Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria

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 OPEN ACCESS Remote Sens. 2014, 6 7763 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.

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

“…For instance, in this study, it was found that NDWI and NDVI derived from low (300 m) MERIS images were generally higher over water surfaces than those generated from high-resolution (30 m) TM images. This finding is in accordance with [45], who found that MERIS had higher reflectance values over water surfaces in the green band compared to TM. Most studies [53][54][55] that make use of coarse-resolution optical imagery for flood mapping do not adequately account for the effects of mixed pixels.…”

“…This is done by assuming that the observed pixel-wise spectral reflectance is a linear combination of the spectral reflectance of soil, water and vegetation endmembers, then using the pixel-wise spectral reflectance to determine NDWI and NDVI. The assumption is that the different components in a pixel contribute independently to its reflectance [45]. The NDWI SU equation for the estimation of γ w is rewritten by substituting the (pixel) spectral reflectance values as linear combinations of the ones of the three endmembers, together with their abundances, where only γ w is unknown and can be determined from observed NDWI.…”

Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu: Implications for lake ecological restoration

“…In addition, remote sensing can markedly decrease aquatic vegetation data acquisition costs. The satellite data used for these purposes include satellite multi-spectral moderate spatial resolution data (TM, ETM, MODIS, MERIS), satellite multi-spectral high-spatial resolution data (Quickbird 2), and airborne hyper-spectral (CASI 2) data 22 23 24 25 26 48 .…”

“…Aside from conventional techniques, such as field mapping and photography, remote sensing can be an effective tool for mapping aquatic vegetation distributions over large scales. Many remote sensing techniques and methods, including spectral inversion, classification trees, the normalized difference vegetation index (NDVI), the floating algae index (FAI) and vegetation presence frequency (VPF), have been developed to accurately identify aquatic vegetation using different types of remote sensing imagery (TM, MODIS, MERIS and airborne data) 22 23 24 25 26 27 28 . In addition, long-term site-specific observations of water quality can generate long, data-rich time series and yield insights into the dynamics of water quality 6 29 .…”

Growth of Invasive Aquatic Macrophytes Over Tapi River

“…These plants may be free-floating or rooted in bottom sediment and submersed. However, on the other side, aquatic weed infestation is one of the major environmental challenges globally, threatening the integrity and functioning of most hydrological ecosystems (Cheruiyot et al, 2014). This invasion of foreign species leads to a number of ecological impacts, including reduced light and oxygen levels in the water column, which in turn reduces native macrophyte diversity.…”