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

Publicado en Conference: Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International Volume: 2
Autores

Arvor, D., Jonathan, M., Meirelles, M.S.P., Dubreuil, V. and Lecerf, R.
Año de publicación 2008
DOI https://doi.org/10.1109/igarss.2008.4779155
Afiliaciones
  • COSTEL UMR CNRS 6554 LETG - IFR 90 CAREN, Université Rennes 2, Place du Recteur H. Le Moal 35043, RENNES CEDEX, France
  • Embrapa Solos, Rua Jardim Botânico, 1024 &ndash Rio de Janeiro, RJ
  • Universidade do Estado do Rio de Janeiro - UERJ, Departamento de Engenharia de Sistemas e Computação &ndash Pos Graduação em Geomatica
  • Invited professor by CAPES at the University of Brasília
Programa

CRN3
Proyecto CRN3036
Keywords
Image classification
Smoothing methods
Vegetation mapping
MODIS
Mato Grosso

Abstract

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.