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dc.contributor.authorHakala, Taina
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorNäsi, Roope
dc.contributor.authorHakala, Teemu
dc.contributor.authorLindfors, Antti
dc.contributor.editorRemondino, F.
dc.contributor.editorToschi, I.
dc.contributor.editorFuse, T.
dc.date.accessioned2018-06-04T05:11:44Z
dc.date.available2018-06-04T05:11:44Z
dc.date.issued2018
dc.identifier.citationHakala, T., Pölönen, I., Honkavaara, E., Näsi, R., Hakala, T., & Lindfors, A. (2018). Variability of remote sensing spectral indices in Boreal lake basins. In F. Remondino, I. Toschi, & T. Fuse (Eds.), <i>ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”</i> (pp. 411-417). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2. <a href="https://doi.org/10.5194/isprs-archives-xlii-2-411-2018" target="_blank">https://doi.org/10.5194/isprs-archives-xlii-2-411-2018</a>
dc.identifier.otherCONVID_28075947
dc.identifier.otherTUTKAID_77760
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/58296
dc.description.abstractRemotely sensed hyperspectral data has widely been used to determine water quality parameters in oceanic waters. However in freshwater basins the dependence between the hyperspectral data and the parameters is more complicated. In this work some ideas are presented concerning the study of this dependence. The data used in this study were collected from the lake Hiidenvesi in southern Finland. The hyperspectral data consists of reflectances in 36 bands in the wavelength area 508...878 nm and the separately measured water quality parameters are turbidity, blue-green algae, chlorophyll, pH and dissolved oxygen. Hyperspectral data was used as bare band reflectances, but also in the form of two simple spectral indices: ratio A/B and difference A-B, where A and B go through all the bands. The correlations of the indices with the parameters were presented visually as 1- or 2-dimensional arrays. To examine the significance on the results of different variables, the data was classified in two different ways: the natural basins and the values of the water quality parameters. It was noticed that the variability of the correlation arrays was particularly strong among different basins in both the magnitude of correlation and the best performing indices. Further studies are needed to clarify which features of the basins are of most importance in predicting the shapes of the correlation arrays.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInternational Society for Photogrammetry and Remote Sensing
dc.relation.ispartofISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”
dc.relation.ispartofseriesInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.rightsCC BY 4.0
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subject.otheroptically complex waters
dc.subject.otherhyperspectral imaging
dc.subject.otherspectral indices
dc.titleVariability of remote sensing spectral indices in Boreal lake basins
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201805312950
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-05-31T09:15:07Z
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange411-417
dc.relation.issn1682-1750
dc.relation.numberinseriesVolume XLII-2
dc.type.versionacceptedVersion
dc.rights.copyright© Authors 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceCongress of the International Society for Photogrammetry and Remote Sensing
dc.subject.ysovedenlaatu
dc.subject.ysokaukokartoitus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p15738
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
dc.relation.doi10.5194/isprs-archives-xlii-2-411-2018
dc.type.okmA4


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