Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2017-08-31T19:33:36Z No. of bitstreams: 2 Willyan_Becker2017.pdf: 12083877 bytes, checksum: 7a9e90225376028c123e5c6e1c568603 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-08-31T19:33:36Z (GMT). No. of bitstreams: 2 Willyan_Becker2017.pdf: 12083877 bytes, checksum: 7a9e90225376028c123e5c6e1c568603 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-02-10 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Orbital remote sensing techniques have proved to be a valuable tool, since they enable the agricultural monitoring of the vigor and the type of vegetation coverage in a regional scale, bringing results with greater anticipation and precision, and lower operational cost when compared to traditional techniques. Automatic identification of cultivated areas is one of the most important steps in the crop forecasting process. The improvement in the estimate of area cultivated with each crop directly influences the result of the forecast of each crop year, since the agricultural production is a function of the cultivated area. The general objective of this research was to create an automatic methodology for the separation of agricultural crops from soybean and maize by means of data mining (Article 1) and a methodology for forecasting the harvest date from the date of maximum vegetative development (Article 2). The methods used corresponded to the application of the seasonal trends analysis and data mining for soybean and corn agricultural areas in the state of Paraná, with images of the EVI vegetation index of MODIS sensors, TERRA and AQUA satellites. The results obtained in Article 1 show that, through the decision tree, one of the techniques of data mining, it was verified that, among eleven variables that characterize the spectral-temporal pattern of the EVI of each culture, five were enough to perform the separation of soybean and maize crops, in the year 2014/2015, with an accuracy of 96.3% and a kappa index of 0.92, being the maximum value of EVI, the date of sowing (DS), the Date of maximum vegetative development (DMDV), Cycle, and Major Integral. In Article 2 the DS, DMDV and Harvest Date (DC) of the EVI temporal profile were estimated for each mapped soybean and maize pixel in the crop years 2011/2012 to 2013/2014. Then, for each crop and crop year, the variables Delta1 (DMDV minus DS) and Delta2 (DC minus DMDV) were created. The results of the differences (DCDifference) between DC estimated by EVI (DCEVI) and predicted by mean time (DCDelta2) show that, for soybeans, it is possible to use only the mean value of the interval between DMDV and DC in the three harvested years studied, with 55 days for soybeans. For corn, the mean interval between DMDV and DC was 60 days, but it is verified that there is a large difference between the results obtained with DCEVI and DCDelta2. For corn DCDelta2 there were large variations among the mesoregions. Differences in DC (DCDifference), when using the means by mesoregions, presented better results than for Paraná as a whole. / Técnicas de sensoriamento remoto orbital têm se mostrado uma ferramenta valiosa, pois possibilitam o monitoramento agrícola do vigor e do tipo de cobertura vegetal em escala regional, trazendo resultados com maior antecedência e precisão e menor custo operacional em relação às técnicas tradicionais. A identificação automática de áreas cultivadas constitui uma das etapas mais importantes no processo de previsão de safras. A melhoria na estimativa de área cultivada com cada cultura influencia diretamente o resultado da previsão de cada ano-safra, uma vez que a produção agrícola é função da área cultivada. O objetivo geral desta pesquisa foi criar uma metodologia automática para separação das culturas agrícolas de soja e milho, por meio da mineração de dados (Artigo 1) e uma metodologia de previsão da data de colheita das culturas a partir da data de máximo desenvolvimento vegetativo (Artigo 2). Os métodos utilizados corresponderam à aplicação da análise de padrões sazonais e mineração de dados para áreas agrícolas de soja e milho no estado do Paraná, com imagens do índice de vegetação EVI dos sensores MODIS, satélites TERRA e AQUA. Os resultados obtidos no Artigo 1 mostram que, por meio da árvore de decisão, uma das técnicas de mineração de dados, constatou-se que, dentre onze variáveis que caracterizam o padrão espectro-temporal do EVI de cada cultura, cinco foram suficientes para realizar a separação das culturas de soja e milho, ano-safra 2014/2015, com uma exatidão de 96,3% e um índice kappa de 0,92, sendo elas o valor máximo de EVI, a data de semeadura (DS), a data de máximo desenvolvimento vegetativo (DMDV), o ciclo e a integral maior. No Artigo 2 foram estimadas as DS, DMDV e Data de Colheita (DC) do perfil temporal EVI para cada pixel mapeado de soja e milho nos anos-safra 2011/2012 a 2013/2014. Posteriormente criaram-se, para cada cultura e ano-safra, as variáveis Delta1 (DMDV menos a DS) e o Delta2 (DC menos a DMDV). Os resultados das diferenças (DCDiferença) entre a DC estimada pelo EVI (DCEVI) e a prevista por média temporal (DCDelta2) apontam que, para a soja, há a possibilidade de utilizar-se apenas do valor médio do intervalo entre a DMDV e a DC nos três anos-safra estudados, sendo 55 dias para a soja. Para a cultura do milho, o intervalo médio entre a DMDV e a DC foi de 60 dias, porém verifica-se que existe grande diferença entre os resultados obtidos com a DCEVI e a DCDelta2. Para a DCDelta2 do milho houve grandes variações entre as mesorregiões. As diferenças nas DC (DCDiferença), quando utilizadas as médias por mesorregiões, apresentam melhores resultados que para o Paraná como um todo.