Abstract
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.
Highlights
Originating from the Amazon Basin, water hyacinth (Pontederia crassipes) has spread to more than 80 countries over the past century (Jafari, 2010)
We have seen that a wide variety of technological approaches for monitoring water hyacinth infestation are available, though the full potential of most of these is currently underexploited
Remote sensing has considerable advantages over other methods, as it is low in cost and can provide extensive spatial and temporal coverage, enabling ongoing surveillance and reaching inaccessible locations
Summary
Originating from the Amazon Basin, water hyacinth (Pontederia crassipes) has spread to more than 80 countries over the past century (Jafari, 2010). Trends in urbanization and increased eutrophication of inland and coastal waterbodies imply that these problems will only grow worse in future (Williams et al, 2005) Tackling this menace requires accurate and timely monitoring of potential water hyacinth habitats within aquatic ecosystems (Shekede et al, 2008). Water hyacinth infestation monitoring has relied on field surveys with limited spatial coverage, using methods that are time and labor intensive (Ritchie et al, 2003). This limited the amount of data that can be collected, leading to poor understanding of factors affecting the emergence and spread of water hyacinth in different geographies. We review a range of technological methods that can be applied and end by proposing a novel, multi-modal approach
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