358
A. Dekker, V. Brando, J. Anstee, S. Fyfe, T. Malthus and E. Karpouzli
for quantitative remote sensing, will further advance our understanding of the remotely sensed
signal over a water body with seagrass cover. As
the spectral response of seagrasses to environmental pressures is better understood, sophisticated remote sensing of this spectral response may open up
many more applications in seagrass biology. Spatially comprehensive maps of changes in seagrass
species or epiphytic algae abundance, will lead to
new insights into cause and effect of seagrass ecosystem change, not possible by aerial photography
analysis.
The insight into the relationship between light and
photosynthesis (Zimmerman, Chapter 13) and the
insight into advanced remote sensing based methods
for estimating water column and substratum vegetation cover both point to a future convergence where
photosynthetic studies in seagrass meadows will use
information derived from remote sensing and vice
versa.
A challenge will be to merge all the historical
information provided by aerial photograph interpretation and field knowledge gathered on seagrass
meadows in the last century with these spectral measurement and analysis methodologies (be it remote
sensing or field-based), in order to create improved
knowledge on seagrass extent and distribution, leading to improved understanding, and management
practices. Another challenge is to provide accurate
estimation of a sloping seabed; for instance P. australis beds, which, in the Australian Bight, may go
down to >30 m, sometimes below the optical depth
of the water column.
References
Aas E (1987) Two-stream irradiance model for deep waters. Appl
Opt 26: 2095–2101
Alberotanza L, Brando VE, Ravagnan G and Zandonella A
(1999) Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello Lagoons, Italy.
Int J Remote Sensing 20: 523–533
Andrefouet S, Payri C, Hochberg EJ, Mao Che L and Atkinson
M (2003) Airborne hyperspectral detection of microbial mat
pigmentation in Rangiroa atoll (French Polynesia). Limnol
Oceanogr 48: 426–430
Anstee JM, Dekker AG and Brando VE (2004) Retrospective
change detection in a shallow coastal tidal lake: Mapping
seagrasses in Wallis Lake, Australia. In: Analysis of Multitemporal remote sensing images, Series in Remote Sensing,
Vol 3, pp 277–285. World Scientific Publishing Co., Singapore
Bajjouk T, Guillaumont B and Populus J (1996) Application
of airborne imaging spectrometry system data to intertidal
seaweed classification and mapping. Hydrobiologia 326/327:
463–471
Bierwirth PN, Lee TJ and Burne RV (1993) Shallow sea-floor
reflectance and water depth derived by unmixing multispectral
imagery. Photogrammetric Eng Remote Sensing 59: 331–338
Clark CD, Ripley HT, Green EP, Edwards AJ and Mumby PJ
(1997) Mapping and measurement of tropical coastal environments with hyperspectral and high spatial resolution data.
Int J Remote Sensing 18: 237–242
Dekker AG, Brando VE, Anstee JM, Pinnel N, Kutser T, Hoogenboom J, Peters SWM, Pasterkamp R, Vos RJ, Olbert C and
Malthus TJ (2001) Imaging spectrometry of water. Imaging
Spectrometry: Basic Principles and Prospective Applications,
IV, pp 307–359. Kluwer Academic Publishers, Dordrecht, The
Netherlands
Dierssen H, Zimmerman RC, Leather RA, Downes V and
Davis CO (2003) Ocean color remote sensing of seagrass and
bathymetry in the Bahamas Banks by high resolution airborne
imagery. Limnol Oceanogr 48: 444–455
Fyfe SK (2003) Spatial and temporal variation in spectral reflectance: Are seagrass species spectrally distinct? Limnol
Oceanogr 48: 464–479
Green EP and Short FT (eds) (2003) World Atlas of Seagrasses,
p 310. University of California Press, LA
Hochberg EJ and Atkinson MJ (2000) Spectral discrimination of
coral reef benthic communities. Coral Reefs 19: 164–171
Hochberg EJ, Atkinson MJ and Andr´ efou¨ et S (2003) Spectral
reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing Environ
85: 159–173
Holden H and Ledrew E (1999) Hyperspectral identification of
coral reef features. International Journal of Remote Sensing,
20: 2545–2563
Jakubauskas ME, Kindscher K, Fraser A, Debinski DM and Price
KP (2000) Close-range remote sensing of aquatic macrophyte
vegetation cover. Int J Remote Sensing 21: 3533–3538
Jensen JR, Rutchey K, Koch MS and Narumalani S (1995) Inland
wetland change detection in the everglades water conservation area 2a using a time-series of normalized remotely-sensed
data. Photogrammetric Eng Remote Sensing 61: 199–209
Joyce K and Phinn SR (2003) Hyperspectral analysis of chlorophyll content and photosynthetic capacity of coral reef substances. Limnol Oceanogr 48: 489–496
Karpouzli E, Malthus TJ and Place CJ (2004) Hyperspectral discrimination of coral reef benthic communities in the western
Caribbean. Coral Reefs 23: 141–151
Karpouzli E, Malthus TJ, Place C, Mitchell Chui A, Ines Garcia
M and Mair JD (2003) Underwater light characterisation for
correction of remotely sensed images. Int J Remote Sensing
24: 2683–2702
Kirk JTO (1989) The upwelling light stream in natural waters.
Limnol Oceanogr 34: 1410–142
Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems.
