Field Sampling from a Segmented Image

  • Pravesh Debba
  • Alfred Stein
  • Freek D. van der Meer
  • Emmanuel John M. Carranza
  • Arko Lucieer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5072)


This paper presents a statistical method for deriving the optimal prospective field sampling scheme on a remote sensing image to represent different categories in the field. The iterated conditional modes algorithm (ICM) is used for segmentation followed by simulated annealing within each category. Derived field sampling points are more intense in heterogenous segments. This method is applied to airborne hyperspectral data from an agricultural field. The optimized sampling scheme shows superiority to simple random sampling and rectangular grid sampling in estimating common vegetation indices and is thus more representative of the whole study area.


Remote Sensing Sampling Scheme Vegetation Index Leaf Area Index Hyperspectral Image 
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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  1. 1.
    Baret, F., Guyot, G.: Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173 (1991)CrossRefGoogle Scholar
  2. 2.
    Besag, J.: On the statistical analysis of dirty pictures. Royal Statistical Society B-48(3), 259–302 (1986)MathSciNetGoogle Scholar
  3. 3.
    Broge, N.H., Leblanc, E.: Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172 (2000)CrossRefGoogle Scholar
  4. 4.
    Brown, L., Jin, M.C., Lablanc, S.G., Cihlar, J.: A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote Sensing Environment 71, 16–25 (2000)CrossRefGoogle Scholar
  5. 5.
    Brus, D.J., Spatjens, L.E.E.M., de Gruitjer, J.J.: A sampling scheme for estimating the mean extractable phosphorus concentration of fields for environmental regulation. Geoderma 89, 129–148 (1999)CrossRefGoogle Scholar
  6. 6.
    Chappelle, E.W., Kim, M.S., McMurtrey III, J.E.: Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and the carotenoids in soybean leaves. Remote Sensing of Environment 39, 239–247 (1992)CrossRefGoogle Scholar
  7. 7.
    Chen, J.: Evaluation of vegetation indices and modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22, 229–242 (1996)Google Scholar
  8. 8.
    Chen, J., Cihlar, J.: Retrieving leaf area index of boreal conifer forests using Landsat Thematic Mapper. Remote Sensing of Environment 55, 153–162 (1996)CrossRefGoogle Scholar
  9. 9.
    Curran, P.J., Williamson, H.D.: The accuracy of ground data used in remote-sensing investigations. International Journal of Remote Sensing 6(10), 1637–1651 (1985)CrossRefGoogle Scholar
  10. 10.
    Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E., McMurtrey III, J.E.: Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74, 229–239 (2000)CrossRefGoogle Scholar
  11. 11.
    Fassnacht, K.S., Gower, S.T., MacKenzie, M.D., Nordheim, E.V., Lillesand, T.M.: Estimating the leaf area index of north central Wisconsin forest using Landsat Thematic Mapper. Remote Sensing Environment 61, 229–245 (1997)CrossRefGoogle Scholar
  12. 12.
    Forbes, F., Peyrard, N.: Hidden Markov Random Field Model selection criteria based on mean field-like approximations. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1089–1101 (2003)CrossRefGoogle Scholar
  13. 13.
    Fraley, C., Raftery, A.E.: Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Technical Report 424, University of Washington, Department of Statistics (2003)Google Scholar
  14. 14.
    Gamon, J.A., Penuelas, J., Field, C.B.: A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41, 35–44 (1992)CrossRefGoogle Scholar
  15. 15.
    Gitelson, A., Rundquist, D., Derry, D., Ramirez, J., Keydan, G., Stark, R., Perk, R.: Using remote sensing to quantify vegetation fraction in corn canopies. In: Proceedings of Third Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado, November 2001, pp. 3–7 (2001)Google Scholar
  16. 16.
    Haboudanea, D., Millera, J.R., Patteyc, E., Zarco-Tejadad, P.J., Strachane, I.B.: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352 (2004)CrossRefGoogle Scholar
  17. 17.
    Kardeván, P., Vekerdy, Z., Róth, L., Sommer, S., Kemper, T., Jordan, G., Tamás, J., Pechmann, I., Kovács, E., Hargitai, H., László, F.: Outline of scientific aims and data processing status of the first Hungarian hyperspectral data acquisition flight campaign, HYSENS 2002 Hungary. In: Habermeyer, M., Mülle, A., Holzwarth, S. (eds.) Proceedings of the 3rd EARSeL workshop on imaging spectroscopy, Herrsching, Germany: EARSeL, pp. 324–332 (2003)Google Scholar
