Ecological vulnerability and influencing factors along an expressway in the agro-pastoral ecotone of northern China: a case study of the Su-Hua expressway.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Apr 13;16:17260. doi: 10.1038/s41598-026-47732-y Ecological vulnerability and influencing factors along an expressway in the agro-pastoral ecotone of northern China: a case study of the Su-Hua expressway Yuwen Tang 1 , Yan Li 1 , Lin Lin 1, ✉ Author information Article notes Copyright and License information 1 School of Geographical Sciences, Harbin Normal University, Harbin, 150025 China ✉ Corresponding author. Received 2025 May 27; Accepted 2026 Apr 2; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13234279  PMID: 41974828 Abstract Due to economic development, the construction of expressways has increased. This study used gross primary productivity (GPP) data from 2010 to 2021 to assess the ecological vulnerability along the Su-Hua Expressway. The geographical detector method was used to analyze the spatiotemporal variations in ecological vulnerability and its drivers in a 2000-m buffer zone around the Su-Hua Expressway. The goal was to provide crucial information for regional environmental monitoring and management along expressways in the agro-pastoral ecotone of northern China. The following was observed. (1) Ecological vulnerability was relatively high from 2010 to 2021, with 36.5% of the region experiencing an increase before and 86.3% after road construction. (2) Human activity and natural factors were the primary factors influencing ecological vulnerability. The key environmental factors were annual mean precipitation, elevation, annual mean temperature, slope, and land use type. Expressway construction and converting farmland to grassland were the dominant human activities. (3) The ranking of the influencing natural factors was mean annual precipitation > elevation > temperature > slope > land use type. Expressway construction increased the mean value of ecological vulnerability by 0.59. Human activities had adverse effects on the environment. Keywords: Ecosystem vulnerability, Gross primary productivity, Geographic detector, Agro-pastoral ecotone Subject terms: Ecology, Environmental sciences Introduction The expansion of the road network has significantly shortened the geographic distance between cities 1 . Although expressways have positive impacts, expressway construction projects can have negative environmental impacts during the construction and operation phases 2 . Therefore, monitoring ecological changes in areas along expressway construction routes is critical. Ecological vulnerability refers to an ecosystem’s susceptibility to natural and anthropogenic disturbances and its ability to recover from disturbances 3 . It is a critical indicator of ecological change, and monitoring these changes along expressways is required. Most research has focused on the quantitative evaluation and analysis of ecologically vulnerable areas 4 – 7 , the sustainable management of ecologically vulnerable areas, and the restoration of degraded ecosystems 8 , 9 . The sensitivity-resilience-pressure (SRP) model, principal component analysis, hierarchical analysis, and other methods 10 – 12 are commonly used to assess ecological vulnerability, its spatiotemporal evolution, and influencing factors. For example, Kang 13 established an evaluation index system based on meteorological and socioeconomic data to examine the sensitivity of ecosystems to disturbance and their adaptive capacity and predict vulnerability in the Jiaozhou Bay region from 1981 to 2050. Li 14 used an exposure-sensitivity-adaptive model based on land use, meteorological, and socioeconomic data to evaluate the social-ecological vulnerability of the middle reaches of the Black River. These studies predominantly focused on large regions, such as river basins, or single ecosystem types like forest areas, with limited attention paid to complex transitional zones like agro-pastoral belts along expressways. Research methodologies regarding ecological vulnerability factors are relatively simplistic, often overlooking anthropogenic impacts and failing to account for the spatiotemporal variations of influencing factors. Therefore, this study utilizes gross primary productivity (GPP) data to quantify ecological vulnerability because GPP is substantially affected by external disturbances and reflects ecosystem health. The geographic detector method is used to identify the dominant factors affecting ecological vulnerability and the underlying causes based on a thorough analysis of spatial heterogeneity 15 , 16 . The Su-Hua Expressway covers a large area with complex regional landforms in northern China’s agro-pastoral areas and runs through many ecologically sensitive areas. The ecological vulnerability of this area is characterized by significant spatial variability due to the combination