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Poster Session 3
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Mapping Seagrass From Satellite Remote Sensing Data
Results
The substrate reflectances or the depth invariant index (DII) computed using the first two TM bands is shown in figure 3. Sea-truth samples collected during satellite overpass were used to calibrate the DII. Using the pseudo color image transformation the sea-bottom feature map is produced (figure 4). A line map representing the sea-bottom features were then produced (figure 5).
Figure 3. Classifications of sea bottom features using sea-truth samples.
Figure 4. Pseudo color image showing the location and type of sea bottom features where seagrass is shown in shade of red.
Figure 5. Final seagrass map- line map which is readily input to GIS
where
a and f are unknown layers
b is coarse sand
c is fine sand
d is mud
e is seagrass
Summary
In this study, remote sensing technique for mapping shallow-water bottom features namely seagrass has been presented. As this information is semi-dynamic, remote sensing techniques forms one of the best methods that can report the distribution of seagrass over large area, at economical rate.
References
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Lyzenga, D. R., (1981), "Remote Sensing of Bottom Reflectance And WaterAttenuation Parameters". International Journal of Remote Sensing, vol 2, no 1, pg 71-82.
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P. N. Bierwirth, T. J. Lee, R. V. Burne, (1993). "Shalow Sea-Floor Reflectance and Water Depth Derived by Unmixing multispectral Imagery ". Photogrammetric Engineering & Remote Sensing, vol. 59, No. 3, March, pg 331-338.
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Sturm B., (1981b), " The Atmospheric Correction of Remotely Sensed Data and the Quantitative Determination of Suspended Matter in Marine Water Surface Layers. In Remote Sensing in meteorology, Oceanography and Hydrology ", Edited by A. P. Cracknell, Halsted Press, ScotlandHalsted Press, Scotland.
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