Item Infomation

Full metadata record
DC FieldValueLanguage
dc.contributor.authorAlibakhski, S.
dc.date.accessioned2020-02-18T02:29:19Z-
dc.date.available2020-02-18T02:29:19Z-
dc.date.issued2017
dc.identifier.citationIn: Remote Sensing : open accessVolume 9 (2017), Isuue 4, article 352
dc.identifier.urihttp://tailieuso.tlu.edu.vn/handle/DHTL/4930-
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.
dc.description.urihttps://www.mdpi.com/2072-4292/9/4/352/htm
dc.languageeng
dc.subjectMNDWI
dc.subjectwetland
dc.subjectspectral index
dc.subjectmodified vegetation water index
dc.subjecttime series
dc.subjectresilience
dc.subjectearly warning signals
dc.subjectcritical transitions
dc.titleRemotely-sensed early warning signals of a critical transition in a wetland ecosystem
dc.typeBB
dc.date.update20190226151621.0
dc.date.submitte130605s2017
Appears in Collections:Tài liệu mở

Files in This Item:
There are no files associated with this item.

Bạn đọc là cán bộ, giáo viên, sinh viên của Trường Đại học Thuỷ Lợi cần đăng nhập để Xem trực tuyến/Tải về



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.