Orientadores: Rubens Augusto Camargo Lamparelli, Jansle Vieira Rocha / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-25T02:30:27Z (GMT). No. of bitstreams: 1 Mello_JeffersonRodrigoBatistade_M.pdf: 5764136 bytes, checksum: 961de7126fa48297c992a3db8b810cfa (MD5) Previous issue date: 2014 / Resumo: Cultivada no Brasil desde as primeiras décadas após a chegada dos Portugueses, a cana-de-açúcar vem desde então exercendo notável influência nos ciclos econômicos e no desenvolvimento do país, ganhando grande destaque com a percepção do etanol como opção de energia limpa e renovável e ainda em função da crescente demanda mundial por este tipo de energia. Estima-se que na Safra 2012/2013 a área cultivada no Brasil totalizou 8,49 milhões de hectares, os quais geraram uma moagem de 588,9 milhões de toneladas de cana-de-açúcar destinadas principalmente à produção de açúcar, etanol e energia elétrica. Diante da magnitude dos números que envolvem a produção canavieira no Brasil, surge a importância do planejamento da produção da cultura, alicerçado nas estimativas de safra, imprescindíveis para o gerenciamento adequado da lavoura e para as negociações antecipadas do produto, inclusive da matéria seca que será a matéria-prima para a geração e comercialização da energia elétrica cogerada e num futuro próximo para a produção do etanol celulósico ou etanol de segunda geração. Diante desta problemática, este trabalho surge com o objetivo específico de comprovar a hipótese de que o modelo agrometeorológico-espectral baseado na abordagem clássica de MONTEITH (1972) sobre a eficiência dos sistemas vegetais em aproveitar a energia radiante para a produção e acúmulo de matéria seca pode ser utilizado com eficácia, em nível aceitável, para a estimativa de produtividade de matéria seca da cana-de-açúcar. Este estudo foi realizado em duas propriedades agrícolas localizadas na região de Matão (SP), cultivadas com o cultivar RB855156, em seu 3º ciclo de colheita mecanizada, na Safra 2011/2012, a qual teve sua produtividade de matéria seca avaliada em campo, pela coleta e análise de amostras, em quatro distintos períodos durante o ciclo de desenvolvimento da cultura, representando cada uma de suas fases de desenvolvimento. Os modelos agrometeorológico-espectrais foram implementados para as áreas de estudo variando a combinação (em pares) de quatro variáveis espectrais (valores de fAPAR derivados dos índices de vegetação obtidos pelas imagens dos sensores MODIS ¿ a: NDVI do Satélite Aqua, b: NDVI do Satélite Terra, c: NDVI médio entre os Satélites Aqua e Terra e, d: NDVI máximo entre os Satélites Aqua e Terra) e quatro variáveis meteorológicas (dados obtidos por meio de estação meteorológica de superfície ¿ a: dados diários, b: dados médios em agrupamento de 8 dias, c: dados médios decendiais e dados médios obtidos pelo modelo ECMWF/ERA-Interim d: dados decendiais), os quais resultaram em dezesseis diferentes conjuntos de dados modelados que, comparados as observações de campo e submetidos às análises estatísticas (testes de normalidades, de igualdade de médias, coeficientes de correlação de Pearson, de concordância e de desempenho), demonstraram a viabilidade do uso deste modelo para a estimativa de produtividade de matéria seca da cana-de-açúcar em nível local, com boa precisão (r = 0,9707) e acurácia (d = 0,9821) quando da utilização das variáveis espectrais do Satélite Aqua, bem como com o valor médio das observações espectrais dos Satélites Aqua e Terra, ao longo de todo o ciclo de desenvolvimento. Demonstraram ainda que os dados meteorológicos provenientes do ERA Interim, disponibilizados pelo ECMWF, podem ser utilizados nesta modelagem (r = 0,9617 e d = 0,9769), em opção aos dados das estações de superfície sem grandes perdas, como alternativa de obtenção de dados meteorológicos em locais onde haja carência de cobertura por estações meteorológicas de superfície / Abstract: Cultivated in Brazil since the early decades after the arrival of the Portuguese, sugarcane has since then been exerting considerable influence on economic cycles and the development of the country, gaining great prominence with the perception of ethanol as an option for clean, renewable energy and also because of the growing global demand for