Uvarov Recognition of arable lands using multi-annual satellite data from spectroradiometer modis and locally adaptive supervised classification
S.A. Bartalev, V.A. Egorov, E.A. Loupian, D.E. Plotnikov, I.A.

Space Research Institute of the Russian Academy of Sciences

Full text of article: Russian language.

Abstract:

The arable lands recognition method is developed based on multi-annual time-series of remote sensing data acquired by spectroradiometer MODIS on board of Terra and Aqua satellites. The method involves producing of the recognition features set, which exploits differences of seasonal and inter-annual changes of spectral reflectance for arable lands on one hand and other types of agricultural lands and natural vegetation on another hand. The arable lands recognition utilizes the locally-adaptive supervised classification algorithm, which accounts the spatial variability of the considered features for classes to be discriminated.
The developed method has been applied to produce the arable lands map for entire Russia. The arable lands map validation based on Pareto optimum approach and reference data has been performed for the test region in order to estimate the method’s accuracy and potential for its further enhancement

Key words:
remote sensing, satellite spectroradiometer, supervised classification, recognition features, agricultural lands monitoring.

References:

  1. Cihlar, J. Classification by progressive generalization: a new automated methodology for remote-sensing multi-channel data. / Cihlar J., Xiao Q., Beaubien J., Fung K., Latifovic R. // International Journal of Remote Sensing. – 1998. – V.19. – P.2685-2704.
  2. Davis, S.M. Remote sensing: the quantitative approach / Davis S.M., Landgrebe D.A., Phillips T.L. // Moscow, Nedra, - 1983. – 415 p.
  3. Uvarov, I.A. Algorithm and a software package for vegetation types locally-adaptive supervised classification / Uvarov I.A., Bartalev S.A. // Contemporary problems of Earth remote sensing: Physical basics, methods and technologies of environmental and hazardous phenomena monitoring. Scientific papers compilation. M.: - “Domira”, - 2010. – V.7, - 1. – P.353-365.
  4. Loupian, E.A. The remote sensing technologies for agricultural monitoring of Russia / Loupian E.A., Bartalev S.A., Savin I.U. // Aerospace Courier. – 2009. - 6. – P.47-49.
  5. Bartalev, S.A. Several crops detection using MODIS in the South of Russia / Bartalev S.A., Loupian E.A., Neishtadt I.A., Savin I.U. // Issledovanie Zemli iz kosmosa. – 2006. – 3. – P.68-75.
  6. Bartalev, S.A. Arable lands detection method based on remote sensing data. / Bartalev S.A., Loupian E.A., Neishtadt I.A. // Contemporary problems of Earth remote sensing: Physical basics, methods and technologies of environmental and hazardous phenomena monitoring. Scientific papers compilation. M.: - «Azbuka-2000», - 2006. – Issue 3. - V.2. – P.271-280.
  7. Plotnikov, D.E. The recognition features to map arable lands based on multi-annual MODIS Earth observation data. / Plotnikov D.E., Bartalev S.A., Loupian E.A. // Contemporary problems of Earth remote sensing: Physical basics, methods and technologies of environmental and hazardous phenomena monitoring. Scientific papers compilation. M.: - “Domira”, - 2010. – V.7, - 1. – P.330-341.
  8. Neishtadt, I.A. Method of cloudless MODIS images creation for vegetation monitoring // Contemporary problems of Earth remote sensing. Scientific papers compilation. M.: - «Azbuka-2000», - 2006. – V.2. – P.359-365.
  9. Keeney, R.L. Decisions with multiple objectives: Preferences and value tradeoffs / New York: - Wiley, - 1976.
  10. Boschetti, L. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary / Boschetti L., Stéphane P.F., Pietro A.B. // Remote Sensing of Environment, - 2004 – Vol. 91. – P. 280-292.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: ko@smr.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20