Geography Reference
In-Depth Information
a silver bullet capable of meeting all our sampling needs.
A very obvious and simple reminder of this fact is the
inapplicability of these methods to small channels with
significant overarching vegetation (i.e. tunnelling). If we
cannot see the channel from the air, even hyperspatial
imagery will be ineffective as a sampling tool. Another
crucial area which still requires fieldwork is the sampling
of biota. The recent progress in river sciences has left us in a
situation where our ability to map abiotic parameters and
physical habitat greatly exceeds our ability to map lotic
biota. This discrepancy will clearly need to be addressed.
However in the near future, hyperspatial imagery can
be a powerful addition to the river sciences 'toolbox'
when used in conjunction with other data sources and
field-based sampling.
river basin (Western Amazonia). Remote Sensing of Environ-
ment , 87 (4), 429-445.
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ence of the marine atmospheric boundary layer on ERS 1
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of Geophysical Research-Oceans , 102 (C3), 5799-5814.
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Through-water close range digital photogrammetry in flume
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Carbonneau, P.E., Bergeron, N., and Lane, S.N. 2005. Auto-
mated grain size measurements from airborne remote sensing
for long profile measurements of fluvial grain sizes. Water
Resources Research , 41 (11). DOI:10.1029/2005WR003994
Carbonneau, P.E., Dugdale, S.J., and Clough, S. 2010. An auto-
mated georeferencing tool for watershed scale fluvial remote
sensing. River Research and Applications , 26 (5), 650-658.
Carbonneau, P.E., Fonstad, M.A., Marcus, W.A., and Dug-
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Acknowledgements
The authors thank all the persons who very kindly par-
ticipated in the Drone Pixy project applied to the fluvial
corridor, all students and colleagues who helped dur-
ing the pixy campaigns, especially Thierry Fournier and
Marie-Laure Tremelo and the ZABR which funded and
supported this effort. We also thank Rodolphe Mon-
tagnon the pilot of the power paraglider, A. Hervouet,
M. Gagnage, B. MacVicar, James, T.D., and J. Riquier,
for kindly providing data and figures for this chapter.
Furthermore, we would like to thank Dr Mark Fonstad
for discussions and assistance in the resolution of the
shadow removal problem.
S.J.
2011.
Making
riverscapes
real. Geomorphology .
DOI:10.1016/j.geomorph.2010.09.030
Carbonneau, P.E., Lane, S.N., and Bergeron, N. 2006. Fea-
ture based image processing methods applied to bathymetric
measurements from airborne remote sensing in fluvial envi-
ronments. Earth Surface Processes and Landforms , 31 (11),
1413-1423.
Carbonneau, P.E., Lane, S.N., and Bergeron, N.E. 2004.
Catchment-scale mapping of surface grain size in gravel
bed rivers using airborne digital imagery. Water Resources
Research , 40 (7). 10.1029/2003WR002759
Chen, S.S., Fang, L.G., Zhang, L.X., and Huang, W.R. 2009.
Remote sensing of turbidity in seawater intrusion reaches
of Pearl River Estuary - A case study in Modaomen
water way, China. Estuar Coast Shelf S , 82 (1), 119-127.
10.1016/j.ecss.2009.01.003
Chubey, M.S., Franklin, S.E., and Wulder, M.A. 2006. Object-
based analysis of Ikonos-2 imagery for extraction of for-
est inventory parameters. Photogrammetric Engineering and
Remote Sensing , 72 (4), 383-394.
Conyers, M.M. and Fonstad, M.A. 2005. The unusual channel
resistance of the Texas Hill Country and its effect on flood
flow predictions. Physical Geography , 26 (5), 379-395.
Dai, M., He, B., Huang, W., Liu, Q., Chen, H., and Xu, L. 2010.
Sources and accumulation of organic carbon in the Pearl River
Estuary surface sediment as indicated by elemental, stable car-
bon isotopic, and carbohydrate compositions. Biogeosciences ,
7 (10), 3343-3362.
Defourny, P., Desclee, B., and Bogaert, P. 2006. Forest change
detection by statistical object-based method. Remote Sensing
of Environment , 102 (1-2), 1-11. 10.1016/j.rse.2006.01.013
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