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Remote Sensing Data
Pre and post monsoon images of IRS IC LISS III were used to generate waterlogged area and landuse/landcover maps of the study area. Fig. 2 shows the false color composite consisting of bands 1 to 3 of post monsoon season as visualized in ER Mapper software. Image has been visually enhanced by transforming the input limits to 99% histogram (0.5% from the lower and 0.5% from the higher end). IGNP canal can be clearly seen in the lower part of the imagery spanning from north east to south west. Ghaggar river bed is also visible in the upper part of the imagery.


Fig. 2: Visually enhanced FCC LISS III image of study area
Bands 3 and 4 were used to derive the waterlogged and sensitive to waterlogged area as shown in Fig. 3. In the backdrop, RGB composite of bands 3, 4 and 2 of the study area to be modeled is seen. Groundwater data of 100 piezometers in the area was collected from Command Area Development, IGNP, Bikaner and was used to validate the waterlogged area, interpreted by remote sensing imagery. It was found that the area estimated by remote sensing was fairly comparable with the waterlogged map of the CAD, IGNP.


Fig. 3: Waterlogged & sensitive to waterlogging area in study area



Fig. 4: Landuse/landcover classifications as useful for groundwater modeling
Landuse/landcover maps were also developed on the basis of unsupervised classification, which were then improved on the basis of ground truth. Various collateral data with respect to landuse and waterlogged areas and other information were collected from various IGNP departments, research farms and farmers. Fig. 4 shows the landuse/landcover map of the area. Though it would be possible to choose more number of classes, only limited number of classes which were useful for groundwater modeling purpose were chosen. Table 2 gives the statistics of the various landuse/landcover classes.

Table 2: Landuse/lancover classification

S. No.	Classification	Percentage area
1.	Waterlogged	5.9%
2.	Sensitive to waterlogging	10.5%
3.	Agriculture	22.9%
4.	Sparse vegetation	22.6%
5.	Sandy & Barren	38.1%
	Total	100.00%

Elevation data of more than 800 points were used to develop the digital terrain model (DTM) of the area. Depressions, low lying and elevated areas are highlighted due to magnification of the Z axis by about 6000 times. Fig. 5 shows the 3D view of the study area as developed in ER Mapper software.


Fig. 5: 3D IRS imagery with Z magnification showing depressions & low lying area.
Conclusions
Following conclusions can be drawn on the basis of present study:

• A rapid and accurate assessment of the waterlogged area can be made by the use of remotely sensed data. Low lying lands which were not indicated by the water table observations as waterlogged could be identified on the IRS imagery.
• Groundwater modeling requires limited landuse/landcover classification, which can be done with the help of remote sensing data using unsupervised classification which is then refined on the basis of ground truth.
• IGNP Stage I command area faces severe problem of waterlogging resulting from over irrigation, seepage losses through canal and distributory system and Ghaggar depressions.
• The integrated use of GIS and remote sensing techniques can be successfully used to develop conceptual groundwater model, which can then be converted into mathematical finite difference groundwater flow model of the area.

References

• Arora, A.N. and Goyal, Rohit (2002). Environmental and Socio-Economical Impacts of Waterlogging in Hanumangarh and Sriganganagar Districts. Nature Environment and Pollution Technology, 1 (3), pp. 307-316.
• Arora, A.N. and Goyal, Rohit (2003). Conceptual Groundwater Modeling using GIS. GIS India 2003, National Conference on GIS/GPS/RS/Digital Photogrammetry and CAD, Jaipur.
• Choubey, V.K. (1996). Assessment of waterlogged area in IGNP Stage I by remotely sensed and field data. Hydrology Journal, Vol. XIX (2), pp. 81-93.
• IGNP Status Report (2001). Monitoring of Water Table IGNP Command Stage I. CAD Groundwater Department, Government of Rajasthan.
• Sahai, B., Kalubarme, M.H., Bapar, M.V. and Jadav, K.L. (1982). Identification of Waterlogged and Salt Affected Soils through Remote Sensing Techniques. Proc. 3rd Asian Conference on Remote Sensing, Dhaka.
• Sharma, K.D. (1996). Remote Sensing and Watershed Modeling: Towards a Hydrological Interface Model. Indo-U.S. Symposium Workshop on Remote Sensing and its Applications, Mumbai (India).
• Sidhu, P.S., Sharma and Bajwa, M.S. (1991). Characteristics, Distribution and Genesis of Salt Affected Soils in Punjab. Photonirvachak, 19 (4), pp. 269-276.

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