Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices
Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices
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DOI：
10.3390/rs9010081
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发表时间：
2017-01-01
期刊：
影响因子：
5
通讯作者：
Schellberg, Juergen
中科院分区：
文献类型：
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作者：
Hollberg, Jens L.;Schellberg, Juergen
监测草地冠层对施肥的反应对于实现良好的管理以支持牧草作物的可持续生产具有重要意义。目前，草地管理者通过昂贵且耗时的实地调查来估计草地的营养状况和生长动态，这只能提供低时空密度的数据。利用遥感植被指数（维斯）绘制草原图有可能有助于解决这些问题。在这项研究中，我们探讨了潜在的维斯区分五个不同施肥草地群落。因此，我们在德国的长期施肥实验（自1941年以来）中收集了这些群落在2012-2014年整个生长季节的光谱特征。计算了15个维斯，并对其季节性发展进行了研究。Welch试验表明，维斯区分这些草地群落的准确性在整个生长季节各不相同。因此，选择最有前途的单一VI草地制图是依赖于光谱采集的日期。使用所有计算的维斯随机森林分类减少了生长季节内的分类精度的变化，并提供了更高的整体精度的分类。因此，我们建议仔细选择用于草地制图的维斯或使用时间稳定的方法，即，在随机森林算法中包括一组维斯。
Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and time-consuming field surveys, which only provide low temporal and spatial data density. Grassland mapping using remotely-sensed Vegetation Indices (VIs) has the potential to contribute to solving these problems. In this study, we explored the potential of VIs for distinguishing five differently-fertilized grassland communities. Therefore, we collected spectral signatures of these communities in a long-term fertilization experiment (since 1941) in Germany throughout the growing seasons 2012-2014. Fifteen VIs were calculated and their seasonal developments investigated. Welch tests revealed that the accuracy of VIs for distinguishing these grassland communities varies throughout the growing season. Thus, the selection of the most promising single VI for grassland mapping was dependent on the date of the spectra acquisition. A random forests classification using all calculated VIs reduced variations in classification accuracy within the growing season and provided a higher overall precision of classification. Thus, we recommend a careful selection of VIs for grassland mapping or the utilization of temporally-stable methods, i.e., including a set of VIs in the random forests algorithm.