Multifactor-based spatio-temporal analysis of effects of urbanization and policy interventions on ecosystem service capacity: A case study of Pingshan River Catchment in Shenzhen city, China

Li, Qian; Yang, Yu; Catena, Michela Rose; Ahmad, Shakeel; Jia, Haifeng; Guan, Yuntao

doi:10.1016/j.ufug.2021.127263

Cited by 4 publications

(3 citation statements)

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“…As a manageable external driver [25], human activities often lead to the degradation of ecological environment quality [44], and the influence on ecosystem services is more significant in a short period [45]. Urbanization-related built-up areas and transportation systems are essential indicators of human activities' stress and disturbance intensity [17]. In urbanization, more natural and semi-natural ecosystems are converted to impervious surfaces, so timely and accurate information on impervious surfaces is crucial in socioeconomic and natural environments [46].…”

Section: Driving Factors Selection and Data Processingmentioning

confidence: 99%

“…The equivalent factor assessment method can directly link regional land use and ecological quality [15,16]. Such a method is more convenient for assessing the spatial-temporal distribution of ESV in regional and global studies [17]. This method has been widely used in evaluating ESV in China, including provisioning services, regulating services, support services, and cultural services [7].…”

Section: Introductionmentioning

confidence: 99%

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Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Ding

Shu

Tang

et al. 2022

Land

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Ecosystem service value (ESV) is a crucial indicator for evaluating ecosystem health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study took the Liuxi River Basin in China as the research object and used the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area was spatially stratified by using the local bivariate spatial correlation pattern to mine the potential driving factors of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV had high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation patterns were of statistical significance. (2) The spatial distribution characteristics of ESV were affected by various driving factors to varying degrees. The order of their degree of influence on ESV was per capita green area > slope > the proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers spatial autocorrelation and spatial heterogeneity. It effectively captured the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

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Section: Driving Factors Selection and Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Ding

Shu

Tang

et al. 2022

Land

View full text Add to dashboard Cite

show abstract

“…As a manageable external driver (Dai et al, 2020;Peng et al, 2017b), human activities often lead to the degradation of ecological environment quality (Yang et al, 2021), and the in uence on ecosystem services is more signi cant in a short period (Nan et al, 2018). Urbanization-related built-up areas and transportation systems are essential indicators of human activities' stress and disturbance intensity (Li et al, 2021). In urbanization, more natural and semi-natural ecosystems are converted to impervious surfaces, so timely and accurate information on impervious surfaces is crucial in socioeconomic and natural environments (Gong et al, 2019).…”

Section: Driving Factors Selection and Data Processingmentioning

confidence: 99%

Identifying driving factors of basin ecosystem service value based on local bivariate spatial correlation patterns

Ding

Shu

Tang

et al. 2022

Preprint

View full text Add to dashboard Cite

Ecosystem service value (ESV) is a crucial indicator for evaluating ecological environment health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study takes the Liuxi River Basin in China as the research object and uses the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area is spatially stratified by using the local bivariate spatial correlation pattern to mine the potential drivers of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV has high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation pattern are of statistical significance. (2) The spatial distribution characteristics of ESV are affected by various driving factors to varying degrees. The order of their degree of influence on ESV is per capita green area > slope > proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers the spatial autocorrelation and spatial heterogeneity. It effectively captures the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

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Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

Zhang,

Lei,

Tong

et al. 2023

J. Arid Land

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In the Anthropocene era, human activities have become increasingly complex and diversified. The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities, especially in arid and semi-arid areas. In the study, we chose the economic belt on the northern slope of the Tianshan Mountains (EBNSTM) in Xinjiang Uygur Autonomous Region of China as a case study. By collecting geographic data and statistical data from 2010 and 2020, we constructed an ecological resilience assessment model based on the ecosystem habitat quality (EHQ), ecosystem landscape stability (ELS), and ecosystem service value (ESV). Further, we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis, and explored its responses to climate change and human activities using the geographically weighted regression (GWR) model. The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020. The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of “high in the western region and low in the eastern region”, and the spatial clustering trend was enhanced during the study period. Desert, Gobi and rapidly urbanized areas showed low level of ecological resilience, and oasis and mountain areas exhibited high level of ecological resilience. Climate factors had an important impact on ecological resilience. Specifically, average annual temperature and annual precipitation were the key climate factors that improved ecological resilience, while average annual evapotranspiration was the main factor that blocked ecological resilience. Among the human activity factors, the distance from the main road showed a negative correlation with ecological resilience. Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions, whereas in the areas with poorer ecological conditions, the correlations were positive. The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

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Multifactor-based spatio-temporal analysis of effects of urbanization and policy interventions on ecosystem service capacity: A case study of Pingshan River Catchment in Shenzhen city, China

Cited by 4 publications

References 44 publications

Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Identifying driving factors of basin ecosystem service value based on local bivariate spatial correlation patterns

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

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