vegetation indices), were mined and analyzed through the vetorial quantization method called Self-Organizing Maps (SOM; Kohonen, 1982). Inga veraWilld. subsp. affinis(DC.) T.D. Penn (INVE) and Calophyllum brasiliense Cambess. (CABR) demonstrated to be associated to the floodplain, including paleochannels (Alluvial Deposits sequence), with clayey-silty sediments. Qualea grandifloraMart. (QUGR) and Tabebuia ochracea(Cham.) Standl. (TAOC) were sampled on hills and plateaus of the Aquidauna Formation, which stood out for higher fine, medium and coarse sand contents and lower silt content. Cedrela fissilisVell. (CEFI) and Zeyheria tuberculosa(Vell.) Bur. (ZETU) showed be associated to one outcrop facies of the Aquidauna Formation, with distinctive presence of coarse and very coarse sandas well as lower silt and very fine sand contents. Biochemical and spectral (400-2.500 nm, FieldSpec Hi-Res 3) data were collected from the leaves of the first five species, during both rainy and dry seasons. Their spectra were classified through Multiple Endmember Spectral Mixture Analysis(MESMA). All target species were well discriminated at leaf scale. Considering the best classification results per spectral range, user\'s and producer\'s accuracies were always higher than 87,5%. These results seem to be linked to the great biochemical variability of their leaves. The intra and interspecific variability of biochemical compounds were correlated with spectral variability. The spectral discrimination at crown scale was performed with two endmembers (EM) via MESMA for CEFI, INVE e QUGR. The 1-m pixels of the preprocessed ProSpecTIR-VS images (530-2.532 nm) were modeled by three EM: EM of the target species, EM of other vegetation classes, and photometric shade. The QUGRclass achieved a relatively lower spectral omission and had no spectra erroneously assigned to its class in the two EM classification, which included other vegetation classes. This classification result was reproduced in the three EM image unmixing. The distribution tendency of that species clearly indicated the hills and plateaus of the Aquidauana Formation in the study area.