Mono and multitemporal Modis imagery for soybean area estimate in Mato Grosso State, Brazil

O objetivo deste trabalho foi avaliar uma nova metodologia para mapeamento da cultura da soja no Estado de Mato Grosso, por meio de imagens Modis e de diferentes abordagens de classificação de imagens. Foram utilizadas imagens diárias e imagens de 16 dias. As imagens diárias foram diretamente classificadas pelo algoritmo Isoseg. As duas séries de imagens de 16 dias, referentes ao ciclo total e à metade do ciclo da cultura da soja, foram transformadas pela análise de componentes principais (ACP), antes de serem classificadas. Dados de referência, obtidos por interpretação visual de imagens do sensor TM/Landsat-5, foram utilizados para a avaliação da exatidão das classificações. Os melhores resultados foram obtidos pela classificação das imagens do ciclo total da soja, transformadas pela ACP: índice global de 0,83 e Kappa de 0,63. A melhor classificação de imagens diárias mostrou índice global de 0,80 e Kappa de 0,55. A ACP aplicada às imagens do ciclo total da soja permitiu o mapeamento das áreas de soja com índices de exatidão melhores do que os obtidos pela classificação derivada das imagens de data única.The objective of this work was to evaluate a new methodology to map soybean crop area in Mato Grosso State, Brazil, using Modis imagery and different image classification approaches. Single-day and 16-day images were used. The single-day images were classified using the Isoseg algorithm. Two series of 16-day composite images, covering the full and the half soybean crop cycles, were transformed using principal component analysis (PCA) prior to the classification. A reference data set, achieved by visual interpretation of TM/Landsat-5 images, was used to evaluate the accuracy of the classifications. The best results were reached using the image classification of soybean full cycle, transformed by PCA: overall accuracy of 0.83, and Kappa of 0.63. The best single-day classification showed an overall index of 0.80, and 0.55 Kappa. PCA applied to the images of the full cycle allowed for the mapping of soybean crop areas with better accuracy indices than those obtained by the single-day classification

Validação de modelos estacionário e prescritivo do manejo da cana-de-açúcar para o ano-safra 2010 nas regiões de Jaú e Ribeirão Preto, São Paulo

Brazil is the world biggest producer of sugar and ethanol derived from sugarcane. The estimated sugarcane cultivated area lies around 8 million hectares, 60% of which are found within São Paulo state, unevenly scattered throughout its territory. Since 2006, the National Institute for Space Research (INPE), in association with the São Paulo State Secretary for the Environment (SMASP), has been monitoring the pre-harvest burning of sugarcane by means of remotely sensed images. In June 2007, the Sugarcane Industry Association (UNICA) and SMASP signed an Agrienvironmental Protocol, designed to anticipate the end of straw burning in areas with slope under 12% until 2014. Two years later, in 2009, stationary and prescriptive scenarios for the year 2010 were generated for some test areas in Sao Paulo State, considering the maintenance of the ongoing trend of harvesting practice dynamics in the first case, and a compliance with the Protocol in the second case. The objective of this study is to perform a comparative analysis between the scenarios generated in 2009 and the real expansion of sugarcane tillages and changes in the sugarcane harvesting practices that took place in 2010 for two of such test areas, namely Jaú and Ribeirão Preto, discussing on the observed conformity with the Agrienvironmental Protocol in both cases.Pages: 474-48

Uso do método de pesos de evidência na análise do manejo da cana-de-açúcar para o estado de São Paulo

Assuming that several spatial variables can influence the harvest practice of sugarcane straw burning, this study aims to evaluate the influence of these variables using a dynamic model based on the concept of weights of evidence and cellular automata. Thematic maps of sugarcane harvest with and without the practice of straw burning for the years of 2006 to 2008 were used together with other land uses such as urban areas, rivers and conservation units. The model was adequate to understand the conditioning variables and the likelihood of the transition of sugarcane harvest practice from burning to not-burning. Variables such as slope and distance to rivers, roads, power plants, classes of sugarcane and urban areas were very influential to understand the straw burning practice.Pages: 415-42

Cenários prognósticos baseados em modelagem dinâmica espacial para o manejo da colheita da cana-de-açúcar no estado de São Paulo

Since 2006, the National Institute for Space Research (INPE), in conjunction with the Environmental Department of São Paulo State (SMA-SP) and the Brazilian Sugarcane Industry Association (UNICA), is monitoring the practice of sugarcane straw burning during harvest in São Paulo State, Brazil, using remote sensing images. An environmental protocol signed between SMA-SP and the private agro-industrial sugarcane sector in 2007 agreed to stop sugarcane straw burning by the year 2014 for mechanized areas. This study aims to evaluate the use of dynamic modeling scenarios through a stationary model based on Markov chain to generate spatial predictions of sugarcane harvest practice for the year 2014. The model was based on the harvest practice of burned and not burned sugarcane fields during the period of 2006 to 2008 in the municipalities of Ribeirão Preto, Jau and its surroundings. Due to its high dynamic and sensitivity to a large number of economic factors, in addition to a reduced period of observations, the model could not correctly assure projections expected for the year of 2014.Pages: 407-41