Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System
2018
Kulshreshtha, Anuj | Shanmugam, Palanisamy
Accurate assessment and monitoring of coastal and inland water quality by satellite optical remote sensing is challenging due to improper atmospheric correction algorithm, inaccurate quantification of in-water constituents' concentration and a lack of efficient models to predict the water quality status. The present study aims to address the latter two parts in conjugation with an appropriate atmospheric correction algorithm to assess trophic status and water quality conditions of two coastal lagoons using Landsat-8 OLI data. Three vital underwater light attenuating factors, directly related to water quality, are considered namely, turbidity, chlorophyll and colored dissolved organic matter (aCDOM). These water quality parameters are quantified based on certain sensitive normalised water-leaving radiance band ratios and threshold values. To assess the accuracy of the derived products, these algorithms were applied to independent in-situ data and statistical evaluation of the results showed good agreement between the estimated and measured values with the errors within desirable limits. Being a primary nutrient indicator, the chlorophyll concentration was used to evaluate Trophic State Index. The Water Quality Index was derived from three parameters namely, chlorophyll concentration, turbidity and aCDOM(443) which were expressed in terms of Trophic State Index, Turbidity Index and Humic-Fulvic Index, respectively. The Water Quality Index maps, derived using a Fuzzy Inference System based on the Centre of Gravity method, provided insights into spatial structures and temporal variability of water quality conditions of the coastal lagoons which are influenced by anthropogenic factors, hydrographic changes and land-ocean-atmospheric interaction processes.
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by National Agricultural Library