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


de Jong, Rogier; de Bruin, S (2012). Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences, 9(1):71-77.

Abstract

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 wellknown 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.

Abstract

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 wellknown 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2012
Deposited On:26 Feb 2013 14:19
Last Modified:01 Sep 2017 15:33
Publisher:Copernicus Publications
ISSN:1726-4170
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.5194/bg-9-71-2012

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