implemented in practice. Many of these studies suggest that, prior to any land cover
analysis from remotely sensed air- and spaceborne imagery at the species level,
having knowledge about species spectral separability is vital. Yet this is not a trivial
task. This is because comparison of spectral signatures obtained from the field to
satellite hyperspectral remote sensing sensors is hindered by complications involved
in accounting for various factors, such as the physical setup of the sensors and the
measurement environment, the latter being especially variable in time and space on
Earth’s surface. Also, scale-related factors have to be taken into account to minimize
the remote measurement discrepancy between what is actually at ground level and
what is perceived from remotely sensed imagery before data can be of use.
All in all, to improve estimation of land cover from hyperspectral remote sensing
data, it is important to acquire an integrated knowledge of the spectral properties of the
land cover targets coupled with an understanding of the factors that affect the
variations of their spectral signature at given spectral and spatial scales. In this
framework, the synergy of contemporary image processing techniques combined with
sophisticated hyperspectral imagery available nowadays from a range of highly
sophisticated hyperspectral sensing systems supported by hyperspectral data collection acquired at different observational scales should be further investigated at
different ecosystem settings.
ACKNOWLEDGMENTS
Authors wish to thank the anonymous reviewers for useful comments on the
manuscript. Dr. Petropoulos wishes to thank INFOCOSMOS E.E. (http://www.
infocosmos.eu/rsgis/index.html) for the support and encouragement provided in
completing the present work.
REFERENCES
Abdel-Rahman, E. M., Ahmed, F. B., and van den Berg, M. 2010. Estimation of sugarcane leaf
nitrogen concentration using in situ spectroscopy. International Journal of Applied Earth
Observation and Geoinformation 12:S52–S57.
Abdi, H. 2007. Bonferroni and Sidak Corrections for Multiple Comparisons. Thousand Oaks,
CA: Sage.
Adam, E., and Mutanga, O. 2009. Spectral discrimination of papyrus vegetation (Cyperus
papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry
and Remote Sensing 64(6):612–620.
Adler-Golden, S. M., Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., Richtsmeier,
S. C., and Burke, H. H. 1999. Atmospheric correction for short-wave spectral imagery based
on MODTRAN4. Imaging Spectrometry V 3753:61–69.
Artigas, F. J., and Yang, J. S. 2006. Spectral discrimination of marsh vegetation types in the
New Jersey Meadowlands. USA Wetlands 26(1):271–277.
312
HYPERSPECTRAL REMOTE SENSING WITH EMPHASIS ON LAND COVER MAPPING
analysis from remotely sensed air- and spaceborne imagery at the species level,
having knowledge about species spectral separability is vital. Yet this is not a trivial
task. This is because comparison of spectral signatures obtained from the field to
satellite hyperspectral remote sensing sensors is hindered by complications involved
in accounting for various factors, such as the physical setup of the sensors and the
measurement environment, the latter being especially variable in time and space on
Earth’s surface. Also, scale-related factors have to be taken into account to minimize
the remote measurement discrepancy between what is actually at ground level and
what is perceived from remotely sensed imagery before data can be of use.
All in all, to improve estimation of land cover from hyperspectral remote sensing
data, it is important to acquire an integrated knowledge of the spectral properties of the
land cover targets coupled with an understanding of the factors that affect the
variations of their spectral signature at given spectral and spatial scales. In this
framework, the synergy of contemporary image processing techniques combined with
sophisticated hyperspectral imagery available nowadays from a range of highly
sophisticated hyperspectral sensing systems supported by hyperspectral data collection acquired at different observational scales should be further investigated at
different ecosystem settings.
ACKNOWLEDGMENTS
Authors wish to thank the anonymous reviewers for useful comments on the
manuscript. Dr. Petropoulos wishes to thank INFOCOSMOS E.E. (http://www.
infocosmos.eu/rsgis/index.html) for the support and encouragement provided in
completing the present work.
REFERENCES
Abdel-Rahman, E. M., Ahmed, F. B., and van den Berg, M. 2010. Estimation of sugarcane leaf
nitrogen concentration using in situ spectroscopy. International Journal of Applied Earth
Observation and Geoinformation 12:S52–S57.
Abdi, H. 2007. Bonferroni and Sidak Corrections for Multiple Comparisons. Thousand Oaks,
CA: Sage.
Adam, E., and Mutanga, O. 2009. Spectral discrimination of papyrus vegetation (Cyperus
papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry
and Remote Sensing 64(6):612–620.
Adler-Golden, S. M., Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., Richtsmeier,
S. C., and Burke, H. H. 1999. Atmospheric correction for short-wave spectral imagery based
on MODTRAN4. Imaging Spectrometry V 3753:61–69.
Artigas, F. J., and Yang, J. S. 2006. Spectral discrimination of marsh vegetation types in the
New Jersey Meadowlands. USA Wetlands 26(1):271–277.
312
HYPERSPECTRAL REMOTE SENSING WITH EMPHASIS ON LAND COVER MAPPING