Snow and lake ice have very high albedos compared to other surfaces found in nature. Surface albedo is an important component of the surface energy budget especially when albedos are high since albedo governs how much shortwave radiation is absorbed or reflected at a surface. In particular, snow and lake ice albedos have been shown to affect the timing of lake ice break-up. Lakes are found throughout the Northern Hemisphere and lake ice has been shown to be sensitive to climatic variability. Therefore, the modelling of lake ice phenology, using lake ice models such as the Canadian Lake Ice Model (CLIMo), is important to the study of climatic variability in the Arctic and sub-Arctic regions and accurate snow and lake ice albedo measurements are required to ensure the accuracy of the simulations. However, snow and lake ice albedo can vary from day-to-day depending on factors such as air temperature, presence of impurities, age, and composition. Some factors are more difficult than others to model (e.g. presence of impurities). It would be more straight forward to just gather field measurements, but such measurements would be costly and lakes can be in remote locations and difficult to access. Instead, CLIMo contains an albedo parameterization scheme that models the evolution of snow and lake ice albedo in its simulations. However, parts of the albedo parameterization are based on sea-ice observations (which inherently have higher albedos due to brine inclusions) and the albedo parameterization does not take ice type (e.g. clear ice or snow ice) into account. Satellite remote sensing via the Moderate Resolution Imaging Spectroradiometer (MODIS) provides methods for retrieving albedo that may help enhance CLIMo’s albedo parameterization. CLIMo’s albedo parameterization as well the MODIS daily albedo products (MOD10A1 and MYD10A1) and 16-day product (MCD43A3) were evaluated against in situ albedo observations made over Malcolm Ramsay Lake near Churchill, Manitoba, during the winter of 2012. It was found that the snow albedo parameterization of CLIMo performs well when compared to average in situ observations, but the bare ice parameterization overestimated bare ice albedo observations. The MODIS albedo products compared well when evaluated against the in situ albedo observations and were able to capture changes in albedo throughout the study period. The MODIS albedo products were also compared against CLIMo’s melting ice parameterization, because the equipment had to be removed from the lake to prevent it from falling into the water during the melt season. Cloud cover interfered with the MODIS observations, but the comparison suggests that MODIS albedo products retrieved higher albedo values than the melting ice parameterization of CLIMo. The MODIS albedo products were then integrated directly into CLIMo in substitution of the albedo parameterization to see if they could enhance break-up date (ice off) simulations. MODIS albedo retrievals (MOD10A1, MYD10A1, and MCD43A3) were collected over Back Bay, Great Slave Lake (GSL) near Yellowknife, Northwest Territories, from 2000-2011. CLIMo was then run with and without the MODIS albedos integrated and compared against MODIS observed break-up dates. Simulations were also run under three difference snow cover scenarios (0%, 68%, and 100% snow cover). It was found that CLIMo without MODIS albedos performed better with the 0% snow cover scenario than with the MODIS albedos integrated in. Both simulations (with and without MODIS albedos) performed well with the snow cover scenarios. The MODIS albedo products slightly improved CLIMo break-up simulations when integrated up to a month in advance of actual lake ice break-up for Back Bay. With the MODIS albedo products integrated into CLIMo, break-up dates were simulated within 3-4 days of MODIS observed break-up. CLIMo without the MODIS albedos still performed very well simulating break-up within 4-5 days of MODIS observed break-up. It is uncertain whether this was a significant improvement or not with such a small study period and with the investigation being conducted at a single site (Back Bay). However, it has been found that CLIMo performs well with the original albedo parameterization and that MODIS albedos could potentially complement lake-wide break-up simulations in future studies.

