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Frontiers of Earth Science

, Volume 13, Issue 2, pp 385–397 | Cite as

Ecological vulnerability analysis of Beidagang National Park, China

  • Xue Yu
  • Yue Li
  • Min XiEmail author
  • Fanlong KongEmail author
  • Mingyue Pang
  • Zhengda Yu
Research Article
  • 9 Downloads

Abstract

Ecological vulnerability analysis (EVA) is vital for ecological protection, restoration, and management of wetland-type national parks. In this study, we assessed the ecological vulnerability of Beidagang National Park based upon remote sensing (RS) and geographic information system (GIS) technologies. To quantify the ecological vulnerability, 10 indices were collected by the ‘exposure-sensitivity-adaptive capacity’ model and spatial principal component analysis (SPCA) was then applied to calculate the ecological vulnerability degree (EVD). Based on the numerical values, EVD of the study area was classified into five levels: moderate, light, medium, strong, and extreme. Results showed that the average EVD value was approximately 0.39, indicating overall good ecological vulnerability in Beidagang National Park. To be specific, 80.42% of the whole area was assigned to a moderate level of EVD with the highest being the tourism developed areas and the lowest being the reservoirs and offshore areas. Ecological vulnerability of the region was determined to be affected by the natural environment and anthropogenic disturbance jointly. The primary factors included tourism disturbance, traffic interference, exotic species invasion, land use/land cover, and soil salinization. We expected to provide some insights of the sustainable development of Beidagang National Park and would like to extend the results to other wetland-type national parks in the future.

Keywords

Beidagang National Park ecological vulnerability degree exposure-sensitivity-adaptive capacity spatial principal component analysis 

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Notes

Acknowledgements

The authors greatly appreciate the State Forestry Administration of the People’s Republic of China for providing basic data and investigation reports. This work is financially supported by the National Natural Science Foundation of China (Grant No. 41771098) and Shandong Natural Science Foundation (Nos. ZR2014DQ028 and ZR2015DM004).

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Environmental Science and EngineeringQingdao UniversityQingdaoChina

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