CHAPTER - 5 EFFECT OF CROPPING AREA ON THE INFLOWS OF OSMANSAGAR AND HIMAYATHSAGAR RESERVOIRS

Transcription

1 117 CHAPTER - 5 EFFECT OF CROPPING AREA ON THE INFLOWS OF OSMANSAGAR AND HIMAYATHSAGAR RESERVOIRS 5.1 PREAMBLE Agriculture is the dominant economic sector in many South Asian countries including India. It provides livelihoods to nearly 70% of the population and employment for about 60% of the labor force. In the last few decades, there has been a phenomenal increase in ground water extraction for irrigation, municipal and industrial use worldwide. Higher rates of depletion are observed in many countries like the USA, China, India and Mexico. In India, the withdrawal of water for non-agricultural, i.e., domestic and industrial purposes, accounted for 8% and for agriculture uses it is 90% (Rosegrant et al., 2002 and FAO, 2003). Between 1970 and 1994, the area under ground water irrigation increased by 105%, while the area under surface water irrigation grew by only 28% (Shah, 2002). This has reversed the roles of ground and surface water irrigation; today ground water irrigation accounts for about 60% of the 50 million ha of irrigated land in the country. The availability of institutional credit for setting up tube wells and highly subsidized electricity for pumping water have induced a remarkable increase in the number of wells (<1 million in 1960 to 19 million in 2000) (Shiferaw et al.,2002) particularly in drier areas where surface water is scarce. While this has made a substantial contribution in terms of raising agricultural productivity and farm incomes for the poor and marginal farmers, excessive extraction without reinvesting in

2 118 recharging facilities has led to depletion of scarce ground water resources in many parts of the country. Even when the level of recharging has increased, the ground water level in many watersheds is declining due to unregulated over-exploitation by large number of irrigators (Shiferaw et al., 2002). Due to this over exploitation, lot of rainfall is utilized to meet the depleted soil moisture and to recharge the ground water preventing runoff to the reservoirs (Venkateswara Rao et al., 2008). This phenomenon is observed in the catchments of Osmansagar and Himayathsagar reservoirs. 5.2 ANALYSIS OF CHANGE IN CROPPED AREA IN THE CATCHMENTS OF OSMANSAGAR AND HIMAYATHSAGAR RESERVOIRS In the Osmansagar and Himayathsagar catchments agriculture is the predominant occupation for the two-thirds of local population for their livelihood, the Irrigation in the study area is mainly depending on monsoon and ground water resources. In some areas of Vikarabad and Pargi mandals, the Kotapally and Lakanpur medium projects are being used for irrigation. The agro-geographical conditions of the study area are very much suitable of the cultivation of crops throughout the year. The agricultural year is divided into the three seasons - generally recognised seasons of harvests which go by the local names of Kharif, Rabi and Summer seasons. The kharif crops are sown in July to August (Asadha to Sravana) and reaped in November to December (Ashwin to Kartika), after the cessation of the rains, usually well before the preparation of the fields for the rabi sowings, which begin in December to January (Kartika to Margasira) and are harvested in April to May (Chaitra to Jyaistha). The principal crops in the kharif are cereals like paddy, jowar, maize,

3 119 bajra, ragi, pulses, vegetables, fruits which account for nearly 83% and the non food crops like cotton, groundnut, sunflower, castor nearly accounts for 17% where as in Rabi, the principal crops are paddy, jowar, maize, pulses, vegetables, fruits and the nonfood crops like sunflower, groundnut. The summer season is relatively low productive and consists of vegetables, fruits like mango, water melons, spices, legumes and a number of low grade cereals. In view of the rapid urbanization of the Hyderabad City, which is very close (9.6 km) to the catchments of Osmansagar and Himayathsagar there may be lot of demand for agricultural produce (such as vegetables, fruits, grains, pulses etc.) that must be necessarily being supplied from these catchments by tapping more surface and ground water by the farmers in the catchments and reducing inflows to the reservoirs (Venkateswara Rao et al., 2010 ). In order to verify whether the cropped area has been continuously increasing or not over the last few decades, the NDVI analysis is carried out by using the medium to high spatial resolution multi spectral data provided by remote sensing satellites, such as Landsat TM, IRS-1C/1D LISS-III and IRS-P6 LISS-III for the years1989, 1994, 1998, 2002 and 2008 of Kharif season, 1975, 1994, 1998, 2003 and 2008 during rabi season. The data is collected from NRSC (National Remote Sensing Centre) and downloaded from the website These years are selected based on the satellite data availability. The details of the satellite and sensor and period of acquisition are tabulated in Table 5.1.

