Human-elephant conflict (HEC) occurs across Africa and is a major threat to the continued existence of the African elephant. To effectively implement mitigation measures, a thorough understanding of the spatial and temporal patterns of HEC is required. This study used a systematic, grid-based geographical information system (GIS) to analyse the spatial and temporal relations of HEC intensity in 2004 and 2008 with underlying environmental variables in a forest habitat, the Bia Conservation Area (BCA), Ghana. Relationships between crop-raiding incident data, Moderate Image Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) values and remotely sensed derived data were investigated at a 10 km2 scale using principal components analysis (PCA) and correlation analysis. Crop-raiding was found to be clustered into distinct areas. The onset of crop-raiding in 2004 and 2008 can be attributed to seasonal variation in vegetation biomass. Decreases in EVI values were matched with crop-raiding incidents. The high number of crop-raiding incidents in 2004 could be attributed to the large fluctuations in vegetation biomass in comparison to 2008. HEC intensity was not significantly related to the environmental variables analysed at the 10 km2 scale. These results suggest that HEC intensity may be influenced by vegetation quality, soil mineral content and/or human density. A grid-based GIS system with a 10 km2 resolution used in combination with remotely sensed data and statistical tools is useful for identifying spatial patterns of HEC, even with relatively small incident data sets. The methods used in this study could be applied to other forest habitats experiencing HEC for comparative analysis. The influence of vegetation quality, soil mineral content and human density on HEC intensity in forest habitats requires further analysis.

The earth surface is monitored periodically by numerous satellite sensors which have different spectral response functions, image acquisition heights, atmosphere correction schemes, overpass times, and sun/view angle geometries. Temporal and spatial variations of land surface properties, such as vegetation index, Leaf Area Index (LAI), land surface temperature, and soil moisture, have been provided by long-term time series of various remote sensing datasets. Inter-sensor translation equations are required to build long-term time series by the combination of multiple sensors from historical to advanced and new satellite datasets. In the first chapter, inter-sensor translation equations of band reflectances and two vegetation indices (e.g. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) were derived using linear regression equations relative to Moderate Resolution Imaging Spectroradiometer (MODIS) values. The consistency and validation of inter-sensor transforms were investigated through statistical student's t-test and the root mean square error (RMSE).In the second chapter, cross-sensor extension of EVI and a 2-band EVI (without the blue band; EVI2) were investigated based on the continuity of both EVI's. Sensor specific red-blue coherencies were examined for the possibility of the EVI and EVI2 extension from MODIS sensor. The EVI continuity to MODIS was particularly problematic for the Visible Infrared Imager / Radiometer Suite (VIIRS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have dissimilar blue bands from that of MODIS. The cross-sensor extension and compatibility of EVI2 were improved and provided the possibility to be lengthened to the Advanced Very High Resolution Radiometer (AVHRR) using its translation equation.Finally, we evaluated the use of sensor-specific EVI and NDVI data sets, using a time sequence of Hyperion images over Amazon rainforest in Tapajos National Forest, Brazil for the 2001 and 2002 dry seasons. We computed NDVI, EVI, and EVI2 with the convolution data of different global monitoring and high temporal resolution sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) from Hyperion, and evaluated their spectral deviations and continuity in the characterization of tropical forest phenology. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated.

Knowledge og the crop yield with sufficient lead time prior to harvest is crucial for the farm management or national agro-food policy. Spectral characteristics provided by satellite based remote sensing have both spatial and temporal resolution which allow crop yields from agricultural fields. The aim of this thesis was to test feasibility of developing crop yield. The study was focused on three major crops in the Czech Republic: spring barely, winter wheat and oilseed rape. The crop yield data were collected from 14 districts that represent regions with more intensive agricultural production and include a variety of climate, topographic and soil conditions. As a main data source for this thesis was series of digital images acquired by MODIS (Moderate Resolution Imaging Spectroradiometr) aboard Terra satellite from 2001-2014 period. Were analyzed two vegetation idices NDVI (Noramized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) from the 16-days composite product with a spatial resolution of 250 m. In most cases, EVI showed higher correlations to the crop yied, which can be explained due to the negative saturation effect of NDVI.