of anthropogenic and natural influence 17 , 18 . This study utilizes MODIS GPP data (2010–2021) to quantify ecological vulnerability along the Suhua Expressway. The primary impacts of road construction on ecological vulnerability are examined in a buffer zone around the highway using the geodetector method. The proposed approach is an effective method for evaluating ecological changes along highway corridors, improving monitoring during road construction, and providing insights into environmental protection and restoration of surrounding areas. The research framework is shown in Fig. 1 . Fig. 1. Open in a new tab Research framework. Overview of the study area The Su-Hua Expressway is located in the eastern part of the Inner Mongolia Autonomous Region (41°52′-42°30′N, 112°48′-114°17′E) (Fig. 2 ). It is a 156-km-long section of the Su–Zhang Expressway from Sonid Right Banner to Huade (Mongolia–Hebei border). The construction of the expressway began in 2018. The construction area, which covers three flag counties in Inner Mongolia, is located in an arid and semiarid region. The region has a temperate continental climate. The area has a complex geomorphology, with higher elevations in the east than in the west. It covers the southwestern part of the Inner Mongolia Plateau, the Daxingan Mountains, the Yinshan Mountains, and the Taihang Mountains. The Su-Hua Expressway runs through many ecologically sensitive areas and complex and diverse natural environments. Therefore, we chose the construction area along the Su-Hua Expressway for evaluating ecological vulnerability. Fig. 2. Open in a new tab Overview of the study area. Data sources and research methodology Data sources The following data covering the period from 2010 to 2021 were used: (1) The MODIS GPP monthly vegetation dataset obtained from the National Aeronautics and Space Administration (NASA) ( https://www.nasa.gov/ ), with a spatial resolution of 500 m. (2) Meteorological data were obtained from the China Meteorological Data Network ( http://data.cma.cn/ ). It included average annual temperature and precipitation data from 10 meteorological stations around the expressway. (3) The China Land Cover Dataset (CLCD) with a 30-m resolution was sourced from Wuhan University. The first-level land use/cover classes were used: cropland, grassland, water, bare land, and impervious surfaces. (4) A digital elevation model (DEM) with a spatial resolution of 12.5 m was obtained from NASA ( https://www.nasa.gov/ ), and slope data were extracted. (5) Expressway road network data were obtained from OpenStreetMap (OSM) ( https://www.openstreetmap.org/ ). Research methods Kriging interpolation Kriging interpolation, also known as spatial local interpolation, is a method based on the spatial correlation between data points to obtain unbiased and optimal estimates of regional variables in a finite region 19 : 1 where is the predicted value at position W 0 ; B(W i ) represents the measured values of n known samples at W i locations (i = 1,2,…,n); i is the residual weight corresponding to the measured value. Ecological vulnerability estimation Ecological vulnerability encompasses ecological sensitivity and adaptability 20 , 21 . An interaction exists between ecological sensitivity and adaptability, reflecting ecosystem vulnerability. Therefore, these two factors should be quantified 22 to reflect the impact of driving factors on the ecosystem. GPP was used in this study as a proxy of ecological vulnerability 23 , 24 . Ecological sensitivity refers to the degree of disturbance affecting the system. The more significant the disturbance, the greater the degree of ecological sensitivity 25 , 26 , 27 . We chose GPP data from 2010 to 2021: 2 where i denotes the ith year (n is 12 years), Fi denotes the GPP in the ith period, F denotes the mean GPP in the n period, and S denotes the variability of GPP, i.e., ecological sensitivity. Ecological adaptability arises from long-term natural selection and is influenced by changes in the natural environment and anthropogenic factors 25 – 27 . The slope of the linearly fitted trend line of the interyear GPP variation was used to reflect changes in ecological adaptability: 3 The slope A of the fitted straight line was extrapolated to obtain the following equation: 4 where X is the interyear variation in GPP, i.e., the absolute GPP for each year. It is the GPP for each year minus the average GPP from 2010 to 2021; y is the ecosystem time series, which refers to 1, 2, 3, …, and corresponds to the years 2010–2021; A is the slope of the regression line of y and x of the dataset, which is the trend in the variability of GPP, reflecting ecological adaptability; B is the intercept. The ecological sensitivity and adaptability results were standardized, and the ecological vulnerability was calculated: 5 where V is the ecological vulnerability, S' is the system’s sensitivity, and A’ is the system’s adaptability. Vulnerability indicators In order to show the difference of ecological vulnerability along the highway more intuitively, the ecological vulnerability index is normalized. 