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Improved land cover mapping using aerial photographs and satellite images

Katalin Varga / Szilárd Szabó
  • Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem tér 1, Debrecen, H-4032 Hungary
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Gergely Szabó
  • Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem tér 1, Debrecen, H-4032 Hungary
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ György Dévai / Béla Tóthmérész
  • MTA-DE Biodiversity and Ecosystem Services Research Group, Debrecen, Egyetem tér 1, H-4032 Hungary
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Published Online: 2014-10-28 | DOI: https://doi.org/10.1515/geo-2015-0002


Manual Land Cover Mapping using aerial photographs provides sufficient level of resolution for detailed vegetation or land cover maps. However, in some cases it is not possible to achieve the desired information over large areas, for example from historical data where the quality and amount of available images is definitely lower than from modern data. The use of automated and semiautomated methods offers the means to identify the vegetation cover using remotely sensed data. In this paper automated methods were tested on aerial photographs and satellite images to extract better and more reliable information about vegetation cover. These testswere performed by using automated analysis of LANDSAT7 images (with and without the surface model of the Shuttle Radar Topography Mission (SRTM)) and two temporally similar aerial photographs. The spectral bands were analyzed with supervised (maximum likelihood) methods. In conclusion, the SRTM and the combination of two temporally similar aerial photographs from earlier years were useful in separating the vegetation cover on a floodplain area. In addition the different date of the vegetation season also gave reliable information about the land cover. High quality information about old and present vegetation on a large area is an essential prerequisites ensuring the conservation of ecosystems

Keywords : LANDSAT; vegetation season; vegetation type; maximum likelihood; mapping accuracy; SRTM


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About the article

Received: 2014-01-14

Accepted: 2014-06-24

Published Online: 2014-10-28

Citation Information: Open Geosciences, Volume 7, Issue 1, ISSN (Online) 2391-5447, DOI: https://doi.org/10.1515/geo-2015-0002.

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© 2015 K. Varga et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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