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.

Made available in DSpace on 2017-05-12T14:47:12Z (GMT). No. of bitstreams: 1 Parte_ 1.pdf: 4908182 bytes, checksum: 66963491c16f98c75db1942dbc323ede (MD5) Previous issue date: 2015-02-13 / In order to propose an objective methodology to estimate soybean crop yield in Western Paraná, the present research explored three different models for estimating soybean crop yield. One agrometeorological model (FAO model), presented four estimates based on agrometeorological data from remote sensing (ECMWF) and data collected in loco; one spectral model (GLO-PEM2 model), which used data from a MODIS sensor to estimate soybean crop yield in the state; and one agro-spectral model (CASA model), which presented two estimates, based in agrometeorological data and MODIS sensor data. The present work determined water balance and real evapotranspiration through two methods (BHTM and BHFAO). The soil data was collected from EMBRAPA, the culture s was from FAO, the dates of planting and harvest were determined by Becker (2013) and the agrometeological data was obtained from ECMWF. The spectral data utilized was provided by the MODIS sensor, namely Terra and Aqua platforms. The GLO-PEM2 proved to be rather weak and requires further investigation; the CASA and FAO models presented good results, lightly overestimating productivity, while compared to the official data. Therefore, this research presented an objective metholodogy for estimating soybean crop yeild in the state of Paraná. / Buscando apresentar uma metodologia objetiva para estimar a produtividade de soja no estado do Paraná, o presente trabalho explorou três diferentes modelos para estimação da produtividade de soja. Um modelo agrometeorológico (modelo FAO), que apresentou quatro estimativas baseadas em dados agrometeorológicos provenientes de sensoriamento remoto (ECMWF) e dados observados a campo; um modelo espectral (modelo GLO-PEM2), que utilizou dados do sensor MODIS para estimar a produtividade de soja no estado; e, um modelo agro-espectral (modelo CASA), que apresentou duas estimativas baseadas em dados agrometeorológicos e dados do sensor MODIS. O trabalho determinou o balanço hídrico e a evapotranspiração real por dois métodos (BHTM e BHFAO). Os dados do solo foram obtidos da EMBRAPA, os dados da cultura provenientes da FAO, as datas de semeadura e colheita foram determinadas por Becker (2013) e os dados agrometeorológicos foram obtidos do ECMWF. Os dados espectrais utilizados foram obtidos do sensor MODIS, plataformas Terra e Aqua. O modelo GLO-PEM2 mostrou-se bastante fraco e deve ser investigado mais profundamente, os modelos CASA e FAO apresentaram bons resultados, superestimando levemente a produtividade, quando comparados com dados oficiais. Logo, o trabalho apresentou uma metodologia objetiva para a estimação da produtividade de soja para o estado do Paraná

Made available in DSpace on 2017-07-10T19:23:58Z (GMT). No. of bitstreams: 1 Parte_ 1.pdf: 4908182 bytes, checksum: 66963491c16f98c75db1942dbc323ede (MD5) Previous issue date: 2015-02-13 / In order to propose an objective methodology to estimate soybean crop yield in Western Paraná, the present research explored three different models for estimating soybean crop yield. One agrometeorological model (FAO model), presented four estimates based on agrometeorological data from remote sensing (ECMWF) and data collected in loco; one spectral model (GLO-PEM2 model), which used data from a MODIS sensor to estimate soybean crop yield in the state; and one agro-spectral model (CASA model), which presented two estimates, based in agrometeorological data and MODIS sensor data. The present work determined water balance and real evapotranspiration through two methods (BHTM and BHFAO). The soil data was collected from EMBRAPA, the culture s was from FAO, the dates of planting and harvest were determined by Becker (2013) and the agrometeological data was obtained from ECMWF. The spectral data utilized was provided by the MODIS sensor, namely Terra and Aqua platforms. The