Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-41906
de Jong, Rogier; de Bruin, S; de Wit, A; Schaepman, M E; Dent, D L (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2):692 - 702.
Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Geography|
|DDC:||910 Geography & travel|
|Uncontrolled Keywords:||Seasonal Mann-Kendall|
|Deposited On:||08 Mar 2011 17:17|
|Last Modified:||28 Nov 2013 00:31|
|Citations:||Web of Science®. Times Cited: 34|
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