A república de Moçambique é um país localizado ao longo da costa Leste da África Austral, com a economia baseada essencialmente na prática da agricultura. A cultura do milho (Zea mays L.) é a mais importante, cultivada em regime de sequeiro, com rendimentos dependentes das condições meteorológicas. Modelos agrometeorológicos de estimativa de rendimentos de culturas alimentares são alternativas viáveis para tomada de decisão em medidas de segurança alimentar e abastecimento. O calendário agrícola e o sistema de produção tornam o uso de geotecnologias uma importante ferramenta para o monitoramento de culturas e o desenvolvimento de modelos de estimativa de rendimentos. Produtos de dados de sensoriamento remoto, como índices espectrais combinados com parâmetros agrometeorológicos podem melhorar as representações espaciais de rendimentos do milho em Moçambique. O ajuste de um modelo agrometeorológico espectral para estimativa de rendimentos do milho por regressão linear múltipla na província de Manica-Moçambique constituiu o objetivo do estudo. Foi realizado um mapeamento de áreas agrícolas por análise multitemporal do NDVI/MODIS e também foi avaliada a eficiência de variáveis agrometeorológicas e espectrais na estimativa de rendimentos do milho em uma área da província de Manica que envolve os distritos de Gondola, Manica, Mossurize e Sussundenga, responsáveis por mais de 80% da produção de milho na província nos anos de 2000 a 2009. Foi desenvolvido um modelo de início do ciclo do milho baseado em critérios de chuva, e estabelecendo um ciclo fixo do milho em 130 dias. A metodologia de mapeamento de áreas agrícolas consistiu em somatórios de imagens binárias geradas por diferença de NDVI máximo e mínimo ao longo do ciclo e estabelecimento de níveis de restrição com base em comparações com estatísticas oficiais por distrito. As variáveis agrometeorológicas testadas foram evapotranspiração relativa (ETr/ETm) e o índice de satisfação das necessidade de água (ISNA) calculados a partir de dados de estimativas de elementos meteorológicos do modelo do ECMWF. O conjunto de variáveis espectrais compreendiam composições de 16 dias de índices de vegetação EVI e NDVI provenientes do produto MOD13Q1 do sensor MODIS e o LSWI, gerado por diferença normalizada de bandas de refletância de superfície do infravermelho próximo e médio contidas no mesmo produto. O modelo agrometeorológico espectral envolveu as variáveis meteorológicas e espectrais como independentes sendo o rendimento médio e relativo, as variáveis dependentes ajustadas em um modelo de regressão múltipla. Todos os distritos, a exceção de Mossurize, geraram modelos com bom desempenho nas estimativas de rendimentos do milho e significado físico. O modelo regional, incluindo Gondola, Manica e Sussundenga e envolvendo o rendimento relativo foi o mais recomendado para estimativa de rendimentos do milho na região com r2 = 0,762 e RMSE de 9,46%. / Mozambique is a country located along the east coast of southern Africa, with an economy based primarily on agriculture. The Maize crop (Zea mays L.) is the most important crop, growing in rainfed conditions, with its yield dependent only on weather conditions. Agrometeorological models to forecast yields of food crops are viable alternatives for decision making on food safety measures and supply. The agricultural calendar and the production system make use of geotechnologies an important tool for crop monitoring and yield forecasting. Products from remote sensing data, combined with spectral indices and agrometeorological parameters can improve the spatial representations of maize yields in Mozambique. Setting an agrometeorological model to estimate the spectral yield of corn by multiple linear regression in Manica province, Mozambique was the objective of the study. Were conducted a mapping of agricultural areas by analyzing multitemporal NDVI / MODIS and also evaluated the effectiveness of spectral and meteorological variables in the estimated maize yield in an area of Manica province involving the districts of Gondola, Manica, Mossurize and Sussundenga responsible for more than 80% of corn production in the province in the years 2000 to 2009. A model was developed to estimate the beginnig of the corn cycle, using as a criteria the rainfall, and setting a fixed cycle of corn in 130 days. The methodology for mapping agricultural areas consisted of sums of binary images generated by the difference of maximum and minimum NDVI throughout the cycle and establishing levels of restriction based on comparisons with official statistics by district. Were tested the meteorological variables: the relative evapotranspiration (ETr / ETm) and the index of satisfaction of water needs (ISNA) calculated from data from meteorological model of ECMWF. The set of spectral variable were comprised of 16 days composition of vegetation indices NDVI and EVI from the MODIS product MOD13Q1 and LSWI generated from normalized difference of surface reflectance bands of near-infrared and medium infrared contained the same product. The meteorological and spectral variables was the set of independent variables and the average and relative yield were the set of dependent variables used to adjusted a multiple regression model, called agrometeorological-spectral model. To all districts, except for Mossurize were generated models with good performance in estimating the corn yield and with physical meaning. The regional model, including Gondola, Manica and Sussundenga and involving the relative yield was the most suitable for estimating corn yield in the region with r2 = 0.762 and RMSE of 9.46%.

