Extraction of Irrigation Network Details from Medium Resolution Satellite Imagery: a Comparative Assessment of LISS-III and ASTER Data Products

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1 Extraction of Irrigation Network Details from Medium Resolution Satellite Imagery: a Comparative Assessment of LISS-III and ASTER Data Products Rajesh Bhakar Centre for Studies in Science Policy, Jawaharlal Nehru University, New Delhi ( rajeshxbhakar@gmail.com) Abstract Non-availability of spatially referenced and detailed digital maps of the irrigation network, wherein each farm is linked to irrigation outlet, hampers the monitoring and realistic evaluation of the irrigation projects performance. This research is aimed at filling this data gap by use of satellite images. Low-cost, medium resolution satellite imagery from ASTER and LISS-III sensors was used for extracting the details of irrigation hierarchy, consisting of main-canal, branch, distributary, minor, sub-minor, and watercourse, of the one of the branches of the Indira Gandhi Nahar Project. Problem of mixed pixels and narrow width of irrigation channels in the lower rung of irrigation hierarchy hampered the use of digital classification algorithms. Visual interpretation technique allowed the extraction of network up to the level of main canal, distributary, minor and sub-minor level canals from the medium resolution satellite imagery. It was not possible to extract the watercourses and high resolution satellite imagery is need for generating their data base. Keywords: medium resolution imagery, irrigation network data base, mixed-pixels, visual interpretation. Introduction Remotely sensed data from the space based sensors has emerged as a reliable, economic and efficient source for spatio-temporal information needed for environmental monitoring, developmental planning and administrative decision making. Medium spatial resolution (5-30 m) based studies of changes in vegetation cover, snow-ice pack, land-sea surface temperature, and slight changes in atmosphere s composition have informed research questions in the realm of earth sciences at local, regional and global scales and enriched our understanding of the complex environmental interactions over last two decades (Jensen, 2003; Verdin, et. al., 2005). Meanwhile, social scientists eagerly awaited for high spatial resolution imagery as it promised opening up of host of opportunities for extraction of vector data (point, line and polygon format) for social science research (Geoghegan, J., 1998) and for administrative and project management activities like land registration, revenue collection, monitoring of multi-purpose irrigation projects and supervision of communication network like roads, electricity lines, and pipe lines supplying water, crude and gas. 1

2 In the first decade of 21st century, high spatial resolution (below 5 m) products have become available with the launch and operationalzation of the IKONOS, ORB- VIEW, and CARTOSAT sensors. But high resolution imagery comes with a price tag which is often beyond the scope of researchers working in college and university system in India. However, the medium spatial resolution (5-30 m) data products from the LANDSAT, LISS III and IV, and SPOT sensors are now available at a fraction of their price in the 1990s. This research attempts assessment of suitability of the low-cost medium resolution imageries from the LISS III sensor (on-board the Indian Remote Sensing satellite) and the ASTER sensor (on-board the US-Japanese Terra satellite) for the extraction of irrigation network details in the command area of the Charanwala branch of the Indira Gandhi Nahar Project (IGNP). This kind of information is prerequisite for addressing some important social science research problems. Background The middle and tail reaches of the irrigation command of the IGNP suffer from erratic and uncertain supplies (Ramanathan and Rathore, 1994; MottMacdonald, 1999). The summary statistics released by the irrigation authorities indicate that the project has always returned favourable cost-benefit ratio (WAPCOS, 1993; IDS, 1995; IGNB, 2002). However, this lumped information often conceals more than what it reveals. Empirical evidence indicates that the middle and tail reaches of the irrigation channels in Bikaner and Jaiselmer district are chocked by wind blown sand from mid-april to early- September and irrigation is possible only in Rabi (winter crop) season (URMUL, 1992; Goldman, 1995; MottMacdonald, 1999; Bhakar, 2006). As a result, the farmers in the head reaches appropriate much of available irrigation supplies. This leads not only to under performance of the project as a whole but also has negative implications from the perspective of equality and spatially-just distribution of the irrigation benefits over the entire command. Preparation of a spatially referenced digital data base of the irrigation command in Geographical Information System (GIS) environment, wherein each farm is linked to an outlet from the irrigation network, is needed for evaluation of the actual performance of the irrigation system in a spatially distributed manner. This data base along with timeseries of irrigation inflows to each farm can be used to assess the irrigation supply efficiencies of the irrigation project. In addition, time series of classified satellite images, can be correlated with data base for the evaluation of the performance of irrigation project for any given period. Since IGNP is still undergoing the development phase, creation of such data base will also provide useful inputs to the planners of IGNP. However, generation of such data base is problematic because of non-availability of detailed maps of the irrigation network in the public domain. Medium to high resolution satellite imagery could be used to fill this data gap. Objective This piece of research seeks to extract the irrigation network supplying the command of the Charanwala Branch of IGNP from the medium resolution LISS III and ASTER imagery. 2

