Computational Sustainability pp 121-147

Part of the Studies in Computational Intelligence book series (SCI, volume 645)

Global Monitoring of Inland Water Dynamics: State-of-the-Art, Challenges, and Opportunities

  • Anuj Karpatne
  • Ankush Khandelwal
  • Xi Chen
  • Varun Mithal
  • James Faghmous
  • Vipin Kumar
Chapter

Abstract

Inland water is an important natural resource that is critical for sustaining marine and terrestrial ecosystems as well as supporting a variety of human needs. Monitoring the dynamics of inland water bodies at a global scale is important for: (a) devising effective water management strategies, (b) assessing the impact of human actions on water security, (c) understanding the interplay between the spatio-temporal dynamics of surface water and climate change, and (d) near-real time mitigation and management of disaster events such as floods. Remote sensing datasets provide opportunities for global-scale monitoring of the extent or surface area of inland water bodies over time. We present a survey of existing remote sensing based approaches for monitoring the extent of inland water bodies and discuss their strengths and limitations. We further present an outline of the major challenges that need to be addressed for monitoring the extent and dynamics of water bodies at a global scale. Potential opportunities for overcoming some of these challenges are discussed using illustrative examples, laying the foundations for promising directions of future research in global monitoring of water dynamics.

References

  1. 1.
    Adam, S.: Glacier snow line mapping using ERS-1 SAR imagery. Remote Sens. Environ. 61(1), 46–54 (1997)CrossRefGoogle Scholar
  2. 2.
    Alvarez-Guerra, M., González-Piñuela, C., Andrés, A., Galán, B., Viguri, J.R.: Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality. Environ. Int. 34(6), 782–90 (2008)Google Scholar
  3. 3.
    Astel, A., Tsakovski, S., Barbieri, P., Simeonov, V.: Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res. 41(19), 78–4566 (2007)CrossRefGoogle Scholar
  4. 4.
    Balthrop, C., Hossain, F.: Short note: a review of state of the art on treaties in relation to management of transboundary flooding in international river basins and the global precipitation measurement mission. Water Policy 12(5), 635–640 (2010)CrossRefGoogle Scholar
  5. 5.
    Barnett, P.T., Adam, J.C., Lettenmaier, D.P.: Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438(7066), 303–309 (2005)CrossRefGoogle Scholar
  6. 6.
    Bartsch, A., Wagner, W., Scipal, K., Pathe, C., Sabel, D., Wolski, P.: Global monitoring of wetlands-the value of ENVISAT ASAR Global mode. J. Environ. Manage. 90(7), 33–2226 (2009)CrossRefGoogle Scholar
  7. 7.
    Battin, J.T., Luyssaert, S., Kaplan, L.A., Aufdenkampe, A.K., Richter, A., Tranvik, L.J.: The boundless carbon cycle. Nat. Geosci. 2(9), 598–600 (2009)CrossRefGoogle Scholar
  8. 8.
    Birkett, C.M.: Radar altimetry: a new concept in monitoring lake level changes. Am. Geophys. Union Trans. 75(24), 273 (1994)CrossRefGoogle Scholar
  9. 9.
    Birkett, C.M.: Surface water dynamics in the Amazon Basin: application of satellite radar altimetry. J. Geophys. Res. 107(D20), 8059 (2002)CrossRefGoogle Scholar
  10. 10.
    Birkett, C.M.: Contribution of the TOPEX NASA Radar Altimeter to the global monitoring of large rivers and wetlands. Water Resour. Res. 34(5), 1223–1239 (1998)CrossRefGoogle Scholar
  11. 11.
    Birkett, C.M., Mason, I.M.: A new global lakes database for a remote sensing program studying climatically sensitive large lakes. J. Great Lakes Res. 21(3), 307–318 (1995)CrossRefGoogle Scholar
  12. 12.
    Bishop, M.P., Shroder, J.F., Jr, Hickman, B.L.: SPOT panchromatic imagery and neural networks for information extraction in a complex mountain environment. Geocarto Int. (1999)Google Scholar
  13. 13.
    Burns, N.M., Rutherford, J.C., Clayton, J.S.: A monitoring and classification system for new Zealand Lakes and Reservoirs. Lake Reserv. Manag. 15(4), 255–271 (1999)CrossRefGoogle Scholar
  14. 14.
