Next Article in Journal
Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index
Previous Article in Journal
Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(1), 34; doi:10.3390/rs9010034

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

1
Division of Forest, Nature and Landscape, KU Leuven, Leuven 3001, Belgium
2
Division of Crop Biotechnics, KU Leuven, Leuven 3001, Belgium
3
Department of Geoscience & Remote Sensing, Delft University of Technology, 2628 CD Delft, The Netherlands
4
Department of Earth System Science and Policy, University of North Dakota, Grand Forks, ND 58202, USA
5
CSIRO Land and Water, Canberra ACT 2601, Australia
6
Remote Sensing Department, Flemish Institute for Technological Research (VITO), Antwerp Mol 2400, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete, Parth Sarathi Roy, Randolph H. Wynne and Prasad S. Thenkabail
Received: 17 August 2016 / Revised: 15 December 2016 / Accepted: 22 December 2016 / Published: 4 January 2017
View Full-Text   |   Download PDF [7603 KB, uploaded 4 January 2017]   |  

Abstract

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. View Full-Text
Keywords: vegetation stability; non-stationarity; resistance; resilience; variance; Australia; climate change vegetation stability; non-stationarity; resistance; resilience; variance; Australia; climate change
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

De Keersmaecker, W.; Lhermitte, S.; Hill, M.J.; Tits, L.; Coppin, P.; Somers, B. Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006. Remote Sens. 2017, 9, 34.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top