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Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Repositório Alice
BROWN, J. C.; KASTENS, J. H.; COUTINHO, A. C.; VICTORIA, D. de C.; BISHOP, C. R..
MODIS 250-m NDVI and EVI datasets are now regularly used to classify regional-scale agricultural land-use practices in many different regions of the globe, especially in the state of Mato Grosso, Brazil, where rapid land-use change due to agricultural development has attracted considerable interest from researchers and policy makers. Variation exists in which MODIS datasets are used, how they are processed for analysis, and what ground reference data are used. Moreover, various and-use/land-cover classes are ultimately resolved, and as yet, crop-specific classifications (e.g. soy?corn vs. soy?cotton double crop) have not been reported in the literature, favoring instead generalized classes such as single vs. double crop. The objective of this study is to...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Cotton; Cross validation; Decision tree; Land cover; Soybean.
Ano: 2013 URL: http://www.alice.cnptia.embrapa.br/handle/doc/964430
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Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil. Repositório Alice
KASTEN, J. H.; BROWN, J. C.; COUTINHO, A. C.; BISHOP, C. R.; ESQUERDO, J. C. D. M..
Abstract - Previous research has established the usefulness of remotely sensed vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the spatial dynamics of agriculture in the state of Mato Grosso (MT), Brazil. With these data it has become possible to track MT agriculture, which accounts for ~85% of Brazilian Amazon soy production, across periods of several years. Annual land cover (LC) maps support investigation of the spatiotemporal dynamics of agriculture as they relate to forest cover and governance and policy efforts to lower deforestation rates. We use a unique, spatially extensive 9-year (2005?2013) ground reference dataset to classify, with approximately 80% accuracy, MODIS VI data, merging the...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Modis; Dados espaciais; Soja; Desmatamento; Sensoriamento remoto; Glycine Max; Soybeans; Moderate resolution imaging spectroradiometer; Oilseeds; Seed oils; Deforestation.
Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1069623
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