this type of energy. It is estimated that in 2012/2013 the planted crop area in Brazil totalized 8.49 million hectares, which generated a crushing of 588.9 million tons of sugarcane mainly for the production of sugar, ethanol and electric energy. Considering the magnitude of the numbers involving sugarcane production in Brazil, emerge the importance of planning in crop yield, based on crop estimates, essential for proper management of the crop and for early negotiations of the product, including the dry matter that will be the prime matter for the generation and sale of electricity and cogeneration for the production of cellulosic ethanol and second generation ethanol in the near future. Faced with this problem, this paper comes up with the specific purpose of testing the hypothesis that the agrometeorological-spectral model based on the classical approach of MONTEITH (1972) on the efficiency of plant systems in harnessing the radiant energy for production and dry matter accumulation can be used effectively at an acceptable level for estimating dry matter yield of sugarcane. This study was conducted on two farms in the region of Matão (SP), cultivated with the RB855156 cultivar in its 3rd cycle of mechanical harvesting in the 2011/2012 Harvest, which had its dry matter productivity evaluated in the field by collection and analysis of samples at four different times during the development cycle of culture, representing each phase of their of development. The agrometeorological-spectral models were implemented for the study areas using a combination of four spectral variables, in pairs, (values of fAPAR derived from the vegetation index obtained by the images from the MODIS sensors - a: NDVI from Sattelite Aqua b: NDVI from Sattelite Terra, c: Average NDVI between Aqua and Terra Sattelites and d: Maximum value of NDVI between Aqua and Terra Sattelites) and four meteorological variables (data obtained from weather station on surface ¿ a: daily data, b: average datas grouped by 8 days, c: average decendials datas e datas obtained by ECMWF/Era-Interim d: average decendials datas), which resulted in sixteen different sets of modeled data which, compared to field observations and subjected to statistical analysis (normality tests, equality of means test, Pearson correlation coefficients of agreement and performance), demonstrated the feasibility of using this model for estimating dry matter yield of sugarcane locally with good precision (r = 0.9707) and accuracy (d = 0.9821) when using the Aqua satellite spectral variables, as well as the average value of spectral observations of the satellites and the Earth Aqua over the entire development cycle. Also demonstrated that the meteorological data from the ERA Interim, provided by ECMWF, can be used in this model (r = 0.9617 e d = 0.9769) as an option to the data of surface stations without heavy losses, as an alternative of obtaining weather data in places where there is lack of coverage of ground meteorological stations / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestre em Engenharia Agrícola

Orientadores: Rubens Augusto Camargo Lamparelli, Jansle Vieira Rocha / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-22T16:59:27Z (GMT). No. of bitstreams: 1 Bispo_RafaelCarlos_M.pdf: 6682271 bytes, checksum: 3672bc3cd1d328c4edf329eabbd31ffe (MD5) Previous issue date: 2013 / Resumo: A produção de café esteve intimamente ligada ao desenvolvimento econômico do Brasil e ainda hoje o café é um importante produto da agricultura nacional. O Estado de Minas Gerais responde atualmente por 52% de toda a área de café do Brasil. Dessa forma, dada a importância da cafeicultura para a economia brasileira, é necessário desenvolver e melhorar as metodologias para seu monitoramento. Dados de sensoriamento remoto podem fornecer informações para o monitoramento e o mapeamento de café de maneira mais rápida e menos onerosa do que os métodos convencionais. Nesse contexto, os objetivos desta pesquisa foram identificar a bienalidade da cultura de café por meio de dados do sensor MODIS, juntamente com dados de estações meteorológicas, entre os anos de 2004 a 2012, e avaliar a eficácia das imagens-fração derivadas do sensor MODIS no mapeamento automático das áreas de café do