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and West, C. 2003. Processing HYPERION and ALI for Forest Classification. IEEE Trans.
Geosci. Remote Sensing 41(2):1321–1331.
Hadjimitsis, D. G., Padavid, G., and Agapiou, A. 2009. Surface reflectance retrieval from
Landsat TM/ETM+ images for monitoring irrigation demand in cyprus. Paper presented at
the EARSeL Symposium “Imagine Europe,” Chania, Greece.
Harris, J. W., and Stocker, H. 1998. Maximum likelihood method. §21. 10. 4 in Handbook of
Mathematics and Computational Science. New York: Springer, p. 824.
Hemmer, T. H., and Westphal, T. L. 2000. Lessons learned in the post-processing of field
spectroradiometric data covering the 0.4 to 2.5 im wavelength region. In S. S. Shen and
M. R. Descour (Eds.) Algorithms for Multispectral, Hyperspectral, and Ultraspectral
Imagery VI, Proceedings of SPIE, Vol. 4049, p. 249.
Henkabail, P. S., Smith, R. B., and Pauw, E. D. 2000. Hyperspectral vegetation indices and
their relationships with agricultural crop characteristics. Remote Sensing Environment
71:158–182.
Herold, M. 2009. Assessment of the status of the development of the standards for the
Terrestrial Essential Climate Variables. Rome, Italy: The Global Terrestrial Observing
System-GTOS.
Holden, H., and LeDrew, E. 1998. Spectral discrimination of healthy and non-healthy corals
based on cluster analysis, principal components analysis, and derivative spectroscopy.
Remote Sensing of Environment 65(2):217–224.
Hostert, P., Roder, A., and Hill, J. 2003. Coupling spectral unmixing and trend analysis for
monitoring of long-term vegetation dynamics in Mediterranean rangelands. Remote Sensing
of Environment 87(2–3):183–197.
Hueni, A., Nieke, J., Schopfer, J., Kneubuhler, M., and Itten, K. I. 2009. The spectral database
SPECCHIO for improved long-term usability and data sharing. Computers & Geosciences
35(3):557–565.
Hughes, G. F. 1968. On the mean accuracy of statistical pattern recognition. IEEE Transactions
on Information Theory IT 14:55–63.
Jacobsen, A., Broge, N. H., and Hansen, B. U. 1995. Monitoring wheat fields and grasslands
using spectral reflectance data. Paper presented at the 79 International Symposium on
Spectral Sensing Research (ISSSR), Victoria, Australia.
Jurado-Exposito, M., Lopez-Granados, F., Atenciano, S., Garcia-Torres, L., and GonzalezAndujar, J. L. 2003. Discrimination of weed seedlings, wheat (Triticum aestivum) stubble
and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop
Protection 22(10):1177–1180.
Karimi, Y., Prasher, S. O., Patel, R. M., and Kim, S. H., 2006. Application of support vector
machines technology for weed and nitrogen stress detection in corn. Computers and
Electronics in Agriculture 51:99–109.
Kavzoglu, T., and Colkesen, I. 2009. A kernel functions analysis for support vector machines
for land cover classification. International Journal of Applied Earth Observation and
Geoinformation 11:352–359.
Kochubey, S. M., and Kazantsev, T. A. 2007. Changes in the first derivatives of leaf reflectance
spectra of various plants induced by variations of chlorophyll content. Journal of Plant
Physiology 164(12):1648–1655.
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315
and West, C. 2003. Processing HYPERION and ALI for Forest Classification. IEEE Trans.
Geosci. Remote Sensing 41(2):1321–1331.
Hadjimitsis, D. G., Padavid, G., and Agapiou, A. 2009. Surface reflectance retrieval from
Landsat TM/ETM+ images for monitoring irrigation demand in cyprus. Paper presented at
the EARSeL Symposium “Imagine Europe,” Chania, Greece.
Harris, J. W., and Stocker, H. 1998. Maximum likelihood method. §21. 10. 4 in Handbook of
Mathematics and Computational Science. New York: Springer, p. 824.
Hemmer, T. H., and Westphal, T. L. 2000. Lessons learned in the post-processing of field
spectroradiometric data covering the 0.4 to 2.5 im wavelength region. In S. S. Shen and
M. R. Descour (Eds.) Algorithms for Multispectral, Hyperspectral, and Ultraspectral
Imagery VI, Proceedings of SPIE, Vol. 4049, p. 249.
Henkabail, P. S., Smith, R. B., and Pauw, E. D. 2000. Hyperspectral vegetation indices and
their relationships with agricultural crop characteristics. Remote Sensing Environment
71:158–182.
Herold, M. 2009. Assessment of the status of the development of the standards for the
Terrestrial Essential Climate Variables. Rome, Italy: The Global Terrestrial Observing
System-GTOS.
Holden, H., and LeDrew, E. 1998. Spectral discrimination of healthy and non-healthy corals
based on cluster analysis, principal components analysis, and derivative spectroscopy.
Remote Sensing of Environment 65(2):217–224.
Hostert, P., Roder, A., and Hill, J. 2003. Coupling spectral unmixing and trend analysis for
monitoring of long-term vegetation dynamics in Mediterranean rangelands. Remote Sensing
of Environment 87(2–3):183–197.
Hueni, A., Nieke, J., Schopfer, J., Kneubuhler, M., and Itten, K. I. 2009. The spectral database
SPECCHIO for improved long-term usability and data sharing. Computers & Geosciences
35(3):557–565.
Hughes, G. F. 1968. On the mean accuracy of statistical pattern recognition. IEEE Transactions
on Information Theory IT 14:55–63.
Jacobsen, A., Broge, N. H., and Hansen, B. U. 1995. Monitoring wheat fields and grasslands
using spectral reflectance data. Paper presented at the 79 International Symposium on
Spectral Sensing Research (ISSSR), Victoria, Australia.
Jurado-Exposito, M., Lopez-Granados, F., Atenciano, S., Garcia-Torres, L., and GonzalezAndujar, J. L. 2003. Discrimination of weed seedlings, wheat (Triticum aestivum) stubble
and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop
Protection 22(10):1177–1180.
Karimi, Y., Prasher, S. O., Patel, R. M., and Kim, S. H., 2006. Application of support vector
machines technology for weed and nitrogen stress detection in corn. Computers and
Electronics in Agriculture 51:99–109.
Kavzoglu, T., and Colkesen, I. 2009. A kernel functions analysis for support vector machines
for land cover classification. International Journal of Applied Earth Observation and
Geoinformation 11:352–359.
Kochubey, S. M., and Kazantsev, T. A. 2007. Changes in the first derivatives of leaf reflectance
spectra of various plants induced by variations of chlorophyll content. Journal of Plant
Physiology 164(12):1648–1655.
REFERENCES
315