3 Research Question Is it possible to extract the details of irrigation network (varying in width from 20 m to 0.25 m) from the medium spatial resolution (15 m -30 m) satellite imagery? Study area The command area served by the Charanwala branch of the IGNP was chosen for this study. Charanwala irrigation system is situated between latitudes N to N, and longitudes E to E (Figure 1). The gross command area investigated for this study is about ha. The six tier irrigation system hierarchy comprises of: Indira Gandhi main canal, branch; distributary; minor; sub-minor; watercourse. The Charanwala branch is about 81 km long while the subminors are about 3-5 km in length. Their cross-sections vary from about from 20 m for Indira Gandhi main canal and 14 m for Charanwala branch to for a watercourse (Table 1). The length of these irrigation channels (excluding the watercourses) and the cultural command area (CCA) served by each is given in Table 2. Database Satellite data: LISS III (on-board IRS-1D satellite) imagery acquired on September 14, 1999 and ASTER (on-board TERRA satellite) imagery captured on December 14, Ancillary data: Planning map of the Indira Gandhi Canal Project area (Figure 2). Software used: ARC GIS 9.1 and ILWIS 3.3 Methodology The ASTER image was obtained as a geo-coded product from the ASTER science team ERSDAC, Japan. LISS III image was registered to the ASTER image by image-toimage registration method in the ILWIS (v 3.3) GIS. Image registration process used WGS84 as the ellipsoid and datum, and UTM as the projection. The RMS error of registration sigma was A general purpose planning map of IGNP, indicating the estimated contours of depth to hydrologic barrier from ground surface and the proposed irrigation network was obtained from the CAD office and scanned (Personal communication, Mr M.S.Mahar, Director, Ground Water Wing, CAD, 5th August 2007). This scanned map was automated in ARC-GIS environment (Figure 2). However, this map could not be used as such because of its spatial referencing in the Indian datum. Hence, this map was also registered to the ASTER image which uses WGS84 datum. The RMS error of registration was 7.8. The registration accuracy was poor as the planning map uses the Indian datum while ASTER imagery uses the WGS 84 datum. Accurate conversion from the Indian datum to the WGS 84 datum is not possible as the Indian datum parameters are classified information. Nevertheless, the georeferencing of the these different data sets in the similar projection (UTM), datum (WGS 84) and ellipsoid (WGS 84) systems made them somewhat compatible to each other for visual interpretation. Ground truth campaign was conducted in August-September 2007 to gain knowledge about the land cover units in study area, with special emphasis on the features associated with irrigation channels. While the Indira Gandhi Main Canal (IGMC) is 3

4 always carrying substantial amounts of water, many of the minor, sub-minor channels and most of watercourses were dry and few of them were even chocked with wind blown sand (see Figure 3). Results and discussion Irrigation network is one of the many land-cover units/features in the study area. The other land-cover units in the study area are: Agriculture (both under crop and fallow ); Plantation (afforestation along the irrigation channels); Sand Dune (bare sand dunes); and Pasture (sand dunes dotted with shrubs). Extraction of land cover units from satellite imagery could be attempted either by digital method using various classification algorithms like Minimum Distance, Minimum Mahalanobis Distance, Maximum Likelihood etc. (Lillesand and Kiefer, 2002) or using Visual Interpretation (Janseen, 2004). The sandy expanse overwhelmed every other land cover feature and led to the problem of missed-pixels. It has been reported by Okin et al. (2001) and Collado et al. (2002) as well that the digital extraction of feature vectors in a sandy landscape is hampered because of mixed pixels. The problem of mixed pixels in the study area in reference to the irrigation network is most pertinent as the spatial resolution of the ASTER and LISS images is 15 m and 23.5 m respectively, while the irrigation channels have width between 20 to 0.25 m (Table 1). As one moves down the irrigation hierarchy - from the IGMC to a watercourse - the cross-section of the channels steadily gets constricted because lower level channels increasingly cater to smaller and smaller area and hence need to carry lesser and lesser water. This regularly decreasing width of channels poses difficult for their extraction from the imagery. Moreover, much of the irrigation network apart from the Indira Gandhi main canal is often dry (i.e. without water), as different parts of network are supplied with water on a rotational basis. As a result, the feature vectors of dry irrigation channels mixes with the land cover classes Fallow and Pasture. Thus the automatic extraction of irrigation network details was not possible. Hence, it was decided to extract the irrigation network by on screen digitisation using visual interpretation. From the IRS LISS-III image, it was possible to extract the network up to the distributary and minor level in irrigation channel hierarchy by on screen digitisation with the aid of visual interpretation. Visual interpretation was based on knowledge of study area and overlay of the planning map over the image in ILWIS GIS. However, in the case of sub-minors, especially in the areas where there is no vegetation cover close to the canals, the channel lines mix with the linear dune chains rendering the visual extraction difficult. A Laplacian high pass filter Laplace plus (Table 2), was created and applied to the IV band of the LISS-III image. This made it made possible the extraction of the network up to the sub-minor level from the LISS-III imagery (Figure 4). However the watercourse (width 0.25 m) could not be detected even after the application of the high pass filter. It was possible to extract the network up to sub-minor level canals from the ASTER image with visual interpretation only (Figure 5). No edge enhancement filter was needed to enhance the linear features. The interpretation keys of shape and association (e.g. plantations close to the irrigation channels) were used for the purpose. Although the spatial resolution of the image pixels is 15 m it was possible to identify the even the 4

