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Utility of Normalized Difference Water Index and GIS for Mapping Surface Water Dynamics in Sub-Upper Krishna Basin

  • Avinash S. AshtekarEmail author
  • M. A. Mohammed-Aslam
  • Ali Raza Moosvi
Research Article
  • 23 Downloads

Abstract

Mapping of the surface water dynamics in parts of Upper Krishna River basin, Maharashtra State of India, was attempted in this study. It comprises an upland watershed and a major tributary of Krishna River in the Upper Krishna basin. Keeping in view that it is essential to appraise the accessibility of surface water in a basin containing the non-perennial river to deal with the water resource management, the present study area was chosen for investigations. This study modelled the surface water dynamics of the Sub-upper Krishna basin (SUKB) over a period of 17 years from 1999 to 2016 with cloud-free Landsat data sets. The technique of normalized difference water index (NDWI) was used to detect the surface water bodies of the basin. The NDWI-generated images were classified into water and non-water pixels, and an accuracy assessment was performed to find out producer’s, user’s, and overall accuracies of output. The NDWI method displays a good result of water surface detection with a Kappa coefficient ranging from 0.81 to 1.00 for the classified images. The surface water-mapped images were used to classify the water into permanent water, seasonal water and new permanent water. The change detection is performed and mapped using geographic information system (GIS) operations to understand the surface water dynamics from 1999 to 2016. The results illustrated the effectiveness of the NDWI approach for surface water mapping and GIS for change detection analysis, especially in detecting the changes in different times, simultaneously.

Keywords

Landsat NDWI Surface water GIS Change detection 

Notes

References

  1. Bhattacharya, A. (2019). Lecture notes on introduction to image classification. www.csre.iitb.ac.in/~avikb/GNR401/DIP/DIP_401_lecture_7.pdf. Accessed 26 Jan 2019.
  2. Campbell, J. B. (2007). Introduction to remote sensing (4th ed.). New York: The Guilford Press.Google Scholar
  3. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.  https://doi.org/10.1016/0034-4257(91)90048-B.CrossRefGoogle Scholar
  4. De Souza, C. H. W., Mercante, E., Prudente, V. H. R., & Justina, D. D. D. (2013). Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes. Ciencia e Investigación Agraria, 40(2), 419–428.CrossRefGoogle Scholar
  5. Downing, J. A., & Duarte, C. M. (2006). Abundance and size distribution of lakes, ponds and impoundments. Limnology and Oceanography, 51(5), 2388–2397.  https://doi.org/10.1016/B978-0-12-409548-9.03867-7.CrossRefGoogle Scholar
  6. Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of pre-diction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49.  https://doi.org/10.1017/S0376892997000088.CrossRefGoogle Scholar
  7. Hung, M.-C., & Wu, Y.-H. (2005). Mapping and visualizing the Great Salt Lake landscape dynamics using multi-temporal satellite images, 1972–1996. International Journal of Remote Sensing, 26(9), 1815–1834.  https://doi.org/10.1080/0143116042000298324.CrossRefGoogle Scholar
  8. Jain, S. K., Singh, R. D., Jain, M. K., et al. (2005). Water Resources Management, 19, 333.  https://doi.org/10.1007/s11269-005-3281-5.CrossRefGoogle Scholar
  9. Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  10. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.  https://doi.org/10.1080/01431169608948714.CrossRefGoogle Scholar
  11. McFeeters, S. K. (2013). Using the Normalized Difference Water Index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561.  https://doi.org/10.3390/rs5073544.CrossRefGoogle Scholar
  12. Mohammed-Aslam, M. A., Rokhmatloh, R., Salem, Z. E., & Javzandulam, T. (2006). Linear mixture model applied to the land-cover classification in an alluvial plain using Landsat TM data. (International) Journal of Environmental Informatics, 7(2), 95–101.CrossRefGoogle Scholar
  13. Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature.  https://doi.org/10.1038/nature20584.Google Scholar
  14. Ratna, S. B. (2012). Summer monsoon rainfall variability over Maharashtra, India. Pure and Applied Geophysics, 169, 259–273.CrossRefGoogle Scholar
  15. Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 2017, 611–622.  https://doi.org/10.4236/ijg.2017.84033.CrossRefGoogle Scholar
  16. Sethre, P. R., Rundquist, B., & Todhunter, P. (2005). Remote detection of Prairie pothole ponds in the devils Lake Basin, North Dakota. GIScience and Remote Sensing., 42, 277–296.  https://doi.org/10.2747/1548-1603.42.4.277.CrossRefGoogle Scholar
  17. Sisodia, P. S. (2014). Analysis of Supervised Maximum Likelihood Classification for Remote Sensing Image. IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09–11, 2014, Jaipur, India, pp 9–12.  https://doi.org/10.1109/ICRAIE.2014.6909319.
  18. Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., et al. (2017). Open surface water mapping algorithms: A comparison of water-related spectral indices. Water, 9, 256.  https://doi.org/10.3390/w9040256.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Geography, School of Earth SciencesCentral University of KarnatakaKalaburagiIndia
  2. 2.Department of Geology, School of Earth SciencesCentral University of KarnatakaKalaburagiIndia

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