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摘要

德国联合项目DeCover 2正在制订一种方法框架,以应付利用遥感技术获得最新土地覆盖资料的日益增加的需求。像RapidEye这样的新卫星系统提供了高几何分辨率和高重复率的数据。由于德国各地自然条件的多样性,相同的采伐日期并不对应于相同的物候阶段。因此,需要对全年可用图像进行物候结构,以评估快速眼成像对植被分类和区分的适用性。以2010年冬小麦的物候期“黄熟”为例,所提出的算法展示了德国全境如何根据需要,实时地并考虑到插值精度,自动插入每日物候期。作为输入,使用了来自德国气象局广泛网络的每日提供的温度和物候阶段点数据以及SRTM数字高程模型。建模结果能够识别特定测试地点的时间物候窗口。
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Automatic interpolation of phenological phases in Germany
The German joint project DeCover 2 is developing a methodological framework to cope with the increasing demand for up-to-date land cover information using remote sensing techniques. New satellite systems like RapidEye provide both data of high geometric resolution and high repetition rates. Because of the Germany-wide diversity of natural conditions, same acquisition dates don't correspond to same phenological phases. Thus, a phenological structuring of the available imagery over the year is needed for the assessment of Rapid-Eye imagery regarding their suitability for the classification and distinction of vegetation classes. On the example of the phenological phase ‘Yellow Ripeness’ of Winter Wheat in 2010, the presented algorithm demonstrates for the total area of Germany how daily phenological phases can be automatically interpolated on demand, in real-time and considering interpolation accuracies. As input, daily provided point data on temperature and phenological phases from the extensive network of the German Weather Service as well as a SRTM digital elevation model are used. The modeling results enable the identification of temporal phenological windows for specific test sites.
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