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Grasslands of the Flint Hills are often burned as a land management practice. Remote sensing can be used to help better manage prairie landscapes by providing useful information about the long-term trends in grassland vegetation greenness and helping to quantify regional differences in vegetation development. Using MODIS 16-day NDVI composite imagery between the years 2001-10 for the entire Flint Hills ecoregion, BFAST was used to determine trend, seasonal, and noise components of the image time series. To explain the trend, 4 factors were considered including hydrologic soil group, burn frequency, and precipitation deviation from the 30 year normal. In addition, the time series data was processed using TIMESAT to extract eight different phenometrics: Growing season length, start of season, end of season, middle of season, maximum value, small integral, left derivative, and right derivative. Phenometrics were produced for each year of the study and an ANOVA was performed on the means of all eight phenometrics to assess if significant differences existed across the study area. A K-means cluster analysis was also performed by aggregating pixel-level phenometrics at the county level to identify administrative divisions exhibiting similar vegetation development. For the study period, the area of negatively and positively trending grassland were similar (41-43%). Logistic regression showed that the log odds of a pixel experiencing a negative trend were higher in sites with clay soils and higher burning frequencies and lower for pixels having higher than normal precipitation and loam soils. Significant differences existed for all phenometrics when considering the ecoregion as a whole. On a phenometric-by-phenometric basis, unexpected groupings of counties often showed statistically similar values. Similarly, when considering all phenometrics at the same time, counties clustered in surprising patterns. Results suggest that long-term trends in grassland conditions warrant further attention and may rival other sources of grassland change (e.g., conversion, transition to savannah) in importance. Analyses of phenometrics indicates that factors other than natural gradients in temperature and precipitation play a significant role in the annual cycle of grassland vegetation development. Unanticipated, and sometimes geographically disparate, groups of counties were shown to be similar in the context of specific phenology metrics and this may prove useful in future implementations of smoke management plans within the Flint Hills.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Time series of normalized difference vegetation index (NDVI) data from satellite spectral measurements can be used to characterize and quantify changes in vegetation phenology and explore the role of natural and anthropogenic activities in causing those changes. Several programs and methods exist to process phenometric data from remotely-sensed imagery, including TIMESAT, which extracts seasonality parameters from time-series image data by fitting a smooth function to the series. This smoothing function, however, is dependent upon user-defined input parameter settings which have an unknown amount of influence in shaping the final phenometric estimates. To test this, a sensitivity analysis was conducted using MODIS maximum value composite NDVI time-series data acquired for Fort Riley, Kansas during the period 2001-2012. The phenometric data generated from the different input setting files were compared against that from a base scenario using Pearson and Lin’s Concordance Correlation Analyses. Findings show that small changes to parameter settings results in insignificant differences in phenometric estimates, with the exception of end of season data and growing season length. Next, a time-series analysis of the same MODIS NDVI data for Fort Riley and nearby Konza Prairie Biological Station (KPBS) was conducted to determine if significant differences existed in selected vegetation phenometrics. Phenometrics of interest were estimated using TIMESAT and based on a Savitzky-Golay filter with parameter settings found optimal in the previous study. The phenometrics start of season, end of season, length of season, maximum value, and small seasonal integral were compared using Kolmogorov-Smirnov (K-S) and showed significant differences existed for all phenometrics in the comparison of Fort Riley training areas and KPBS, as well as low- versus high-training intensity areas within Fort Riley. Fort Riley and high-intensity training areas have earlier dates for the start and end of the growing season, shorter growing season lengths, lower maximum NDVI values, and lower small seasonal integrals compared to KPBS and low-intensity training areas, respectively. Evidence was found that establishes a link between military land uses and/or land management practices and observed phenometric differences.

