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Data Processing, Algorithm and Modelling
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Collaborating Remote Sensing with historical limnological data to map primary productivity at a Eutrophic lake
Results and discussion
As seen from the graphical representation of the results
(fig.3), the model could represent the winter productivity at
lake Kasumigaura quite well from the input variables of chl-a,
SS, SDD and WT satisfactorily. Composite coefficient of
determination (R 2 ) taking into account both the training and
validation dataset was 0.76. With satisfactory validation, the
model was now ready for estimation of productivity or
productivity mapping for the month of January. Pixel by pixel
values of generated chl-a, SDD, SS and W.Temp from
LandsatTM imagery of 19 th January, 2001 were put as inputs
to the developed model. With one forward pass, the gross
productivity map of lake Kasumigaura for 19 th January, 2001
was generated. Figure 4(a) shows the chl-a, SS, SDD and
WT (not validated) maps for 19th January, 2001 and fig. 4(b)
shows the resultant GP map. The banding effect seen in the
GP product is inherited from input water quality maps which
are again a result of band effect in LandsatTM bands over
water. No de-stripping was performed.
There were no GP data available for 19 th January, 2001 for validating the resultant image. However, as
the model is independent of the time scale, in the sense decades of data have been incorporated, and as
the lake ecosystem has not been undergoing considerable changes (as evident from the temporal water
quality data from Kasumigaura databook, 2001) it can be concluded that, the resultant product gives a
satisfactory picture to the present situation. However, it will always give better and more practical results
by incorporating recent data to the model. The developed model, however, can always be used with
From some simple analysis of the fig. 4(b), it is evident that, productivity was highest at the points of
intermediate chl-a (30~40 µg/l) and highest SDD (75 ~ 80 cm). Lowest values were seen in areas where
lowest SDD and/or highest SS prevailed. Maximum chlorophyll-a concentration regions were found to
coincide with that of maximum SDD and minimum SS. However, these regions were not the most
productive regions indicating that either temperature or nutrients had influential role to play in the process.
Looking at the pattern of productivity it is possible that, due to shallower depth (hence more mixing) and
inflow from the Sakura and Bizen rivers (refer fig.1), nutrients might be playing a dominant role in making
Tsuchiura harbor side the most productive one. However, further analysis covering several days or
months would be necessary to reach at some confident conclusion. As the discussion on this topic is out
of the scope of this paper, we wind up our discussion at this point.
Figure 4 (a) Chl-a, SS, SDD and WT estimated from LandsatTM imagery on 19 th January, 2001 at lake
Kasumigaura. (b) Gross productivity on 19 th January, 2001 generated by productivity model from maps in (a)
as inputs.
Conclusion
Productivity model based on historical limnological data can be quite useful in mapping the same in
synoptic scale while accommodating remote sensing derived products. While doing so, however, the
number of input variables for modeling becomes fewer and the representation of the complex process of
productivity becomes almost impossible. Making separate models for each month of the year, i.e.
reducing the time-independency, was found to be effective in reducing the complexity of the problem and
finding a satisfactory solution. In our demonstration, the model for month of January produced quite
satisfactory results in estimating productivity from chlorophyll-a, suspended sediment, secchi disk depth
and water temperature. The developed model can thus produce maps of primary productivity at the lake
provided maps of the four input parameters are generated from remote sensing imagery and fed to the
model. Similar models for other months can provide a fully functional productivity-mapping product where
simultaneous sampling of the productivity is not necessary with satellite overpass. Moreover, productivity
maps of any past date can also be generated with confidence. It is our hope that, such a scheme would
provide for better scientific and management tools towards providing a cleaner and healthier environment
in and around this treasure of nature.
Acknowledgement: We express our sincere gratitude to NASDA, Japan for providing with the
LandsatTM images under the project “Development of an algorithm to identify wetland vegetation”. Our
heartfelt thanks go to Dr. K. Matsushige and Dr. A. Imai for providing us the limnological data.
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