The increasing interest of modeling global carbon cycling during the past two decades has driven this research to map leaf area index (LAI) at multiple spatial resolutions by combining LAI field observations with various sensor images at local, regional, and global scale. This is due to its important role in process based models that are used to predict carbon sequestration of terrestrial ecosystems. Although a substantial research has been conducted, there are still many challenges in this area. One of the challenges is that various images with spatial resolutions varying from few meters to several hundred meters and even to 1 km have been used. However, a method that can be used to collect LAI field measurements and further conduct multiple spatial resolution mapping and accuracy assessment of LAI is not available. In this study, a pilot study in a complex landscape located in the Southern Illinois was carried out to map LAI by combining field observations and remotely sensed images. Multi-scale mapping and accuracy assessment of LAI using aerial photo, Landsat TM and MODIS images were explored by developing a multi-scale sampling design. The results showed that the sampling design could be used to collect LAI observations to create LAI products at various spatial resolutions and further conduct accuracy assessment. It was also found that the TM derived LAI maps at the original and aggregated spatial resolutions successfully characterized the heterogeneous landscape and captured the spatial variability of LAI and were more accurate than those from the aerial photo and MODIS. The aerial photo derived models led to not only over- and under-estimation, but also pixilated maps of LAI. The MODIS derived LAI maps had an acceptable accuracy at various spatial resolutions and are applicable to mapping LAI at regional and global scale. Thus, this study overcame some of the significant gaps in this field.

A república de Moçambique é um país localizado ao longo da costa Leste da África Austral, com a economia baseada essencialmente na prática da agricultura. A cultura do milho (Zea mays L.) é a mais importante, cultivada em regime de sequeiro, com rendimentos dependentes das condições meteorológicas. Modelos agrometeorológicos de estimativa de rendimentos de culturas alimentares são alternativas viáveis para tomada de decisão em medidas de segurança alimentar e abastecimento. O calendário agrícola e o sistema de produção tornam o uso de geotecnologias uma importante ferramenta para o monitoramento de culturas e o desenvolvimento de modelos de estimativa de rendimentos. Produtos de dados de sensoriamento remoto, como índices espectrais combinados com parâmetros agrometeorológicos podem melhorar as representações espaciais de rendimentos do milho em Moçambique. O ajuste de um modelo agrometeorológico espectral para estimativa de rendimentos do milho por regressão linear múltipla na província de Manica-Moçambique constituiu o objetivo do estudo. Foi realizado um mapeamento de áreas agrícolas por análise multitemporal do NDVI/MODIS e também foi avaliada a eficiência de variáveis agrometeorológicas e espectrais na estimativa de rendimentos do milho em uma área da província de Manica que envolve os distritos de Gondola, Manica, Mossurize e Sussundenga, responsáveis por mais de 80% da produção de milho na província nos anos de 2000 a 2009. Foi desenvolvido um modelo de início do ciclo do milho baseado em critérios de chuva, e estabelecendo um ciclo fixo do milho em 130 dias. A metodologia de mapeamento de áreas agrícolas consistiu em somatórios de imagens binárias geradas por diferença de NDVI máximo e mínimo ao longo do ciclo e estabelecimento de níveis de restrição com base em comparações com estatísticas oficiais por distrito. As