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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Monitoring the spread of water hyacinth (Pontederia crassipes): challenges and future developments
Author(s): Datta, Aviraj
Maharaj, Savitri
Prabhu, G Nagendra
Bhowmik, Deepayan
Marino, Armando
Akbari, Vahid
Rupavatharam, Srikanth
Sujeetha, J Alice R
Anantrao, Girish G
Poduvattil, Vidhu K
Kumar, Saurav
Kleczkowski, Adam
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Keywords: remote sensing
Synthetic Aperture Radar
Ground sensor network
Unmanned Aerial Vehicle
citizen science
machine learning
aquatic weeds
Issue Date: 28-Jan-2021
Date Deposited: 28-Jan-2021
Citation: Datta A, Maharaj S, Prabhu GN, Bhowmik D, Marino A, Akbari V, Rupavatharam S, Sujeetha JAR, Anantrao GG, Poduvattil VK, Kumar S & Kleczkowski A (2021) Monitoring the spread of water hyacinth (Pontederia crassipes): challenges and future developments. Frontiers in Ecology and Evolution, 9, Art. No.: 631338. Biogeography and Macroecology, Invaders on the Horizon! Scanning the Future of Invasion Science and Management.
Series/Report no.: Biogeography and Macroecology, Invaders on the Horizon! Scanning the Future of Invasion Science and Management
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.
DOI Link: 10.3389/fevo.2021.631338
Rights: © 2021 Datta, Maharaj, Prabhu, Bhowmik, Marino, Akbari, Rupavatharam, Sujeetha, Anantrao, Poduvattil, Kumar and Kleczkowski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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