GLO-PEM2 proved to be rather weak and requires further investigation; the CASA and FAO models presented good results, lightly overestimating productivity, while compared to the official data. Therefore, this research presented an objective metholodogy for estimating soybean crop yeild in the state of Paraná. / Buscando apresentar uma metodologia objetiva para estimar a produtividade de soja no estado do Paraná, o presente trabalho explorou três diferentes modelos para estimação da produtividade de soja. Um modelo agrometeorológico (modelo FAO), que apresentou quatro estimativas baseadas em dados agrometeorológicos provenientes de sensoriamento remoto (ECMWF) e dados observados a campo; um modelo espectral (modelo GLO-PEM2), que utilizou dados do sensor MODIS para estimar a produtividade de soja no estado; e, um modelo agro-espectral (modelo CASA), que apresentou duas estimativas baseadas em dados agrometeorológicos e dados do sensor MODIS. O trabalho determinou o balanço hídrico e a evapotranspiração real por dois métodos (BHTM e BHFAO). Os dados do solo foram obtidos da EMBRAPA, os dados da cultura provenientes da FAO, as datas de semeadura e colheita foram determinadas por Becker (2013) e os dados agrometeorológicos foram obtidos do ECMWF. Os dados espectrais utilizados foram obtidos do sensor MODIS, plataformas Terra e Aqua. O modelo GLO-PEM2 mostrou-se bastante fraco e deve ser investigado mais profundamente, os modelos CASA e FAO apresentaram bons resultados, superestimando levemente a produtividade, quando comparados com dados oficiais. Logo, o trabalho apresentou uma metodologia objetiva para a estimação da produtividade de soja para o estado do Paraná

In this essay I present my creative input and development as a musician since fall 2020 and the compositions that has come out of it. Furthermore, I reflect on preparing these songs for my exam concert with my band Paradise League, as well as adding a trumpet synth called EVI (Electric Valve Instrument) to the instrumentation. Having experienced embouchure fatigue and a lack of stamina to follow through with longer sets, I am also pursuing a more effective technique. Hence, I analyze my practice methods and discuss my trumpet teachers, their tools and the effects thereof. Through the combination of fundamental, soloistic and flexibility exercises I developed a more effortless playing style with a wider artistic expression. There were vast technical differences between the EVI and the trumpet. Despite some minor challengers when switching between the two instruments, the EVI opened up a different set of improvisational ideas, broadening my soloistic framework.

Made available in DSpace on 2017-05-12T14:46:46Z (GMT). No. of bitstreams: 1 Carlos Henrique Wachholz de Souza.pdf: 9172700 bytes, checksum: 974911a1d2a6ffa227da2b1e9937a56d (MD5) Previous issue date: 2013-01-30 / The use of remote sensing technology has been studied as a way to make the current system of monitoring and crop forecasting in Brazil more efficient, dynamic and reliable. One of the difficulties found in the use of medium spatial resolution images as MODIS (250 Meters) is that the spectral profiles of temporary crops, as soybean and corn, may present similar curves, difficulting the separation of these cultures at the time of classification of the seeded areas. In this sense, the aim of this work was analyzing the pattern of temporal profiles, from the vegetation index (VI) EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index) and WDRVI (Wide Dynamic Range Vegetation Index), obtained by the MODIS images for the crops of corn and soybean in the crop years of 2010/2011 and 2011/2012 in the state of Paraná. The aim was performing the spectral separation of these cultures to make its mapping. The applied methodology allowed the discrimination of areas with soybean and corn (masks) for each crop year. The areas of the masks were extracted and compared with SEAB official data, finding adjustments in "R ²" between 0.89 and 0.94 for soybean and from 0.43 to 0.83 for corn. For the Willmott coefficient (d) values were between 0.85 to 0.87 for the soybean crop and 0.63 to 0.76 for corn. The accuracy of spatial masks using images with high spatial resolution achieved the best results with the IV WDRVI with overall accuracy (OA) = 86% and = 0.78, and Kappa Index (KI) IV EVI with OA and KI = 83% = 0.74. Based on these results, it can be conclude that the proposed methodology is promising and may be used for mapping of these crops in the estimation of the state area. / A utilização de tecnologias de Sensoriamento Remoto vem sendo estudada como forma de tornar o sistema atual de monitoramento e previsão de safras no Brasil mais eficiente, dinâmica e confiável. Uma das dificuldades encontrada na utilização de imagens de média resolução espacial como as do sensor MODIS (250 metros), é que os perfis espectrais de culturas temporárias, como a soja e o milho, podem apresentar curvas semelhantes, dificultando a separação dessas culturas na hora da classificação das áreas semeadas. Neste sentido, o objetivo da realização deste trabalho foi analisar o padrão de perfis temporais, provenientes dos índices de vegetação (IV) EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index) e WDRVI (Wide Dynamic Range Vegetation Index), obtidos por meio de imagens do sensor MODIS, para as culturas do milho e soja, nos anos-safra 2010/2011 e 2011/2012, no estado do Paraná. Para realizar a separação espectral das referidas culturas e efetuar o seu mapeamento. A metodologia aplicada permitiu a discriminação das áreas com soja e milho (máscaras) para cada ano-safra. As áreas das máscaras foram extraídas e comparadas com os dados oficiais da SEAB, encontrando-se ajustes de coeficiente de correlação (R²) entre 0,89 a 0,94 para a cultura da soja e de 0,43 a 0,83 para milho. Para o coeficiente de Willmott d foram encontrados valores entre 0,85 e 0,87 para a cultura soja e de 0,63 a 0,76 para milho. A exatidão espacial das máscaras utilizando imagens de alta resolução espacial obteve os melhores resultados com o IV WDRVI com Exatidão Global (EG) = 86% e Índice Kappa (IK) = 0,78 e o IV EVI com EG = 83% e IK = 0,74. Com base nestes resultados, pode-se concluir que a metodologia proposta é promissora, podendo ser utilizada para mapeamento dessas culturas na estimação da área estadual.

Made available in DSpace on 2017-07-10T19:23:35Z (GMT). No. of bitstreams: 1 Carlos Henrique Wachholz de Souza.pdf: 9172700 bytes, checksum: 974911a1d2a6ffa227da2b1e9937a56d (MD5) Previous issue date: 2013-01-30 / The use of remote sensing technology has been studied as a way to make the current system of monitoring and crop forecasting in Brazil more efficient, dynamic and reliable. One of the difficulties found in the use of medium spatial resolution images as MODIS (250 Meters) is that the spectral profiles of temporary crops, as soybean and corn, may present similar curves, difficulting the separation of these cultures at the time of classification of the seeded areas. In this sense, the aim of this work was analyzing the pattern of temporal profiles, from the vegetation index (VI) EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index) and WDRVI (Wide Dynamic Range Vegetation Index), obtained by the MODIS images for the crops of corn and soybean in the crop years of 2010/2011 and 2011/2012 in the state of Paraná. The aim was performing the spectral separation of these cultures to make its mapping. The applied methodology allowed the discrimination of areas with soybean and corn (masks) for each crop year. The areas of the masks were extracted and compared with SEAB official data, finding adjustments in "R ²" between 0.89 and 0.94 for soybean and from 0.43 to 0.83 for corn. For the Willmott coefficient (d) values were between 0.85 to 0.87 for the soybean crop and 0.63 to 0.76 for corn. The accuracy of spatial masks using images with high spatial resolution achieved the best results with the IV WDRVI with overall accuracy (OA) = 86% and = 0.78, and Kappa Index (KI) IV EVI with OA and KI = 83% = 0.74. Based on these results, it can be conclude that the proposed