variáveis agrometeorológicas testadas foram evapotranspiração relativa (ETr/ETm) e o índice de satisfação das necessidade de água (ISNA) calculados a partir de dados de estimativas de elementos meteorológicos do modelo do ECMWF. O conjunto de variáveis espectrais compreendiam composições de 16 dias de índices de vegetação EVI e NDVI provenientes do produto MOD13Q1 do sensor MODIS e o LSWI, gerado por diferença normalizada de bandas de refletância de superfície do infravermelho próximo e médio contidas no mesmo produto. O modelo agrometeorológico espectral envolveu as variáveis meteorológicas e espectrais como independentes sendo o rendimento médio e relativo, as variáveis dependentes ajustadas em um modelo de regressão múltipla. Todos os distritos, a exceção de Mossurize, geraram modelos com bom desempenho nas estimativas de rendimentos do milho e significado físico. O modelo regional, incluindo Gondola, Manica e Sussundenga e envolvendo o rendimento relativo foi o mais recomendado para estimativa de rendimentos do milho na região com r2 = 0,762 e RMSE de 9,46%. / Mozambique is a country located along the east coast of southern Africa, with an economy based primarily on agriculture. The Maize crop (Zea mays L.) is the most important crop, growing in rainfed conditions, with its yield dependent only on weather conditions. Agrometeorological models to forecast yields of food crops are viable alternatives for decision making on food safety measures and supply. The agricultural calendar and the production system make use of geotechnologies an important tool for crop monitoring and yield forecasting. Products from remote sensing data, combined with spectral indices and agrometeorological parameters can improve the spatial representations of maize yields in Mozambique. Setting an agrometeorological model to estimate the spectral yield of corn by multiple linear regression in Manica province, Mozambique was the objective of the study. Were conducted a mapping of agricultural areas by analyzing multitemporal NDVI / MODIS and also evaluated the effectiveness of spectral and meteorological variables in the estimated maize yield in an area of Manica province involving the districts of Gondola, Manica, Mossurize and Sussundenga responsible for more than 80% of corn production in the province in the years 2000 to 2009. A model was developed to estimate the beginnig of the corn cycle, using as a criteria the rainfall, and setting a fixed cycle of corn in 130 days. The methodology for mapping agricultural areas consisted of sums of binary images generated by the difference of maximum and minimum NDVI throughout the cycle and establishing levels of restriction based on comparisons with official statistics by district. Were tested the meteorological variables: the relative evapotranspiration (ETr / ETm) and the index of satisfaction of water needs (ISNA) calculated from data from meteorological model of ECMWF. The set of spectral variable were comprised of 16 days composition of vegetation indices NDVI and EVI from the MODIS product MOD13Q1 and LSWI generated from normalized difference of surface reflectance bands of near-infrared and medium infrared contained the same product. The meteorological and spectral variables was the set of independent variables and the average and relative yield were the set of dependent variables used to adjusted a multiple regression model, called agrometeorological-spectral model. To all districts, except for Mossurize were generated models with good performance in estimating the corn yield and with physical meaning. The regional model, including Gondola, Manica and Sussundenga and involving the relative yield was the most suitable for estimating corn yield in the region with r2 = 0.762 and RMSE of 9.46%.

