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Lookup NU author(s): Dr Deepayan BhowmikORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. Water hyacinth (Pontederia crassipes, also known as Eichhornia crassipes) is a highly invasive aquatic macrophyte species, indigenous to Amazonia, Brazil and tropical South America. It was introduced to India in 1896 and has now become an environmental and social challenge throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering its large speed of propagation on the water surface under conducive conditions and the adverse impact the infesting weed has, constant monitoring is needed to aid civic bodies, governments and policy makers involved in remedial measures. The synoptic coverage provided by satellite imaging and other remote sensing practices make it convenient to find a solution using this type of data. While there is an established background for the practice of remote sensing in the detection of aquatic plants, the use of Synthetic Aperture Radar (SAR) has yet to be fully exploited in the detection of water hyacinth. This research focusses on detecting water hyacinth within Vembanad Lake, Kuttanad, India. Here, results show that the monitoring of water hyacinth has proven to be possible using Sentinel-1 SAR data. A quantitative analysis of detection performance is presented using traditional and state-of-the-art change detectors. Analysis of these more powerful detectors showed true positive detection ratings of ~95% with 0.1% false alarm, showing significantly greater positive detection ratings when compared to the more traditional detectors. We are therefore confident that water hyacinth can be monitored using SAR data provided the extent of the infestation is significantly larger than the resolution cell (bigger than a quarter of a hectare).
Author(s): Simpson MD, Akbari V, Marino A, Prabhu GN, Bhowmik D, Rupavatharam S, Datta A, Kleczkowski A, Sujeetha JARP, Anantrao GG, Poduvattil VK, Kumar S, Maharaj S, Hunter PD
Publication type: Article
Publication status: Published
Journal: Remote Sensing
Year: 2022
Volume: 14
Issue: 12
Online publication date: 14/06/2022
Acceptance date: 13/06/2022
Date deposited: 20/09/2023
ISSN (electronic): 2072-4292
Publisher: MDPI
URL: https://doi.org/10.3390/rs14122845
DOI: 10.3390/rs14122845
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