município de Monte Santo de Minas/MG. Foi utilizada uma série temporal com 163 imagens da banda NIR do MODIS, produto MOD13Q1, para se extrair os valores de refletância dos pixels com pelo menos 80% de café. Dados diários de temperatura e precipitação foram agrupados de acordo com a resolução temporal das imagens (16 dias) para o cálculo do balanço hídrico. Para o mapeamento das áreas de café, foram utilizadas imagens do MODIS, bandas MIR, NIR e RED, dos períodos seco e chuvoso. Através do Modelo Linear de Mistura Espectral foram derivadas imagens-fração de solo, café e água/sombra. Estas imagens-fração serviram como dados de entrada para a classificação automática supervisionada com o método SVM - Support Vector Machine. Os resultados mostraram que para o monitoramento do café os dados de refletância dos períodos de colheita apresentaram maior correlação com a alternância da quantidade da produção. A partir da matriz de erro montada entre as classificações e as máscaras de referência, observou-se que os melhores resultados de Exatidão Global e Índice Kappa foram obtidos na classificação do período seco, sendo 67% e 0,41, respectivamente. Análises estatísticas de correlação e coeficiente de variação aplicadas sobre as imagens-fração de café permitiram melhor compreensão da complexidade do mapeamento do café / Abstract: Coffee production was closely linked to the economic development of Brazil and even today coffee is an important product of national agriculture. The State of Minas Gerais currently accounts for 52% of the whole area of coffee in Brazil. Thus, given the importance of the coffee crops to Brazilian economy, it is necessary to develop and improve methodologies for its monitoring. Then, remote sensing data can provide information for monitoring and mapping of coffee crops faster and cheaper than conventional methods. In this context, the objectives of this study were to identify the biennial yield of the coffee crop using data from MODIS and meteorological stations, over the period between 2004 and 2012, and assess the effectiveness of the fraction-images derived from MODIS in the automatic mapping of the areas of coffee in Monte Santo de Minas/MG. Were used a time series of 163 images of NIR band from MODIS, MOD13Q1 product, to extract the values of reflectance of pixels with at least 80% of coffee. Daily data of air temperature and precipitation were compiled to 16-day intervals to match the temporal resolution of MODIS imagery and to calculate the water balance. For coffee mapping, we used MODIS imagery, MIR, NIR and RED bands, of dry and rainy seasons. Through the Spectral Linear Mixing Model were derived fraction images of soil, coffee and water/shadow. These fraction images served as input data for supervised classification with SVM - Support Vector Machine approach. The results showed that for coffee monitoring the reflectance data of harvest period presented higher correlation with the alternation of coffee production. From the error matrix between the classifications and reference masks, it was observed that the best results of Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively. Statistical analyses of correlation and coefficient of variation applied over images fraction of coffee allowed a better understanding about the complexity of mapping coffee / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestre em Engenharia Agrícola

The subtropical thicket biome in the Eastern Cape Province of South Africa has been heavily degraded and transformed due overutilization during the last century. The highly degraded and transformed areas exhibit a significant loss of above ground carbon stocks (AGC) and loss of SOC content. Information about land use /cover change and fragmentation dynamics is a prerequisite for measuring carbon stock changes. The main aim of this study is to assess the trends of land use/cover change, fragmentation dynamics, model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, quantify and map the spatial distribution of SOC concentrations in the partial subtropical thicket cover in the Great Fish River Nature Reserve and environs (communal rangelands). Multi-temporal analyses based on 1972 Landsat MSS, 1982 and 1992 Landsat TM, 2002 Landsat ETM and 