Die Brasilianische Savanne, auch bekannt als der Cerrado, bedeckt ca. 24% der Landoberfläche Brasiliens. Der Cerrado ist von einer einzigartigen Biodiversität und einem starken Gradienten in der Vegetationsstruktur gekennzeichnet. Großflächige Landnutzungsveränderungen haben dazu geführt, dass annähernd die Hälfte der Cerrado in bewirtschaftetes Land umgewandelt wurde. Die Kartierung ökologischer Prozesse ist nützlich, um naturschutzpolitische Entscheidungen auf räumlich explizite Informationen zu stützen, sowie um das Verständnis der Ökosystemdynamik zu verbessern. Neue Erdbeobachtungssensoren, frei verfügbare Daten, sowie Fortschritte in der Datenverarbeitung ermöglichen erstmalig die großflächige Erfassung saisonaler Vegetationsdynamiken mit hohem räumlichen Detail. In dieser Arbeit wird der Mehrwert von Landsat-basierten Landoberflächenphänologischen (LSP) Metriken, für die Charakterisierung der Cerrado-Vegetation, hinsichtlich ihrer strukturellen und phänologischen Diversität, sowie zur Schätzung des oberirdischen Kohlenstoffgehaltes (AGC), analysiert. Die Ergebnisse zeigen, dass LSP-Metriken die saisonale Vegetatiosdynamik erfassen und für die Kartierung von Vegetationsphysiognomien nützlich sind, wobei hier die Grenzen der Einteilung von Vegetationsgradienten in diskrete Klassen erreicht wurden. Basierend auf Ähnlichkeiten in LSP wurden LSP Archetypen definiert, welche die Erfassung und Darstellung der phänologischen Diversität im gesamten Cerrado ermöglichten und somit zur Optimierung aktueller Kartierungskonzepte beitragen können. LSP-Metriken ermöglichten die räumlich explizite Quantifizierung von AGC in drei Untersuchungsgebieten und sollten bei zukünftigen Kohlenstoffschätzungen berücksichtigt werden. Die Erkenntnisse dieser Dissertation zeigen die Vorteile und Nutzungsmöglichkeiten von LSP Metriken im Bereich der Ökosystemüberwachung und haben demnach direkte Implikationen für die Entwicklung und Bewertung nachhaltiger Landnutzungsstrategien. / The Brazilian savanna, known as the Cerrado, covers around 24% of Brazil. It is characterized by a unique biodiversity and a strong gradient in vegetation structure. Land-use changes have led to almost half of the Cerrado being converted into cultivated land. The mapping of ecological processes is, therefore, an important prerequisite for supporting nature conservation policies based on spatially explicit information and for deepening our understanding of ecosystem dynamics. New sensors, freely available data, and advances in data processing allow the analysis of large data sets and thus for the first time to capture seasonal vegetation dynamics over large extents with a high spatial detail. This thesis aimed to analyze the benefits of Landsat based land surface phenological (LSP) metrics, for the characterization of Cerrado vegetation, regarding its structural and phenological diversity, and to assess their relation to above ground carbon. The results revealed that LSP metrics enable to capture the seasonal dynamics of photosynthetically active vegetation and are beneficial for the mapping of vegetation physiognomies. However, the results also revealed limitations of hard classification approaches for mapping vegetation gradients in complex ecosystems. Based on similarities in LSP metrics, which were for the first time derived for the whole extent of the Cerrado, LSP archetypes were proposed, which revealed the spatial patterns of LSP diversity at a 30 m spatial resolution and offer potential to enhance current mapping concepts. Further, LSP metrics facilitated the spatially explicit quantification of AGC in three study areas in the central Cerrado and should thus be considered as a valuable variable for future carbon estimations. Overall, the insights highlight that Landsat based LSP metrics are beneficial for ecosystem monitoring approaches, which are crucial to design sustainable land management strategies that maintain key ecosystem functions and services.

