Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.

中文翻译：

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通过卫星图像量化德国森林对干旱事件的响应

森林系统为我们的环境提供了至关重要的生态系统功能，例如平衡碳储量并影响当地，区域和全球气候。因此，气候变化频率增加导致极端天气事件（包括干旱）的趋势成为森林管理的主要挑战。在这种情况下，遥感数据的应用为在较大的空间尺度和时间上进行快速，可操作和廉价的调查提供了有力的手段。这项研究致力于探索卫星数据与谐波分析相结合的潜力，以量化植被对德国森林干旱事件的响应。谐波建模方法与z分数标准化方法进行了比较，并与气象和地形数据相关。来自Landsat的光学卫星图像和中分辨率成像光谱仪（MODIS）与三种常用的植被指数结合使用。基于谐波建模技术的最高相关分数被计算为6次谐波度。MODIS影像结合归一化植被指数（NDVI）生成，因此是测量干旱条件下光谱响应的最佳结果。观测到的遥感数据与气象措施之间的土壤湿度和自校准的帕尔默干旱严重度指数（scPDSI）之间的最强相关性。此外，已确定以松树为主要树木类型的沙质土壤上的森林地区特别容易受到干旱的影响。此外，地形分析表明，缓解了山坡上的干旱影响。