4 120 Table 5.1 Satellite Data used for the NDVI Analysis Kharif Year Path/Row Satellite/ Sensor Rabi Year Path/Row Satellite/ Sensor /48 Landsat-TM /48 Landsat-TM /48 Landsat-TM /48 Landsat-TM /60 and 100/ /60 and 100/ /60 and 100/60 IRS-1C LISS-III IRS-1C LISS-III IRS-P6 LISS-III /60 and 100/ /60 and 100/ /60 and 100/60 IRS-1C LISS-III IRS-ID LISS-III IRS-P6 LISS-III To carry out the change analysis in the cropped area of the Osmansagar and Himayathsagar catchment areas, the satellite data is used. For identifying appropriate satellite data, spatial resolution and spectral resolution plays an important role. Spatial resolution is the size of the smallest object that can be discriminated by the sensor of the satellite. In fact, area coverage and resolution are interdependent and these two factors determine the scale of imagery. In case of spectral resolution, the information is collected by satellite sensors in multi-band or multi spectral format i.e., individual images have been separately recorded in discrete spectral bands. The position in the spectrum, width and number of spectral bands will determine the degree to which individual targets can be determined on the multi spectral image. The use of multi spectral imagery can lead to a higher degree of discriminating power than any single band taken on its own and facilitates in identifying various surface features. Keeping this view in mind and the study objectives, the NDVI map is prepared by using medium to high spatial resolution multi

5 121 spectral data provided by remote sensing satellites, such as Landsat TM, IRS- 1C/1D LISS-III and IRS-P6 LISS-III. The images are processed using ERDAS imagine software. The processing sequence has the following steps. (1) Downloading the data supplied by NDC (2) Precision Geometric Rectification (3) NDVI Image Generation (4) Separation of Forest area from Vegetation (5) Extraction of Statistics and Final Report Downloading the Data Supplied by NDC While downloading the individual scene supplied in CDROM Products using the IMPORT option in ERDAS imagine, the satellite ID, Date of pass, the exact scan and pixel size of the images and the gain settings from header file are found. Then the images of all bands as per the size provided in the header file are downloaded. The downloaded file is given a name as the raw image. After down loading, quality of the data in terms of its usability due to clouds, line loss, pixels dropout etc has to be ascertained by displaying the data through viewer Precision Geometric Rectification The satellite data is geometrically corrected with respect to Survey of India toposheets of scale 1: 50,000. To carry out the same, ground control points (GCPs) were identified on the maps and raw satellite data. The coefficients for two co-ordinate transformation equations were computed based on polynomial regression between GCPs on map and satellite data. Alternate

6 122 GCPs were generated till the r.m.s error was less than 0.5 pixel and then both the images were co-registered NDVI Image Generation The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the Visible and Near-Infrared (NIR) bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not. Generally, healthy vegetation will absorb most of the visible light that falls on it, and reflects a large portion of the Near-Infrared light. Unhealthy or sparse vegetation reflects more visible light and less nearinfrared light. Bare soils on the other hand reflect moderately in both the Red and Infrared portion of the electromagnetic spectrum (Tucker, 1979; Jackson, 1983; Holme et al., 1987 and Tucker et al., 1991). Since we know the behavior of plants across the electromagnetic spectrum, we can derive NDVI information by focusing on the satellite bands that are most sensitive to vegetation information (Near-Infrared and Red). Therefore the bigger the difference between the Near-Infrared (NIR) and the Red Reflectance (RED), the more the vegetation there has to be. The NDVI algorithm subtracts the red reflectance values from the near-infrared and divides it by the sum of near-infrared and red bands. NDVI= (NIR-RED)... (5.1 ) (NIR+RED) This formulation allows us to cope with the fact that two identical patches of vegetation could have different values if one were, for example in

