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Detection of Hydromorphologic Characteristics of Indus River Estuary, Pakistan, Using Satellite and Field Data

  • Research Article - Earth Sciences
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Abstract

Natural and anthropogenic factors directly determine the hydromorphologic and ecologic equilibrium of riverine environment. The present study was designed to detect the hydromorphologic characteristics of Indus River Estuary (IRE) using medium and high spatial resolution multispectral satellite imagery along with field data. Qualitative (visual) and quantitative (analytical) analysis was undertaken, and accuracy of each method as well as remotely sensed data was assessed. Single-band density slicing method was used for water bodies, while multiband supervised and unsupervised classification methods were adopted for the identification of hydromorphologic habitat along with key ecologic features of the IRE. The analysis of satellite imagery showed that the shortwave infrared-2 (band 7) of Landsat-8 Operational Land Imager (OLI) sensor performed better than its visible bands for delineating water bodies. The overall classification accuracy was 89%. Supervised classification with the maximum likelihood algorithm performed better for OLI imagery (30 m) than high spatial resolution RapidEye (5 m) imagery. However, unsupervised classification method was not suitable due to the significant overlapping of inter- and intra-class pixels. Overall, due to its adequate spectral range Landsat OLI imagery was utilized for monitoring of terrestrial water bodies and their morphologic features. Thus, we recommend that selecting the spatial resolution of the imagery should be based on the size of the objects to be recognized.

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References

  1. Hazarika, N.; Das, A.K.; Borah, S.B.: Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra plains, India, using RS-GIS techniques. Egypt. J. Remote Sens. Space Sci. 18(1), 107–118 (2015)

    Google Scholar 

  2. Jagers, H.R.A.: Modelling Planform Changes of Braided Rivers. University of Twente, Enschede (2003)

    Google Scholar 

  3. Gilvear, D.; Bryant, R.: Analysis of aerial photography and other remotely sensed data. Red 600(5.8), 23 (2003)

    Google Scholar 

  4. Goetz, S.J.; Gardiner, N.; Viers, J.H.: Monitoring freshwater, estuarine and near-shore benthic ecosystems with multi-sensor remote sensing: an introduction to the special issue. Remote Sens. Environ. 112(11), 3993–3995 (2008)

    Article  Google Scholar 

  5. Leuven, R.S.E.W.; Poudevigne, I.; Teeuw, R.M.: Application of Geographic Information Systems and Remote Sensing in River Studies. Backhuys Publishers, Leiden, The Netherlands (2002)

  6. Okin, G.S.; Roberts, D.A.; Murray, B.; Okin, W.J.: Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 77(2), 212–225 (2001)

    Article  Google Scholar 

  7. Shaw, G.A.; Burke, H.H.K.: Spectral imaging for remote sensing. Linc. Lab. J. 14(1), 3–28 (2003)

    Google Scholar 

  8. Gilvear, D.J.; Bryant, R.; Hardy, T.: Remote sensing of channel morphology and in-stream fluvial processes. Prog. Environ. Sci. 1, 257–284 (1999)

    Google Scholar 

  9. Giri, C.; Long, J.; Abbas, S.; Murali, R.M.; Qamer, F.M.; Pengra, B.; Thau, D.: Distribution and dynamics of mangrove forests of South Asia. J. Environ. Manag. 148, 101–111 (2015)

    Article  Google Scholar 

  10. Johnston, R.M.; Barson, M.M.: Remote sensing of Australian wetlands: an evaluation of Landsat TM data for inventory and classification. Mar. Freshw. Res. 44(2), 235–252 (1993)

    Article  Google Scholar 

  11. Frazier, P.S.; Page, K.J.: Water body detection and delineation with Landsat TM data. Photogramm. Eng. Remote Sens. 66(12), 1461–1468 (2000)

    Google Scholar 

  12. Gilvear, D.J.; Davids, C.; Tyler, A.N.: The use of remotely sensed data to detect channel hydromorphology; River Tummel, Scotland. River Res. Appl. 20(7), 795–811 (2004)

    Article  Google Scholar 

  13. Du, Z.; Li, W.; Zhou, D.; Tian, L.; Ling, F.; Wang, H.; Sun, B.: Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett. 5(7), 672–681 (2014)