5 minor (4 m wide) and sub-minors (2-3 m wide). However the watercourses could not be detected even from this image. Conclusion This research demonstrate that the low cost medium resolution satellite images could be successfully used to extract linear vector features like irrigation network with width as little as 2-3 meters. Moreover it is concluded said that the ASTER imagery, with its relatively higher spatial resolution of 15 m is comparatively advantageous than the rather coarser LISS-III image (spatial resolution 23.5 m) for extraction of irrigation channels. However it was not possible to extract still narrower features like watercourses which are about 0.5 to 0.25 m wide. For such an enterprise, expensive higher resolution satellite imageries acquired by sensors like the CARTOSAT-1 (spatial resolution 2.5 m), CARTOSAT-2 (spatial resolution 1 m), IKONOS (spatial resolution 1 m) and ORB- VIEW (spatial resolution 0.6 m) is required. Acknowledgement: This piece of research was carried out as part of the joint IIRS- ITC Hazard and Risk analysis training programme at IIRS, Dehradun and ITC, Netherlands. The LISS III image was provided by NRSA, India and the ASTER image by ITC, Netherlands The author records his sincere thank to Dr V.K Dadhwal, Dean Indian Institute of Remote Sensing, Dehradun for his keen support and encouragement. References Bhakar, R. (2006). Analysis of hydrogeological system and land cover for assessment of risks to irrigated agriculture in Thar desert: Charanwala system of the Indira Gandhi canal project, Thesis (MSc). ITC, Enschede. Collado, A.D., Chuvieco, E. and Camarasa, A. ( 2002). Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina. Journal of Arid Environments, 52(1): Goldman, M.R. (1994). There's a Snake on Our Chests: State and Development Crisis in India's Desert. Ph.D. Thesis, University of California, Santa Cruz. IDS (1995). Economic Viability of Lift Irrigation Schemes IGNP Stage-II. Institute of Development Studies, Jaipur. IGNB (2002). History of Indira Gandhi Nahar: A Venture to Turn Desert of Rajasthan into Granary. Indira Gandhi Nahar Board, Bikaner. Janseen, L.L.F., (2004). Visual Image Interpretation. In: N. Kerle, L.L.F. Janseen and G.C. Huurnemann (Editors), Principles of Remote Sensing. The International Institute for Geo-information Science and Earth Observation, Enschede, pp

6 Jensen, J.R. (2003). Remote Sensing of Environment: An Earth Resource Perspective. Pearson Education, New Delhi, 544 pp. Lillesand, T.M. and Kiefer, R.W. (2002). Remote Sensing and Image Interpretation. John Wiley and Sons Inc., New York, 724 pp. MottMacdonald (1999). IGNP Studies for the State of Rajasthan: Annex H- Environmental Review, Mott Macdonald (Consultants for the Government of Rajasthan for Evaluation of IGNP). Okin, G.S., Roberts, D.A., Murray, B. and Okin, W.J. (2001). Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment, 77(2): Ramanathan, S. and Rathore, M.S. (1994). Sustainability of Indira Gandhi Canal and the Need for Correct Responses: a Social Scientist Perspective. In: R. Hooja and P.S. Kavadia (Editors), Planning for Sustainability in Irrigation. Rawat Publishers, Jaipur, pp URMUL (1992). The Nahar Yatra: a Report on Indira Gandhi Canal, URMUL Trust, Bikaner. WAPCOS (1993). Ecological and Environmental Studies for Integrated Development of Indira Gandhi Project Stage-II. Water and Power Consultancy Services (India) Limited, New Delhi. Verdin, J., Funk, C., Senay, G., and Choularton, R. (2005). Climate Science and Famine Early Warning. Philosophical Transactions of the Royal Society B, 360(1463): Geoghegan, J., Pritchard, L., Sanderson, S., Chowdhury, R.R., and Turner II, B.L., (1998). Socializing the Pixel" and "Pixelizing the Social" in Land-Use and Land-Cover Change. In: Diana Liverman, D., Moran, E.F., Rindfuss, R.R., and Stern, P.C. (Editors), People and Pixels: Linking Remote Sensing and Social Science. National Academy Press, Washington, D.C. pp

7 Table 1: Width of irrigation channels in IGNP system. Class Width (m) Main canal 20 Branches Distributary 4-7 Minor and sub-minor 2-4 Watercourse Table 2: Edge enhancement Laplace plus filter Figure 1 7

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10 Figure 5 Citation: Rajesh Bhakar, Extraction of Irrigation Network Details from Medium Resolution Satellite Imagery: a Comparative Assessment of LISS-III and ASTER Data Products. International Journal of Engineering and Earth Sciences, Volume 3 (6),