Identificação de eventos de seca em cana-de-açúcar com base em índices de seca derivados do sensor Modis

The objective of this work was to evaluate the potential of several spectral indices, calculated using moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane (Saccharum officinarum) crops. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.O objetivo deste trabalho foi avaliar o potencial de diversos índices, calculados com o uso de imagens do sensor Modis (“moderate resolution imaging spectroradiometer”), em identificar eventos de seca na cana-de-açúcar (Saccharum officinarum). As imagens dos satélites Terra e Aqua foram utilizadas para calcular os índices espectrais, com bandas na região do visível (vermelho), infravermelho próximo e infravermelho médio, e oito índices foram selecionados: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI e MSI. Os índices foram calculados com base em imagens de outubro a abril de quatro anos agrícolas: 2007/08, 2008/09, 2009/10 e 2013/14. Esses índices foram correlacionados com o índice de seca meteorológica SPEI, calculado para 1, 3 e 6 meses. Quatro deles tiveram correlação significativa com o índice SPEI: GVMI, MSI, NDI7 e NDWI. Os índices espectrais derivados do sensor Modis a bordo do satélite Aqua (MYD) são mais adequados para o reconhecimento de eventos de seca, e março proporcionou os índices mais relevantes para esse propósito. Índices de seca calculados com base em dados Modis são efetivos em detectar eventos de seca em cana-de-açúcar, além de serem capazes de apontar flutuações sazonais

Diagnóstico da expansão da cana-de-açúcar:: aplicação do Barômetro da Sustentabilidade nos municípios de Barretos e Jaboticabal (SP)

With the premise of reducing emissions of greenhouse gases, international interest in biofuelshas grown considerably in recent years. With the increasing demand and consequent expansion,the need to assess the impacts on the environment and society is unquestioned. Brazil, with largeareas of arable and large current production of sugarcane, in addition to the added knowledgefrom decades of research and production of bioethanol, has the potential for expansion, but theconsequences should be considered alongside to increased economic returns. For this, theapplication of the Barometer of Sustainability was made for two municipalities in the state of SãoPaulo: one with strong sugarcane expansion (Barretos), and another with stagnant production(Jaboticabal). The main objective was to identify the impacts based on the comparison ofmunicipalities and identify causality in relation to the expansion of the culture under study. Theapplication of sustainability barometer indicates a negative impact in the municipality withsugarcane expansion. In addition, the municipality without sugarcane expansion improves itshuman welfare performance against a stabilization of the municipality with expansion, indicatinga relationship between the expansion of sugarcane and precariousness of human development.Para reduzir emissões dos gases do efeito estufa e aumentar a segurança de suprimento energético, o interesse internacional por biocombustíveis tem crescido consideravelmente nos últimos anos. Com a crescente demanda e consequente expansão da produção, é inquestionável a necessidade de avaliar os impactos sobre a sociedade e o meio ambiente. O Brasil, com grandes áreas agricultáveis e a grande produção de cana-de-açúcar, além do conhecimento agregado por décadas de pesquisa e produção de bioetanol, possui potencial para expansão, mas as consequências devem ser analisadas além da simples viabilidade econômica. Para uma avaliação de impactos sociais da atividade canavieira foi aplicada a metodologia conhecida como Barômetro da Sustentabilidade para dois municípios do estado de São Paulo: um com forte expansão canavieira (Barretos), e outro com produção estagnada (Jaboticabal) no mesmo período de 10 anos. Com a comparação dos indicadores dos dois municípios buscou-se identificar a causalidade em relação à expansão da cultura em estudo. A aplicação do Barômetro da Sustentabilidade indica um impacto ambiental negativo no município com significativa expansão canavieira. Além disso, o município sem expansão teve melhora em seu desempenho de bem-estar humano, contra uma estabilização do indicador no município em que houve expansão

Estimation of summer crop areas in the state of Paraná, Brazil, using multitemporal EVI/Modis images

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.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

Estimation of summer crop areas in the state of Paraná, Brazil, using multitemporal EVI/Modis images

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.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.1295130

Precision production environments for sugarcane fields

Sugarcane (saccharum spp.) in Brazil is managed on the basis of “production environments”. These “production environments” are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the "production environments" in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new “production environments”, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production