7 123 bright sunshine, and another under a cloudy sky. The bright pixels would all have larger values, and therefore a larger absolute difference between the bands. This is avoided by dividing by the sum of the reflectance. Theoretically, NDVI values are represented as a ratio ranging in value from -1 to 1 but in practice extreme negative values represent water, values around zero represent bare soil and values over 0.6 represent dense green vegetation Separation of Forest Area from Vegetation The cropped area image is generated by generating forest map of study area and masked on the vegetation index map to get cropped area map. The forest map is digitised from the Landsat TM, IRS-1C/1D and IRS-P6 LISS-III images acquired during the summer months where the cropped area is minimal and the contrast between forest and nonforest area was larger in the False Color Composite of Landsat TM and IRS LISS-III. The digitized forest vector was converted into raster image. Once the forest area is masked on the vegetation index image, the remaining unmasked region represents the cropped area in the study area Extraction of Statistics and Final Report Using the interpreter icon of ERDAS IMAGINE main menu and by selecting summary option available in the GIS analysis submenu, the NDVI histogram is computed. Then running the software programme written in C language the NDVI statistic is extracted Calculation of Percentage of Deviation The change in the cropped area between two years is obtained by comparing the current season cropped area with the pervious reference

8 124 seasons cropped area of similar period. The percentage deviation of current year cropped area with previous/reference year cropped area is computed using the formula of Vegetation condition = (Current year cropped area- Previous cropped area ) 100 Previous cropped area... (5.2) Analysis of Kharif Data Digitally classified output of cropping area in the catchments of Osmansagar and Himayathsagar reservoirs and the corresponding raw data are shown in Figures 5.1 to The classification has been made using NDVI. In the figures the crop response is indicated in green color where as dark blue, cyan and red color indicates water bodies, dried water bodies and river respectively. The areas are tabulated in the Table 5.2. Table 5.2 Change Analysis in the Study Area during Kharif Season from 1989 to 2008 Year Land use category in Hectares Reservoirs/Lakes/ Tanks with water (Ha.) Crops (Ha.)

9 125 Fig 5.1 Satellite Imagery of the Study Area during 1989 Kharif Season Fig 5.2 NDVI Map of the Study Area during the year 1989 Kharif Season

10 126 Fig 5.3 Satellite Imagery of the Study Area during 1994 Kharif Season Fig 5.4 NDVI Map of the Study Area during the year 1994 Kharif Season

11 127 Fig 5.5 Satellite Imagery of the Study Area during 1998 Kharif Season Fig 5.6 NDVI Map of the Study Area during the year 1998 Kharif Season

12 128 Fig 5.7 Satellite Imagery of the Study Area during 2002 Kharif Season Fig 5.8 NDVI Map of the Study Area during the year 2002 Kharif Season

13 129 Fig 5.9 Satellite Imagery of the Study Area during 2008 Kharif Season Fig 5.10 NDVI Map of the Study Area during the year 2008 Kharif Season

14 130 The spatial estimate of crop area from 1989 to 2008 indicating the progressive increment in the total cropping area. For validation of the satellite based estimate, the ground reported crop sown area during kharif season is collected from the Bureau of Economics and Statistics, Govt. of Andhra Pradesh. This is presented in the Table 5.3. Table 5.3 Comparison of Land Use between Satellite and Ground Data during Kharif Season Year Crops Satellite estimated area (Ha.) Ground reported area estimate (Ha.) It is evident that the total cropping area was increased by 41.38% and Reservoirs/ Tanks/ Lakes occupied with water is reduced by 43% from 1989 to The decrement in the cropping area in 2002 is due to severe drought Analysis of Rabi Data The spatial variation of the Rabi crops is shown in Figures5.11 to The crop response is indicated in green color where as dark blue, cyan and red color indicates water bodies, dried water bodies and river respectively. The resulting areas are tabulated in the Table 5.4.

