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Advances in Meteorology
Volume 2014 (2014), Article ID 798428, 9 pages
Research Article

Spatially Explicit Assessment of Ecosystem Resilience: An Approach to Adapt to Climate Changes

1State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
2College of Geomatics, Shandong University of Science and Technology, No. 579 Qianwangang Road, Economic & Technical Development Zone, Qingdao, Shandong 266590, China
3Department of Agricultural Economics and Rural Development, Georg-August-Universität Göttingen, Raum MZG 2016, Platz der Göttinger Sieben 5, 37073 Göttingen, Germany
4Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5University of Chinese Academy of Sciences, Beijing 100049, China
6Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China

Received 2 November 2013; Revised 4 January 2014; Accepted 11 January 2014; Published 19 February 2014

Academic Editor: Dong Jiang

Copyright © 2014 Haiming Yan et al. 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.


The ecosystem resilience plays a key role in maintaining a steady flow of ecosystem services and enables quick and flexible responses to climate changes, and maintaining or restoring the ecosystem resilience of forests is a necessary societal adaptation to climate change; however, there is a great lack of spatially explicit ecosystem resilience assessments. Drawing on principles of the ecosystem resilience highlighted in the literature, we built on the theory of dissipative structures to develop a conceptual model of the ecosystem resilience of forests. A hierarchical indicator system was designed with the influencing factors of the forest ecosystem resilience, including the stand conditions and the ecological memory, which were further disaggregated into specific indicators. Furthermore, indicator weights were determined with the analytic hierarchy process (AHP) and the coefficient of variation method. Based on the remote sensing data and forest inventory data and so forth, the resilience index of forests was calculated. The result suggests that there is significant spatial heterogeneity of the ecosystem resilience of forests, indicating it is feasible to generate large-scale ecosystem resilience maps with this assessment model, and the results can provide a scientific basis for the conservation of forests, which is of great significance to the climate change mitigation.