    Cole, J.J., Prairie, Y.T., Caraco, N.F., McDowell, W.H., Tranvik, L.J., Striegl, R.G., Duarte, C.M., Kortelainen, P., Downing, J.A., Middelburg, J.J., et al.: Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10(1), 172–185 (2007)CrossRefGoogle Scholar
  15. 15.
    Crist, E.P., Cicone, R.C.: A physically-based transformation of thematic mapper data—the tm tasseled cap. IEEE Trans. Geosci. Remote Sens. GE-22(3), 256–263 (1984)Google Scholar
  16. 16.
    Daya Sagar, B.S., Gandhi, G., Prakasa Rag, B.S.: Applications of mathematical morphology in surface water body studies. Int. J. Remote Sens. 16(8), 1495–1502 (1995)CrossRefGoogle Scholar
  17. 17.
    Dekker, A.G., Phinn, S.R., Anstee, J.: Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnol. and Oceanogr. Methods (2011)Google Scholar
  18. 18.
    Di, K., Wang, J., Ma, R., Li, R.: Automatic shoreline extraction from high-resolution IKONOS satellite imagery. In: Proceeding of ASPRS 2003 Annual Conference (2003)Google Scholar
  19. 19.
    Doxaran, D., Froidefond, J.-M., Lavender, S., Castaing, P.: Spectral signature of highly turbid waters. Remote Sens. Environ. 81(1), 149–161 (2002)CrossRefGoogle Scholar
  20. 20.
    Duan, Z., Bastiaanssen, W.G.M.: Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sens. Environ. 134, 403–416 (2013)CrossRefGoogle Scholar
  21. 21.
    Feyisa, G.L., Meilby, H., Fensholt, R., Proud, S.R.: Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, 23–35 (2014)CrossRefGoogle Scholar
  22. 22.
    Foody, G.M., Muslim, A.M., Atkinson, P.M.: Super-resolution mapping of the waterline from remotely sensed data. Int. J. Remote Sens. 26(24), 5381–5392 (2005)CrossRefGoogle Scholar
  23. 23.
    Frappart, F., Minh, K.D., L’Hermitte, J., Cazenave, A., Ramillien, G., Toan, T.L., Mognard-Campbell, N.: Water volume change in the lower Mekong from satellite altimetry and imagery data. Geophys. J. Int. 167(2), 570–584 (2006)CrossRefGoogle Scholar
  24. 24.
    Frappart, F., Papa, F., Famiglietti, J.S., Prigent, C., Rossow, W.B., Seyler, F.: Interannual variations of river water storage from a multiple satellite approach: a case study for the Rio Negro River basin. J. Geophys. Res. 113(D21), D21104 (2008)CrossRefGoogle Scholar
  25. 25.
    Frazier, P.S., Page, K.J., et al.: Water body detection and delineation with landsat tm data. Photogram. Eng. Remote Sens. 66(12), 1461–1468 (2000)Google Scholar
  26. 26.
    Frohn, R.C., Hinkel, K.M., Eisner, W.R.: Satellite remote sensing classification of thaw lakes and drained thaw lake basins on the North Slope of Alaska. Remote Sens. Environ. 97(1), 116–126 (2005)CrossRefGoogle Scholar
  27. 27.
    Gao, Bo-cai: NDWIA normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58(3), 257–266 (1996)Google Scholar
  28. 28.
    Gao, H., Birkett, C., Lettenmaier, D.P.: Global monitoring of large reservoir storage from satellite remote sensing. Water Resour. Res. 48(9), n/a–n/a (2012)Google Scholar
  29. 29.
    Giordano, M.A., Wolf, A.T.: Sharing waters: post-rio international water management. In: Natural Resources Forum, vol. 27, pp. 163–171. Wiley Online Library (2003)Google Scholar
  30. 30.
    Gleason, C.J., Smith, L.C.: Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proc. Natl. Acad. Sci. 111(13), 91–4788 (2014)CrossRefGoogle Scholar
  31. 31.
    Gleick, H.P.: Global freshwater resources: soft-path solutions for the 21st century. Science 302(5650), 8–1524 (2003)CrossRefGoogle Scholar
  32. 32.
