Publication Date:
2020
Description:
〈p〉Publication date: Available online 25 January 2020〈/p〉
〈p〉〈b〉Source:〈/b〉 Estuarine, Coastal and Shelf Science〈/p〉
〈p〉Author(s): Hiroto Higa, Shogo Sugahara, Salem Ibrahim Salem, Yoshiyuki Nakamura, Takayuki Suzuki〈/p〉
〈div xml:lang="en"〉
〈h5〉Abstract〈/h5〉
〈div〉〈p〉Blue tide, which occurs in eutrophic semi-enclosed bays, is recognized as an important environmental problem because it causes mass mortality of fish and shellfish. In Tokyo Bay, which is a typical eutrophic semi-enclosed bay in Japan, fishery damage due to blue tide is frequently reported. Continuous monitoring is needed for effective water environment management and conservation because blue tides can be too short in duration to observe in the course of field observations. The aim of this study is to develop a model for estimating the distribution of blue tide based on sulfur concentration, which is a key element associated with blue tide. Using real-time data from the Geostationary Ocean Color Imager (GOCI) to estimate sulfur concentrations, we can clearly identify the spatial distributions of blue tides. We developed an empirical model using 〈em〉in situ〈/em〉 measurement of sulfur and remote sensing reflectance to obtain sulfur concentration (R〈sup〉2〈/sup〉 = 0.851). The developed model was applied to GOCI images acquired shortly after field observations during a blue tide on 24 August 2015 in Tokyo Bay in order to validate the accuracy of the sulfur estimation. Estimated and measured sulfur concentration showed less difference when the time between the start of field observations and GOCI image capture was comparatively small (R〈sup〉2〈/sup〉 = 0.704). Further, the blue tide distributions could be estimated by applying the developed model to other images acquired during blue tide occurrences at other dates and years. In conclusion, spatial distributions of blue tide in Tokyo Bay can be adequately estimated based on sulfur concentration determined from GOCI images.〈/p〉〈/div〉
〈/div〉
Print ISSN:
0272-7714
Electronic ISSN:
1096-0015
Topics:
Biology
,
Geography
,
Geosciences
