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MatoGrosso/2012_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/2012_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.

Single Fused Map Cache: false

Extent: Initial Extent: Full Extent: Pixel Size X: 240.0

Pixel Size Y: 240.0

Band Count: 1

Pixel Type: U8

RasterFunction Infos: {"name":"MattoGrassoLandcover","description":"A raster function template.","help":""}, {"name":"None","description":"","help":""}

Mensuration Capabilities: Basic

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

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

Service Data Type: esriImageServiceDataTypeThematic

Min Values: 1

Max Values: 9

Mean Values: 4.2196475550294

Standard Deviation Values: 0.98389192874426

Object ID Field:

Fields: None

Default Mosaic Method: Center

Allowed Mosaic Methods:

SortField:

SortValue: null

Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Nearest

Max Record Count: null

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: null

Max Mosaic Image Count: null

Allow Raster Function: true

Allow Compute TiePoints: false

Supports Statistics: false

Supports Advanced Queries: false

Use StandardizedQueries: true

Raster Type Infos: Has Raster Attribute Table: true

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources:   Info   Raster Attribute Table   Histograms   Key Properties   Legend   MultiDimensionalInfo   rasterFunctionInfos

Supported Operations:   Export Image   Identify   Measure   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics