Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 411-417, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 411-417, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

VARIABILITY OF REMOTE SENSING SPECTRAL INDICES IN BOREAL LAKE BASINS

T. Hakala1, I. Pölönen1, E. Honkavaara2, R. Näsi2, T. Hakala2, and A. Lindfors3 T. Hakala et al.
  • 1Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
  • 2Finnish Geospatial Research Institute FGI, Kirkkonummi, Finland
  • 3Luode Consulting Ltd, Espoo, Finland

Keywords: Water quality, Optically complex waters, Remote sensing, Hyperspectral imaging, Spectral indices

Abstract. 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.