methodology is promising and may be used for mapping of these crops in the estimation of the state area. / A utilização de tecnologias de Sensoriamento Remoto vem sendo estudada como forma de tornar o sistema atual de monitoramento e previsão de safras no Brasil mais eficiente, dinâmica e confiável. Uma das dificuldades encontrada na utilização de imagens de média resolução espacial como as do sensor MODIS (250 metros), é que os perfis espectrais de culturas temporárias, como a soja e o milho, podem apresentar curvas semelhantes, dificultando a separação dessas culturas na hora da classificação das áreas semeadas. Neste sentido, o objetivo da realização deste trabalho foi analisar o padrão de perfis temporais, provenientes dos índices de vegetação (IV) EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index) e WDRVI (Wide Dynamic Range Vegetation Index), obtidos por meio de imagens do sensor MODIS, para as culturas do milho e soja, nos anos-safra 2010/2011 e 2011/2012, no estado do Paraná. Para realizar a separação espectral das referidas culturas e efetuar o seu mapeamento. A metodologia aplicada permitiu a discriminação das áreas com soja e milho (máscaras) para cada ano-safra. As áreas das máscaras foram extraídas e comparadas com os dados oficiais da SEAB, encontrando-se ajustes de coeficiente de correlação (R²) entre 0,89 a 0,94 para a cultura da soja e de 0,43 a 0,83 para milho. Para o coeficiente de Willmott d foram encontrados valores entre 0,85 e 0,87 para a cultura soja e de 0,63 a 0,76 para milho. A exatidão espacial das máscaras utilizando imagens de alta resolução espacial obteve os melhores resultados com o IV WDRVI com Exatidão Global (EG) = 86% e Índice Kappa (IK) = 0,78 e o IV EVI com EG = 83% e IK = 0,74. Com base nestes resultados, pode-se concluir que a metodologia proposta é promissora, podendo ser utilizada para mapeamento dessas culturas na estimação da área estadual.

Made available in DSpace on 2017-07-10T19:24:17Z (GMT). No. of bitstreams: 1 DENISE_M_GR_ZEGOZEWSKI.pdf: 8188144 bytes, checksum: 045f54782a1ea2161edf5aa7046a8c1c (MD5) Previous issue date: 2016-02-03 / Estimates of agricultural production are greatly important especially in economy field. However, they depend on area knowledge and cropping yield. Thus, this study aimed to propose a methodology to estimate the areas cropped with soybeans and corn in Paraná State according to multi-temporal EVI/MODIS vegetation index images for 2010/2011, 2011/2012 and 2012/2013 crop years. In addition, there was a research with spatial autocorrelation soybean yield in Paraná, with EVI vegetation index and meteorological variables in a decennial scale and estimate yield using CAR, SAR and GWR models. In Paraná State, there is a drawback to map soybeans crop since corn sowing period is very close to the first one. Therefore, images from the maximum and minimum vegetative vigour were drawn of each studied crop for mapping soybean and corn crops in order to obtain both cropping areas. Although, for the separation, Spectro Angle Mapper algorithm (SAM) was applied by one of the studied crops, while mapping was obtained by multiplying the other bands. Thus, for spatial statistics application of mapped data, the average EV profile of each municipality was extracted as well as for each multi-temporal image, in order to change them into a decennial scale. According to the spatial statistics of such areas, the descriptive analysis of univariate spatial autocorrelation (global and local) of each ten-day variable was used based on the soybean cycle. A bivariate autocorrelation analysis between soybean yield and the studied varieties were also performed. Finalizing the methodology, variables with the highest significant level by stepwise method were selected and SAR, CAR and GWR models were generated to estimate soybean yield. As results, regarding mappings, the following answers for soybean were found out: r = 0.95 and r = 0.99, and while for corn, the answers were: r = 0.72 and r = 0.95 for 2012/2013 and 2013/2014 crop years in relation to the official