Imagens adquiridas por sensores orbitais possibilitam observações da Terra e auxiliam estudos de grande áreas. Este trabalho utilizou imagens orbitais para analisar os componentes que modificam as características óticas da água na Lagoa dos Patos, localizada no sul do Brasil. A partir de imagens multiespectrais dos sensores MODIS/Terra e OLI/Landsat-8, foi possível avaliar a variação espaço-temporal de sólidos em suspensão (SS) e estimar alguns parâmetros de qualidade da água na Lagoa dos Patos. Na primeira etapa desse trabalho, o padrão anual e espacial de SS na laguna foi determinado com base em uma série de 15 anos do produto MOD09Q1 (reflectância na faixa do vermelho do sensor MODIS). Foi observado que a reflectância possui um padrão sazonal com aumento do outono até o final da primavera, e que no corpo lagunar e no estuário a reflectância varia ao longo do ano. Interpretações da variação da reflectância, relacionadas a descarga fluvial e a ação dos ventos, foram apresentadas. Na segunda etapa desse trabalho, o modelo linear de mistura espectral (MLME) foi aplicado em uma imagem do sensor OLI para separar águas espectralmente distintas do estuário da Lagoa dos Patos. A partir das imagens frações, geradas pelo MLME, e das bandas do sensor OLI e TIRS modelos empíricos foram desenvolvidos para estimar alguns parâmetros de qualidade da água. Os modelos obtidos foram capazes de estimar a concentração de clorofila-a (R2 = 0,82) e de sólidos em suspensão (R2 = 0,62), a turbidez (R2 = 0,67) e a profundidade do disco Secchi (R2 = 0,64). Conclui-se que o MLME e os dados do satélite Landsat-8 apresentam grande potencial para estimativa de parâmetros da água por imagens orbitais. / Images acquired by orbital sensors enable observations of the Earth and assist studies of large areas. This work used orbital images to analyze the components that modify the optical characteristics of the water of Lagoa dos Patos, located in the southern region of Brazil. By using multispectral images from MODIS/Terra and OLI/Landsat-8 sensors it was possible to evaluate the spatio-temporal variation of suspended solids (SS) and estimate some parameters of water quality. In the first stage of this work, the annual and spatial pattern of SS were estimated based on a series of 15 years of MOD09Q1 product (reflectance in the red channel of MODIS sensor). It was observed that the reflectance has a seasonal pattern with increase in the autumn until the late spring, and that in the lagoon body and estuary the reflectance varies throughout the year. Interpretations of the spatio-temporal variation of reflectance related to river discharge and the action of the winds were presented. In second stage of this study, the Linear Spectral Mixing Model (LSMM) was applied to an image of the OLI sensor to separate spectrally distinct waters of Lagoa dos Patos estuary. From the fraction images generated by LSMM and the bands of OLI and TIRS sensors, empirical models were developed to estimate some parameters of water quality. The obtained models were able to estimate the concentration of chlorophyll (R2 = 81,56) and the suspended solids (R2 = 61,57), turbidity (R2= 67,14) and Secchi disk depth (R2 = 64,29). Combination of LSMM and the Landsat- 8 satellite have shown great potential for water parameters estimation from orbital images.

Imagens adquiridas por sensores orbitais possibilitam observações da Terra e auxiliam estudos de grande áreas. Este trabalho utilizou imagens orbitais para analisar os componentes que modificam as características óticas da água na Lagoa dos Patos, localizada no sul do Brasil. A partir de imagens multiespectrais dos sensores MODIS/Terra e OLI/Landsat-8, foi possível avaliar a variação espaço-temporal de sólidos em suspensão (SS) e estimar alguns parâmetros de qualidade da água na Lagoa dos Patos. Na primeira etapa desse trabalho, o padrão anual e espacial de SS na laguna foi determinado com base em uma série de 15 anos do produto MOD09Q1 (reflectância na faixa do vermelho do sensor MODIS). Foi observado que a reflectância possui um padrão sazonal com aumento do outono até o final da primavera, e que no corpo lagunar e no estuário a reflectância varia ao longo do ano. Interpretações da variação da reflectância, relacionadas a descarga fluvial e a ação dos ventos, foram apresentadas. Na segunda etapa desse trabalho, o modelo linear de mistura espectral (MLME) foi aplicado em uma imagem do sensor OLI para separar águas espectralmente distintas do estuário da Lagoa dos Patos. A partir das imagens frações, geradas pelo MLME, e das bandas do sensor OLI e TIRS modelos empíricos foram desenvolvidos para estimar alguns parâmetros de qualidade da água. Os modelos obtidos foram capazes de estimar a concentração de clorofila-a (R2 = 0,82) e de sólidos em suspensão (R2 = 0,62), a turbidez (R2 = 0,67) e a profundidade do disco Secchi (R2 = 0,64). Conclui-se que o MLME e os dados do satélite Landsat-8 apresentam grande potencial para estimativa de parâmetros da água por imagens orbitais. / Images acquired by orbital sensors enable observations of the Earth and assist studies of large areas. This work used orbital images to analyze the components that modify the optical characteristics of the water of Lagoa dos Patos, located in the southern region of Brazil. By using multispectral images from MODIS/Terra and OLI/Landsat-8 sensors it was possible to evaluate the spatio-temporal variation of suspended solids (SS) and estimate some parameters of water quality. In the first stage of this work, the annual and spatial pattern of SS were estimated based on a series of 15 years of MOD09Q1 product (reflectance in the red channel of MODIS sensor). It was observed that the reflectance has a seasonal pattern with increase in the autumn until the late spring, and that in the lagoon body and estuary the reflectance varies throughout the year. Interpretations of the spatio-temporal variation of reflectance related to river discharge and the action of the winds were presented. In second stage of this study, the Linear Spectral Mixing Model (LSMM) was applied to an image of the OLI sensor to separate spectrally distinct waters of Lagoa dos Patos estuary. From the fraction images generated by LSMM and the bands of OLI and TIRS sensors, empirical models were developed to estimate some parameters of water quality. The obtained models were able to estimate the concentration of chlorophyll (R2 = 81,56) and the suspended solids (R2 = 61,57), turbidity (R2= 67,14) and Secchi disk depth (R2 = 64,29). Combination of LSMM and the Landsat- 8 satellite have shown great potential for water parameters estimation from orbital images.