6 where S i denotes the normalized value of the ecological vulnerability index; E i , E i,max and E i,min represent the unnormalized value, maximum value and minimum value of the ecological vulnerability index, respectively. The ecological vulnerability was classified using the natural breakpoint method 28 into five levels.In this study: not (0–0.27), mild (0.27–0.43), moderate (0.43–0.57), severe (0.57–0.72), and extreme (0.72–1). Geographic detector The geographic or geo-detector method is an effective means for assessing the spatial dissimilarity of single or multiple factors 29 . We use the geo-detector’s factor and interaction identification modules to determine the degree of factor influence. 7 where h = 1,2…, L is the stratification of variable Y or factor X; N h and N are the number of units in layer h and the entire region, respectively; and σ 2 are the variances in the Y values of layer h and the entire region, respectively; and SSW and SST are the sum of the layer’s variance and the region’s total variance, respectively. Results and discussion The ecological vulnerability was analyzed in six buffer zones (500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m) centered around the expressway(Fig. 3 ). As the radius of the buffer zone increased, the proportion of areas with lower ecological vulnerability levels (not vulnerable and mildly vulnerable) decreased, and that of areas with higher vulnerability levels (severely vulnerable and extremely vulnerable) increased. This study focused on the 2000 m buffer zone, which exhibited the largest changes in ecological vulnerability. Fig. 3. Open in a new tab Spatial variation of ecosystem vulnerability in the study area. Spatiotemporal changes in ecological vulnerability The normalized ecological vulnerability ranges from 0 to 1,the average value of ecological vulnerability from 2010 to 2021 was 0.51, with significant spatial variability. The spatial variability was high in the southeast and low in the northwest (Fig. 4 ). Areas with relatively high ecological vulnerability were located east of the central part of the study area in Xianghuang Banner and Huade County. These regions have high elevations, extensive grassland areas, and relatively sensitive ecosystems. Thus, the expressway construction resulted in relatively high ecological vulnerability. Areas with lower ecological vulnerability were located in Sonid Right Banner in the west, which has a step-like terrain, high biodiversity, and high ecological adaptability. Therefore, highway construction had a minor effect on ecological vulnerability. The ecological vulnerability of the study area was high, with the largest proportion of mild, moderate, and severe vulnerability classes, which comprised 74% of the study area. Fig. 4. Open in a new tab Time variation of ecosystem vulnerability. A Sankey diagram was used to assess the temporal evolution of ecosystem variability. The ecological vulnerability was examined before (2013) and after (2017) the highway construction by dividing the period into three phases (Fig. 5 ). The ecological vulnerability was relatively low from 2010 to 2013 (the first stage). The dominant level was mildly vulnerable, accounting for 32.7% of the area, followed by not vulnerable (22.1%) and moderately vulnerable (24.5%). The ecological vulnerability decreased from 2014 to 2017 (the second stage). The dominant level was not vulnerable, accounting for 71.6% of the area, followed by mildly vulnerable (26.1%). From 2018 to 2021 (the third phase), ecological vulnerability increased significantly, with extremely vulnerable areas accounting for 92.2% of the total, followed by severely vulnerable areas (7.7%). Fig. 5. Open in a new tab Sankey diagram of temporal evolution of ecosystem vulnerability. The spatial distribution of ecological vulnerability in the three stages is shown in Fig. 6 . In the first stage, ecological vulnerability was high at both ends and low in the middle, with high vulnerability in the central-eastern region. In the second stage, ecological vulnerability exhibited a patchy distribution, with low spatial continuity. In the third stage, ecological vulnerability increased from west to east and was the highest among the three stages. In the second stage, only 36.5% of the area exhibited higher ecological vulnerability than in the first stage. The changes in vulnerability differed in different areas, with the central west area showing the largest increase. In the third stage, 86.3% of the area had higher vulnerability than in the second stage, with the ecologically sensitive central eastern area displaying the largest increase. Expressway construction had a more significant impact on ecologically sensitive areas. Fig. 