2010 SPOT 5 High Resolution images were used for land use/cover change detection and fragmentation analysis. Object oriented post-classification comparison was applied for land use/cover change detection analysis. Fragmentation dynamics analysis was carried out by computing and analyzing landscape metrics in land use/cover classes. Landscape fragmentation analyses revealed that thicket vegetation has increasingly become fragmented, characterized by smaller less linked patches of intact thicket cover. Landscape metrics for intact thicket and degraded thicket classes reflected fragmentation, as illustrated by the increase in the Number of Patches (NP), Patch Density (PD), Landscape Shape Index (LSI), and a decrease in Mean Patch Size (MPS). The use of remote sensing techniques and landscape metrics was vital for the understanding of the dynamics of land use/cover change and fragmentation. Baseline land use/cover maps produced for 1972, 1982, 1992 2002 and 2010 and fragmentation analyses were then used for analyzing carbon stock changes in the study area. To model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, a method based on the integration of RS and GIS was employed for the estimation of AGC stocks in a time series. A non-linear regression model was developed using NDVI values generated from SPOT 5 HRG satellite imagery of 2010 as the independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks for the entire study area on the 2010 SPOT 5 HRG and also extrapolated to the 1972 Landsat MSS, 1982 and 1992 Landsat TM, and 2002 Landsat ETM. The AGC stocks for the period 1972 -1982, 1982-1992, 1992-200) and 2002-2010 were compared by means of change detection analysis. The comparison of AGC stocks was carried out at subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation which were caused by anthropogenic activities. To map and quantify SOC concentration in partial (fractional) thicket vegetation cover, the spectral reflectance of both thicket vegetation and bare-soils was measured in situ. Soil samples were collected from the sampling sites and transported to the laboratory for spectral reflectance and SOC measurements. Thicket vegetation and bare soil reflectance were measured using spectroscopy both in situ and under laboratory conditions. Their respective endmembers were extracted from ASTER imagery using the Pixel Purity Index (PPI). The endmembers were validated with in situ and laboratory thicket and bare-soil reflectance signatures. The spectral unmixing technique was applied to ASTER imagery to discriminate pure pixels of thicket vegetation and bare-soils; a residual spectral image was produced. The Residual Spectral Unmixing (RSU) procedure was applied to the residual spectral image to produce an RSU soil spectrum image. Partial Least Squares Regression (PSLR) model was developed using spectral signatures of a residual soil spectrum image as the independent variable and SOC concentration measured from soil samples as the dependent variable. The PSLR prediction model was used to predict SOC concentration on the RSU soil spectral image. The predicted SOC concentration was then validated with SOC concentration measured from the field plots. A Strong correlation (R2 = 0.82) was obtained between the predicted SOC concentration and the SOC concentration measured from field samples. The PSLR was then used to generate a map of SOC concentration for the Great Fish River Nature Reserve and its environs. Areas with very low SOC concentrations were found in the degraded communal villages, as opposed to the higher SOC values in the protected area. The results confirmed that RS techniques are key to estimating and mapping the spatial distribution of SOC concentration in partial subtropical thicket vegetation. Partial thicket vegetation has a huge influence on the soil spectra; it can influence the prediction of SOC concentration. The use of the RSU approach eliminates partial thicket vegetation cover from bare soil spectra. The residual soil spectrum image contains enough information for the mapping of SOC concentration. The technique has the potential to augment the applicability of airborne imaging spectroscopy for soil studies in the sub-tropical thicket biome and similar environments.