    Article  Google Scholar 

  14. Isikdogan, F.; Bovik, A.; Passalacqua, P.: Automatic channel network extraction from remotely sensed images by singularity analysis. IEEE Geosci. Remote Sens. Lett. 12(11), 2218–2221 (2015)

    Article  Google Scholar 

  15. Donchyts, G.; Schellekens, J.; Winsemius, H.; Eisemann, E.; van de Giesen, N.: A 30 m resolution surface water mask including estimation of positional and thematic differences using landsat 8, srtm and openstreetmap: a case study in the Murray-Darling Basin, Australia. Remote Sens. 8(5), 386 (2016)

    Article  Google Scholar 

  16. Khan, T.M.A.; Razzaq, D.A.; Chaudhry, Q.U.Z.; Quadir, D.A.; Kabir, A.; Sarker, M.A.: Sea level variations and geomorphological changes in the coastal belt of Pakistan. Mar. Geod. 25(1–2), 159–174 (2002)

    Article  Google Scholar 

  17. Siddiqui, M.N.; Jamil, Z.; Afsar, J.: Monitoring changes in riverine forests of Sindh-Pakistan using remote sensing and GIS techniques. Adv. Space Res. 33(3), 333–337 (2004)

    Article  Google Scholar 

  18. Qamer, F.M.; Ashraf, M.S.; Hussain, N.; Saleem, R.; Ali, H.; Mirza, H.; Raza, S.M.: Pakistan Wetlands GIS-a multi-scale national wetlands inventory. In: 33rd International Symposium on Remote Sensing of Environment (2008)

  19. Syvitski, J.P.; Overeem, I.; Brakenridge, G.R.; Hannon, M.: Floods, floodplains, delta plains—a satellite imaging approach. Sed. Geol. 267, 1–14 (2012)

    Article  Google Scholar 

  20. Mahar, G.A.; Zaigham, N.A.: Examining spatio-temporal change detection in the indus river delta with the help of satellite data. Arab. J. Sci. Eng. 40(7), 1933–1946 (2015)

    Article  Google Scholar 

  21. Gilvear, D.; Tyler, A.; Davids, C.: Detection of estuarine and tidal river hydromorphology using hyper-spectral and LiDAR data: forth estuary, Scotland. Estuar. Coast. Shelf Sci. 61(3), 379–392 (2004)

    Article  Google Scholar 

  22. Kalhoro, N.A.; Saleem, M.; Muhammad, F.; Bhutto, A.H.: Dynamics of a polluted Tidal Estuary, Gizri Creek during south west monsoon. Sch. Acad. J. Biosci. 3(1A), 46–51 (2015)

    Google Scholar 

  23. Ouma, Y.O.; Tateishi, R.: A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data. Int. J. Remote Sens. 27(15), 3153–3181 (2006)

    Article  Google Scholar 

  24. Ji, L.; Geng, X.; Sun, K.; Zhao, Y.; Gong, P.: Target detection method for water mapping using landsat 8 OLI/TIRS imagery. Water 7(2), 794–817 (2015)

    Article  Google Scholar 

  25. Klemenjak, S.; Waske, B.; Valero, S.; Chanussot, J.: Automatic detection of rivers in high-resolution SAR data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(5), 1364–1372 (2012)

    Article  Google Scholar 

  26. Tapsall, B.; Milenov, P.; Tasdemir, K.: Analysis of RapidEye imagery for annual landcover mapping as an aid to European Union (EU) Common Agricultural Policy. In: Wagner, W., Szekely, B. (eds.) ISPRS TC VII Symposium 100 Years ISPRS, Vienna, Austria, 5-7 July, Vienna, Austria, vol. 38, part 7B, pp. 586–573. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2010)

  27. Yao, F.; Wang, C.; Dong, D.; Luo, J.; Shen, Z.; Yang, K.: High-resolution mapping of urban surface water using ZY-3 multi-spectral imagery. Remote Sens. 7(9), 12336–12355 (2015)

    Article  Google Scholar 

  28. Banko, G.: A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods including Remote Sensing Data in Forest Inventory. International Institute for Applied Systems Analysis, Laxenburg (1998)

    Google Scholar 

  29. Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective. Univ. of South Carolina, Columbus (1986)

