Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Miscellanea Geographica

Regional Studies on Development

4 Issues per year

CiteScore 2016: 0.40

SCImago Journal Rank (SJR) 2016: 0.227
Source Normalized Impact per Paper (SNIP) 2016: 0.404

Covered by e.g. Web of Science Core Collection by Clarivate Analytics, and SCOPUS by Elsevier
14 points in the Ministerial journal value rating scale

Open Access
See all formats and pricing
More options …

Multi-temporal analysis of vegetation reflectance using MERIS data in the Czech Republic

Přemysl Štych / Lucie Malíková / Jan Kříž / Lukáš Holman
Published Online: 2014-06-17 | DOI: https://doi.org/10.2478/mgrsd-2014-0015


Accurate high temporal resolution data is a very important source of information for understanding processes in the landscape. High temporal and spectral resolution data enable the monitoring of dynamic landscape processes. For this reason, since 2008 a receiving station for Metosat, NOAA and Envisat data has been installed at the Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague. The aim of this study is to analyse the spectral characteristics of vegetation using MERIS data in the Czech Republic. Spectral characteristics of vegetation were examined both by analysing changes in reflectivity as well as by utilising vegetation indices. Vegetation in forests and agricultural land was evaluated. The results present the spectral characteristics of selected associations of vegetation based on MERIS data and a discussion of the methods of multitemporal classification of land cover.

Keywords: High temporal data; MERIS; Czech Republic; land cover; classification


  • Aurdal, LRBH, Vikhamar, D & Solberg, A 2005, Use of hidden Markov models and phenology for multitemporal satellite image classification: applications to mountain vegetation classification. Available from: <http://citeseerx.ist.psu.edu> [12 April 2012].Google Scholar

  • Bacour, C, Baret, V, Beal, D, Weiss, M & Pavageau, K 2006, ‛Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: Principles and validation’. Remote Sensing of Environment, vol. 105, no. 4, pp. 313‑325.Google Scholar

  • Brodsky, L, Vobora, V, Sourkova, L & Kodesova, R 2008, ‛Supervised crop classification from midle-resolution multitemporal images’, Proc. of the 2nd MERIS/(A)ATSR User Workshop, Frascati, Italy, 22‑26 September 2008, pp. 34- 49.Google Scholar

  • Dash, J, Mathur, A, Foody, GM, Curran, PJ, Chipman, J & Lillesand, TM 2005, ‛Land cover classification using multitemporal MERIS vegetation indices’, International Journal of Remote Sensing, vol. 28, no. 6, pp.1137-1159.Google Scholar

  • European Space Agency 2009, EOLI (Earth Observation Link). Available from: <http://earth.esa.int/EOLi/EOLi.html> [20 April 2013].Google Scholar

  • European Space Agency 2010, BEAM Earth Observation Toolbox and Development Platform. Available from: <http://www.brockmann-consult.de/cms/web/beam/>. [25 June 2013].Google Scholar

  • European Space Agency 2010, GlobCorine. Available from: <http://due.esrin.esa.int/prjs/prjs114.php>. [14 June 2010].Google Scholar

  • European Space Agency 2010, GlobCover. Available from: <http://ionia1.esrin.esa.int/>. [14 June 2010].Google Scholar

  • Goddard Earth Sciences Data and Information Services Center 2013, Giovanni - Interactive Visualization and Analysis. Available from: <http://disc.sci.gsfc.nasa.gov/giovanni>. [5 July 2012].Google Scholar

  • Guanter, L, Gonzalez-Sanpedro, M & Moreno, J 2007, ‛A method for atmospheric correction of ENVISAT/MERIS data over land targets’, International Journal of Remote sensing, vol. 28, no. 3-4, pp. 709-728.Google Scholar

  • Junxiang, L, Liangjun, D, Yujie, W & Yongchang, S 2006, ‛Vegetation classification of East China with multi-temporal NOAA-AVHRR data’, Front. Biol. China, vol. 1, no. 3, pp. 303-309.Google Scholar

  • LPIS Sitewell 2004. Available from: <http://www.lpis.cz>. [20 September 2013].Google Scholar

  • Zhang, XY, Friedl, MA, Schaaf, CB, Strahler, AH, Hodges, JCF, Gao, F, Reed, BC & Huete, A 2003, ‛Monitoring vegetation phenology using MODIS’. Remote Sensing of Environment, vol. 84, no. 2, pp. 471-475.Google Scholar

  • Zurita-Milla, R 2008, ‛Mapping and monitoring heterogenous landscapes: spatial, spectral and temporal unmixing of MERIS data’, PhD Thesis, Wageningen University, p. 138. Google Scholar

About the article

Received: 2013-10-11

Accepted: 2014-03-14

Published Online: 2014-06-17

Published in Print: 2014-06-01

Citation Information: Miscellanea Geographica, Volume 18, Issue 2, Pages 30–34, ISSN (Online) 2084-6118, DOI: https://doi.org/10.2478/mgrsd-2014-0015.

Export Citation

© by Přemysl Štych. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in