    Grabs, T., Seibert, J., Bishop, K., Laudon, H.: Modeling spatial patterns of saturated areas: a comparison of the topographic wetness index and a dynamic distributed model. J. Hydrol. 373(1–2), 15–23 (2009)CrossRefGoogle Scholar
  33. 33.
    Grossmann, M.: Cooperation on africa’s international waterbodies: information needs and the role of information-sharing. Editors 173 (2006)Google Scholar
  34. 34.
    Harrison, J.A., Maranger, R.J., Alexander, R.B., Giblin, A.E., Jacinthe, P.-A., Mayorga, E., Seitzinger, S.P., Sobota, D.J., Wollheim, W.M.: The regional and global significance of nitrogen removal in lakes and reservoirs. Biogeochemistry 93(1–2), 143–157 (2009)CrossRefGoogle Scholar
  35. 35.
    Hess, L.: Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens. Environ. 87(4), 404–428 (2003)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Hinkel, K.M., Eisner, W.R., Bockheim, J.G., Nelson, F.E., Peterson, K.M.: Spatial extent, age, and carbon stocks in drained thaw lake basins on the barrow peninsula, alaska. Arct. Antarct. Alp. Res. 35(3), 291–300 (2003)CrossRefGoogle Scholar
  37. 37.
    Huang, L., Li, Z., Tian, B.-S., Chen, Q., Liu, J.-L., Zhang, R.: Classification and snow line detection for glacial areas using the polarimetric SAR image. Remote Sens. Environ. 115(7), 1721–1732 (2011)CrossRefGoogle Scholar
  38. 38.
    Hui, F., Bing, X., Huang, H., Qian, Y., Gong, P.: Modelling spatial-temporal change of poyang lake using multitemporal landsat imagery. Int. J. Remote Sens. 29(20), 5767–5784 (2008)CrossRefGoogle Scholar
  39. 39.
    Immerzeel, W.W., Droogers, P., De Jong, S.M., Bierkens, M.F.P.: Large-scale monitoring of snow cover and runoff simulation in himalayan river basins using remote sensing. Remote Sens. Environ. 113(1), 40–49 (2009)CrossRefGoogle Scholar
  40. 40.
    Islam, A.S., Bala, S.K., Haque, M.A.: Flood inundation map of Bangladesh using MODIS time-series images. J. Flood Risk Manag. 3(3), 210–222 (2010)CrossRefGoogle Scholar
  41. 41.
    Jain, S.K., Lohani, A.K., Singh, R.D., Chaudhary, A., Thakural, L.N.: Glacial lakes and glacial lake outburst flood in a Himalayan basin using remote sensing and GIS. Nat. Hazards 62(3), 887–899 (2012)CrossRefGoogle Scholar
  42. 42.
    Jain, S.K., Lohani, A.K., Singh, R.D., Chaudhary, A., Thakural, L.N.: Delineation of flood-prone areas using remote sensing techniques. Water Resour. Manage. 19(4), 333–347 (2005)CrossRefGoogle Scholar
  43. 43.
    Jiang, Z., Qi, J., Shiliang, S., Zhang, Z., Jiaping, W.: Water body delineation using index composition and HIS transformation. Int. J. Remote Sens. 33(11), 3402–3421 (2012)CrossRefGoogle Scholar
  44. 44.
    Kloiber, S.M., Brezonik, P.L., Bauer, M.E.: Application of Landsat imagery to regional-scale assessments of lake clarity. Water Res. 36(17), 4330–4340 (2002)CrossRefGoogle Scholar
  45. 45.
    Kloiber, S.M., Brezonik, P.L., Olmanson, L.G., Bauer, M.E.: A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens. Environ. 82(1), 38–47 (2002)CrossRefGoogle Scholar
  46. 46.
    Knight, A.W., Tindall, D.R., Wilson, B.A.: A multitemporal multiple density slice method for wetland mapping across the state of queensland, australia. Int. J. Remote Sens. 30(13), 3365–3392 (2009)CrossRefGoogle Scholar
  47. 47.
    Lacava, T., Cuomo, V., Di Leo, E.V., Pergola, N., Romano, F., Tramutoli, V.: Improving soil wetness variations monitoring from passive microwave satellite data: the case of April 2000 Hungary flood. Remote Sens. Environ. 96(2), 135–148 (2005)CrossRefGoogle Scholar
  48. 48.