    Google Scholar 

  30. Munechika, C.K.; Warnick, J.S.; Salvaggio, C.; Schott, J.R.: Resolution enhancement of multispectral image data to improve classification accuracy. Photogramm. Eng. Remote Sens. 59(1), 67–72 (1993)

    Google Scholar 

  31. Gao, J.: A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int. J. Remote Sens. 20(14), 2823–2833 (1999)

    Article  Google Scholar 

  32. Purkis, S.J.; Klemas, V.V.: Remote Sensing and Global Environmental Change. Wiley, New York (2011)

    Book  Google Scholar 

  33. Green, E.P.; Clark, C.D.; Mumby, P.J.; Edwards, A.J.; Ellis, A.C.: Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 19(5), 935–956 (1998)

    Article  Google Scholar 

  34. Lee, T.M.; Yeh, H.C.: Applying remote sensing techniques to monitor shifting wetland vegetation: a case study of Danshui River estuary mangrove communities, Taiwan. Ecol. Eng. 35(4), 487–496 (2009)

    Article  Google Scholar 

  35. Lu, D.; Batistella, M.; Moran, E.; de Miranda, E.E.: A comparative study of Landsat TM and SPOT HRG images for vegetation classification in the Brazilian Amazon. Photogramm. Eng. Remote Sens. 74(3), 311–321 (2008)

  36. Mahavir, : High (spatial) resolution cs. low resolution images : a planner’s view point. Photogramm. Eng. Remote Sens. 33(B7), 6 (2000)

    Google Scholar 

  37. Ridd, M.K.: Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities\({\dagger }\). Int. J. Remote Sens. 16(12), 2165–2185 (1995)

    Article  Google Scholar 

  38. Takara, K.; Kojima, T.: GIS-aided land cover classification assessment based on remote sensing images with different spatial resolutions. IAHS Publ. Ser. Proc. Rep. Intern Assoc Hydrol. Sci. 235, 659–668 (1996)

    Google Scholar 

  39. Carleer, A.P.; Debeir, O.; Wolff, E.: Assessment of very high spatial resolution satellite image segmentations. Photogramm. Eng. Remote Sens. 71(11), 1285–1294 (2005)

    Article  Google Scholar 

  40. Dare, P.M.: Shadow analysis in high-resolution satellite imagery of urban areas. Photogramm. Eng. Remote Sens. 71(2), 169–177 (2005)

  41. Liu, Z.; Yao, Z.; Wang, R.: Assessing methods of identifying open water bodies using Landsat 8 OLI imagery. Environ. Earth Sci. 75(10), 1–13 (2016)

    Article  Google Scholar 

  42. Breunig, F.M.; Galvão, L.S.; Formaggio, A.R.: Detection of sandy soil surfaces using ASTER-derived reflectance, emissivity and elevation data: potential for the identification of land degradation. Int. J. Remote Sens. 29(6), 1833–1840 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to exclusively acknowledge Dr. Jan Dempewolf, Assistant Research Professor, Department of Geography, University of Maryland, USA, for technical and grammatical review. Moreover, the United States Geological Survey (USGS) and Google Earth are also acknowledged who have voluntarily offered satellite data online for research facilitation.

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Authors and Affiliations

  1. U.S.-Pakistan Centre for Advanced Studies in Water, Mehran University of Engineering and Technology, Jamshoro, Sindh, 76062, Pakistan

    Muhammad Wajid Ijaz, Altaf Ali Siyal, Rasool Bux Mahar & Waqas Ahmed

  2. Environmental Protection Agency, Lahore, Punjab, 54000, Pakistan

    Muhammad Wajid Ijaz

  3. Department of Land and Water Management, Sindh Agriculture University, Tandojam, Sindh, 70060, Pakistan

    Altaf Ali Siyal

  4. Division of Hydrology Water-Land Resources in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, People’s Republic of China

    Muhammad Naveed Anjum

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  1. Muhammad Wajid Ijaz
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Correspondence to Muhammad Wajid Ijaz.

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Ijaz, M.W., Siyal, A.A., Mahar, R.B. et al. Detection of Hydromorphologic Characteristics of Indus River Estuary, Pakistan, Using Satellite and Field Data. Arab J Sci Eng 42, 2539–2558 (2017). https://doi.org/10.1007/s13369-017-2528-9

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  • DOI: https://doi.org/10.1007/s13369-017-2528-9

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