Browsing by Subject "chlorophyll-a"

Now showing 1 - 3 of 3
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    Improving Inland Water Quality Monitoring through Remote Sensing Techniques
    (2014-11) Ogashawara, Igor; Moreno-Madriñán, Max J.
    Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially in those used for human services, as they imply an increased risk of exposure. Remote sensing technology is being increasingly used to monitor water quality, although its application in cases of small urban lakes is limited by the spatial resolution of the sensors. Lake Thonotosassa, FL, USA, a 3.45-km2 suburban lake with several uses for the local population, is being monitored monthly by traditional methods. We developed an empirical bio-optical algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) daily surface reflectance product to monitor daily chl-a. We applied the same algorithm to four different periods of the year using 11 years of water quality data. Normalized root mean squared errors were lower during the first (0.27) and second (0.34) trimester and increased during the third (0.54) and fourth (1.85) trimesters of the year. Overall results showed that Earth-observing technologies and, particularly, MODIS products can also be applied to improve environmental health management through water quality monitoring of small lakes.
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    A transferable bio-optical model for quantification of inland water caynobacterial pigments
    (2012-03-16) Li, Linhai; Li, Lin; Tedesco, Lenore P.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
    Cyanobacterial blooms are currently one of the most important issues faced by environmental agencies, water authorities and public health organizations. Remote sensing provides an advanced approach to monitor cyanobacteria by detecting and quantifying chlorophyll-a (Chl-a) and phycocaynin (PC). In this thesis, an analytical bio-optical model, more typically applied to ocean waters, was modified to accommodate the complexity of inland waters. The newly developed models work well to estimate inherent optical properties, including absorption and backscattering coefficients, in eight different study sites distributed around the globe. Based on derived absorption coefficients, Chl-a and PC concentrations were accurately retrieved for data sets collected annually from 2006 to 2010, and the estimation accuracy exceeded that of currently used algorithms. An important advantage of the model is that low concentrations of Chl-a and PC can be predicted more accurately, enabling early warning of cyanobacterial blooms. In addition, the results also indicated good spatial and temporal transferability of the algorithms, since no specific calibration procedures were required for data sets collected in a different sites and seasons. The compatibility of the newly developed algorithm with MERIS spectra provides the possibility for routine surveillance of cyanobacterial growth in inland waters.
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    The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms
    (MDPI, 2019-06) Ogashawara, Igor; Earth Sciences, School of Science
    Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.