    Larsen, S., Andersen, T., Hessen, D.: Climate change predicted to cause severe increase of organic carbon in lakes. Glob. Change Biol. 17(2), 1186–1192 (2011)CrossRefGoogle Scholar
  49. 49.
    Li, J., Sheng, Y.: An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. Int. J. Remote Sens. 33(16), 5194–5213 (2012)CrossRefGoogle Scholar
  50. 50.
    Li, S., Sun, D., Goldberg, M., Stefanidis, A.: Derivation of 30-m-resolution water maps from TERRA/MODIS and SRTM. Remote Sens. Environ. 134, 417–430 (2013)CrossRefGoogle Scholar
  51. 51.
    Li, S., Sun, D., Yunyue, Y., Csiszar, I., Stefanidis, A., Goldberg, M.D.: A new Short-Wave infrared (SWIR) method for quantitative water fraction derivation and evaluation with EOS/MODIS and Landsat/TM data. IEEE Trans. Geosci. Remote Sens. 51(3), 1852–1862 (2013)CrossRefGoogle Scholar
  52. 52.
    Lira, J.: Segmentation and morphology of open water bodies from multispectral images. Int. J. Remote Sens. 27(18), 4015–4038 (2006)CrossRefGoogle Scholar
  53. 53.
    Loriaux, T., Casassa, G.: Evolution of glacial lakes from the Northern Patagonia Icefield and terrestrial water storage in a sea-level rise context. Glob. Planet. Change 102, 33–40 (2013)CrossRefGoogle Scholar
  54. 54.
    Martinez, J., Letoan, T.: Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens. Environ. 108(3), 209–223 (2007)CrossRefGoogle Scholar
  55. 55.
    McFeeters, S.K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17(7), 1425–1432 (1996)CrossRefGoogle Scholar
  56. 56.
    Micklin, P.P.: Desiccation of the aral sea: a water management disaster in the soviet union. Science 241(4870), 1170–1176 (1988)CrossRefGoogle Scholar
  57. 57.
    Niu, Z.G., Gong, P., Cheng, X., Guo, J.H., Wang, L., Huang, H.B., Shen, S.Q., Wu, Y.Z., Wang, X.F., Wang, X.W., Ying, Q., Liang, L., Zhang, L.N., Wang, L., Yao, Q., Yang, Z.Z., Guo, Z.Q.,Dai, Y.J.: Geographical characteristics China’s of wetlands derived from remotely sensed data. Sci. China Ser. D: Earth Sci. 52(6), 723–738 (2009)Google Scholar
  58. 58.
    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)CrossRefGoogle Scholar
  59. 59.
    Overton, I.C.: Modelling floodplain inundation on a regulated river: integrating GIS, remote sensing and hydrological models. River Res. Appl. 21(9), 991–1001 (2005)CrossRefGoogle Scholar
  60. 60.
    Palmer, M.A., Reidy Liermann, C.A., Nilsson, C., Flörke, M., Alcamo, J., Lake, P.S., Bond, N.: Climate change and the world’s river basins: anticipating management options. Front. Ecol. Environ. 6(2), 81–89 (2008)Google Scholar
  61. 61.
    Paul, F., Huggel, C., Kääb, A.: Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers. Remote Sens. Environ. 89(4), 510–518 (2004)CrossRefGoogle Scholar
  62. 62.
    Phan, V.H., Lindenbergh, R., Menenti, M.: ICESat derived elevation changes of Tibetan lakes between 2003 and 2009. Int. J. Appl. Earth Obs. Geoinformation 17, 12–22 (2012)CrossRefGoogle Scholar
  63. 63.
    Prost, G.L.: Remote Sensing for Geologists: A Guide to Image Interpretation. CRC Press (2002)Google Scholar
  64. 64.
    Pulvirenti, L., Chini, M., Pierdicca, N., Guerriero, L., Ferrazzoli, P.: Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sens. Environ. 115(4), 990–1002 (2011)CrossRefGoogle Scholar
  65. 65.
    Quincey, D.J., Richardson, S.D., Luckman, A., Lucas, R.M., Reynolds, J.M., Hambrey, M.J., Glasser, N.F.: Early recognition of glacial lake hazards in the Himalaya using remote sensing datasets. Global and Planet. Change 56(1–2), 137–152 (2007)CrossRefGoogle Scholar
  66. 66.
