Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Landscape Ecological Risk Index Model Construction
2.3.2. Analysis of the Spatial and Temporal Variation of LERI
3. Results
3.1. Evolution of Land Use Landscape Pattern
3.2. Spatiotemporal Dynamics of Landscape Ecological Risk
3.2.1. Spatial Patterns of Landscape Ecological Risk
- The improved landscape ecological risk model was used to calculate the LERI of 5048 evaluation units in five periods and to find the LERI of the YREEB and each province respectively (Figure 4). The average LERI values of the YREEB in 2000, 2005, 2010, 2015, and 2020 were 1.3443, 1.3275, 1.3142, 1.3107, and 1.2180, respectively. This leads to no particularly significant change in the value at risk for the region, but there was a significant downward trend. The provincial-scale value showed a similar trend. The overall ecological situation in most provinces improved significantly from 2000 to 2015, as evidenced by decreasing risk, and then stabilized after 2015. The provinces of Shanxi, Ningxia, and Qinghai had relatively higher risks (Figure 4). The overall landscape structure of these regions were relatively fragmented, and the stability and resilience of ecosystems were poor. The risk to Shandong and Henan were relatively low, which indicated that the landscape resilience of these regions was better than that of the western regions. The ecological risks of Shandong and Henan showed an increasing trend from 2000 to 2010, indicating that the ecological protection and development were ignored while developing the economy in this period, which increased the vulnerability of the environment and the ecological risks. The overall risk of most provinces in this period was reduced, indicating that the ecological situation was significantly improved, thus reducing the ecological risks. Since 2010, each province has basically shown a stable development trend.
- The ecological risk index is divided into five grades by using the Natural Breaks method: low ecological risk (LERI < 0.60), medium-low ecological risk (0.60 ≤ LERI < 1.18), medium ecological risk (1.18 ≤ LERI < 1.72), medium-high ecological risk (1.72 ≤ LERI < 2.40), and high ecological risk (LERI > 2.4). The proportion of the risk index of each region is obtained and shown in Figure 5. The proportion of high ecological risk areas for the entire region has decreased from 2000 to 2020, from 9.5% to 7.4% (Figure 5a), and the number of units has decreased by 103. The proportion of low ecological risks remained almost unchanged, while the proportion of medium and low risks continued to increase. The trend of risk structure for each province was similar to that for the whole YREEB. The proportion of high risk categories in Qinghai, Gansu, and Shaanxi generally declined, while the proportion of low- and medium-risk areas increased (Figure 5b–h). The proportion of high-risk categories in Ningxia increased first and then decreased. The proportion of medium- and high-risk categories was relatively high (Figure 5d). The proportion of high-risk categories in Shanxi decreased first and then increased, reaching the highest amount in 2010, but only 1.1%. However, the proportion of medium- and high-risk categories in Shanxi was the largest in all regions, reaching 65.5% in 2000. The proportion of low- and medium-low-risk categories was the lowest in all regions (Figure 5f). The proportion of medium- and low-risk categories in Henan was the highest in all regions, with a value higher than 58% (Figure 5g). It can be concluded from the average number of units with different risk levels in each province that the regions with a high proportion of high risks were mainly concentrated in Gansu and Qinghai, and low-risk and medium-low-risk units are mainly concentrated in Henan and Shandong (Figure 5i). Henan Province had no high-risk areas, and Ningxia was dominated by medium- and high-risk units. However, due to the small size of the province, the impact on changes in the overall YREEB risk structure was weak.
- At the grid scale, the low-risk areas are generally widely distributed, mainly in Yellow River estuary areas of Shandong and Henan province (Figure 6). Human activities in the above areas were relatively infrequent, and the loss degree after human interference and the landscape interference index was small. In addition, the land use type in this area was mainly cultivated land with high land cover continuity, so the vulnerability and fragmentation of this landscape was low. The risks in the north and south sides of Qinghai were also low, because the main dominant landscapes in this area were large areas of grassland and unused land, with less human interference, good vegetation growth conditions, and good ecological protection. The medium-risk areas were mainly distributed in the middle of the study area in blocks, and only scattered in the southwest and northeast, which are mostly located in the transition area between the lower-risk areas and the higher-risk areas. The medium- and high-risk areas are relatively concentrated, mainly located in the western region, including the areas around Qinghai Lake, Qilian Mountain, and southern Gansu, where the surface vegetation was sparse, the biodiversity is low, and the land use type is single. The spatial change of ecological risk had a typical zone transition, which showed that the relatively medium-risk area appeared in the periphery of the relatively low-risk area and the relatively high-risk area, forming a gradient change of ecological risk. From 2000 to 2020, low-risk and medium-low-risk areas expanded, especially in Shanxi and Shaanxi.
