Monitoring the spread of water hyacinth (pontederia crassipes): challenges and future developments

Aviraj Datta, Savitri Maharaj, G. Nagendra Prabhu, Deepayan Bhowmik, Armando Marino, Vahid Akbari, Srikanth Rupavatharam, J. Alice R. P. Sujeetha, Girish Gunjotikar Anantrao, Vidhu Kampurath Poduvattil, Saurav Kumar, Adam Kleczkowski

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Water hyacinth (Pontederia crassipes, also referred to as Eichhornia crassipes) is one of the most invasive weed species in the world, causing significant adverse economic and ecological impacts, particularly in tropical and sub-tropical regions. Large scale real-time monitoring of areas of chronic infestation is critical to formulate effective control strategies for this fast spreading weed species. Assessment of revenue generation potential of the harvested water hyacinth biomass also requires enhanced understanding to estimate the biomass yield potential for a given water body. Modern remote sensing technologies can greatly enhance our capacity to understand, monitor, and estimate water hyacinth infestation within inland as well as coastal freshwater bodies. Readily available satellite imagery with high spectral, temporal, and spatial resolution, along with conventional and modern machine learning techniques for automated image analysis, can enable discrimination of water hyacinth infestation from other floating or submerged vegetation. Remote sensing can potentially be complemented with an array of other technology-based methods, including aerial surveys, ground-level sensors, and citizen science, to provide comprehensive, timely, and accurate monitoring. This review discusses the latest developments in the use of remote sensing and other technologies to monitor water hyacinth infestation, and proposes a novel, multi-modal approach that combines the strengths of the different methods.

Original languageEnglish
Article number631338
Number of pages8
JournalFrontiers in Ecology and Evolution
Publication statusPublished - 28 Jan 2021


  • aquatic weeds
  • citizen science
  • ground sensor network
  • machine learning
  • remote sensing
  • synthetic aperture radar
  • unmanned aerial vehicle
  • wetlands


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