6. Open in a new tab Spatial distribution map of influencing factors. Factors affecting ecological vulnerability Ecological vulnerability is an ecosystem’s susceptibility to natural and anthropogenic disturbances. The relationships between these factors and ecological vulnerability were analyzed. Influence of environmental factors on ecological vulnerability Five factors were selected: land use type, multiyear average temperature, multiyear average precipitation, elevation, and slope. The spatial distributions of the influencing factors are shown in Fig. 7 . Pearson correlation analysis was used to determine the linear correlation between the five factors and ecological vulnerability. The results are listed in Table 1 . Fig. 7. Open in a new tab Spatial distribution of influencing factors. Table 1. Correlation between ecological vulnerability and environmental factors. Land use type Annual average temperature Annual average precipitation Elevation Slope Correlation coefficient − 0.122 − 0.54 0.712 0.716 0.138 p-value 0.00 0.00 0.00 0.00 0.00 Open in a new tab The correlations show that land use type and ecological vulnerability are negatively and weakly correlated. Figure 7 a and b depict the relationship between land use type and ecological vulnerability for grassland, cropland, bare ground, and impervious surfaces. The results indicate that the land use types in this area are relatively simple.The environment is relatively vulnerable, and an inappropriate transformation of the land use type increases ecological vulnerability. Table 1 shows that air temperature is negatively correlated and precipitation is positively correlated with ecological vulnerability, and the correlation between the two factors is high. As shown in Fig. 7 a and d, the average multiyear temperature ranges from 3.8 to 4.6 °C. The vegetation in areas with high temperatures grows for a relatively long period, and the snow melts relatively fast, increasing the amount of water used for irrigation and reducing ecological vulnerability. Thus, a negative correlation exists between the temperature and ecological vulnerability. Figure 7 a and c indicates that the average precipitation ranges from 315 to 430 mm, exhibiting spatial variability. Grasslands dominate the study area in regions with annual precipitation of 200 mm to 400 mm. An increase in precipitation has adverse effects, resulting in a positive relationship between ecological vulnerability and precipitation. Elevation, slope, and ecological vulnerability are positively correlated, with the highest coefficient for elevation and the lowest for slope. As shown in Fig. 7 a, e, and f, the elevation ranges from 1052 to 1599 m. Precipitation and heat conditions are generally more favorable for vegetation growth in low-altitude areas than in high-altitude areas, resulting in a greater variety of vegetation types and lower vulnerability. The slope range is 0–24°, and most slopes are less than 5°, indicating gentle slopes. Thus, the correlation between slope and ecological vulnerability is low. Dominant factors influencing ecological vulnerability The results of the geo-detector and grey relational analyses are listed in Table 2 . The ranking of the factors was multiyear average precipitation (P) > elevation (E) > multiyear average temperature (T) > slope (S) > land use type (L). The factor with the most significant explanatory power was multiyear average precipitation. The southeast of the study area has abundant precipitation, high elevations, and steep slopes. Intense precipitation events, coupled with the complex high-elevation topography, likely increase soil erosion and land degradation, increasing the system’s vulnerability. Due to long-term water scarcity in the agro-pastoral ecotone of northern China, high precipitation, particularly when coupled with high variability, may result in significant vegetation growth, leading to increased water consumption, long-term hydrological stress, and high ecological vulnerability. Thus, precipitation significantly affects ecological vulnerability. Elevation was the factor with the second-highest explanatory power. The study area has undulating terrain, and the ecological vulnerability increases with elevation. The grassland growing season is short, and temperature changes rarely occur in spring and summer. Therefore, the influence of the multiyear average temperature was high. The low explanatory power of the slope and land use type occurred because both factors are relatively homogeneous, with low spatial variability, resulting in a minor influence on ecological vulnerability. Table 2. Results of geo-detector and grey relational analyses. Independent variable T P E S L Q-value 0.321 0.569 0.535 0.035 0.017 Open in a new tab V: Vulnerability; T: Mean annual temperature; P: Mean annual precipitation; E: Elevation; S: Slope; L: Land-use type. The pairwise interaction between the