Leaf Area index is widely used metric for the assessment of vegetation dynamics and can be used to assess the impact of regional/local climate conditions. The underlying continuity of high resolution spatio-temporal phenological processes in the presence of extensive missing values poses a number of challenges in the detection of changes at a local and regional level. The feasibility of functional data analysis methods were evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation of leaf area index (LAI) is conducted in the Columbia Watershed, as detected by NOAA AVHRR satellite imaging, and its inter- and intra-annual correlation with maximum temperature and precipitation using the ERA-Interim Reanalysis from 1996 to 2017. A functional cluster analysis model was implemented to identify regions in the Columbia Watershed that exhibit similar long-term greening trends. Across these several regions, the primary source of annual LAI variation is a trend toward seasonally earlier and higher recordings of regional average maximum LAI. Further exploratory analysis reveals that although strongly correlated to LAI, maximum temperature and precipitation do not exhibit clear longitudinal trends.
翻译:在本文件中,对功能性数据分析方法的可行性进行了评估,以改进对这些数据的勘探,哥伦比亚河流域对叶叶面积指数的多十年变异进行了调查,如诺阿AVHRR卫星成像所探测的,以及1996年至2017年使用ERA-I临时再分析发现的最高温度和降水量与最高温度和降水量的年间和年内关联性。实施了功能性集群分析模型,以确定具有类似长期绿化趋势的哥伦比亚河流域区域。在这三个区域,年度LAI变化的主要来源是区域平均降水面积指数的季节性更早和更高记录的趋势。进一步探索分析显示,虽然与LAI密切相关,但最高温度和降水量没有明显纵向趋势。