Submitted by NATÁLIA SOARES DA SILVA null (nataliasoasilva@hotmail.com) on 2017-01-25T15:52:11Z No. of bitstreams: 1 Natalia Tese com ficha.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-01-27T12:21:22Z (GMT) No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Made available in DSpace on 2017-01-27T12:21:22Z (GMT). No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) Previous issue date: 2016-11-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O sensoriamento remoto tem sido uma ferramenta bastante utilizada em diferentes campos das ciências e não seria diferente na agricultura sendo um dos principais motivos de sua utilização a facilidade para a obtenção de dados dos sensores, já que muitos são disponibilizados gratuitamente em plataformas na internet. É indiscutível que a agricultura é um dos maiores consumidores dos recursos hídricos e que seu uso quando de forma adequada pode gerir excelentes resultados na produção dos cultivos agrícolas. A hipótese do estudo é que técnicas de sensoriamento remoto, aplicadas na área de interesse, podem se transformar em ferramenta para a gestão dos recursos hídricos dedicados à agricultura irrigada com cobertura de palha no solo. Nesse contexto o objetivo principal da pesquisa foi monitorar através do sensoriamento remoto o desenvolvimento do feijoeiro conduzido em sistema de Pivô Central cultivado com cobertura de palha no solo, na região de Paranapanema-SP, determinando quais parâmetros poderão ser utilizados para a gestão da irrigação. O estudo foi desenvolvido através da análise de imagens Landsat e Terra para obtenção do índice de vegetação por diferença normalizada (NDVI) por sensoriamento remoto e suas relações com outras variáveis (umidade do solo, índice de área foliar e evapotranspiração) a fim de parametrizar o desenvolvimento do feijoeiro, além da aplicação do modelo de índice de umidade do solo (IUS). Observou-se uma similaridade no comportamento do NDVI tanto nas imagens obtidas pelo satélite Landsat quanto Terra, onde no início do cultivo o NDVI é baixo devido à baixa porcentagem de cobertura verde e à medida que a cultura se desenvolve esses valores aumentam com o acréscimo da cobertura vegetal onde o ponto máximo é verificado na fase de enchimento do grão e decréscimo na maturação. Com a determinação de um índice (IUS) por sensoriamento remoto infere-se a umidade do solo e é possível monitorar as condições do feijoeiro durante o período de cultivo. / Remote sensing has been a tool widely used in different fields of science and it would not be different for agriculture. It is ease to obtaining data from sensors, since many are available on platforms on the internet. There is no doubt that agriculture is one of the largest consumers of water resources, and when properly manage, excellent results are obtained from agricultural crops production. The main objective of the study was to monitor through remote sensing the development of bean conducted under Central Pivot irrigation, cultivated with no-till and direct seedling, in the region of Paranapanema-SP. Additionally determining which parameters may be used for the irrigation management. The study hypothesis was that remote sensing techniques, applied in the area of interest, can become a tool for the management of water resources devoted to irrigated agriculture with no-till and direct seedling. The study was developed through the analysis of Landsat and Terra images, obtaining the normalized difference vegetation index (NDVI) by remote sensing and its relations with other variables (soil moisture, leaf area index and evapotranspiration) in order to the parametrization of the development of common bean, as well as the application of the model index of soil moisture (IUS). There were similarities in the behavior of NDVI for images from Landsat satellite and Terra. At the beginning of bean development NDVI was low due to the low percentage of cover; as the crop develops these values increased with the development of vegetation cover. The maximum value of NDVI was obtained during the filling phase of the grain in the pods and a decrease in maturation. With the determination of the IUS by remote sensing it can be infers soil water content.

