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Linear trends in seasonal vegetation time series and the modifiable temporal unit problem

机译：季节性植被时间序列的线性趋势和可修正的时间单位问题

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pstrongAbstract./strong Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results./p.
机译：> >摘要。从卫星图像得出的植被指数（VI）的时间序列为陆生植物生产力提供了一致的监控系统。它们使得能够在涵盖的时间范围内检测和量化逐渐变化，例如，这在全球变化研究中至关重要。但是，VI时间序列通常包含较强的季节性信号，这会使更改检测变得复杂。通常，趋势是使用线性回归方法进行量化的，而串行自相关的影响是通过具有固定宽度的条带上的时间聚集来补救的。以这种方式聚合数据会产生可修改的时间单位。类似于众所周知的可修改区域单位问题（MAUP），定义这些时间单位的方式可能会影响拟合的模型参数，并因此影响检测到的变化量。本文说明了此可修改的时间单位问题（MTUP）对综合数据集和实际VI数据集的影响。发现在营养循环的全长不匹配的区域上，由于聚合引起的检测变化存在较大差异，这表明数据中的非周期性可能影响模型结果。使用26年的VI数据并在全长期间进行汇总，发现年度期间的VI收益偏差小于1％（相对于季节性调整后的数据），而对于5年的汇总窗口，偏差增加至24％ 。这表明为了避免虚假的模型结果，需要谨慎进行时间聚合。

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