    Reis, S.: Temporal monitoring of water level changes in Seyfe Lake using remote sensing. Hydrol. Process. 22(22), 4448–4454 (2008)CrossRefGoogle Scholar
  67. 67.
    Ryu, J., Won, J., Min, K.: Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea. Remote Sens. Environ. 83(3), 442–456 (2002)CrossRefGoogle Scholar
  68. 68.
    Sakamoto, T., Van Nguyen, N., Kotera, A., Ohno, H., Ishitsuka, N., Yokozawa, M.: Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sens. Environ. 109(3), 295–313 (2007)CrossRefGoogle Scholar
  69. 69.
    Sass, G.Z., Creed, I.F., Bayley, S.E., Devito, K.J.: Understanding variation in trophic status of lakes on the Boreal Plain: a 20 year retrospective using Landsat TM imagery. Remote Sens. Environ. 109(2), 127–141 (2007)CrossRefGoogle Scholar
  70. 70.
    Schumann, G.J.-P., Neal, J.C., Mason, D.C., Bates, P.D.: The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sens. Environ. 115(10), 2536–2546 (2011)CrossRefGoogle Scholar
  71. 71.
    Shibuo, Y., Jarsjö, J., Destouni, G.: Hydrological responses to climate change and irrigation in the aral sea drainage basin. Geophys. Res. Lett. 34(21) (2007)Google Scholar
  72. 72.
    Simpson, J.J., Keller, R.H.: An improved fuzzy logic segmentation of sea ice, clouds, and ocean in remotely sensed arctic imagery. Remote Sens. Environ. 54(3), 290–312 (1995)CrossRefGoogle Scholar
  73. 73.
    Sivanpillai, R., Miller, S.N.: Improvements in mapping water bodies using ASTER data. Ecol. Inf. 5(1), 73–78 (2010)CrossRefGoogle Scholar
  74. 74.
    Song, C., Huang, B., Ke, L.: Modeling and analysis of lake water storage changes on the Tibetan Plateau using multi-mission satellite data. Remote Sens. Environ. 135, 25–35 (2013)CrossRefGoogle Scholar
  75. 75.
    Stave, K.A.: A system dynamics model to facilitate public understanding of water management options in Las Vegas, Nevada. J. Environ. Manage. 67(4), 13–303 (2003)CrossRefGoogle Scholar
  76. 76.
    Steinbacher, F., Pfennigbauer, M., Aufleger, M., Ullrich, A.: High resolution airborne shallow water mapping. ISPRS-Int. Arch. Photogrammetry Remote Sens. Spat. Inf. Sci. 1, 55–60 (2012)CrossRefGoogle Scholar
  77. 77.
    Subramaniam, S., Babu, A.V.S., Roy, P.S.: Automated water spread mapping using resourcesat-1 awifs data for water bodies information system. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(1):205–215, 2011Google Scholar
  78. 78.
    Sun, D., Yunyue, Y., Goldberg, M.D.: Deriving water fraction and flood maps from MODIS images using a decision tree approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(4), 814–825 (2011)CrossRefGoogle Scholar
  79. 79.
    Sun, D., Yunyue, Y., Zhang, R., Li, S., Goldberg, M.D.: Towards operational automatic flood detection using EOS/MODIS data. Photogram. Eng. Remote Sens. 78(6), 637–646 (2012)CrossRefGoogle Scholar
  80. 80.
    Sun, F., Sun, W., Chen, J., Gong, P.: Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens. 33(21), 6854–6875 (2012)CrossRefGoogle Scholar
  81. 81.
    Temimi, M., Leconte, R., Chaouch, N., Sukumal, P., Khanbilvardi, R., Brissette, F.: A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness. J. Hydrol. 388(1–2), 28–40 (2010)CrossRefGoogle Scholar
  82. 82.
    Temimi, M., Leconte, R., Brissette, F., Chaouch, N.: Flood monitoring over the Mackenzie River Basin using passive microwave data. Remote Sens. Environ. 98(2–3), 344–355 (2005)CrossRefGoogle Scholar
  83. 83.
