RESUMO

Climate is the set of average atmospheric conditions that characterizes a region. It directly influences the majority of human activities, especially agriculture. Climate classification systems (CCSs) are important tools in the study of agriculture, enabling knowledge of the climatic characteristics of a region. Thus, we aimed to perform the climatic characterization of the State of Paraná using the methods proposed by Köppen and Geiger (1928), modified by Trewartha (1954) (KT), Thornthwaite (1948) (TH) and Camargo (1991) and modified by Maluf (2000) (CM), using data from the European Center for Medium-Range Weather Forecast (ECMWF) model. The results of spatial interpolation (virtual stations) were performed using the Kriging method in spherical shape with one neighbour and resolution of 0.25°. The CCSs displayed the ability to separate the warm and dry from cold and wet regions. The most predominant climates were Cfa (temperate humid with hot summers), C 1 rA'a' (sub-humid with little water deficiency, megathermal) and ST-UMi (humid subtropical with dry winter), according to KT, TH and CM, respectively. CM is an intermediate CCS between KT and TH.

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Resumo -O objetivo deste trabalho foi estimar e mapear as áreas com as culturas de soja e milho, no Paraná, com uso de imagens multitemporais EVI/Modis. Foram avaliados os anos-safra de 2004/2005 a 2007/2008. Em razão da alta dinâmica temporal e da heterogeneidade de datas de semeadura das culturas no estado, foram utilizadas cenas que contemplavam as fases de pré-plantio e de desenvolvimento inicial das culturas, para gerar a imagem de mínimo EVI (IMIE), e cenas que consideravam o pico vegetativo das culturas, para gerar a imagem de máximo EVI (IMAE). Estas imagens foram utilizadas para gerar a composição colorida RGB (R, IMAE; GB, IMIE), o que permitiu a confecção de máscara das áreas com soja e milho. As estimativas das áreas de máscara por município foram comparadas com dados oficiais de produção agrícola municipal, tendo-se observado bons ajustes (R²>0,84, d>0,95, c>0,85) entre os dados. Para a avaliação da exatidão espacial das máscaras, imagens Landsat-5/TM e AWiFS/IRS foram usadas como referência para construção da matriz de erros. Os resultados obtidos são indicativos de que a metodologia proposta é altamente eficiente e pode ser utilizada para mapeamento dessas culturas.Termos para indexação: classificação de imagens, distribuição espacial de culturas, índice de vegetação, mapeamento, previsão de safras, sensoriamento remoto. Estimation of summer crop areas in the state of Paraná, Brazil, using multitemporal EVI/Modis imagesAbstract -The objective of this work was to estimate and map crop areas with soybean and corn in the state of Paraná, Brazil, using EVI/Modis images. The crop seasons from 2004/2005 to 2007/2008 were evaluated. Due to the high temporal dynamics and difference in sowing dates of the cultures within the state, scenes containing the pre-planting and initial crop development phases were used to obtain the minimum EVI image (IMIE), and scenes at the peak of the crop cycle were used to obtain the maximum EVI image (IMAE). These images were used to generate the RGB color composition (R, IMAE; GB, IMIE), which allowed for the creation of masks of the areas planted with soybean and corn. The estimation of masked areas by municipality was compared with the municipal agricultural production official data, and good fits (R²>0. 84, d>0.95, c>0.85) were observed between data. For spatial accuracy assessment, Landsat-5/TM and AWiFS/IRS images were used as references to build the error matrix. The obtained results indicate that the proposed methodology is highly efficient and may be used as a model for cropland mapping.

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This work aimed to study the spatial autocorrelation of the total static capacity storage, the total number of warehouses in 2013/2014 (CONAB) and the average of the total grain production (soybean, corn 1 st and 2 nd crops and wheat) in the harvest years 2008/2009 to 2013/2014 (SEAB) in Paraná State, Brazil. The study was based on Moran's global autocorrelation index, Moran's local and Moran's bivariate correlation. It was possible to identify regions with low and high total grain production. There was positive spatial autocorrelation for the Total Static Storage Capacity (TSSC) and Total Quantity of Warehouses (TQW). For the total grain production, significant spatial autocorrelation were found. The total static storage capacity showed similarity between the studied regions. When evaluating the bivariate spatial correlation between Total Production of Harvested Grains (TPHG) in relation to the total static storage capacity and total quantity of warehouses, the presence of positive spatial correlation was observed. The results indicated that Moran's global autocorrelation and local indexes showed significant patterns of spatial autocorrelation, as well as the bivariate spatial correlation indexes in the studied variables.

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