data from SEAB. So, it has been proved some great efficiency of this methodology to separate and identify crops. When the descriptive statistics of municipalities for each variable was carried out, it was found out that some regions began an early sowing in relation to other ones in Paraná by the decennial vegetation index. The ten-day scale was also possible to be identified according to the climatic factors that caused soybean yield damage. Based on the analysis of spatial autocorrelation, the greatest similarities occurred in 2011/2012 crop year, the one affected by the weather change, whose yields were similar in the municipalities of Paraná State. For spatial modelling, it was observed that selection of decennial variables was different for each studied crop year, and the best model selected by the validation. And GWR was chosen as the best model by the AIC, BIC and adjusted R² validation criteria. The residuals were randomly distributed throughout all the State, so that spatial autocorrelation could be eliminated. / As estimativas das produções agrícolas têm grande importância, principalmente, no âmbito econômico. No entanto, elas são dependentes do conhecimento da área de cultivo e da produtividade da cultura. Desta forma, este trabalho teve por objetivo propor uma metodologia para estimar as áreas cultivadas com soja e milho em escala municipal no Estado do Paraná a partir de imagens multi-temporais do índice de vegetação EVI/MODIS, para os anos-safras 2010/2011, 2011/2012 e 2012/2013. Além disto, trabalhar com a autocorrelação espacial da produtividade da soja nesse Estado, com o índice de vegetação EVI e variáveis agrometeorológicas em escala decendial bem como estimar a produtividade a partir dos modelos CAR, SAR e GWR. No Paraná, há o inconveniente para mapear a soja devido à proximidade de datas de semeadura do milho. Assim, para o mapeamento da soja e do milho, utilizaram-se imagens englobando o período de máximo e mínimo vigor vegetativo de cada cultura, para se obter a área cultivada das duas. Para a separação, utilizou-se o algoritmo Spectro Angle Mapper (SAM) para uma das culturas e obteve-se o mapeamento da outra pela multiplicação de bandas. Para aplicação da estatística espacial dos dados mapeados, extraiu-se o perfil médio do EVI de cada município e para cada imagem multi-temporal para transformá-los em escala decendial. De acordo com a estatística espacial de áreas, utilizou-se a análise descritiva, de autocorrelação espacial univariada (global e local) de cada variável decendial com foco no ciclo da soja. Também realizou-se a análise de autocorrelação bivariada entre a produtividade da soja com as variáveis em estudo. Finalizando a metodologia, selecionaram-se as variáveis com maior índice de significância pelo método de stepwise e, em seguida, foram gerados os modelos estimados (SAR, CAR e GWR) da produtividade da soja. Como resultados, foram encontradas as seguintes respostas para os mapeamentos da soja r= 0,95 e 0,99, e para o milho de r = 0,72 e r= 0,95 para os anos-safras 2012/2013 e 2013/2014 em relação aos dados oficiais da SEAB. Logo, comprovou-se a grande eficiência da metodologia para separação e identificação das culturas. Quando realizada a estatística descritiva dos municípios para cada variável, verificaram-se regiões que iniciam as semeaduras antecipadas em relação a outras regiões do Estado pelos decêndios do índice de vegetação. Foi também possível identificar os decêndios em que os fatores climáticos causaram danos à produtividade da soja. Na análise da autocorrelação espacial, as maiores similaridades ocorreram no ano-safra 2011/2012, ano afetado pela variação climática, cujas produtividades foram semelhantes nos municípios do Paraná. Para a modelagem espacial, verificou-se que a seleção das variáveis decêndiais foi diferente para cada ano-safra estudado, e o GWR foi escolhido como melhor modelo pelos critérios de validação, AIC, BIC e R² ajustado. Foram encontrados resíduos distribuídos aleatoriamente por todo o Estado, para que assim se eliminasse a autocorrelação espacial