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Frontiers of Earth Science

Front. Earth Sci.    2019, Vol. 13 Issue (2) : 385-397
Ecological vulnerability analysis of Beidagang National Park, China
Xue YU, Yue LI, Min XI(), Fanlong KONG(), Mingyue PANG, Zhengda YU
College of Environmental Science and Engineering, Qingdao University, Qingdao 266071, China
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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     
Corresponding Authors: Min XI,Fanlong KONG   
Just Accepted Date: 09 November 2018   Online First Date: 11 December 2018    Issue Date: 16 May 2019
 Cite this article:   
Xue YU,Yue LI,Min XI, et al. Ecological vulnerability analysis of Beidagang National Park, China[J]. Front. Earth Sci., 2019, 13(2): 385-397.
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Xue YU
Yue LI
Min XI
Fanlong KONG
Mingyue PANG
Zhengda YU
Fig.1  Location of the study area.
Fig.2  Ecological vulnerability evaluation model in Beidagang National Park.
Type Indicator Source
Remote sensing Land use/land cover Forest, grassland, water, mudflat, rearing pond, agricultural land, constructed land
NDVI Eq. (1)
Landscape fragmentation Eq. (2)
Monitoring data Average temperature Chinese Meteorological Data Sharing Service System (
Average precipitation Chinese Meteorological Data Sharing Service System (
Water pollution Water monitoring data
Socio economic survey data Traffic interference Social and economic investigation
Tourism disturbance Wetland ecotourism investigation
Wetland survey data Exotic species invasion Wild plant investigation
Soil salinization Wetland soil survey
Tab.1  Indicator’s classification and source
Standardized value 2 4 6 8 10
Land use/land cover Forest, grassland,
Mudflat Rearing pond Agricultural land Constructed land
Traffic interference >4000 3000–4000 2000–3000 1000–2000 0–1000
Tourism disturbance >4000 3000–4000 2000–3000 1000–2000 0–1000
Exotic species invasion >6000 3000–6000 1500–3000 500–1500 0–500
Tab.2  Grade-weighted method
EVD Range
Moderate <0.42
Light 0.42–0.51
Medium 0.51–0.60
Strong 0.60–0.69
Extreme >0.69
Tab.3  Classification of EVD
Principle component Aspects
Ecological vulnerability Exposure Sensitivity Adaptive capacity
PC1 Eigenvalue 0.06 0.05 0.02 0.02
Contribution ratio/% 37.73 59.55 50.43 58.32
Cumulative contribution/% 37.73 59.55 50.43 58.32
PC2 Eigenvalue 0.03 0.02 0.02 0.01
Contribution ratio/% 20.72 17.30 37.74 26.54
Cumulative contribution/% 58.45 76.85 88.17 84.86
PC3 Eigenvalue 0.03 0.01
Contribution ratio/% 17.21 12.97
Cumulative contribution/% 75.66 89.82
PC4 Eigenvalue 0.01
Contribution ratio/% 7.03
Cumulative contribution/% 82.69
PC5 Eigenvalue 0.01
Contribution ratio/% 5.37
Cumulative contribution/% 88.06
Tab.4  The eigenvalue and contribution ratio of each PC
Item PC1 PC2 PC3
Tourism disturbance 0.61 −0.07 0.79
Exotic species invasion 0.28 0.50 −0.21
Traffic interference 0.54 −0.39 −0.49
Land use/land cover 0.51 0.77 −0.31
Tab.5  The detailed loading of exposure
Item PC1 PC2
Landscape fragmentation −0.26 0.85
Soil salinization 0.92 −0.07
Water pollution −0.30 0.52
Tab.6  The detailed loading of sensitivity
Item PC1 PC2
Average temperature 0.78 0.56
Average precipitation 0.52 0.32
NDVI 0.36 0.76
Tab.7  The detailed loading of adaptive capacity
Item PC1 PC2 PC3 PC4 PC5 Weight
Landscape fragmentation 0.23 −0.23 0.47 −0.01 0.27 0.08
Tourism disturbance 0.53 0.25 0.15 0.78 −0.14 0.20
Exotic species invasion 0.17 0.42 0.10 −0.10 0.71 0.14
Traffic interference 0.43 0.38 0.24 −0.57 −0.44 0.18
Land use/land cover 0.53 −0.04 −0.44 −0.21 0.17 0.11
Soil salinization −0.26 0.57 −0.32 0.07 −0.11 0.10
Water pollution 0.05 −0.01 0.48 −0.08 0.30 0.07
Average temperature 0.15 0.41 0.30 0.01 0.03 0.06
Average precipitation −0.24 0.22 −0.11 0.07 0.03 0.02
NDVI −0.17 0.17 0.25 −0.01 0.27 0.04
Tab.8  The eigenvalue and weight of each factor
Fig.3  The distribution of (a) land use/land cover, (b) traffic interference, (c) tourism disturbance, (d) exotic species invasion, (e) exposure index.
Item Evaluation index
EVD Exposure Sensitivity Adaptive capacity
Moderate Areas/km2 961.64
Percent/% 80.42
Light Areas/km2 97.62
Percent/% 8.16
Medium Areas/km2 68.94
Percent/% 5.77
Strong Areas/km2 47.21
Percent/% 3.95
Extreme Areas/km2 20.34
Percent/% 1.70
I Areas/km2 804.10 976.13 23.91
Percent/% 66.82 81.12 1.97
II Areas/km2 319.43 69.76 157.23
Percent/% 26.55 5.80 12.96
III Areas/km2 67.72 55.39 419.43
Percent/% 5.63 4.60 34.59
IV Areas/km2 5.40 58.77 270.88
Percent/% 0.45 4.88 22.34
V Areas/km2 6.70 73.21 341.26
Percent/% 0.56 6.08 28.14
Tab.9  The areas of each degree of exposure, sensitivity, adaptive capacity, and vulnerability
Fig.4  The distribution of (a) landscape fragmentation, (b) soil salinization, (c) water pollution, (d) sensitivity index.
Fig.5  The distribution of (a) average temperature, (b) average precipitation, (c) NDVI, and (d) adaptive capacity.
Fig.6  The spatial distribution of EVD.
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