Study of the Ecological Impact of Sewage Irrigated Vegetable Farming of Calcutta Metropolis using Remote Sensing & GIS
Nitai Kundu(1), Mausumi Pal(1), Anjana Saha(1), Sushama Panigrahy(2) and Shibendu Shekhar Ray(2)
(1) Institute of Environmental Studies and Wetland Ma, B-04, LA Block, Sector-III, Salt Lake, Kolkata-98, 700 098, India
(2) Space Applications Centre, Ambawadi Vistar, Ahmedabad- 380 015, India
OBJECTIVES: 1. Creating a spatial database of sewage flow and distribution pattern of Calcutta Metropolis in GIS Environment using space borne data and collateral data. 2. Monitoring the change in the sewage distribution pattern over the time period, crop growth status in the sewage fed farms and condition of sewage fed fishery ponds using ALOS AVINIR-II data. 3. Stimulating long term sustainability of sewage water irrigation with respect to crop and soil. 4. Comparative of Landuse study PAN and LISS III merged data and AVINIR –II data.
METHODOLOGY: Spatial database are created for different components of the system like canals, sewage channels, soil, vegetable farms, crop status at different times of the year, fishery sites etc. Evaluation of accuracy of identification and classification of the LISS III PAN merged data with the ground truth data. The databases are updated using ALOS data (AVINIR-II) Data was collected at various sample points on sewage availability, toxic chemicals are various channels at different times of the year. This will be modeled to study the flow concentration art various channels, the intensity of toxicity and its effect on vegetables, soil. Landuse classes are identified using non supervised classification technique.
RESULTS: Spatial database creation of the study area for canals, sewage channels and vegetable farming area is already done using LISS III and PAN merged data. Plot level landuse of the study area is delineated using cadastral map and satellite data. Landuse classes are identified using non supervised technique of classification method. Regarding the PAN and LISS III merged data the urban area signature mixing with water class. But in AVINIR II data urban signature have identification problem. Thus it appears that identification of urban class signature in both AVINIR-II and LISS-III - PAN merged is not properly identified.