15 131 Table 5.4 Change Analysis in the Study Area during Rabi Season from 1989 to 2008 Year Land use category Reservoirs/Lakes/ Tanks Crops (in Ha.) with water (in Ha.) The areas of various NDVI classes pertaining to the study area are given in Table 5.4. This table presents the change of water bodies, dried water bodies and cropped area from 1975 to From the table it is evident that there is a continuous increment in the cropped area. For validation of the satellite based estimate, the ground reported crop sown area during rabi season is collected from the Bureau of Economics and Statistics, Govt. of Andhra Pradesh. This is presented in the Table 5.5. Table 5.5 Comparison of Land Use between Satellite and Ground Data during Rabi Season Year Satellite estimated area (Ha.) Crops Not Available , , , ,811 Ground reported area estimate (Ha.)

16 132 Fig 5.11 Satellite Imagery of the Study Area during 1975 Rabi Season Fig 5.12 NDVI Map of the Study Area during the year 1975 Rabi Season

17 133 Fig 5.13 Satellite Imagery of the Study Area during 1990 Rabi Season Fig NDVI Map of the Study Area during the year 1990 Rabi Season

18 134 Fig 5.15 Satellite Imagery of the Study Area during 1998 Rabi season Fig 5.16 NDVI Map of the Study Area during the year 1998 Rabi season

19 135 Fig 5.17 Satellite Imagery of the Study Area during 2003 Rabi season Fig 5.18 NDVI Map of the Study Area during the year 2003 Rabi season

20 136 Fig 5.19 Satellite Imagery of the Study Area during 2008 Rabi season Fig 5.20 NDVI Map of the Study Area during the year 2008 Rabi season

21 137 From the Table 5.4 it is evident that the total cropping area was increased by % and Reservoirs/ Tanks/ Lakes occupied with water is reduced by 67.47% from 1975 to The decrement in the cropping area in 2003 is due to continuously low rainfall received during 2002 and 2003 years and suffered with most severe drought. From the Figures 5.11 to 5.20 it is observed that in the year 1975 the more cultivation took place near reservoirs only but later due to the continuous raising demand of Hyderabad city for vegetables, fruits etc. and construction of conservation structures like check dams, farm pits etc. the cultivation has increased in the upper catchment area. Due to the Starting of shamshabad airport near Himayathsagar reservoir in the year 2008 the most of the agriculture land converted into settlement in that area. The area of increment is more in rabi season (55.55%) than in the kharif season (25.84%) from 1989 to This shows that more and more ground water has been increasingly used over the years to raise the rabi crop. This may be due to continuous raising demand of the Hyderabad city for various agricultural produce consequently these activities lead to the changes in the cropping pattern in the catchment area of Osmansagar and Himayathsagar. The area under rice has remained almost static. Sugarcane, grapes, sorghum has reduced. The cropped area of pigeon pea, Pulses like green gram and black gram and vegetables, fruits have shown an increasing trend. There is a drastic increment in the number of irrigation wells also between 1993 to 2005 shown in Table 5.6. This is due to the increasing demand of ground water for irrigation. This shows that the inflows were

22 138 reduced to the reservoirs in the recent years and which was due to the increased tapping of surface and ground water with in the catchment areas. Table 5.6 Number of Wells in the Study Area Mandal Name Number of wells Chevella Moinabad Shabad Shankarpally Shamshabad Nawabpet Vikarabad Pudur Pargi Maheswaram Rajendranagar Bantaram Kandukur Mominopet Kondurg Kothur Shadnagar Total RESULTS OF THE NDVI ANALYSIS From the NDVI analysis it is found that the total cropping area was increased in the catchments of Osmansagar and Himayathsagar during both Kharif and Rabi seasons. From 1989 to 2008 the change of percentage in Kharif season is % where as in Rabi season it is 55.55%. which means that ground water is being increasingly utilized in the catchment consequently the inflows were reduced to the reservoirs in the recent years what ever the rainfall that occurs in the catchment first utilised for meeting the depleted soil moisture and ground water reducing the surface runoff. It is also observed that

23 139 in the year 1975 the more cultivation took place near reservoirs only. But later due to the continuous raising demand of Hyderabad city for vegetables, fruits and pulses, construction of conservation structures like check dams, farm pits etc. took place consequently the cultivation increased in the upper catchment area. Due to the Starting of shamshabad airport near Himayathsagar reservoir in the year 2008 the most of the agriculture land has been converted into settlement in that area.