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


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.


Water Body Normalize Difference Vegetation Index Shuttle Radar Topography Mission Glacial Lake Local Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the National Science Foundation Awards 1029711 and 0905581, and the NASA Award NNX12AP37G.


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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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