Monitoring water hyacinth in Lake Victoria using Sentinel-2, Sentinel-1 and cloud computing: a case study of Winam Gulf, Kenya
dc.contributor.advisor
Nichol, Caroline
dc.contributor.author
Hick, Denise
dc.date.accessioned
2022-11-08T11:25:42Z
dc.date.available
2022-11-08T11:25:42Z
dc.date.issued
2022-11
dc.description.abstract
Water hyacinth is considered one of the world’s worst invasive aquatic weeds. Originally from the Amazon, it was introduced in Lake Victoria in 1989, causing periodic waves of infestation bearing socioeconomic and environmental issues.
Studies monitored water hyacinth invasions in Lake Victoria to inform management practices until 2017. This study proposes a Google Earth Engine framework to monitor the weed in Winam Gulf (Kenya) over the period 2018-2021. Sentinel-2 data was used to test two different water hyacinth classification methods: Red Edge Normalised Difference Vegetation Index (reNDVI) thresholding and Random Forest classification. Both yielded mean Overall Accuracies of ~92% for the 22 images classified. Additionally, Sentinel-1 data was used to classify the weed by thresholding 56 bi-weekly VV mean composites and to create yearly invasion heatmaps. The Sentinel-2 and Sentinel-1-derived water hyacinth timeseries indicated a Nov 2018 water hyacinth peak (~155 km2), which could have been the worst invasion in the Gulf since 2013. In accordance with previous studies, water hyacinth invasions exhibited Autumnal peaks, albeit with a decreasing trend (~95% decrease Dec 2018-Dec 2021). Weather data from Kisumu (Kenya) suggested that increased rainfall and cloudiness in 2019-2021 might be the culprit. The Sentinel-1 yearly composites also revealed that Osodo and Kusa Bay were the areas most affected by water hyacinth invasions in 2018-2021. Limitations of this study include Sentinel-2 L2A data unavailability in GEE and Sentinel-1 biweekly averaging. Finally, the proposed cloud-based water hyacinth monitoring solution could ensure continued observation of the Gulf and/or be applied to other geographical locations.
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dc.identifier.uri
https://hdl.handle.net/1842/39448
dc.identifier.uri
http://dx.doi.org/10.7488/era/2698
dc.language.iso
en
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dc.publisher
The University of Edinburgh
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dc.relation.references
Simpson, M.D., Akbari, V., Marino, A., Prabhu, G.N., Bhowmik, D., Rupavatharam, S., Datta, A., Kleczkowski, A., Sujeetha, J.A.R.P., Anantrao, G.G. and Poduvattil, V.K. (2022). Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery. Remote Sensing, 14(12), p.2845.
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dc.relation.references
Ongore, C.O., Aura, C.M., Ogari, Z., Njiru, J.M. and Nyamweya, C.S. (2018). Spatial-temporal dynamics of water hyacinth, Eichhornia crassipes (Mart.) and other macrophytes and their impact on fisheries in Lake Victoria, Kenya. Journal of Great Lakes Research, 44(6), pp.1273-1280.
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dc.relation.references
Datta, A., Maharaj, S., Prabhu, G.N., Bhowmik, D., Marino, A., Akbari, V., Rupavatharam, S., Sujeetha, J.A.R., Anantrao, G.G., Poduvattil, V.K. and Kumar, S. (2021). Monitoring the spread of water hyacinth (Pontederia crassipes): challenges and future developments. Frontiers in Ecology and Evolution, 9.
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dc.relation.references
Albright, T.P., Moorhouse, T.G. and McNabb, T.J. (2004). The rise and fall of water hyacinth in Lake Victoria and the Kagera River Basin, 1989-2001. Journal of Aquatic Plant Management, 42(42), pp.73-84.
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dc.subject
Sentinel-2
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dc.subject
Sentinel-1
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dc.subject
Google Earth Engine
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dc.subject
Water Hyacinth
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dc.subject
Supervised Classification
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dc.subject
Machine Learning
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dc.title
Monitoring water hyacinth in Lake Victoria using Sentinel-2, Sentinel-1 and cloud computing: a case study of Winam Gulf, Kenya
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Masters
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dc.type.qualificationname
MSc Master of Science
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