Abstract
Tanks are one of the main sources for the irrigation in the country. Out of average net irrigated area of 62 Mha in India during 2001–2015, 3% of area is irrigated through tanks, as against 15% in the 1950s. The deterioration of tanks was a matter of concern for most of the states in South India. Government of Telangana launched five-year long programme called 'Mission Kakatiya' in 2014 to harness the benefits of tank irrigation by increasing command area and water supply available for irrigation. In order to assess volume of water stored in a tank, the information on water level and corresponding water spread area is essential through elevation-area-capacity (EAC) curve. However, such information is not available for most of the irrigation tanks in the country. Generation of terrain information of tank storage area using remote sensing technology is one of the options available today. Stereo images from space borne and aerial platforms are not sufficient enough for generating terrain information for very shallow water bodies like irrigation tanks. In this study, an alternative approach is proposed to use images acquired from unmanned aerial vehicle platform instead of stereo images from other sources/platforms for generating EAC curve. UAV is flown when the tanks are almost dry, for generation of very high resolution digital terrain model. This can be used to establish EAC curve. This EAC curve in combination with the near real-time water spread area obtained from satellite remote sensing can be used to estimate the volume of water available in a tank. In order to assess the feasibility, a drone survey was carried out over Teegalanarayana Cheruvu, a small tank near Godumakunta village of Keesara Mandal in the outskirts of Hyderabad city, Telangana. Images collected were processed to generate digital terrain model and this was used to derive the EAC curve for the tank. Using satellite-based water spread area from LISS-IV sensor along with EAC curve, the volume of water stored was estimated as 60,812 m
3
, 88,830 m
3
and 22,160 m
3
as on November 30, 2016, November 1, 2017, and March 25, 2018, respectively. The study proves that this approach is cost-effective, feasible and less time-consuming.