Remote sensing as a tool for digital agriculture usage in sugarcane

A agricultura digital é uma abordagem multidisciplinar que diz respeito ao uso de informações digitais detalhadas para orientar a tomada de decisão ao longo da cadeia agrícola. Seu uso é cada vez mais necessário e deve trazer muitos benefícios para a segurança alimentar e energética nos próximos anos. O grande ponto é a aquisição de dados de maneira contínua para gerar informações e guiar processos. Como a agricultura, principalmente no Brasil, é feita em grandes escalas de área e por conta disso, a aquisição de dados com o uso de sensores na propriedade é dificultada, o sensoriamento remoto aparece como uma ferramenta importante para a obtenção de dados e validação de operações. O sensoriamento remoto é utilizado de forma contínua desde a década de 80, porém tem ganhado mais força com o aparecimento da computação de alta performance e com o barateamento dela. Embora em algumas culturas estejam bastante desenvolvidas no uso de sensoriamento remoto para geração de informações, a cana-de-açúcar ainda possui poucos trabalhos e em escala local. A cana-de-açúcar é a principal cultura para a produção de açúcar e etanol no Brasil. O país é responsável por mais da metade da produção mundial dessa planta e hoje ela é cultivada no Nordeste e na região Centro-Sul. Devido à extensa área de cultivo, existem diversas condições edafoclimáticas em que a cultura da cana-de-açúcar se desenvolve e desta forma, separar essas regiões é extremamente importante para poder aplicar modelos em escala homogênea. Além disso, fazer uma separação morfológica dos canaviais é importante para não gerar modelos sem essa variável e consequentemente trazer incertezas ao processo. No intuito de criar regiões homogêneas foi feito um trabalho de zoneamento e regionalização levando-se em conta as variáveis agrometeorológicas, solo e produtividade histórica da cana planta. Criou-se três regiões de alto, médio e baixo potencial de produção de cana para o Centro-Sul do país. Em seguida, avaliando-se o comportamento histórico de índices de vegetação da área de estudo, fez-se uma regionalização levando em conta o potencial de produção e o comportamento do índice de vegetação. Foram propostas dezessete regiões com comportamento homogêneo para aplicações de modelos baseados em sensoriamento remoto. Outra abordagem foi identificar características morfológicas da cana-de-açúcar que podem levar a falhas em modelagens de áreas contínuas. Para isso, foi utilizado o modelo Random Forest e imagens do satélite Sentinel-2 para criar um modelo que identifica diferentes cultivares. O modelo teve uma precisão global de 86% e índice kappa de 81%. Quando aplicado para 4 cultivares em uma região maior, apresentou a precisão variando de 91% a 96%. Desta forma, foi possível concluir que modelos se adaptam com a mesma precisão que o conjunto de treino, se a região de aplicação é homogênea em relação a solo, clima e manejo. Além disso, o trabalho é base para futuras aplicações em agricultura digital para a cultura da cana-de-açúcar que precisem gerar modelos para a região Centro-Sul.Digital agriculture is a multidisciplinary approach that concerns the use of detailed digital information to guide decision making along the agricultural chain. Its use is increasingly and should bring many benefits to food and energy security in the next years. The big point is the data acquisition in a continuous way to generate information and guide processes. As agriculture, mainly in Brazil, is carried out on large scales of area and because of this, the acquisition of data with the use of sensors on the farm level is difficult, remote sensing appears as an important tool for obtaining data and validating field operations. Remote sensing has been used continuously since the 1980s, but it has gained more strength with the emergence of high-performance computing and its low-price tendency. Although in some cultures they are quite developed in the use of remote sensing to generate information, sugarcane still has few works and on a local scale. Sugarcane is the main crop to produce sugar and ethanol in Brazil. The country is responsible for more than half of the world production of this plant and today it is cultivated in the Northeast and in the Center-South region. Due to the extensive cultivation area, there are several edaphoclimatic conditions in which the sugarcane crop develops and, therefore, separating these regions is extremely important to be able to apply models on a homogeneous scale. In addition, making a morphological separation of the sugarcane fields is important not just to generate models without this variable and consequently bring uncertainties to the process. To create homogeneous regions, a zoning and regionalization work was carried out, considering the agrometeorological variables, soil and historical productivity of sugarcane first stage. Three regions of high, medium and low potential for sugarcane production were created for the Center-South of the country. Then, evaluating the historical behavior of vegetation indices in the study area, a regionalization was carried out taking into account the production potential and the behavior of the vegetation index. Seventeen regions with homogeneous behavior were proposed for application of models based on remote sensing. Another approach was to identify morphological characteristics of sugarcane that can lead to failures in continuous area modeling. For this, the Random Forest model and Sentinel-2 satellite images were used to create a model that identifies different cultivars. The model had an overall accuracy of 86% and a kappa index of 81%. When applied to 4 cultivars in a larger region, the accuracy ranged from 91% to 96%. In this way, it was possible to conclude that models adapt with the same precision as the training set, if the application region is homogeneous in relation to soil, climate, and management. In addition, the work is the basis for future applications in digital agriculture for the cultivation of sugarcane that need to generate models for the Center-South region

