Share Email Print
cover

Proceedings Paper

Assessment of biological and physic chemical water quality parameters using Landsat 8 time series
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The Water Framework Directive of the European Union aims to protect water bodies from feature degradation. Monitoring is essential for assessment and comprehensive overview of water status. Annex V of WFD define tree type of water quality parameters which need to be monitored (biological and two supported one – hydro morphological and physic chemical) in order to assess ecological status of water bodies. Remote sensing data can be used for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. However, this technique must to be integrated with traditions in situ sampling method and field surveying in order to provide precise results. Various empirical, semi-analytics and machine learning algorithms exist to derive relationship between multi spectral image surface reflectance and water quality indicators derived from in situ measurement. In this study we evaluate the capabilities of Landsat 8 satellite image for assessment of abundance of phytoplankton’s (biological parameters) and Turbidity, Dissolved oxygen, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia. The Neuron Networks are used to analyzing correlation between in situ measurements and 7 Landsat 8 atmospherically corrected satellite images acquired in 2013. In situ data are obtained from Agency for environment protection of Serbia. Our results shows that satellite-based monitoring, in combination with in situ data, provide an improved basis for more effective monitoring of large number of water bodies over large geographical area. Relationship between derived and WFD quality parameters is established in order to provide usage of remote sensing data for ecological status classification according to WFD.

Paper Details

Date Published: 10 October 2018
PDF: 13 pages
Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107831F (10 October 2018); doi: 10.1117/12.2513277
Show Author Affiliations
Gordana Jakovljević, Univ. of Banja Luka (Bosnia and Herzegovina)
Miro Govedarica, Univ. of Novi Sad (Serbia)
Flor Álvarez-Taboada, Univ. de León (Spain)


Published in SPIE Proceedings Vol. 10783:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XX
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top