influencing factors is shown in Fig. 8 . Most of the five variables exhibited interaction effects. The explanatory power of the factors increased significantly after considering bivariate interactions, indicating that the spatial distribution of ecological vulnerability was the result of the influence of multiple factors. The q value revealed that the dominant interaction was between the multiyear average air temperature and elevation (0.6487), followed by the mean annual air temperature and mean annual precipitation (0.6449), indicating that the influence of air temperature was more apparent when it interacted with other factors. Fig. 8. Open in a new tab Interaction factor score. The land use type exhibited interactions with elevation, slope, and temperature. Changes in environmental factors are often synergistic. More effort is required to improve the environment and reduce ecological vulnerability when addressing environmental factors that are difficult to change. Impact of human activities on ecological vulnerability Figure 5 shows that the ecological vulnerability was the lowest in the second stage. The mean value decreased by 0.18 from the first stage to the second stage, which was due to changes in the ecological environment and the reduction in ecological vulnerability resulting from the policy of converting farmland to forests and grasslands in Inner Mongolia implemented in 2000. The mean ecological vulnerability increased by 0.59 from the second to the third stages because of expressway construction between late August 2018 and December 2021. Demolition and construction preparation occurred in 2017–2018. Most of the grassland ecosystems along the project had low resilience, resulting in adverse effects on the regional ecological balance. In addition, a rare period of high temperatures occurred in the Wulanchabu section of the Su-Hua Expressway, exacerbating the vulnerability of an ecosystem affected by drought and water scarcity. The Su-Hua Expressway passes through Huade County, Wulanchabu City. The illegal disposal of industrial waste occurred in the Xinghe County Xingwangjiao Industrial Park adjacent to the Su-Hua Expressway, from 2017 to 2018, significantly polluting the area and reducing ecological stability near the expressway. In this study, human activities included the return of farmland to grassland, expressway construction, and industrial pollution. Favorable human activities can reduce ecological vulnerability and improve the environment. Ecological critical areas and ecological management The distribution of severely and extremely vulnerable areas in this study aligns with the extent of ecologically vulnerable areas outlined in the National Ecologically Vulnerable Area Protection plan . These are key areas for agriculture in the northern and eastern parts of the route. The study area is located in an ecologically critical area vulnerable to climate change and other external disturbances. Its vulnerability is high. Due to increased expressway construction, disturbances to ecosystems have become more significant. Therefore, monitoring and evaluating changes in the environment along expressways is crucial. Many domestic and international scholars have investigated environmental changes along expressways. Wen 30 analyzed ecologically vulnerable areas. Zhang et al. 31 employed the technique for order of preference by similarity to an ideal solution (TOPSIS)–adversarial interpretive structural model (AISM) model to assess ecological risks along major linear engineering projects. Alshetty & Nagendra 32 investigated the impact of expressway construction on air quality. Most studies used single evaluation indices to assess ecological vulnerability along expressways. This study examined the effects of several factors on ecological vulnerability along an expressway. The changes in the environment before and after the start of expressway construction were compared. The proposed evaluation method is effective for assessing ecological vulnerability along expressways. The vulnerability is high under the joint influence of climate and human activities. Protecting ecologically critical areas is everyone’s responsibility. The negative impact of expressway construction and operation on ecosystems can be reduced by increasing vegetation cover along the expressway and strengthening restoration and protection measures. Transportation convenience can be utilized to expedite the transportation and deployment of materials and personnel for environmental management, improve management efficiency, and ensure the effective implementation of environmental protection measures. Conclusion This study investigated spatial differences in ecological vulnerability along the Su-Hua expressway using GPP data. The dominant factors affecting ecological vulnerability were analyzed. The conclusions are as follows: The distributions of ecological vulnerability along the