EU Enlargement 1989-2009: Actors, Institutions, and Literature

On 1 May 2004 at a historic, if understated, signing ceremony in Dublin the European Union (EU) formally recognized the accession to the Union of ten new states. These were the Mediterranean ‘micro’ states of Cyprus and Malta, and eight new members from Central and Eastern Europe(CEE) –the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia – which, for more than fifty years, had been cut off from the European integration process by virtue of their geopolitical imprisonment behind the Iron Curtain. The eastern enlargement was completed via the ‘coda enlargement’, with the accessions of Bulgaria and Romania in 2007. At that point the EU completed its extraordinary and cumulative geographic sweep: the first enlargement in 1973 was ‘west’ (UK, Ireland and Denmark), the emphasis in the 1980s was on the ‘south’ (Spain, Portugal and Greece); in the 1990s the Union expanded ‘north’ (Finland, Sweden and Austria). The history of European integration has been one of successive and successful enlargement rounds; ‘widening’ has proved as potent a force as ‘deepening’ in determining how the European Union has evolved as a post-national inter-state and supra-state zone of peace and relative prosperity. For more than three decades after World War Two, the Cold War stood in the way of the realization of the oft-stated ambition to unite ‘east’ and ‘west’ in a single European constellation of states. But with the demise of the Soviet Union and the loosening of its post- War grip on its Central and Eastern European satellite states in the wake of 1989’s so-called ‘geopolitical earthquake’, Jean Monnet’s ambition of a European construction stretching from the Atlantic to the Urals suddenly seemed possible. Thereafter, enlargement quickly made its way to the top of the European Union’s political agenda. Two decades later the EU has applied the successful model of ‘Europeanization East’ in negotiating with states in the Western Balkans and Turkey, though with less than successful results to date. Thus a process which was instituted in the aftermath of the dramatic events that defined the 1989 revolutions and had brought the EU population up to 500 million people now sought to consolidate democracy and European integration in Europe’s most fragile and contested political space. This chapter analyzes the European Union’s enlargement process in the two decades that followed the ‘annus mirabilis’ of 1989. The 1989 Revolutions opened up the possibility of a vast and voluntary framework of economic and political integration extending to a genuinely pan-European scale. At the centre of this historic project the European Union initially demonstrated great hesitation in response to what Jacques Delors termed the ‘acceleration of history’, but gradually found its stride as the European Commission assumed responsibility for the practical implementation of, if not a utopian ‘Return to Europe’ by ‘Yalta Europe’, then a process whereby gradual ‘catchup’ could be pursued and adaptation of CEE states to existing legal and procedural norms of the European Union could be achieved

Agrupamento de perfis espectro-temporais do índice de vegetação EVI/Modis para culturas agrícolas de verão entre os anos-safra 2004/2005 e 2007/2008 no Estado do Paraná

This study aimed group spectrum-temporal profiles of vegetation index EVI/Modis, for major country of soybean and maize producers in the State of Parana, between the crop years 2005/2006 and 2007/2008. From the mapping (masks) of these cultures obtained from Johann et al. (2012), generated average municipal spectro-temporal profiles from EVI/Modis sensor (images of 16 days), between scenes 241 (29/08/year1) and 113 (23/04/year2) for each year crop studied. The EVI pixels values were extracted from the masks for each county and crop year by a system of image extraction data developed in IDL/ENVI platform. The multivariate statistical technique partitioning K-Means, allowed clustering similar spectral pattern counties during these crops development. Were tested 3 to 10 clusters. The best results in terms of similar EVI time series profile, were found when the 322 municipalities studied (responsible for 99% of production and harvested area of soybeans and maize) were organized in six groups. The spatial representation of these counties groups showed that the occurrence wasnt random, but in sub-regions, showing similar agronomic crop patterns of maize and soybeans for each cluster. This information can expedite the use of spectral models for estimating productivity of these crops, adopting a single model for each cluster, optimizing human resources and computer systems in crop forecasting.Pages: 1703-171