Crop area estimate from original and simulated spatial resolution data and landscape metrics

Imagens coletadas no mesmo dia pelos sensores ETM+/Landsat-7 (30 m de resolução espacial) e MODIS/Terra (250 m) foram utilizadas para estimar a área de três importantes culturas agrícolas (soja, cana-de-açúcar e milho) com diferentes padrões de paisagem no Sudeste Brasileiro. Filtragem de Maioria dos resultados da classificação da imagem ETM+ foi aplicada para descrever o comportamento de 15 métricas em diferentes simulações de resolução espacial (90, 150, 210 e 270 m). Utilizando modelos de regressão, o desempenho do MODIS e de suas métricas para predizer a área das culturas, considerando os dados ETM+ como referência, foi analisado. Os resultados mostraram que o sensor MODIS superestimou as áreas de soja (15%) e cana-de-açúcar (1%) e subestimou a área de milho (12%). A regressão múltipla indicou que sensores de resolução espacial grosseira podem ser usados para predizer adequadamente a área vista por instrumentos com 30 m de resolução espacial apenas para culturas com baixo padrão de fragmentação como soja. Estes sensores não podem predizer adequadamente a área de milho devido aos efeitos de agregação de pixels das culturas menos fragmentadas (soja e cana-de-açúcar) sobre a mais fragmentada (milho), conforme demonstrado pela simulação da resolução espacial por filtragem de maioria da imagem ETM+. As métricas da paisagem melhoraram as estimativas de área com o MODIS apenas para a cana-de-açúcar, conforme indicado por maiores valores de R² observados para regressão múltipla do que para regressão simples. Apenas um número pequeno de métricas foi selecionado para compor os modelos de regressão visto que a maior parte delas não foi preservada entre resoluções espaciais diferentes (30 e 250 m).Images acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple regression results indicated that coarse spatial resolution sensors can be used to predict adequately the area viewed by the 30 m spatial resolution instruments only for crops with low fragmentation pattern such as soybean. These sensors cannot be used to predict the area of corn due to aggregation pixel effects of the less fragmented crops (soybean and sugarcane) over the most fragmented one (corn), as demonstrated by the spatial resolution simulation using majority filtering of the ETM+ image. Landscape metrics improved MODIS area estimates only for sugarcane, as indicated by higher values of R² for multiple than for simple regression. Only a small set of metrics was select to compose the multiple regression models because most of them were not preserved across different spatial resolutions (30 m and 250 m)

O SATÉLITE TERRA E AS PESQUISAS EM MUDANÇAS GLOBAIS

On December 18, 1999, the National Aeronautics and Space Administration (NASA) launched the TERRA Satellite, the first one of the Earth Observing System program (EOS). It was developed with the objective to begin a scientific mission never accomplished before: to generate a complete physical check-up of the Earth, which nowadays began to show some symptoms of health problems. One of the main objectives of this mission is to measure main parameters that describe the conditions of the Earth and its atmosphere and begin a long-term monitoring of the human impact on the environment. This article provides a previous literature revision where the main characteristics and objectives of the five sensors onboard TERRA Satellite are presented, as well as its role in global change context. Key words: Earth Observation System; TERRA Satellite; Global Changes; Remote Sensing.Em 18 de dezembro de 1999, a National Aeronautics and Space Administration (NASA) lançou o Satélite TERRA como marco principal do programa Earth Observing System (EOS), visando iniciar a mais abrangente missão científica até então tentada, destinada a gerar uma ampla avaliação física do Planeta Terra. Entre os principais objetivos da missão está o de buscar melhorar o entendimento quanto aos movimentos de carbono e energia em relação ao sistema climático terrestre. O presente artigo consiste numa revisão em que são apresentadas as características dos cinco sensores a bordo do Satélite TERRA, bem como o seu papel no contexto das mudanças globais. Palavras-chave: Sistema de Observação da Terra; mudanças globais; sensoriamento remoto

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics. The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics

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

Análise de Imagem Orientada a Objeto e Mineração de Dados aplicadas ao mapeamento da cana-de-açúcar

The aim of this research was to develop a methodology that can automate the sugar cane mapping task when remote sensing data are used. For this, we tested the integration of two major approaches of Artificial Intelligence: Object Based Image Analysis (OBIA) and Data Mining (DM). The study area comprises the municipalities of Ipuã, Guará and São Joaquim da Barra, located in the northwestern of São Paulo state, which are well representatives of the conditions of agriculture in southern and southeastern regions of Brazil. OBIA was used to emulate the interpreter knowledge in the process of sugar cane mapping, and MD techniques were employed for automatic generation of knowledge model. MD algorithm used was C4.5, which generates decision trees (DT) from a previous prepared training set. A time series of Landsat images was acquired in order to represent the wide patterns variability within the sugar cane crop season. The objects were generated by application of multiresolution segmentation algorithm. Thereafter, the knowledge extraction process has begun, which ends with the acquisition of DT. Once properly trained, the DT was applied to the Landsat time series and then generated the thematic map. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance, indicating that the examined classification routines achieved an overall accuracy of 94% and Kappa of 0,87. The results shows that OBIA and MD are very efficient and promising in the direction of automating the sugar cane classification process.Pages: 467-47