3.2.2. Spatiotemporal Differences in Risk Changes
3.3. Key Areas for Ecological Risk Management
4. Discussion
4.1. Spatiotemporal Differentiation of Factors Influencing LERI
4.2. Implications for Management
4.3. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Resolution | Weight | Unit | Sources |
---|---|---|---|---|
Land use | 30 m | / | / | https://www.resdc.cn/Default.aspx (accessed on 1 June 2022) |
GDP | 1000 m | 0.2421 | 104 yuan/km2 | http://www.geodata.cn (accessed on 2 June 2022) |
Population | 1000 m | 0.1467 | Pop/km2 | https://www.resdc.cn/Default.aspx (accessed on 2 June 2022) |
Elevation | 30 m | 0.1482 | m | http://www.resdc.cn (accessed on 4 June 2022) |
Slope | 30 m | 0.0985 | % | http://www.resdc.cn (accessed on 4 June 2022) |
Temperature | 1000 m | 0.0892 | °C | http://data.cma.cn (accessed on 5 June 2022) |
Precipitation | 1000 m | 0.0647 | Mm | http://data.cma.cn (accessed on 6 June 2022) |
NPP | 1000 m | 0.1023 | g·C/m2 | http://modis.gsfc.nasa.gov (accessed on 7 June 2022) |
NDVI | 1000 m | 0.1083 | / | https://www.usgs.gov (accessed on 7 June 2022) |
Landscape Index | Equation | Implications |
---|---|---|
Landscape disturbance index () | represents a quantitative expression of the magnitude of disturbance to different landscapes within the study area [54], variables a, b, and c represent the weights of and a = 0.5, b = 0.3, c = 0.2 | |
Landscape fragmentation index () | describes the fragmentation of a continuous large area of land use type into smaller patches after being disturbed by human or natural factors [55]. is the number of patches of landscape type i in the kth risk plot, and has the same definition as those given above. | |
Landscape separation index () | reflects the degree of separation or isolation between land use patches [50], the , and have the same definitions as those given above. | |
Landscape dominance index () | indicates the dominant landscape of land use types [56,57], where is the total number of samples in which patch i occurs, is the number of patch i to the total number of patches in the kth risk plot, and is the total area of patch i to the total area of the kth risk plot. | |
Landscape vulnerability index () | captures a quantitative representation of the degree of stability of a land use type, indicating the resilience of a landscape type when it is affected by external factors or disturbances by external forces [57]. is the empirical value of landscape vulnerability of land use type i. Quantification of indicators by assigning values to different land use types through the expert scoring method: unused land = 6, water = 5, cropland = 4, grassland = 3, woodland = 2, and urban land = 1 [58,59]. is an adjustment factor of the kth risk plot. | |
Compound adjustment factor () | is the compound adjustment factor of the kth risk plots. is a composite adjustment factor reflecting the spatial and temporal heterogeneity of landscape vulnerability. is the weighted sum of indicators in the kth risk plot. | |
Weighted sum of indicators () | is the weighted sum of indicators in unit k, is the weight of indicator j, and is the standardized index value. The study mainly selected eight indicators (j = 8), GDP, population density, elevation, slope, temperature, precipitation, NPP, and NDVI [60,61], to comprehensively characterize the vulnerability of the ecological environment, and the weights are determined using the entropy value method [62]. |
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Li, M.; Zhang, B.; Zhang, X.; Zhang, S.; Yin, L. Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method. Int. J. Environ. Res. Public Health 2023, 20, 1837. https://doi.org/10.3390/ijerph20031837
Li M, Zhang B, Zhang X, Zhang S, Yin L. Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method. International Journal of Environmental Research and Public Health. 2023; 20(3):1837. https://doi.org/10.3390/ijerph20031837
Chicago/Turabian StyleLi, Meirui, Baolei Zhang, Xiaobo Zhang, Shumin Zhang, and Le Yin. 2023. "Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method" International Journal of Environmental Research and Public Health 20, no. 3: 1837. https://doi.org/10.3390/ijerph20031837