Bathymetry Derived From Landsat
[12] Bathymetry derived from Landsat imagery was used
to supplement current holdings of traditionally acquired
bathymetric data. Band ratio methods, typically the ratio
of the Landsat bands 1 and 2 (blue and green wavelengths,
respectively), have been suggested as a way to estimate
depth and minimize the effects of changes in bottom albedo [Lyzenga, 1978; Dierssen et al., 2003; Stumpf et al., 2003].
Band ratio methods make an assumption that different
bottom albedoes at the same depth have the same ratio
and, when rescaled using known bathymetry values, provide
an approximation of depth.
[13] For this study, the ratio of the blue and green pixel
intensities (denoted as B1:B2) from 12 Landsat (Table 3)
scenes covering the Torres Strait and the northern Great
Barrier Reef were compared to AHS bathymetric data. For
each Landsat scene, pixels that had colocated bathymetric
data were identified. The average ratio of B1:B2, for each 1 m
of depth, from 1 to 20 m, was then calculated. The resulting
data sets provided an estimation of how B1:B2 changed with
depth over each Landsat scene. Scene-specific algorithms,
based on second-order polynomials, were then calculated to
estimate depth in each scene (Table 3). Owing to the
increasing attenuation of light with increasing depth, the
relationship between B1:B2 and depth is asymptotic in
optically deep water, (i.e., no signal from the seabed will
be received by the satellite). The depth at which this
asymptote occurs is variable between the Landsat scenes
(Table 3) but generally occurs at 15 m. This marks the
maximum depth down to which bathymetry can be estimated
by this technique.
[14] The application of passive remote-sensing techniques
to bathymetric mapping requires shallow and clear water,
minimal changes in bottom type, and no atmospheric contamination.
Concentrations of turbidity and chlorophyll are
assumed to be variable (though not directly measured) in all
the scenes and would account for some of the variability in
the algorithms used to convert B1:B2 to bathymetry. Regions
within Landsat scenes that were observed to be highly turbid
(particularly northern Torres Strait and costal regions around
northern Australia) were masked out of the analysis to limit
the influence of terrestrial runoff. As a result, a good
correlation was observed between measured depths and
B1:B2 (r2 > 0.97 typically). This technique was not extended
to the Gulf of Papua because of the high turbidity in the
region, the greater depths involved, and the lack of adequate
bathymetric data in clear waters.
Bathymetry Derived From Landsat[12] Bathymetry derived from Landsat imagery was usedto supplement current holdings of traditionally acquiredbathymetric data. Band ratio methods, typically the ratioof the Landsat bands 1 and 2 (blue and green wavelengths,respectively), have been suggested as a way to estimatedepth and minimize the effects of changes in bottom albedo [Lyzenga, 1978; Dierssen et al., 2003; Stumpf et al., 2003].Band ratio methods make an assumption that differentbottom albedoes at the same depth have the same ratioand, when rescaled using known bathymetry values, providean approximation of depth.[13] For this study, the ratio of the blue and green pixelintensities (denoted as B1:B2) from 12 Landsat (Table 3)scenes covering the Torres Strait and the northern GreatBarrier Reef were compared to AHS bathymetric data. Foreach Landsat scene, pixels that had colocated bathymetricdata were identified. The average ratio of B1:B2, for each 1 mof depth, from 1 to 20 m, was then calculated. The resultingdata sets provided an estimation of how B1:B2 changed withdepth over each Landsat scene. Scene-specific algorithms,based on second-order polynomials, were then calculated toestimate depth in each scene (Table 3). Owing to theincreasing attenuation of light with increasing depth, therelationship between B1:B2 and depth is asymptotic inoptically deep water, (i.e., no signal from the seabed willbe received by the satellite). The depth at which this
asymptote occurs is variable between the Landsat scenes
(Table 3) but generally occurs at 15 m. This marks the
maximum depth down to which bathymetry can be estimated
by this technique.
[14] The application of passive remote-sensing techniques
to bathymetric mapping requires shallow and clear water,
minimal changes in bottom type, and no atmospheric contamination.
Concentrations of turbidity and chlorophyll are
assumed to be variable (though not directly measured) in all
the scenes and would account for some of the variability in
the algorithms used to convert B1:B2 to bathymetry. Regions
within Landsat scenes that were observed to be highly turbid
(particularly northern Torres Strait and costal regions around
northern Australia) were masked out of the analysis to limit
the influence of terrestrial runoff. As a result, a good
correlation was observed between measured depths and
B1:B2 (r2 > 0.97 typically). This technique was not extended
to the Gulf of Papua because of the high turbidity in the
region, the greater depths involved, and the lack of adequate
bathymetric data in clear waters.
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