Assessment of the abnormal growth of floating macrophytes in Winam Gulf (Kenya) by using MODIS imagery time series

Fusilli, Lorenzo; Collins, Mike; Laneve, Giovanni; Palombo, Angelo; Pignatti, Stefano; Santini, Federico

doi:10.1016/j.jag.2011.09.002

Cited by 30 publications

(23 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We focus on the Winam Gulf because this almost enclosed shallow section of the lake is more vulnerable to vegetation invasion perhaps due to high levels of eutrophication. Earlier work of [15] show that vegetation proliferation is preceded by about two months by high levels of water quality indicators such as total suspended matter (TSM) and phytoplankton measured as a Chlorophyll-a (Chl-a) index. The most prevalent of these invasive weeds include the non-native water hyacinth and hippo-grass.…”

Section: Study Areamentioning

confidence: 99%

“…The challenge, however, is the correct identification of suitable thresholds separating various feature classes in the scene. The authors of [13] applied NDVI = 0.1 as a threshold to detect presence of vegetation, while [15] estimated the aquatic vegetation cover in Lake Victoria using a three-level NDVI scale:…”

Section: Empirical Slicing Of Vegetation Indicesmentioning

confidence: 99%

“…While this slicing clearly provides an excellent display of the spatial distribution of vegetation in the lake as seen in [15], in this study we evaluate the impact of limiting NDVI to a few classes. NDVI was computed from Landsat-7 ETM+ using red and near infrared bands 3 and 4 centred at 660 nm and 825 nm respectively; while MERIS NDVI was computed using bands 7 and 13 centred at 664 nm and 865 nm.…”

Section: Empirical Slicing Of Vegetation Indicesmentioning

confidence: 99%

“…The authors of [14] used NDVI and several of its derivatives to monitor Lake Victoria's water level and drought conditions. More recently [15] used NDVI to map vegetation distribution in the lake, and to develop a floating vegetation index for quantifying its surface extent.…”

Section: Introductionmentioning

confidence: 99%

“…MERIS sensor ended data acquisition in March 2013, but its products are good test data for the anticipated Sentinel 2/3 products. In terms of delineation, we evaluate two methods: NDVI slicing-with special focus on the empirical slicing proposed by [15], and maximum likelihood classifier. In terms of area estimation, we evaluate two methods: NDVI-based fractional cover retrieval model proposed by [16], and linear spectral unmixing (LSU)-which is a form of spectral mixture analysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: Study Areamentioning

confidence: 99%

Section: Empirical Slicing Of Vegetation Indicesmentioning

confidence: 99%

Section: Empirical Slicing Of Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating MERIS-Based Aquatic Vegetation Mapping in Lake Victoria

et al. 2014

View full text Add to dashboard Cite

show abstract

The hydrological response of surface water to recent climate variability: A remote sensing case study from the central tropical Pacific

Higley

Conroy

2019

Hydrological Processes

View full text Add to dashboard Cite

For small tropical islands with limited freshwater resources, understanding how island hydrology is influenced by regional climate is important, considering projected hydroclimate and sea level changes as well as growing populations dependent on limited groundwater resources. However, the relationship between climate variability and hydrologic variability for many tropical islands remains uncertain due to local hydroclimatic data scarcity. Here, we present a case study from Kiritimati, Republic of Kiribati (2°N, 157°W), utilizing the normalized difference vegetation index to investigate variability in island surface water area, an important link between climate variability and groundwater storage. Kiritimati surface water area varies seasonally, following wet and dry seasons, and interannually, due to hydroclimate variability associated with the El Niño/Southern Oscillation. The NIÑO3.4 sea surface temperature index, satellite‐derived precipitation, precipitation minus evaporation, and local sea level all had significant positive correlations with surface water area. Lagged correlations show sea level changes and precipitation influence surface water area up to 6 months later. Differences in the timing of surface water area changes and variable climate‐surface water area correlations in island subregions indicate that surface hydrology on Kiritimati is not uniform in response to climate variations. Rather, the magnitude of the ocean–atmosphere anomalies and island–ocean connectivity determine the extent to which sea level and precipitation control surface water area. The very strong 2015–2016 El Niño event led to the largest surface water area measured in the 18‐year data set. Surface water area decreased to pre‐event values in a similarly rapid manner (<6 months) after both the very strong 2015–2016 event and the 2009–2010 moderate El Niño event. Future changes in the frequency and amplitude of interannual hydroclimate variability as well as seasonal duration will thus alter surface water coverage on Kiritimati, with implications for freshwater resources, flooding, and drought.

show abstract