Monitoring the Spread of Water Hyacinth (Pontederia crassipes): Challenges and Future Developments

Datta, A and Maharaj, S and Prabhu, G N and Bhowmik, D and Marino, A and Akbari, V and Rupavatharam, S and Sujeetha, J A R P and Anantrao, G G and Poduvattil, V K and Kumar, S and Kleczkowski, A (2021) Monitoring the Spread of Water Hyacinth (Pontederia crassipes): Challenges and Future Developments. Frontiers in Ecology and Evolution (TSI), 9 (631338). pp. 1-8. ISSN 2296-701X

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Abstract

Water hyacinth (Pontederia crassipes, also referred to as Eicchornia 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.

Item Type:	Article
Divisions:	Research Program : Asia
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Remote sensing, Synthetic aperture radar, Ground sensor network, Unmanned aerial vehicle, Citizen science, Machine learning, Aquatic weeds, Wetlands
Subjects:	Others > Weed Science
Depositing User:	Mr Arun S
Date Deposited:	14 Apr 2021 08:46
Last Modified:	14 Apr 2021 08:46
URI:	http://oar.icrisat.org/id/eprint/11787
Official URL:	https://doi.org/10.3389/fevo.2021.631338
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	UNSPECIFIED
Links:	Google Scholar

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