    Tidwell, V.C., Moreland, B.D., Zemlick, K.M., Roberts, B.L., Passell, H.D., Jensen, D., Forsgren, C., Sehlke, G., Cook, M.A., King, C.W., Larsen, S.: Mapping water availability, projected use and cost in the western United States. Environ. Res. Lett. 9(6), 064009 (2014)Google Scholar
  84. 84.
    Töyrä, J., Pietroniro, A.: Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sens. Environ. (2005)Google Scholar
  85. 85.
    Töyrä, J., Pietroniro, A., Martz, L.W.: Multisensor hydrologic assessment of a freshwater wetland. Remote Sens. Environ. 75(2), 162–173 (2001)CrossRefGoogle Scholar
  86. 86.
    Töyrä, J., Pietroniro, A., Martz, L.W., Prowse, T.D.: A multi-sensor approach to wetland flood monitoring. Hydrol. Process. 16(8), 1569–1581 (2002)CrossRefGoogle Scholar
  87. 87.
    Tranvik, L.J., Downing, J.A., Cotner, J.B., Loiselle, S.A., Striegl, R.G., Ballatore, T.J., Dillon, P., Finlay, K., Fortino, K., Knoll, L.B., et al.: Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54(6), 2298–2314 (2009)CrossRefGoogle Scholar
  88. 88.
    Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2), 127–150 (1979)CrossRefGoogle Scholar
  89. 89.
    Verpoorter, C., Kutser, T., Seekell, D.A., Tranvik, L.J.: A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41(18), 6396–6402 (2014)CrossRefGoogle Scholar
  90. 90.
    Verpoorter, C., Kutser, T., Tranvik, L.: Automated mapping of water bodies using Landsat multispectral data. Limnol. Oceanogr.: Methods 10, 1037–1050 (2012)CrossRefGoogle Scholar
  91. 91.
    Vörösmarty, C.J., Green, P., Salisbury, J., Lammers, R.B.: Global water resources: vulnerability from climate change and population growth. Science 289(5477), 284–288 (2000)CrossRefGoogle Scholar
  92. 92.
    Vörösmarty, C.J., McIntyre, P.B., Gessner, M.O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S.E., Sullivan, C.A., Liermann, C.R., et al.: Global threats to human water security and river biodiversity. Nature 467(7315), 555–561 (2010)Google Scholar
  93. 93.
    Wang, L., Sousa, W.P., Gong, P.: Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int. J. Remote Sens. 25(24), 5655–5668 (2004)CrossRefGoogle Scholar
  94. 94.
    Wang, X., Gong, P., Zhao, Y., Yue, X., Cheng, X., Niu, Z., Luo, Z., Huang, H., Sun, F., Li, X.: Water-level changes in China’s large lakes determined from ICESat/GLAS data. Remote Sens. Environ. 132, 131–144 (2013)CrossRefGoogle Scholar
  95. 95.
    Worm, B., Barbier, E.B., Nicola Beaumont, J., Duffy, E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., et al.: Impacts of biodiversity loss on ocean ecosystem services. Science 314(5800), 787–790 (2006)CrossRefGoogle Scholar
  96. 96.
    Baiqing, X., Cao, J., Hansen, J., Yao, T., Joswia, D.R., Wang, N., Wu, G., Wang, M., Zhao, H., Yang, W., Liu, X., He, J.: Black soot and the survival of Tibetan glaciers. Proc. Natl. Acad. Sci. 106(52), 8–22114 (2009)Google Scholar
  97. 97.
    Hanqiu, Xu: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27(14), 3025–3033 (2006)CrossRefGoogle Scholar
  98. 98.
    Zhang, G., Xie, H., Kang, S., Yi, D., Ackley, S.F.: Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sens. Environ. 115(7), 1733–1742 (2011)Google Scholar
  99. 99.
    Zhang, S., Gao, H., Naz, B.S.: Monitoring reservoir storage in south asia from multisatellite remote sensing. Water Resour. Res. (2014)Google Scholar
  100. 100.
    Zhu, X.: Remote sensing monitoring of coastline change in pearl river estuary. 22nd Asian Conference on Remote Sensing, vol. 5, p. 9 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Anuj Karpatne
    • 1
  • Ankush Khandelwal
    • 1
  • Xi Chen
    • 1
  • Varun Mithal
    • 1
  • James Faghmous
    • 1
  • Vipin Kumar
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of MinnesotaMinneapolisUSA

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