Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil.

Abstract : Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes represent agricultural practices involving three commercial crops (soybean, maize and cotton) planted in single or double cropping systems. Both classifications are based on five steps: (1) analysis of theMODIS/EVI time series, (2) application of a smoothing algorithm, (3) application of a feature selection/extraction process to reduce the data set dimensionality, (4) application of a classifier and (5) application of a post-classification treatment. The first classification detected 95% of the agricultural areas (5 617 250 ha during the 2006-2007 harvest) and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification (overall accuracy = 74% and kappa index = 0.675) allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.
Document type :
Journal articles
Complete list of metadatas

https://halshs.archives-ouvertes.fr/halshs-00623706
Contributor : Vincent Dubreuil <>
Submitted on : Wednesday, September 14, 2011 - 10:54:07 PM
Last modification on : Friday, July 5, 2019 - 12:42:03 PM

Identifiers

Citation

Damien Arvor, Jonathan Milton, Margareth Simões Penello Meirelles, Vincent Dubreuil, Laurent Durieux. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil.. International Journal of Remote Sensing, Taylor & Francis, 2011, 32 (22), pp.7847-7871. ⟨10.1080/01431161.2010.531783⟩. ⟨halshs-00623706⟩

Share

Metrics

Record views

3847