Print Email Facebook Twitter Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria Title Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria Author Cheruiyot, E.K. Mito, C. Menenti, M. Gorte, B.G.H. Koenders, R. Akdim, N. Faculty Civil Engineering and Geosciences Department Geoscience & Remote Sensing Date 2014-08-20 Abstract 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. Subject accuracy assessmentmapping aquatic vegetationcoarse resolutionLake VictoriaOA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:06427000-06c8-4195-bf01-7bd52e1e99bd DOI https://doi.org/10.3390/rs6087762 Publisher MDPI ISSN 2072-4292 Source http://www.mdpi.com/2072-4292/6/8/7762 Source Remote Sensing, 6 (8), 2014 Part of collection Institutional Repository Document type journal article Rights (c) 2014 Creative Commons Files PDF Menenti_2014.pdf 2.88 MB Close viewer /islandora/object/uuid:06427000-06c8-4195-bf01-7bd52e1e99bd/datastream/OBJ/view