Assessment of Ecological Vulnerability on Northern Sand Prevention Belt of China Based on the Ecological Pressure–Sensibility–Resilience Model

Abstract

Quantitative assessment of ecological vulnerability is of great significance for ecological protection and restoration in ecologically vulnerable regions. Here, the ecological vulnerability of the northern sand prevention belt (NSPB) of China was assessed using an ecological pressure–sensibility–resilience model from 2000 to 2015. Results showed that the ecological vulnerability index (EVI) displayed low values in the eastern part and high values in the western part of the study region. The EVI ranged from 0.29 to 1.32 in 2000, with the mean value of 0.88, whereas it averaged 0.78 in 2015, ranging from 0.21 to 1.26. Decreasing EVI from 2000 to 2015 indicated that the ecological status has been improved. Moreover, the area proportion of moderately, heavily, and extremely ecological vulnerability levels occupied approximately 87% in both 2000 and 2015, indicating a high ecological vulnerability level. Furthermore, the change in area proportion of different ecological vulnerability levels were associated with the change in the spatial distribution of vegetation coverage, indicating that eco-environmental protection projects were indeed effective. These findings indicated that differential strategies in different restoration zones should be adopted, especially in the western parts of the study region, and eco-environmental protection projects should be reinforced to improve the ecological restoration.

A Long-Term Ecological Vulnerability Analysis of the Tibetan Region of Natural Conditions and Ecological Protection Programs

“…In the analysis process, this would result in overlapping and redundant information, as well as erroneous and problematic analytical findings [10,11,15,16,21,68,69]. The SPCA technique may successfully maintain relevant primary components by removing information overlap in the original indicator data [12,21,36,43]. The indicators' weights are determined using SPCA (Spatial Principal Component Analysis) based on PCA (principal component analysis) after the standardization of all indicators in ArcGIS 10.4 [64][65][66][67][68].…”

“…A total of 18 standardized indices were analyzed by principal components analysis to generate 4 new comprehensive indices. In addition, the absolute value of eigenvalue presents the extent of the correlation of indicators and principal components; the more significant the eigenvalue is, the stronger the relationship will be [11,12,17,36,68,72]. The first four principal components whose cumulative contribution rate reached 85% or more were selected; the final principal component result is shown in Table 2, and the whole contribution of original indexes of ecological vulnerability to principal components is shown in Appendix A, Table A1.…”

“…According to the above method, the higher the EVI, the more vulnerable the ecological environment. The index is obtained by the sum of the comprehensive values of multiple principal components and their corresponding weights [11,12,36,[60][61][62][63][64][68][69][70], as shown in the following formula:…”

“…Studies in Europe and coastal areas have mainly focused on the sensitivity of the ecosystem and its response to climate change and pattern change over time [25][26][27][28][29][30], which measure ecosystem tolerance and vegetation response predictions to climate anomalies. In mainland areas, research on ecological vulnerability is more concentrated on land use patterns [31][32][33], desertification soil problems [24,[34][35][36], and human disturbances [37][38][39][40][41]. Typically, vulnerable zones with different topological characteristics are studied [38][39][40] to determine the determinants of vulnerability.…”

Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework

“…Currently, research on vulnerability assessment is dominated by case studies. Some scholars have focused on different scales of study regions, mainly including countries [11,12], geographical areas [13,14], provinces [15], and cities [10,16]. Based on the characteristics of the study region, multiple methods have been proposed and used, such as the analytic hierarchy process (AHP) [17,18], principal component analysis (PCA) [19], spatial principal component analysis (SPCA) [20,21], environmental vulnerability distance index (EVDI) [22], grey system evaluation method [23], and other techniques.…”