A república de Moçambique é um país localizado ao longo da costa Leste da África Austral, com a economia baseada essencialmente na prática da agricultura. A cultura do milho (Zea mays L.) é a mais importante, cultivada em regime de sequeiro, com rendimentos dependentes das condições meteorológicas. Modelos agrometeorológicos de estimativa de rendimentos de culturas alimentares são alternativas viáveis para tomada de decisão em medidas de segurança alimentar e abastecimento. O calendário agrícola e o sistema de produção tornam o uso de geotecnologias uma importante ferramenta para o monitoramento de culturas e o desenvolvimento de modelos de estimativa de rendimentos. Produtos de dados de sensoriamento remoto, como índices espectrais combinados com parâmetros agrometeorológicos podem melhorar as representações espaciais de rendimentos do milho em Moçambique. O ajuste de um modelo agrometeorológico espectral para estimativa de rendimentos do milho por regressão linear múltipla na província de Manica-Moçambique constituiu o objetivo do estudo. Foi realizado um mapeamento de áreas agrícolas por análise multitemporal do NDVI/MODIS e também foi avaliada a eficiência de variáveis agrometeorológicas e espectrais na estimativa de rendimentos do milho em uma área da província de Manica que envolve os distritos de Gondola, Manica, Mossurize e Sussundenga, responsáveis por mais de 80% da produção de milho na província nos anos de 2000 a 2009. Foi desenvolvido um modelo de início do ciclo do milho baseado em critérios de chuva, e estabelecendo um ciclo fixo do milho em 130 dias. A metodologia de mapeamento de áreas agrícolas consistiu em somatórios de imagens binárias geradas por diferença de NDVI máximo e mínimo ao longo do ciclo e estabelecimento de níveis de restrição com base em comparações com estatísticas oficiais por distrito. As variáveis agrometeorológicas testadas foram evapotranspiração relativa (ETr/ETm) e o índice de satisfação das necessidade de água (ISNA) calculados a partir de dados de estimativas de elementos meteorológicos do modelo do ECMWF. O conjunto de variáveis espectrais compreendiam composições de 16 dias de índices de vegetação EVI e NDVI provenientes do produto MOD13Q1 do sensor MODIS e o LSWI, gerado por diferença normalizada de bandas de refletância de superfície do infravermelho próximo e médio contidas no mesmo produto. O modelo agrometeorológico espectral envolveu as variáveis meteorológicas e espectrais como independentes sendo o rendimento médio e relativo, as variáveis dependentes ajustadas em um modelo de regressão múltipla. Todos os distritos, a exceção de Mossurize, geraram modelos com bom desempenho nas estimativas de rendimentos do milho e significado físico. O modelo regional, incluindo Gondola, Manica e Sussundenga e envolvendo o rendimento relativo foi o mais recomendado para estimativa de rendimentos do milho na região com r2 = 0,762 e RMSE de 9,46%. / Mozambique is a country located along the east coast of southern Africa, with an economy based primarily on agriculture. The Maize crop (Zea mays L.) is the most important crop, growing in rainfed conditions, with its yield dependent only on weather conditions. Agrometeorological models to forecast yields of food crops are viable alternatives for decision making on food safety measures and supply. The agricultural calendar and the production system make use of geotechnologies an important tool for crop monitoring and yield forecasting. Products from remote sensing data, combined with spectral indices and agrometeorological parameters can improve the spatial representations of maize yields in Mozambique. Setting an agrometeorological model to estimate the spectral yield of corn by multiple linear regression in Manica province, Mozambique was the objective of the study. Were conducted a mapping of agricultural areas by analyzing multitemporal NDVI / MODIS and also evaluated the effectiveness of spectral and meteorological variables in the estimated maize yield in an area of Manica province involving the districts of Gondola, Manica, Mossurize and Sussundenga responsible for more than 80% of corn production in the province in the years 2000 to 2009. A model was developed to estimate the beginnig of the corn cycle, using as a criteria the rainfall, and setting a fixed cycle of corn in 130 days. The methodology for mapping agricultural areas consisted of sums of binary images generated by the difference of maximum and minimum NDVI throughout the cycle and establishing levels of restriction based on comparisons with official statistics by district. Were tested the meteorological variables: the relative evapotranspiration (ETr / ETm) and the index of satisfaction of water needs (ISNA) calculated from data from meteorological model of ECMWF. The set of spectral variable were comprised of 16 days composition of vegetation indices NDVI and EVI from the MODIS product MOD13Q1 and LSWI generated from normalized difference of surface reflectance bands of near-infrared and medium infrared contained the same product. The meteorological and spectral variables was the set of independent variables and the average and relative yield were the set of dependent variables used to adjusted a multiple regression model, called agrometeorological-spectral model. To all districts, except for Mossurize were generated models with good performance in estimating the corn yield and with physical meaning. The regional model, including Gondola, Manica and Sussundenga and involving the relative yield was the most suitable for estimating corn yield in the region with r2 = 0.762 and RMSE of 9.46%.

