Assessment of Regional Vegetation Response to Climate Anomalies

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

De Keersmaecker, W. (Katholieke Universiteit Leuven)
Lhermitte, S.L.M. (TU Delft Mathematical Geodesy and Positioning)
Hill, M.J. (University of North Dakota; CSIRO Earth Science and Resource Engineering)
Tits, L. (Flemish Institute for Technological Research)
Coppin, P. (Katholieke Universiteit Leuven)
Somers, B. (Katholieke Universiteit Leuven)

2017

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

http://resolver.tudelft.nl/uuid:97b95ad8-a75a-47f5-9bf1-b7de25ca0329

https://doi.org/10.3390/rs9010034

2072-4292

Remote Sensing, 9 (1)

Institutional Repository

journal article

© 2017 W. De Keersmaecker, S.L.M. Lhermitte, M.J. Hill, L. Tits, P. Coppin, B. Somers