What is the relationship between ecosystem services and urbanization? A case study of the mountainous areas in Southwest China

Li, Peng; Wang, Xuxi

doi:10.1007/s11629-019-5390-2

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…In Europe, the area of forested land in a similar context has increased due to the introduction of policies and the designation of nature reserves [60]. "Forest conservation", "Reforestation and Revegetation", and other eco-civilizations promote environmental protection as well as sustainable development, which also leads to the continuous return of cultivated land to forest and grass [61]. This is in line with our results.…”

Section: Mop Coupling Plus Multi-scenario Simulationsupporting

confidence: 88%

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

Luo,

Wang,

Zhang

et al. 2023

Remote Sensing

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Continuously global warming and landscape change have aggravated the damage of flood disasters to ecological safety and sustainable development. If the risk of flood disasters under climate and land-use changes can be predicted and evaluated, it will be conducive to flood control, disaster reduction, and global sustainable development. This study uses bias correction and spatial downscaling (BCSD), patch-generating land-use simulation (PLUS) coupled with multi-objective optimization (MOP), and entropy weighting to construct a 1 km resolution flood risk assessment framework for the Guanzhong Plain under multiple future scenarios. The results of this study show that BCSD can process the 6th Climate Model Intercomparison Project (CMIP6) data well, with a correlation coefficient of up to 0.98, and that the Kappa coefficient is 0.85. Under the SSP126 scenario, the change in land use from cultivated land to forest land, urban land, and water bodies remained unchanged. In 2030, the proportion of high-risk and medium-risk flood disasters in Guanzhong Plain will be 41.5% and 43.5% respectively. From 2030 to 2040, the largest changes in risk areas were in medium- and high-risk areas. The medium-risk area decreased by 1256.448 km2 (6.4%), and the high-risk area increased by 1197.552 km2 (6.1%). The increase mainly came from the transition from the medium-risk area to the high-risk area. The most significant change in the risk area from 2040 to 2050 is the higher-risk area, which increased by 337 km2 (5.7%), while the medium- and high-risk areas decreased by 726.384 km2 (3.7%) and 667.488 km2 (3.4%), respectively. Under the SSP245 scenario, land use changes from other land use to urban land use; the spatial distribution of the overall flood risk and the overall flood risk of the SSP126 and SSP245 scenarios are similar. The central and western regions of the Guanzhong Plain are prone to future floods, and the high-wind areas are mainly distributed along the Weihe River. In general, the flood risk in the Guanzhong Plain increases, and the research results have guiding significance for flood control in Guanzhong and global plain areas.

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Section: Mop Coupling Plus Multi-scenario Simulationsupporting

confidence: 88%

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

Luo,

Wang,

Zhang

et al. 2023

Remote Sensing

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“…The overall level of urbanization was relatively low, but the process of urbanization was gradually accelerating; the average urbanization level increased from 24.61% in 2000 to 42.09% in 2015, and the population became rapidly concentrated in cities and towns. The ecological value of the study area is important because it is the key area of ecosystem services in China and an important component of the crucial ecological security shelter of the Yangtze River [29].…”

Section: Study Areamentioning

confidence: 99%

Understanding the Role of Urbanization on Vegetation Dynamics in Mountainous Areas of Southwest China: Mechanism, Spatiotemporal Pattern, and Policy Implications

Deng

Liu

2021

IJGI

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As an important component of terrestrial ecosystems, the mountainous areas of southwest China are facing eco-environmental stress due to rapid urbanization. This study analyzed the vegetation dynamics during urbanization in 410 counties in the mountainous area of southwest China using trend analysis and bivariate spatial autocorrelation analysis. The results demonstrate the following: (1) The regional differences in the natural background and the stage of economic development resulted in obvious geographical heterogeneity in the relationship between urbanization and vegetation coverage. (2) The relationship between the rate of urbanization level change (RULC) and the slope of the normalized difference vegetation index (NDVI) (Slope−NDVI) in the mountainous areas of southwest China is characterized by an inverted-U-shaped curve. Based on the inflection point (0.206, 57.60) of the inverted-U-shaped curve, the optimal mean annual RULC is 1.37%. (3) The relief degree of the land surface, the gross domestic product (GDP) change rate, and the slope of temperature change are positively correlated with the Slope−NDVI. Overall, an understanding of how urbanization affects vegetation dynamics can inform decisions concerning ecological restoration and urban–rural development in China.

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“…Thus, Nanjing City may be at a later stage of development and thus will fall on the right side of the Ushaped curve. Similarly, the relationships among population, light index, and ESVs are also controversial [45][46][47][48] For example, Han found that the influence trend of the population on ESV is different in the high-ESV region and low-ESV region [45]. Studies on the relationships between ESVs and urbanization generally indicate that urban expansion has positive or negative correlations with the values of different ecosystem services [47,48].…”

Section: Plos Onementioning

confidence: 99%

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Liu

Wang

et al. 2020

PLoS ONE

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The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socioeconomic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.

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What is the relationship between ecosystem services and urbanization? A case study of the mountainous areas in Southwest China

Cited by 27 publications

References 44 publications

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

Understanding the Role of Urbanization on Vegetation Dynamics in Mountainous Areas of Southwest China: Mechanism, Spatiotemporal Pattern, and Policy Implications

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

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