Made available in DSpace on 2014-06-12T16:26:33Z (GMT). No. of bitstreams: 2 arquivo2046_1.pdf: 6954777 bytes, checksum: ddb5bca785659d91216220f7b9426ed0 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Esta pesquisa tem como enfoque mostrar aplicações do sensor MODIS (Moderate Resolution Spectroradiometer) e seus diferentes tipos de produtos. O MODIS foi concebido para análise de mudanças em escala global, possuindo assim diversas aplicações ambientais. Este sensor possui 36 bandas e 44 produtos para diferentes tipos de pesquisas. A resolução espacial das imagens MODIS varia de 250m a 1km dependendo da banda ou produto a ser analisado. O produto MOD13Q1 do nível 3 do sensor MODIS é um produto específico para aquisição de informações do NDVI (Índice de Vegetação por Diferença Normalizada), com resolução espacial de 250 m, e resolução espacial de 16 dias, e dispõe de dois índices de vegetação: NDVI e EVI (Enhanced Vegetation Index); Duas imagens com informações de atestamento da qualidade dos produtos NDVI e EVI (quality NDVI e quality EVI); Imagens de reflectância RED, BLUE, NIR e MIR;Três imagens referentes a forma de aquisição dos dados, correspondentes a ângulo de visada, ângulo zenital solar e azimute. Já produto MOD43B3 é um produto de albedo de superfície do MODIS e contém as sete primeiras bandas do sensor MODIS além de outras três bandas simuladas com larga faixa espectral. O MOD11 é um produto do nível 2 que fornece dados de temperatura da superfície terrestre (LST Land Surface Temperature) e emissividade (E) diárias da superfície terrestre com resolução espacial de 1 Km, este é composto por LSTs diurnos e noturnos, qualidade de avaliação, tempos de observação, ângulos de visada, cobertura de céu claro e emissividades estimadas nas bandas 31 e 32 para tipos de cobertura do solo. Nesta pesquisa foram utilizados exclusivamente produtos relacionados ao NDVI, EVI, temperatura e albedo das imagens MODIS/Terra. Estes produtos foram avaliados em um estudo sobre a bacia hidrográfica do Rio Pajeú (UP9 Unidade de Planejamento Hídrico 9) que está localizada no estado de Pernambuco, especificamente nas mesorregiões do Sertão Pernambucano e do São Francisco. A bacia envolve as microrregiões do Pajeú, em sua totalidade, e parte do Sertão do Moxotó, do Salgueiro e de Itaparica. A divisão políticoadministrativa da área que abrange um total de 27 municípios. Na área existe a predominância de rochas cristalinas na proporção de 86,3%, enquanto 11,7% representam depósitos sedimentares e os 2% restantes pertencem aos solos aluviais. O relevo divide-se entre planos e ondulados, com vegetação de caatinga arbustiva e arbórea em sua maior parte. Os tipos de solos mais predominantes na área são os do tipo Luvissolo Crômico, Argissolo Solodico e Neossolos (NC, Os e R). Neste trabalho, os parâmetros extraídos das imagens foram comparados com dados de solo contidos no ZAPE (Zoneamento Agroecológico de Pernambuco) desenvolvido pela EMBRAPA solos e, sobrepostos a mapas da transposição do Rio São Francisco que corta uma pequena faixa desta bacia mas que pode causar alterações importantes na área com esta obra civil. Os objetivos foram portanto: analisar a variação da vegetação na área da bacia a partir de imagens multitemporais de NDVI e EVI; Analisar o comportamento da temperatura na área da bacia; E analisar a distribuição do albedo sobre a região a partir de uma análise multitemporal. Um mapeamento temático da bacia hidrográfica foi desenvolvido e mostra-se que imagens MODIS são importantes para análise espacial, temporal e espectral da bacia

Biodiversity is a complex concept incorporating genes, species, ecosystems, composition, structure and function. The global scientific and political community has recognized the importance of biodiversity for human well-being, and has set goals and targets for its conservation, sustainable use, and benefit sharing. Monitoring biodiversity will help meet conservation goals and targets, yet observations collected in-situ are limited in space and time, which may bias interpretations and hinder conservation. Remote sensing can provide complementary datasets for monitoring biodiversity that are spatially comprehensive and repeatable. However, further research is needed to demonstrate, for various spatial scales and regions, how remotely sensed datasets represent different aspects of biodiversity. The overall goal of this dissertation is to advance the mapping and monitoring of biodiversity indicators, globally and within Canada, through the use of remote sensing. This research produced maps that were rich with spatially explicit, spatially continuous data, filling gaps in the availability