Remote Sens. 2014, 6(5), 4266-4288; doi:10.3390/rs6054266
Global Ecosystem Response Types Derived from the Standardized Precipitation Evapotranspiration Index and FPAR3g Series
1
Land Resources Management Unit, European Commission Joint Research Centre, 21027 Ispra, Italy
2
Department of Geography, University of Copenhagen, DK-1350 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Received: 16 January 2014 / Revised: 14 April 2014 / Accepted: 21 April 2014 / Published: 8 May 2014
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Abstract
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns in time-series of Standardized Precipitation Evapotranspiration Index (SPEI) and FPAR3g anomalies (1982–2011) by using an extended Principal Component Analysis. The ERTs represent region specific spatio-temporal patterns of ecosystems responding to drought or ecosystems with decreasing severity in drought events as well as ecosystems where drought was not a dominant factor in a 30-year period. Highest explanatory values in the SPEI12-FPAR3g anomalies and strongest SPEI12-FPAR3g correlations were seen in the ERTs of Australia and South America whereas lowest explanatory value and lowest correlations were observed in Asia and North America. These ERTs complement traditional pixel based methods by enabling the combined assessment of the location, timing, duration, frequency and severity of climatic and vegetation anomalies with the joint assessment of wetting and drying climatic conditions. The ERTs produced here thus have potential in supporting global change studies by mapping reference conditions of long term ecosystem changes. View Full-Text
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Remote Sens.
EISSN 2072-4292
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