Milton Jonathan

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

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 the MODIS/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.

Comparison of Multitemporal MODIS-EVI Smoothing Algorithms and its Contribution to Crop Monitoring

Time series of MODIS vegetation indices are widely used to map vegetation. However, some noise can affect the temporal profiles. Thus, many techniques have been developed to smooth them. Four algorithms are applied on crop pixels in the Brazilian Amazonian State of Mato Grosso. Comparisons led to the selection of the Weighted Least Squares (WLS) algorithm and the Savitzky-Golay (SG) filter. Those techniques were computed on MODIS data in order to detect six crop classes. Tests of separability show that the smoothed data improved the potential of separability at each MODIS sub-period. Moreover, supervised classifications were then realized. The WLS data refined efficiently the classification result when using C4.5 decision tree. When using the Maximum Likelihood and Spectral Angle Mapper classifiers, the smoothed data did not improve the classification results as compared with those obtained through original MODIS data. However, it required fewer input MODIS images to reach good results. The SG filter led to better results than the WLS algorithm when using those classifiers.