expressway were consistent across spatial scales. The proportion of the vulnerability degree changed with an increase in the buffer zone. No noticeable change in ecological vulnerability occurred when the buffer zone exceeded 2000 m, indicating this was the maximum range of the impact of expressway construction. The average value of ecological vulnerability from 2010 to 2021 was 0.11, with significant spatial variability. The spatial variability was high in the southeast and low in the northwest. The dominant vulnerability levels were mild, moderate, and severe, accounting for 74% of the study area. Spatial and temporal variability in ecological vulnerability was observed. Ecological vulnerability was high at both ends and low in the middle in the first stage. A patchy distribution occurred in the second stage, with low spatial variability. The third stage exhibited an increase from west to east. Ecological vulnerability decreased and increased during the study period. The geographic detector results revealed that environmental factors, such as temperature, precipitation, elevation, slope, and land use type, had the most significant effects on ecological vulnerability. Different factors had different degrees of influence. The ranking of the influencing factors was multiyear average precipitation (P) > elevation (E) > year average temperature (T) > slope (S) > land use type (L). Multifactor interaction had a larger influence than single factors, and temperature was the key factor in these interactions. Human activities significantly affected ecological vulnerability during different periods. They included expressway construction and converting farmland to grassland. Expressway construction increased the mean value of ecological vulnerability by 0.59, and converting farmland to grassland decreased the mean value by 0.18. Author contributions Tang, Y.W.: Conceptualization, Data Curation, Methodology, Writing—Original Draft; Li, Y.: Formal Curation, Resources, Visualization; Lin, L.: Supervision, Writing—Review & Editing. All authors reviewed the manuscript. Funding The author received no funding for this work. Data availability The datasets generated during the current study are publicly available. (1) GPP:NASA, https://www.nasa.gov/ . (2) Meteorological:China Meteorological Data Network, http://data.cma.cn/ . (3) Comprehensive Land Cover Dataset (CLCD dataset):Wuhan University.(4) DEM: NASA, https://www.nasa.gov/ .(5) Expressway road network:OpenStreetMap (OSM, https://www.openstreetmap.org/ ). Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Zhang, X. Study on the influence of highway construction on urbanization in Gansu Province. Lanzhou University of Finance and Economics. (2019). 2. Han, W. Ecological environment protection in expressway construction—Review of expressway environmental protection engineering (second edition). Environ. Eng. 268 (10), 250–250 (2020). [ Google Scholar ] 3. Weißhuhn, P., Müller, F. & Wiggering, H. Ecosystem vulnerability review: Proposal of an interdisciplinary ecosystem assessment approach. Environ. Manage. 61 (6), 904–915. 10.1007/s00267-018-1023-8 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 4. Huo, T., Zhang, X., Zhou, Y. & Chen, W. Evaluation and correlation analysis of spatial-temporal changes of ecological vulnerability based on VSD model :A case in Suzhou section,Grand Canal of China. Acta Ecol. Cinica 42 (06), 2281–2293 (2022). [ Google Scholar ] 5. He, Y., Yuan, Y., Wang, T., Zhang, H. & Chen, Y. Integrated assessment of marine ecological vulnerability in the Yangtze River Estuary using GIS. Acta. Ecol. Cinica 39 (11), 3918–3925 (2019). [ Google Scholar ] 6. Sun, G., Lu, H., Zheng, J., Liu, Y. & Ran, Y. Spatioa-temporal variation of ecological vulnerability in Xinjiang and driving force analysis. Arid Zone Res. 39 (01), 258–269 (2022). [ Google Scholar ] 7. Li, D., Han, C., Yang, J. & Gu, H. Temporal and spatial distribution changes,driving force analysis and simulation prediction of ecological vulnerability in Liaoning Province, China. Land 11 (7), 1025–1025. 10.3390/land11071025 (2022). [ Google Scholar ] 8. Song, G., Li, Z., Yang, Y., Semakula, H. M. & Zhang, S. Assessment of ecological vulnerability and decision-making application for prioritizing roadside ecological restoration: A method combining geographic information system, Delphi survey and Monte Carlo simulation. Ecol. Ind. 52 , 57–65 (2015). [ Google Scholar ] 9. Zhang, Y., Zhang, J. & Gong, J. Landscape pattern vulnerability and its influencing factors on a semi-arid lake basin: A case study of Liangcheng County. Arid Zone Res. 39 (04), 1259–1269 (2022). [ Google Scholar ] 10. Xu, P. Evaluation Of Ecological Vulnerability in Shennongjia Forestregion Based on SRP-AHP Model. Huazhong Agricultural University. (2022). 10.27158/d.cnki.ghznu.2022.000476. 11. Deng, W., Yuan, X., Sun, R. & Zhang, Y. Eco-vulnerability assessment based on remote sensing in the argo-pastoral ecotone of North China. Environ. Sci. Technol. 