4.8 Article

Trend changes in global greening and browning: contribution of short-term trends to longer-term change

期刊

GLOBAL CHANGE BIOLOGY
卷 18, 期 2, 页码 642-655

出版社

WILEY
DOI: 10.1111/j.1365-2486.2011.02578.x

关键词

GIMMS NDVI; global greening and browning; gradual and abrupt change detection; time series analysis; trend breaks

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Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi-arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Nino event. The net global figure considered over the full length of the time series showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short-term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long-term NDVI time series.

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