Two study plots containing Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) newly infested by Douglas-fir beetle (Dendroctonus pseudotsugae Hopk.) were established and photographed with large-scale (1:1000), colour infrared film on July 29. 1979 - approximately three months after possible insect attack. Ground checking confirmed attacked trees and also showed that at the time of photography all trees had visually green, healthy-appearing foliage. All trees, both attacked and non-attacked in each plot were matched to their photographic images, and visual photo interpretation for damage types and densitometric analysis of the original transparencies were done. For each tree-crown image included, the yellow, magenta and cyan dye layer density measurements were taken and these values plus three ratios derived from them were tested statistically using analysis of variance and stepwise discriminant analysis. Significant differences were found between the optical density values of the images of healthy and attacked trees. The ratio values had much smaller variances than did the individual dye layer densities and all three ratios showed significant differences between healthy and attacked trees. Stepwise discriminant analysis produced significant separation of damage classes. Two-thirds of the successfully attacked trees were correctly classified and were confirmed by a second ground check in January, 1980. It is concluded that successfully beetle-attacked trees have a unique spectral signature than can be detected on colour infrared air photos approximately three months after initial attack when the trees still support visually green, healthy-appearing foliage. / Forestry, Faculty of / Graduate

abstract: Deforestation is a common phenomenon in Bangladesh, leaving the country under a great threat of losing its natural habitat. The increasing rate of natural habitat loss has raised questions regarding the country’s forest resource management practices. These practices were originally adopted to protect the forest ecosystem and secure the livelihood of the people dependent on forest resources. Despite the support from development partners like the United States Agency for International Development (USAID), the country is still struggling to protect its forest resources from human encroachment. One of the major problems is the lack of inconclusiveness in current approaches. Most initiatives are not evidence-based and are project-based for only a certain period of time. This has failed to ensure sustainable outcomes. This study looks at Bangladesh’s Himchari National Park forest management system to generate evidence regarding deforestation from 1991-2018 and highlight existing gaps. To identify and analyze the gaps, the study uses a social-ecological system (SES) lens. Results reveal deforestation across different time periods, articulates the overall governance structure regarding forest resource management, and provides an overview of the major gaps within the system. The study also offers a set of recommendations for improving the existing management system and policy implications. / Dissertation/Thesis / Masters Thesis Urban and Environmental Planning 2020

Satellite wind vector data is integral to atmospheric models and forecasts. Currently, the limited refresh rate of global wind vector measurement systems makes it difficult to observe diurnal variation of mesoscale processes. Using advancements in the underlying subsystem technologies, new satellite wind scatterometers may be possible that increase temporal resolution, among other performance metrics. I propose a method for parametrically modeling the extreme performance range of a complex system. I use this method to develop a model of the space of possible satellite wind scatterometer designs. I validate the model using point designs of heritage scatterometers. Finally, I present two example concepts for constellations of cooperative satellite wind scatterometers capable of measuring global ocean surface vector winds every hour for the same total cost as a single heritage scatterometer.

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The Meseta Purepecha, a volcanic plateau in the Mexican state of Michoacan, is home to one of the most species-rich pine forests in the world. Recent increases in demand for forest products has put added pressure on these resources. Though existing research has suggested significant deforestation in the Meseta, there is little information identifying specific areas of decline. This study focuses on two indigenous communities in the Meseta-Pichataro and Sevina. Both communities have long relied on wood as an economic resource. However, the two communities have reacted differently to increased demand for forest resources. The purpose of this study is to identify the differences in the rate and extent of forest change between Pichataro and Sevina. Three dates of Landsat satellite images - 1976, 1986, and 2000-were used to identify changes in the Meseta's forests. Supervised classification was used to classify the 2000 image into forested and non-forested areas. Change detection was performed on the 1976 through 2000 images to identify areas of forest clearing and forest regrowth. The 2000 image was then used as a reference for generating maps of historic forest extent based on the change detection results. Results show that between 1986 and 2000, Sevina cleared approximately 16% of its forested land between while Pichataro experienced a net gain of 7%. In the same period, 93% of the deforestation in the combined study area occurred within the community boundary of Sevina, which manages only 35% of the study area forests. Sevina's remaining forests are also more isolated and fragmented than the forests of Pichataro. The differences between the two communities appear related to management practices. Sevina has relied on larger-scale timber harvesting to derive economic benefits from its forests. Pichataro has focused on local harvesting and value-added production.

No description available.

No description available.