and spatial resolution or scalability of information regarding ecosystem function (primary productivity) at global scales, tree species composition at regional scales (Saskatchewan, Canada), and ecosystem structure at local scales (coastal British Columbia, Canada). Further, the remotely sensed indicator datasets proposed and tested in each of the research chapters are repeatable, ecologically meaningful, translate to specific biodiversity targets globally and within Canada, and are calculable at multiple spatial scales. Challenges and opportunities for fully implementing these or similar remotely sensed biodiversity indicators and indicator datasets at a national level in Canada are discussed, contributing to the advancement of biodiversity monitoring science. / Graduate

Anthropogenic changes to forests, agriculture and hydrology are being driven by a need to provide water, food and shelter to more than six billion people. Unfortunately, these changes have a major impact on hydrology, biodiversity, climate, socio-economic stability and food security. The most pervasive form of land-cover change in South Africa is human settlement expansion. In many cases, new human settlements and settlement expansion are informal and occur in areas that are typically covered by natural vegetation. Settlements are infrequently mapped on an ad-hoc basis in South Africa which makes information on when and where new settlements form very difficult. Determining where and when new informal settlements occur is beneficial from not only an ecological but also a social development standpoint. The objective of this thesis is to make use of coarse resolution satellite data to infer the location of new settlement developments in an automated manner by making use of machine learning methods. The specific sensor that is considered in this thesis is the MODIS sensor on-board the Terra and Aqua satellites. By using samples taken at regular intervals (8 days), a hyper-temporal time-series is constructed and consequently used to detect new human settlement formations in South Africa. Two change detection methods are proposed in this thesis to achieve the goal of automated new settlement development detection using this high-temporal coarse resolution satellite time-series data. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Land surface albedo describes the proportion of incident solar radiant flux that is reflected from the Earth's surface and therefore is a crucial parameter in modeling and monitoring attempts to capture the current climate, hydrological, and biogeochemical cycles and predict future scenarios. Due to the temporal variability and spatial heterogeneity of land surface albedo, remote sensing offers the only realistic method of monitoring albedo on a global scale. While the distribution of bright, highly reflective surfaces (clouds, snow, deserts) govern the vast majority of the fluctuation, variations in the intrinsic surface albedo due to natural and human disturbances such as urban development, fire, pests, harvesting, grazing, flooding, and erosion, as well as the natural seasonal rhythm of vegetation phenology, play a significant role as well. The development of times series of global snow-free and cloud-free albedo from remotely sensed observations over the past decade and a half offers a unique opportunity to monitor and assess the impact of these alterations to the Earth's land surface. By utilizing multiple satellite records from the MODerate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging Spectroradiometer (MISR) and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, and developing innovative spectral conversion coefficients and temporal gap-filling strategies, it has been possible to utilize the strengths of the various sensors to improve the spatial and temporal coverage of global land surface albedo retrievals. The availability of these products is particularly important in tropical regions where cloud cover obscures the forest for significant periods. In the Amazon, field ecologists have noted that some areas of the forest ecosystem respond rapidly with foliage growth at the beginning of the dry season, when sunlight can finally penetrate fully to the surface and have suggested this phenomenon can continue until reductions in water availability (particularly in times of drought) impact the growth cycle. While it has been difficult to capture this variability from individual optical satellite sensors, the temporally gap-filled albedo products developed during this research are used in a case study to monitor the Amazon during the dry season and identify the extent of these regions of foliage growth.