Imagens adquiridas por sensores orbitais possibilitam observações da Terra e auxiliam estudos de grande áreas. Este trabalho utilizou imagens orbitais para analisar os componentes que modificam as características óticas da água na Lagoa dos Patos, localizada no sul do Brasil. A partir de imagens multiespectrais dos sensores MODIS/Terra e OLI/Landsat-8, foi possível avaliar a variação espaço-temporal de sólidos em suspensão (SS) e estimar alguns parâmetros de qualidade da água na Lagoa dos Patos. Na primeira etapa desse trabalho, o padrão anual e espacial de SS na laguna foi determinado com base em uma série de 15 anos do produto MOD09Q1 (reflectância na faixa do vermelho do sensor MODIS). Foi observado que a reflectância possui um padrão sazonal com aumento do outono até o final da primavera, e que no corpo lagunar e no estuário a reflectância varia ao longo do ano. Interpretações da variação da reflectância, relacionadas a descarga fluvial e a ação dos ventos, foram apresentadas. Na segunda etapa desse trabalho, o modelo linear de mistura espectral (MLME) foi aplicado em uma imagem do sensor OLI para separar águas espectralmente distintas do estuário da Lagoa dos Patos. A partir das imagens frações, geradas pelo MLME, e das bandas do sensor OLI e TIRS modelos empíricos foram desenvolvidos para estimar alguns parâmetros de qualidade da água. Os modelos obtidos foram capazes de estimar a concentração de clorofila-a (R2 = 0,82) e de sólidos em suspensão (R2 = 0,62), a turbidez (R2 = 0,67) e a profundidade do disco Secchi (R2 = 0,64). Conclui-se que o MLME e os dados do satélite Landsat-8 apresentam grande potencial para estimativa de parâmetros da água por imagens orbitais. / Images acquired by orbital sensors enable observations of the Earth and assist studies of large areas. This work used orbital images to analyze the components that modify the optical characteristics of the water of Lagoa dos Patos, located in the southern region of Brazil. By using multispectral images from MODIS/Terra and OLI/Landsat-8 sensors it was possible to evaluate the spatio-temporal variation of suspended solids (SS) and estimate some parameters of water quality. In the first stage of this work, the annual and spatial pattern of SS were estimated based on a series of 15 years of MOD09Q1 product (reflectance in the red channel of MODIS sensor). It was observed that the reflectance has a seasonal pattern with increase in the autumn until the late spring, and that in the lagoon body and estuary the reflectance varies throughout the year. Interpretations of the spatio-temporal variation of reflectance related to river discharge and the action of the winds were presented. In second stage of this study, the Linear Spectral Mixing Model (LSMM) was applied to an image of the OLI sensor to separate spectrally distinct waters of Lagoa dos Patos estuary. From the fraction images generated by LSMM and the bands of OLI and TIRS sensors, empirical models were developed to estimate some parameters of water quality. The obtained models were able to estimate the concentration of chlorophyll (R2 = 81,56) and the suspended solids (R2 = 61,57), turbidity (R2= 67,14) and Secchi disk depth (R2 = 64,29). Combination of LSMM and the Landsat- 8 satellite have shown great potential for water parameters estimation from orbital images.