University of Jyväskylä | JYX Digital Repository

  • English  | Give feedback |
    • suomi
    • English
 
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.
View Item 
  • JYX
  • Artikkelit
  • Informaatioteknologian tiedekunta
  • View Item
JYX > Artikkelit > Informaatioteknologian tiedekunta > View Item

Variability of remote sensing spectral indices in Boreal lake basins

ThumbnailPublisher's PDF
View/Open
2.4 Mb

Downloads:  
Show download detailsHide download details  
Hakala, 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.), ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020” (pp. 411-417). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2. https://doi.org/10.5194/isprs-archives-xlii-2-411-2018
Published in
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Authors
Hakala, Taina |
Pölönen, Ilkka |
Honkavaara, Eija |
Näsi, Roope |
Hakala, Teemu |
Lindfors, Antti
Editors
Remondino, F. |
Toschi, I. |
Fuse, T.
Date
2018
Discipline
TietotekniikkaMathematical Information Technology
Copyright
© Authors 2018.

 
Remotely 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. ...
Publisher
International Society for Photogrammetry and Remote Sensing
Conference
Congress of the International Society for Photogrammetry and Remote Sensing
Is part of publication
ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”
ISSN Search the Publication Forum
1682-1750
Keywords
optically complex waters hyperspectral imaging spectral indices vedenlaatu kaukokartoitus
DOI
https://doi.org/10.5194/isprs-archives-xlii-2-411-2018
URI

http://urn.fi/URN:NBN:fi:jyu-201805312950

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/28075947

Metadata
Show full item record
Collections
  • Informaatioteknologian tiedekunta [1863]

Related items

Showing items with similar title or keywords.

  • Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks 

    Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti (Springer, 2020)
    In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. ...
  • HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender 

    Riihiaho, Kimmo A.; Rossi, Tuomo; Pölönen, Ilkka (Copernicus Publications, 2022)
    Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers ...
  • Editorial for the special issue "Frontiers in spectral imaging and 3D technologies for geospatial solutions" 

    Honkavaara, Eija; Karantzalos, Konstantinos; Liang, Xinlian; Nocerino, Erica; Pölönen, Ilkka; Rönnholm, Petri (MDPI, 2019)
    This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of ...
  • Humuspitoisen järven kaukokartoitussignaalin ja klorofylli-a:n määritys optisilla kenttämittauksilla 

    Kinnunen, Eveliina (2020)
    Kaukokartoituksella on mahdollista määrittää kustannustehokkaasti veden optisiin ominaisuuksiin vaikuttavia ainesosia, mutta sen laajempi hyödyntäminen humuspitoisilla sisävesillä edellyttää menetelmien kehittämistä. ...
  • Discovering knowledge in various applications with a novel hyperspectral imager 

    Pölönen, Ilkka (University of Jyväskylä, 2013)
  • Browse materials
  • Browse materials
  • Articles
  • Conferences and seminars
  • Electronic books
  • Historical maps
  • Journals
  • Tunes and musical notes
  • Photographs
  • Presentations and posters
  • Publication series
  • Research reports
  • Research data
  • Study materials
  • Theses

Browse

All of JYXCollection listBy Issue DateAuthorsSubjectsPublished inDepartmentDiscipline

My Account

Login

Statistics

View Usage Statistics
  • How to publish in JYX?
  • Self-archiving
  • Publish Your Thesis Online
  • Publishing Your Dissertation
  • Publication services

Open Science at the JYU
 
Data Protection Description

Accessibility Statement

Unless otherwise specified, publicly available JYX metadata (excluding abstracts) may be freely reused under the CC0 waiver.
Open Science Centre