39 (11), 174–181 (2016). [ Google Scholar ] 12. Zhao, D. & Wu, S. Responses of vulnerability for natural ecosystem to climate change in China. J. Geogr. Sci. 24 (02), 237–248 (2014). [ Google Scholar ] 13. Kang, T. The research on Vulnerability of Agro-Ecosystem Affected by Climate Change in Jiaozhou Bay area. ( Master’s thesis, Anhui Agricultural University ). (2015). 14. Li, J. Effects of Land Use and Land Cover Change on the Vulnerability of Social Ecosystem in the Middle Reaches of Heihe River. Northwest Normal University. (2016) 15. Ye, Y., Qi, Q., Jiang, L. & Zhang, A. Impact factors of grain output from farms in Heilongjiang reclamation area based on geographical detector. Geogr. Res. 37 (01), 171–182 (2018). [ Google Scholar ] 16. Zhao, X., Zhu, M., Lei, J., Chen, L. & Wu, J. Spatio-temporal changes and driving forces of ecosystem service value in the headwater region of Dongjiang river. J. Hydroecol. 44 (02), 26–33 (2023). [ Google Scholar ] 17. Huang, Y., Cheng, J. & Wang, P. Spatiotemporal evolution pattern and driving factors of ecological vulnerability in agro-pastoral region in northern China: A case of Yanchi County in Ningxia. Arid. Land Geogr. 44 (04), 1175–1185 (2021). [ Google Scholar ] 18. Zhang, X., Wei, W., Zhou, L., Guo, Z. & Li, Z. Analysis on spatio-temporal evolution of ecological vulnerability in arid areas of Northwest China. Acta Ecol. Sin. 41 (12), 4707–4719 (2021). [ Google Scholar ] 19. Liu, S. et al. Analysis of spatial and temporal differences and influencing factors of air quality in plateau cities based on geodetectors. J. Yunnan Normal Univ. Nat. Sci. Ed. 42 (03), 53–59 (2022). [ Google Scholar ] 20. Chen, Y., Luo, Z., Qi, S., Zhao, J. & Yuan, Y. Ecological security pattern construction of Nanchang City based on ecological sensitivity and ecological network. Res. Soil Water Conserv. 28 (04), 342–349 (2021). [ Google Scholar ] 21. Xu, X. & Yu, G. Theories of ecosystem vulnerability, adaptability and catastrophe based on the mechanisms of ecological succession. Chin. J. Appl. Ecol. 33 (03), 623–628 (2022). [ DOI ] [ PubMed ] [ Google Scholar ] 22. Wang, P., Zhao, W. & Ke, X. Evaluation and spatiotemporal evolution of ecological vulnerability of Qianjiang based on SRP model. Res. Soil Water Conserv. 28 (05), 347–354 (2021). [ Google Scholar ] 23. Yang, Y., Zhou, D., Gong, Z., Liu, Z. & Zhang, L. Ecological vulnerability and its drivers of the Loess Plateau based on vegetation productivity. Ecol. Environ. Sci. 31 (10), 1951–1958 (2022). [ Google Scholar ] 24. Wang, H., He, M., Yan, W., Ai, J. & Chu, J. Ecosystem vulnerability in the Tianshan Mountains and Tarim Oasis based on remote sensed gross primary productivity. Acta Ecol. Sin. 41 (24), 9729–9737 (2021). [ Google Scholar ] 25. Li, Y., Feng, Z., Li, L., An, D. & Wang, W. Assessment of ecological sensitivity of terrestrial ecosystem in the Yellow River Basin based on GIS. Environ. Sci. Technol. 44 (04), 219–225 (2021). [ Google Scholar ] 26. Zhang, J., Sun, J., Ma, B., Du, W. & Zhu, L. Assessing the ecological vulnerability of the upper reaches of the Minjiang River. PLoS ONE 12 (7), e0181825. 10.1371/journal.pone.0181825 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Yu, L., Cao, M., Tao, B., Li, K. & Dong, W. Quantitative assessment of the vulnerability of terrestrial ecosystems of China to climate change based on potential vegetation. Chin. J. Plant Ecol. 03 , 521–530 (2008). [ Google Scholar ] 28. Xiao, R., Ouyang, Z., Wang, X. & Zhao, T. Sensitivity of rocky desertification and its spatial distribution in southwestern China. Chin. J. Ecol. 05 , 551–554 (2005). [ Google Scholar ] 29. Zhang, T. & Yu, D. Temporal and spatial dynamics of wetland landscape patterns in the Poyang Lake Eco-economic Zone. J. Hydroecol. 43 (04), 1–7 (2022). [ Google Scholar ] 30. Wen, Y. Research on Ecological Environment Assessment and Greening Design of Fragile Roads Areas. Northeast Forestry University. (2013). 31. Zhang, J., Dong, F., Zeng, S. & Lei, Y. Ecological risk assessment along major linear projects based on the TOPSIS-AISM model. Front. Sci. Technol. Eng. Manag. 44 (04), 83–89 (2025). [ Google Scholar ] 32. Alshetty, D. & Nagendra, S. Impact of vehicular movement on road dust resuspension and spatiotemporal distribution of particulate matter during construction activities. Atm. Pollut. Res. 13 (1), 1–10 (2022). [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The datasets generated during the current study are publicly available. (1) GPP:NASA, https://www.nasa.gov/ . (2) Meteorological:China Meteorological Data Network, http://data.cma.cn/ . (3) Comprehensive Land Cover Dataset (CLCD dataset):Wuhan University.(4) DEM: NASA, https://www.nasa.gov/ .(5) Expressway road network:OpenStreetMap (OSM, https://www.openstreetmap.org/ ). 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