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Remote Sens. 2014, 6(8), 7762-7782; doi:10.3390/rs6087762

Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria

1,2,* , 2
1 Department of Geoscience and Remote Sensing, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands 2 Department of Physics, University of Nairobi, P.O. Box 30197, 00100 Nairobi, Kenya
* Author to whom correspondence should be addressed.
Received: 14 February 2014 / Revised: 4 August 2014 / Accepted: 5 August 2014 / Published: 20 August 2014
(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)
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Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov’s NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov’s method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover.
Keywords: accuracy assessment; mapping aquatic vegetation; coarse resolution; Lake Victoria accuracy assessment; mapping aquatic vegetation; coarse resolution; Lake Victoria
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Cheruiyot, E.K.; Mito, C.; Menenti, M.; Gorte, B.; Koenders, R.; Akdim, N. Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria. Remote Sens. 2014, 6, 7762-7782.

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