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Continuous Innovation of the Quality Control of Remote Sensing Data for Territory Management

  • Elisabetta Carfagna
  • Johnny Marzialetti

Abstract

This chapter deals with the problem of assessing the quality of land-cover databases, since only high-quality products are useful for gaining knowledge about and managing territory. After a brief analysis of the main aspects of quality control and validation of land-cover databases, the main concepts of statistical quality control methods are recalled in order to show how some quality control procedures for land-cover databases can be formalized and improved by taking advantage of statistical quality control methods. Then, sequential and two-step adaptive procedures with various quality indices are proposed that continuously improve the quality of land-cover databases during the production process, in order to satisfy the user’s needs.

Keywords

Sample Unit Adaptive Sampling Remote Sensing Data European Environment Agency Global Land Cover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Agresti, A.: Categorical Data Analysis. Wiley, New York (2002)MATHCrossRefGoogle Scholar
  2. Banerjee, M., Capozzoli, M, McSweeney, L.: Beyond kappa: a review of interrater agreement measures. Can. J. Stat. 27, 3–23 (1999)MATHCrossRefMathSciNetGoogle Scholar
  3. Birkett, M.A., Day, S.J.: Internal pilot studies for estimating sample size. Stat. Med. 13, 2455–2463 (1994)CrossRefGoogle Scholar
  4. Carfagna, E.: Innovazione continua nella elaborazione di dati telerilevati per la geslione del territorio. Proc. Intermediate Meeting of the Research Project of Relevant National Interest “Statistica e tecnologia a sostegno delle imprese”. Department of Statistics, Bologna, 15-16 Feb. (2007a)Google Scholar
  5. Carfagna, E.: Crop area estimates with area frames in the presence of measurement errors. Proceedings of ICAS-IV, 4th Int. Conf. on Agricultural Statistics, Advancing Statistical Integration and Analysis (paper invited by Michael A. Steiner), Beijing, 22-24 Oct. (2007b)Google Scholar
  6. Carfagna, E., Gallego, J.F.: Thematic Maps and Statistics (invited paper). In: Land Cover and Land Use Information Systems for European Union Policy Needs, Office for Official Publications of the European Communities, Luxembourg, pp. 111–121, ISBN 92-828-74450-8 (1998)Google Scholar
  7. Carfagna E., Gallego J.F.: Using remote sensing for agricultural statistics. Int. Stat. Rev. 73, 3, 389–404 (2005)MATHGoogle Scholar
  8. Carfagna, E., Marzialetli, J.: Sequential design in quality control and validation of land cover data bases. In: Vicario, G., Isaia, E.D. (eds.): Proc. Joint ENBIS-DEINDE 2007 Conf. “Computer Experiments versus Physical Experiments,” Torino, Italy, 11-13 April (2007); paper submitted to J. Appl. Stoch. Mod. Bus. Ind. (ASMBI)Google Scholar
  9. Carfagna, E., Marzialetti, J., Maffei, S.: Sequential and two phase sample designs for quality control. Proc. XLIV Sci. Meeting of the Italian Statistical Society, University of Calabria, Italy, 25-27 June (2008)Google Scholar
  10. Carfagna, E., Napoletano, P.: Statistica e cartografia per la creazione e l’utilizzo di basi di dati sull’uso del suolo. Proc. XL Sci. Meeting of the Italian Statistical Society, Florence, Italy, 26-28 April 2000, pp. 747–750 (2000)Google Scholar
  11. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960) Cochran, W.C.: Sampling techniques. 3rd edn. Wiley, New York (1997)Google Scholar
  12. European Environment Agency: The thematic accuracy of Coline land cover 2000-assessment using LUCAS. EEA Tech. Rep. 7 (2006)Google Scholar
  13. Fleiss, J.L., Cohen, J., Everitt, B.S.: Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 72, 323–327 (1969)CrossRefGoogle Scholar
  14. Mayaux, P., Strahler, A., Eva, H., Herold, M., Shefali, A., Naumov, S., et al.: Validation of the Global Land Cover 2000 Map. IEEE Trans. Geosci. Rem. Sens. 44(7), 1728–1739 (2006)CrossRefGoogle Scholar
  15. Montgomery, D.C.: Introduction to statistical quality control, 4th edn. Wiley, New York (2001)Google Scholar
  16. Ohlsson, E.: Coordination of samples using permanent random numbers. In: Cox, B., Binder, D., Chinnapa, B., Christianson, A., Colledge, M., Kott P. (eds.): Business survey methods. Wiley, New York, pp. 153–169(1995)Google Scholar
  17. Ohlsson, E.: SAMU-The system for co-ordination of samples from the business register at Statistics Sweden-a methodological description (R&D Rep. 1992:18). Statistics Sweden, Stockholm (1992)Google Scholar
  18. Scepan, J.: Thematic validation of high-resolution global land-cover data sets. Photogramm. Eng. Rem. S. 65, 1051–1060 (1999)Google Scholar
  19. Strahler, A.S., Boschetti, L., Foody, G.M., Friedl, M.A., Hansen, M.C., Herold, M., et al.: Global land cover validation recommendations for evaluation and accuracy assessment of Global Land Cover Maps (EUR 22156 EN). Office for Official Publication of the European Communities, Luxembourg (2006)Google Scholar
  20. Tanner, M.A., Young, M.A.: Modeling agreement among raters. J. Am. Stat. Assoc. 80, 175–180 (1985)CrossRefGoogle Scholar
  21. Thompson, S.K.: Sampling. Wiley, New York (1992)MATHGoogle Scholar
  22. Thompson, S.K., Seber, G.A.F.: Adaptive Sampling. Wiley, New York, ISBN 0-471-55871-0 (1996)MATHGoogle Scholar
  23. Wald. A.: Sequential Analysis. Wiley, New York (1947)MATHGoogle Scholar

Copyright information

© Springer 2009

Authors and Affiliations

  • Elisabetta Carfagna
    • 1
  • Johnny Marzialetti
    • 1
  1. 1.Department of Statistical SciencesUniversity of BolognaItaly

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