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

dc.contributorAalto Universityen
dc.contributor.authorAlibakhshi, Saraen_US
dc.contributor.authorGroen, Thomas A.en_US
dc.contributor.authorRautiainen, Miinaen_US
dc.contributor.authorNaimi, Babaken_US
dc.contributor.departmentDepartment of Built Environmenten
dc.contributor.departmentDepartment of Electronics and Nanoengineeringen
dc.contributor.organizationUniversity of Twenteen_US
dc.contributor.organizationUniversity of Copenhagenen_US
dc.description.abstractThe 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.en
dc.description.versionPeer revieweden
dc.identifier.citationAlibakhshi, S, Groen, T A, Rautiainen, M & Naimi, B 2017, ' Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem ', Remote Sensing, vol. 9, no. 4, 352, pp. 1-19 .
dc.identifier.otherPURE UUID: d4923d91-14de-4610-b04b-8b3ed5dc2b8fen_US
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dc.relation.ispartofseriesREMOTE SENSINGen
dc.relation.ispartofseriesVolume 9, issue 4en
dc.subject.keywordcritical transitionsen_US
dc.subject.keywordearly warning signalsen_US
dc.subject.keywordtime seriesen_US
dc.subject.keywordmodified vegetation water indexen_US
dc.subject.keywordspectral indexen_US
dc.titleRemotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystemen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi