New approaches for surface water quality estimation in Lake Erken, Sweden, using remotely sensed hyperspectral data
2011 (English)In: WSEAS Transactions on Environment and Development, ISSN 1790-5079, Vol. 7, no 10, 285-314 p.Article in journal (Refereed) Published
This work demonstrates the efficiency of using linear statistical modelling for estimation ofconcentrations of various substances in lake water using remotely sensed multi- and hyperspectral imagestogether with extensive field measurements collected over Lake Erken in Sweden. A linear relationship wasassumed between image data and the corresponding field measurements, and the transformation coefficientswere estimated using the least squares method. The resulting coefficients were used to transform new imagedata into the corresponding substance concentrations. Estimation errors were computed and concentration mapswere generated for chlorophyll-a and phaeophytine-a, suspended particulate organic matter SPOM, suspendedparticulate inorganic matter SPIM, as well as total suspended particulate matter SPM (SPOM+SPIM). Goodcorrelation was obtained between estimated and measured values. Backward elimination was performed to findthe most useful spectral bands for the case study of this work. Descriptive spectral signatures, describing theimpact of underlying processes on the spectral characteristics of water, were generated, analysed and also usedto predict the corresponding water quality parameters in image data, with the same estimation accuracy as thelinear statistical model. Feature vector based analysis FVBA was also employed to generate transformationcoefficients that could be used to estimate water quality parameters from image data, also, with the sameaccuracy as the previous methods. Finally, the impact of performing atmospheric correction was investigated,in addition to applying linear statistical modelling for the purpose of combined atmospheric correction andground reflectance estimation.
Place, publisher, year, edition, pages
2011. Vol. 7, no 10, 285-314 p.
Remote sensing, Statistical modelling, Water quality, Descriptive spectral signatures
IdentifiersURN: urn:nbn:se:kth:diva-91633ScopusID: 2-s2.0-84858024585OAI: oai:DiVA.org:kth-91633DiVA: diva2:510880
This work was financed by a research grant from the Swedish National Space Board (SNSB). QC 201203272012-03-192012-03-192014-02-04Bibliographically approved