  18. 18.
    Keuchel, J., Naumann, S., Heiler, M., Siegmund, A.: Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment 86, 530–541 (2003)CrossRefGoogle Scholar
  19. 19.
    Lehmann, F., Oertel, D., Richter, R., Rothfuss, H., Strobl, P., Muller, A., Tischler, S., Mueller, R., Beran, D., Fries, J., Boehl, R., Obermeier, P.: Hyperspectral applications with a new sensor. In: ISSSR (International Symposium on Spectral Sensing Research) in Melbourne: DAIS-7915, The Digital Airborne Imaging Spectrometer DAIS-7915 (1995)Google Scholar
  20. 20.
    Lu, D., Mausel, P., Brondízio, E., Moran, E.: Classification of successional forest stages in the Brazilian Amazon basin. Forest Ecology and Management 181(3), 301–312 (2003)CrossRefGoogle Scholar
  21. 21.
    Moreau, M., Laffly, D., Joly, D., Brossard, T.: Analysis of plant colonization on an arctic moraine since the end of the Little Ice Age using remotely sensed data and a Bayesian approach. Remote Sensing of Environment 99, 244–253 (2005)CrossRefGoogle Scholar
  22. 22.
    Qi, J., Chehbouni, A., Huete, A.R., Keer, Y.H., Sorooshian, S.: A modified soil vegetation adjusted index. Remote Sensing of Environment 48, 119–126 (1994)CrossRefGoogle Scholar
  23. 23.
    Qi, J., Kerr, Y.H., Moran, M.S., Weltz, M., Huete, A.R., Sorooshian, S., Bryant, R.: Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sensing of Environment 73, 18–30 (2000)CrossRefGoogle Scholar
  24. 24.
    Roberts, D.A., Yamaguchi, Y., Lyon, R.J.P.: Calibration of airborne imaging spectrometer data to percentage reflectance using field spectral measurements. In: Proceedings of the Nineteenth International Symposium on Remote Sensing of the Environment, Ann Arbor, Michigan, pp. 21–25 (1985)Google Scholar
  25. 25.
    Rougean, J.L., Breon, F.M.: Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51, 375–384 (1995)CrossRefGoogle Scholar
  26. 26.
    Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C.: Monitoring the vernal advancements and retrogradation of natural vegetation. In: NASA/GSFC, Final Report, Greenbelt, MD, USA, pp. 1–137 (1974)Google Scholar
  27. 27.
    Spanner, M.A., Pierce, L.L., Peterson, D.L., Running, S.W.: Remote sensing of temperate coniferous forest leaf area index: The influence of canopy closure, understory vegetation and background reflectance. Internation Journal of Remote Sensing 11, 95–111 (1990)CrossRefGoogle Scholar
  28. 28.
    Stanford, D.C., Raftery, A.E.: Approximate bayes factors for image segmentation: The pseudolikelihood information criterion (PLIC). IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1517–1520 (2002)CrossRefGoogle Scholar
  29. 29.
    Thenkabail, P.S., Enclonab, E.A., Ashton, M.S., Legg, C., De Dieu, M.J.: Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment 90, 23–43 (2004)CrossRefGoogle Scholar
  30. 30.
    Thenkabail, P.S.: Optimal hyperspectral narrowbands for discriminating agricultural crops. Remote Sensing Reviews 20(4), 257–291 (2002)Google Scholar
  31. 31.
    Thompson, S.K.: Sampling. John Wiley and Sons, Inc., New York (1992)MATHGoogle Scholar
  32. 32.
    Van Groenigen, J.W., Stein, A.: Constrained optimization of spatial sampling using continuous simulated annealing. Journal Environmental Quality 27, 1078–1086 (1998)CrossRefGoogle Scholar
  33. 33.
    Watson, F.G.R., Anderson, T.N., Newman, W.B., Alexander, S.E., Garrott, R.A.: Optimal sampling schemes for estimating mean snow water equivalents in stratified heterogeneous landscapes. Journal of Hydrology 328, 432–452 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pravesh Debba
    • 1
    • 2
  • Alfred Stein
    • 3
  • Freek D. van der Meer
    • 3
  • Emmanuel John M. Carranza
    • 3
  • Arko Lucieer
    • 4
  1. 1.Council for Scientific and Industrial Research (CSIR)Logistics and Quantitative Methods, CSIR Built EnvironmentSouth Africa
  2. 2.College of Science, Engineering and Technology, Department of StatisticsUniversity of South AfricaPretoriaSouth Africa
  3. 3.International Institute for Geo-Information Science and Earth Observation (ITC)EnschedeThe Netherlands
  4. 4.School of Geography & Environmental Studies, Center for Spatial Information Science (CenSIS)University of TasmaniaTasmaniaAustralia

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