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2014 | 18 | 2 | 30-34

Article title

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

Content

Title variants

Languages of publication

Abstracts

EN
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.

Year

Volume

18

Issue

2

Pages

30-34

Physical description

Dates

published
2014

Contributors

  • Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague
  • Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague
author
  • Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague
  • Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague

References

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  • 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.
  • 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.
  • 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.
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  • 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].
  • 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.[WoS]
  • 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.
  • LPIS Sitewell 2004. Available from: <http://www.lpis.cz>. [20 September 2013].
  • 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.
  • Zurita-Milla, R 2008, ‛Mapping and monitoring heterogenous landscapes: spatial, spectral and temporal unmixing of MERIS data’, PhD Thesis, Wageningen University, p. 138.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
2037394

YADDA identifier

bwmeta1.element.ojs-issn-0867-6046-year-2013-volume-18-issue-2-article-bwmeta1_element_doi-10_2478_mgrsd-2014-0015
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