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Remote Sens. 2017, 9(4), 352; doi:10.3390/rs9040352

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

1
Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, 00076 Espoo, Finland
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
3
Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 14100, 00076 Espoo, Finland
4
Center for Macroecology, Evolution and Climate (CMEC), Natural History Museum of Denmark, University of Copenhagen, Universitetparken 15, DK-2100 Copenhagen, Denmark
5
Department of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran
*
Author to whom correspondence should be addressed.
Academic Editors: Qiusheng Wu, Deepak R. Mishra and Prasad S. Thenkabail
Received: 30 September 2016 / Revised: 20 February 2017 / Accepted: 1 April 2017 / Published: 7 April 2017
View Full-Text   |   Download PDF [11036 KB, uploaded 7 April 2017]   |  

Abstract

The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called ‘regime shifts’ or ‘critical transitions’, can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation-water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone. View Full-Text
Keywords: critical transitions; early warning signals; resilience; time series; modified vegetation water index; spectral index; wetland; MNDWI critical transitions; early warning signals; resilience; time series; modified vegetation water index; spectral index; wetland; MNDWI
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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).

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MDPI and ACS Style

Alibakhshi, S.; Groen, T.A.; Rautiainen, M.; Naimi, B. Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem. Remote Sens. 2017, 9, 352.

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