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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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Time series of vegetation indices (VI) derived from satellite imageryprovide a consistent monitoring system for terrestrial plant productivity.They enable detection and quantification of gradual changes within the timeframe covered, which are of crucial importance in global change studies, forexample. However, VI time series typically contain a strong seasonal signalwhich complicates change detection. Commonly, trends are quantified usinglinear regression methods, while the effect of serial autocorrelation isremediated by temporal aggregation over bins having a fixed width.Aggregating the data in this way produces temporal units which aremodifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP),the way in which these temporal units are defined may influence the fittedmodel parameters and therefore the amount of change detected. This paperillustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on asynthetic data set and a real VI data set. Large variation in detectedchanges was found for aggregation over bins that mismatched full lengths ofvegetative cycles, which demonstrates that aperiodicity in the data mayinfluence model results. Using 26 yr of VI data and aggregation overfull-length periods, deviations in VI gains of less than 1% were foundfor annual periods (with respect to seasonally adjusted data), whiledeviations increased up to 24% for aggregation windows of 5 yr. Thisdemonstrates that temporal aggregation needs to be carried out with care inorder to avoid spurious model results.
机译：来自卫星图像的植被指数（VI）的时间序列为陆生植物生产力提供了一个一致的监控系统，它们可以在涵盖的时间范围内检测和量化渐变，这对于全球变化研究至关重要。但是，VI时间序列通常包含较强的季节性信号，这使更改检测变得复杂。通常，趋势是使用线性回归方法来量化的，而串行自相关的影响是通过在具有固定宽度的bin上进行时间聚合来抵消的。以这种方式聚合数据会产生可修改的时间单位。与众所周知的可修改区域单位问题（MAUP）相似，定义这些时间单位的方式可能会影响拟合模型参数，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）对异步数据集和实际VI数据集的影响。发现在与营养循环的全长不匹配的区域上的聚集中，检测到的变化存在较大差异，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数据和全长的汇总数据，发现年度期间的VI增益偏差小于1％（相对于季节性调整后的数据），而5年的汇总窗口的偏差增加至24％。这表明为了避免虚假模型结果，需要谨慎进行时间聚合。

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