Remotely-sensed early warning signals of a critical transition in a wetland ecosystem

S. Alibakhshi, Thomas A. Groen, Miina Rautiainen, Babak Naimi

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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.
Original languageEnglish
Article number352
Number of pages19
JournalRemote sensing
Volume9
Issue number352
DOIs
Publication statusPublished - 2017

Fingerprint

ecosystem
vegetation
NDVI
time series
remote sensing
water
lake
autocorrelation
MODIS
wetlands ecosystem
index
modeling
indicator
method
satellite image

Keywords

  • METIS-322124
  • ITC-GOLD
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

Alibakhshi, S. ; Groen, Thomas A. ; Rautiainen, Miina ; Naimi, Babak. / Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. In: Remote sensing. 2017 ; Vol. 9, No. 352.
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Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. / Alibakhshi, S.; Groen, Thomas A.; Rautiainen, Miina; Naimi, Babak.

In: Remote sensing, Vol. 9, No. 352, 352, 2017.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - 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.

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