Näytä suppeat kuvailutiedot

dc.contributor.authorErkkilä, Anna-Leena
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorLindfors, A.
dc.contributor.authorHonkavaara, E.
dc.contributor.authorNurminen, K.
dc.contributor.authorNäsi, R.
dc.date.accessioned2017-11-07T11:48:03Z
dc.date.available2017-11-07T11:48:03Z
dc.date.issued2017
dc.identifier.citationErkkilä, A.-L., Pölönen, I., Lindfors, A., Honkavaara, E., Nurminen, K., & Näsi, R. (2017). Choosing of Optimal Reference Samples for Boreal Lake Chlorophyll A Concentration Modeling Using Aerial Hyperspectral Data. In E. Honkavaara, B. Hu, K. Karantzalos, X. Liang, R. Müller, E. Nocerino, . . . , & P. Rönnholm (Eds.), <em>ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions</em> (pp. 39-46). International archives of the photogrammetry, remote sensing and spatial information sciences, Volume XLII-3/W3. International Society for Photogrammetry and Remote Sensing. <a href="https://doi.org/10.5194/isprs-archives-XLII-3-W3-39-2017">doi:10.5194/isprs-archives-XLII-3-W3-39-2017</a>
dc.identifier.otherTUTKAID_75510
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55788
dc.description.abstractOptical remote sensing has potential to overcome the limitations of point estimations of lake water quality by providing spatial and temporal information. In open ocean waters the optical properties are dominated by phytoplankton density, while the relationship between color and the constituents is more complicated in inland waters varying regionally and seasonally. Concerning the difficulties relating to comprehensive modeling of complex inland and coastal waters, the alternative approach is considered in this paper: the raw digital numbers (DN) recorded using aerial remote hyperspectral sensing are used without corrections and derived by means of regression modeling to predict Chlorophyll a (Chl-a) concentrations using in situ reference measurements. The target of this study is to estimate which number of local reference measurements is adequate for producing reliable statistical model to predict Chl-a concentration in complex lake water ecosystem. Based on the data collected from boreal lake Lohjanjarvi, the effect of standard ¨ deviation of Chl-a concentration of reference samples and their local clustering on predictability of model increases when number of reference samples or bands used as model variables decreases. However, the 2 or 3 band models are beneficial and more cost efficient when compared to 5 or 7 band models when the standard deviation of Chl-a concentration of reference samples is over certain level. The simple empirical approach combining remote sensing and traditional sampling may be feasible for regional and seasonal retrieval of Chl-a concentration distributions in complex ecosystems, where the comprehensive models are difficult or even impossible to derive.
dc.language.isoeng
dc.publisherInternational Society for Photogrammetry and Remote Sensing
dc.relation.ispartofISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions
dc.relation.ispartofseriesInternational archives of the photogrammetry, remote sensing and spatial information sciences;Volume XLII-3/W3
dc.relation.urihttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/39/2017/isprs-archives-XLII-3-W3-39-2017.pdf
dc.subject.otheraerial remote sensing
dc.subject.otherlake water color
dc.subject.otherwater quality monitoring
dc.subject.otherhyperspectral imaging
dc.subject.otherchlorophyll a
dc.titleChoosing of Optimal Reference Samples for Boreal Lake Chlorophyll A Concentration Modeling Using Aerial Hyperspectral Data
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201711024122
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-11-02T13:15:39Z
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange39-46
dc.relation.issn1682-1750
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2017. This is an open access article distributed under the terms of the Creative Commons License.
dc.rights.accesslevelopenAccessfi
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-archives-XLII-3-W3-39-2017


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Näytä suppeat kuvailutiedot

© Authors, 2017. This is an open access article distributed under the terms of the Creative Commons License.
Ellei muuten mainita, aineiston lisenssi on © Authors, 2017. This is an open access article distributed under the terms of the Creative Commons License.