University Press, Cambridge, UK 509 pp
Kutser T, Dekker AG and Skirving W (2003) Modeling spectral discrimination of Great Barrier Reef benthic communities
by remote sensing instruments. Limnol Oceanogr 48: 497–
510
Louchard EM, Reid P, Stephens C, Davis CO, Leathers RA
and Downes V (2003) Optical remote sensing of habitats and
bathymetry in coastal environments at Lee Stocking Island,
A. Dekker, V. Brando, J. Anstee, S. Fyfe, T. Malthus and E. Karpouzli
for quantitative remote sensing, will further advance our understanding of the remotely sensed
signal over a water body with seagrass cover. As
the spectral response of seagrasses to environmental pressures is better understood, sophisticated remote sensing of this spectral response may open up
many more applications in seagrass biology. Spatially comprehensive maps of changes in seagrass
species or epiphytic algae abundance, will lead to
new insights into cause and effect of seagrass ecosystem change, not possible by aerial photography
analysis.
The insight into the relationship between light and
photosynthesis (Zimmerman, Chapter 13) and the
insight into advanced remote sensing based methods
for estimating water column and substratum vegetation cover both point to a future convergence where
photosynthetic studies in seagrass meadows will use
information derived from remote sensing and vice
versa.
A challenge will be to merge all the historical
information provided by aerial photograph interpretation and field knowledge gathered on seagrass
meadows in the last century with these spectral measurement and analysis methodologies (be it remote
sensing or field-based), in order to create improved
knowledge on seagrass extent and distribution, leading to improved understanding, and management
practices. Another challenge is to provide accurate
estimation of a sloping seabed; for instance P. australis beds, which, in the Australian Bight, may go
down to >30 m, sometimes below the optical depth
of the water column.
References
Aas E (1987) Two-stream irradiance model for deep waters. Appl
Opt 26: 2095–2101
Alberotanza L, Brando VE, Ravagnan G and Zandonella A
(1999) Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello Lagoons, Italy.
Int J Remote Sensing 20: 523–533
Andrefouet S, Payri C, Hochberg EJ, Mao Che L and Atkinson
M (2003) Airborne hyperspectral detection of microbial mat
pigmentation in Rangiroa atoll (French Polynesia). Limnol
Oceanogr 48: 426–430
Anstee JM, Dekker AG and Brando VE (2004) Retrospective
change detection in a shallow coastal tidal lake: Mapping
seagrasses in Wallis Lake, Australia. In: Analysis of Multitemporal remote sensing images, Series in Remote Sensing,
Vol 3, pp 277–285. World Scientific Publishing Co., Singapore
Bajjouk T, Guillaumont B and Populus J (1996) Application
of airborne imaging spectrometry system data to intertidal
seaweed classification and mapping. Hydrobiologia 326/327:
463–471
Bierwirth PN, Lee TJ and Burne RV (1993) Shallow sea-floor
reflectance and water depth derived by unmixing multispectral
imagery. Photogrammetric Eng Remote Sensing 59: 331–338
Clark CD, Ripley HT, Green EP, Edwards AJ and Mumby PJ
(1997) Mapping and measurement of tropical coastal environments with hyperspectral and high spatial resolution data.
Int J Remote Sensing 18: 237–242
Dekker AG, Brando VE, Anstee JM, Pinnel N, Kutser T, Hoogenboom J, Peters SWM, Pasterkamp R, Vos RJ, Olbert C and
Malthus TJ (2001) Imaging spectrometry of water. Imaging
Spectrometry: Basic Principles and Prospective Applications,
IV, pp 307–359. Kluwer Academic Publishers, Dordrecht, The
Netherlands
Dierssen H, Zimmerman RC, Leather RA, Downes V and
Davis CO (2003) Ocean color remote sensing of seagrass and
bathymetry in the Bahamas Banks by high resolution airborne
imagery. Limnol Oceanogr 48: 444–455
Fyfe SK (2003) Spatial and temporal variation in spectral reflectance: Are seagrass species spectrally distinct? Limnol
Oceanogr 48: 464–479
Green EP and Short FT (eds) (2003) World Atlas of Seagrasses,
p 310. University of California Press, LA
Hochberg EJ and Atkinson MJ (2000) Spectral discrimination of
coral reef benthic communities. Coral Reefs 19: 164–171
Hochberg EJ, Atkinson MJ and Andr´ efou¨ et S (2003) Spectral
reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing Environ
85: 159–173
Holden H and Ledrew E (1999) Hyperspectral identification of
coral reef features. International Journal of Remote Sensing,
20: 2545–2563
Jakubauskas ME, Kindscher K, Fraser A, Debinski DM and Price
KP (2000) Close-range remote sensing of aquatic macrophyte
vegetation cover. Int J Remote Sensing 21: 3533–3538
Jensen JR, Rutchey K, Koch MS and Narumalani S (1995) Inland
wetland change detection in the everglades water conservation area 2a using a time-series of normalized remotely-sensed
data. Photogrammetric Eng Remote Sensing 61: 199–209
Joyce K and Phinn SR (2003) Hyperspectral analysis of chlorophyll content and photosynthetic capacity of coral reef substances. Limnol Oceanogr 48: 489–496
Karpouzli E, Malthus TJ and Place CJ (2004) Hyperspectral discrimination of coral reef benthic communities in the western
Caribbean. Coral Reefs 23: 141–151
Karpouzli E, Malthus TJ, Place C, Mitchell Chui A, Ines Garcia
M and Mair JD (2003) Underwater light characterisation for
correction of remotely sensed images. Int J Remote Sensing
24: 2683–2702
Kirk JTO (1989) The upwelling light stream in natural waters.
Limnol Oceanogr 34: 1410–142
Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems.
University Press, Cambridge, UK 509 pp
Kutser T, Dekker AG and Skirving W (2003) Modeling spectral discrimination of Great Barrier Reef benthic communities
by remote sensing instruments. Limnol Oceanogr 48: 497–
510
Louchard EM, Reid P, Stephens C, Davis CO, Leathers RA
and Downes V (2003) Optical remote sensing of habitats and
bathymetry in coastal environments at Lee Stocking Island,