Improving Inland Water Quality Monitoring through Remote Sensing Techniques

dc.contributor.authorOgashawara, Igor
dc.contributor.authorMoreno-Madriñán, Max J.
dc.date.accessioned2015-02-02T14:48:05Z
dc.date.available2015-02-02T14:48:05Z
dc.date.issued2014-11
dc.description.abstractChlorophyll-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.en_US
dc.identifier.citationOgashawara, I., & Moreno-Madriñán, M. J. (2014). Improving Inland Water Quality Monitoring through Remote Sensing Techniques. ISPRS International Journal of Geo-Information, 3(4), 1234-1255.en_US
dc.identifier.urihttps://hdl.handle.net/1805/5764
dc.language.isoen_USen_US
dc.rightsAttribution 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectchlorophyll-aen_US
dc.subjectcyanobacteria biomassen_US
dc.subjectempirical algorithmsen_US
dc.subjectremote sensingen_US
dc.titleImproving Inland Water Quality Monitoring through Remote Sensing Techniquesen_US
dc.typeArticleen_US
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