Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series
Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series
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DOI：
10.3390/rs70506107
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发表时间：
2015-05
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影响因子：
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通讯作者：
A. Halabuk;M. Mojses;Marek Halabuk;S. David
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作者：
A. Halabuk;M. Mojses;Marek Halabuk;S. David
保持欧洲干草草甸良好状态的主要要求是通过先决条件削减管理。然而，在更大范围内监测这些做法非常困难。我们的研究分析了使用MODIS植被指数产品，即EVI和NDVI，区分切割和未切割的草地在斯洛伐克。我们测试了原始数据序列（季节统计，一阶差分序列）的简单变换的附加值，比较了EVI和NDVI，并分析了最佳周期，场景数量和平滑对分类性能的影响。第一差分序列变换在分类结果中看到了实质性的改善。最好的情况下，NDVI系列分类产生了85%的整体准确性与生产者和用户的准确性为两个类的平衡率。经济脆弱性指数产生的值略低，虽然没有显着差异，但用户的准确性削减草地只达到67%。区分切割和未切割草地的最佳时期在5月16日至8月4日之间，这意味着只有7张连续的图像就足以准确检测干草草地的切割。更重要的是，16天的合成期似乎足以检测切割，这将是可能希望通过即将到来的机载HR传感器实现的时间跨度（例如，Sentinel-2）。
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).