MatoGrosso/2013_CropYear (ImageServer)

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Service Description: Normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used 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. To interpret the satellite data, researchers from Empresa Brasileira de Pesquisa Agropecuária(Embrapa), the Brazilian equivalent of the USDA, collected an unprecedented amount of ground reference data in Mato Grosso by interviewing farmers, tracing field boundaries on printed satellite imagery and obtaining cropping histories for various parcels of land. This unique, spatially extensive 9-year (2005-2013) ground reference dataset was used to classify, with approximately 80% accuracy, the MODIS NDVI data. The results were merged with carefully processed annual forest and sugarcane coverages developed by Brazil's National Institute for Space Research (INPE) to produce land cover maps for MT for the 2001-2014 crop years, where a crop year runs from August of the preceding year through July of the nominal year. Static urban and water layers, obtained from the Brazilian Institute of Geography and Statistics (IBGE), round out the land cover maps.

Name: MatoGrosso/2013_CropYear

Description: Normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used 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. To interpret the satellite data, researchers from Empresa Brasileira de Pesquisa Agropecuária(Embrapa), the Brazilian equivalent of the USDA, collected an unprecedented amount of ground reference data in Mato Grosso by interviewing farmers, tracing field boundaries on printed satellite imagery and obtaining cropping histories for various parcels of land. This unique, spatially extensive 9-year (2005-2013) ground reference dataset was used to classify, with approximately 80% accuracy, the MODIS NDVI data. The results were merged with carefully processed annual forest and sugarcane coverages developed by Brazil's National Institute for Space Research (INPE) to produce land cover maps for MT for the 2001-2014 crop years, where a crop year runs from August of the preceding year through July of the nominal year. Static urban and water layers, obtained from the Brazilian Institute of Geography and Statistics (IBGE), round out the land cover maps.

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Copyright Text: Kansas Biological Survey / Kansas Applied Remote Sensing Program

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Supported Operations: Export Image Identify Measure Compute Histograms Compute Statistics Histograms Get Samples Compute Class Statistics Query Boundary Compute Pixel Location Compute Angles Validate Project