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Remote Sens. 2015, 7(5), 6107-6132; doi:10.3390/rs70506107

Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series

Institute of Landscape Ecology, Slovak Academy of Sciences (ILE-SAS), Branch Nitra, Nitra 94910, Slovak Republic
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Author to whom correspondence should be addressed.
Academic Editors: Norbert Pfeifer, András Zlinszky, Hermann Heilmeier, Heiko Balzter, Bernhard Höfle, Bálint Czúcz and Prasad S. Thenkabail
Received: 26 February 2015 / Revised: 28 April 2015 / Accepted: 7 May 2015 / Published: 15 May 2015
(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)
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Abstract

The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate cut and uncut meadows in Slovakia. We tested the added value of simple transformations of raw data series (seasonal statistics, first difference series), compared EVI and NDVI, and analyzed optimal periods, the number of scenes and the effect of smoothing on classification performance. The first difference series transformation saw substantial improvement in classification results. The best case NDVI series classification yielded overall accuracy of 85% with balanced rates of producer’s and user’s accuracies for both classes. EVI yielded slightly lower values, though not significantly different, although user accuracy of cut meadows achieved only 67%. Optimal periods for discriminating cut and uncut meadows lay between 16 May and 4 August, meaning only seven consecutive images are enough to accurately detect cutting in hay meadows. More importantly, the 16-day compositing period seemed to be enough for detection of cutting, which would be the time span that might be hopefully achieved by upcoming on-board HR sensors (e.g., Sentinel-2). View Full-Text
Keywords: land use management; grasslands; rangelands; CART; decision trees; agricultural management; farmland management; earth observation land use management; grasslands; rangelands; CART; decision trees; agricultural management; farmland management; earth observation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Halabuk, A.; Mojses, M.; Halabuk, M.; David, S. Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series. Remote Sens. 2015, 7, 6107-6132.

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