 |
| Titel |
Linear trends in seasonal vegetation time series and the modifiable temporal unit problem |
| VerfasserIn |
R. Jong, S. Bruin |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 9, no. 1 ; Nr. 9, no. 1 (2012-01-05), S.71-77 |
| Datensatznummer |
250006652
|
| Publikation (Nr.) |
 copernicus.org/bg-9-71-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|