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

Li, Peng; Deng, Wei; Liu, Ying

doi:10.3390/ijgi10090590

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…From the perspective of research methods, there are many common vegetation analysis methods, such as coefficient of variation [19], trend analysis [5], pixel dichotomy model [11], grey model [12], and so on. Coefficient of variation can be used to represent the spatial variability of soil and vegetation characteristics; combining coal mining and non-mining areas can determine the coefficient of variation threshold, which can distinguish the disturbance areas [18].…”

Section: Discussionmentioning

confidence: 99%

“…That is, the stronger the disturbance of vegetation growth. The trend analysis method mainly uses linear regression and the least square method to find the optimal fitting slope of long time series data and can be used to characterize the degeneration and growth trend of vegetation [1,5,8]. It can reflect the annual variation of NDVI.…”

Section: Methodsmentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI) can be used to measure the degradation and improvement trend of vegetation [5]. Many scholars have used remote sensing techniques to indirectly measure vegetation growth status by calculating the vegetation index.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

Ding

Zhao

2022

IJGI

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It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne SAR and Landsat OLI images in the Xishan Coalfield. The relative change rate, coefficient of variation, and trend analysis methods were used to compare the vegetation growth trends in the subsidence center, subsidence edge, and non-subsidence zones; and the vegetation coverage was predicted by the pixel dichotomy and grey model from 2021 to 2025. The results indicated that the proportions of vegetation with high fluctuation and serious degradation were 6.60% and 5.64% in the subsidence center, and its NDVI values were about 10% lower than that in the subsidence edge and non-subsidence zones. In addition, vegetation coverage showed a wedge ascending trend from 2013 to 2020, and the prediction values of vegetation coverage obtained by GM (1,1) model also revealed this trend. The residuals of the predicted values were 0.047, 0.047, and 0.019 compared with the vegetation coverage in 2021, and the vegetation coverage was the lowest in the subsidence center, which was consistent with the law obtained by using NDVI. Research suggested that ground subsidence caused by mining activities had a certain impact on the surface vegetation in the mining areas; the closer to the subsidence center, the greater the fluctuation of NDVI, and the stronger the vegetation degradation trend; conversely, the smaller the fluctuation, and the more stable the vegetation growth.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

Ding

Zhao

2022

IJGI

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“…Regional differences include the following: the carbon stock in the core area decreases throughout the entire area, and the extended area's decreasing area of carbon stock extends from the central part to the northeast; the decreasing area in the new development area is dispersed; the decreasing area in northeast Chongqing only appears in the central and western part, and the overall trend is increasing; the increase in the central and eastern part of southeast Chongqing is the most pronounced, which gradually expands to the whole region with a better ecological environment. The ecological environment has bene ted drastically in recent years as a result of ecological projects like the protection of forests in the middle and upper reaches of the Yangtze River, which have been successively identi ed in China (Peng et al, 2021), and the improvement of the ecological environment in southeast Chongqing corresponds with this trend.…”

Section: Spatial Characteristicsmentioning

confidence: 99%

Theory of the driving model of land use change on the evolution of carbon stock: A case study of Chongqing, China

Zheng

Chen

et al. 2023

Preprint

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Terrestrial ecosystems are significant carbon sinks and are crucial for understanding the regional and global carbon cycles, energy flow, and climate change. As land use change is a significant factor affecting ecosystem carbon stocks, studying it is essential to comprehending the evolution of regional carbon sink functions and achieving sustainable development goals. The drastically diverse land use patterns in each of the study area's functional areas resulted in significant differences in carbon stocks between them. This study explores the evolution traits of carbon stocks based on land use data and their driving mechanisms in Chongqing during the past 30 years by using spatial analysis, the InVEST model, and geographic probes. The results demonstrate the significant change in land use change in the study area, which led to a 5.1078Tg decrease in total carbon stock, a decline of 1.5%. The main pathway for carbon loss pathway in the evolution of carbon stock is the conversion of cropland to construction land, and the primary carbon compensation pathway is the conversion of grassland and cropland to forest land, with a spatial distribution characterized by "higher in the whole area and obvious local differences". The degree of land use contributes most to the evolution of carbon stocks. Moreover, the interaction of pairwise factors played a more important role in affecting the evolution of carbon stocks than did each factor individually. The case study in this paper shows that land use change is a significant driving mechanism for the evolution of carbon stock, and the development of a driving model theory is appropriate for deciphering the trajectory of carbon stock evolution and offering research suggestions for other regions.

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“…In addition, among the anthropogenic factors, the implementation of forestry ecological projects effectively restored the regional vegetation, which was the main reason for the improvement of vegetation cover [31]. The secondary reason is the influence of urbanization, and the recent study on the characteristics of vegetation dynamics under county-scale urbanization found that the impacts of urbanization on vegetation change showed a significant spatial heterogeneity due to the differences in natural background and economic development stages and represented a "U"-shaped relationship between urbanization rate and the slope of NDVI change [32]. Although some analyses have been conducted on the impacts of climate change and human activities on the vegetation change in the SACA, there is still a lack of systematic research on the driving mechanism of this vegetation change.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Vegetation and Its Driving Mechanisms in the Southwest Alpine Canyon Area of China

Lai,

Zhao,

2023

Forests

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The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA.

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

Cited by 9 publications

References 37 publications

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

Theory of the driving model of land use change on the evolution of carbon stock: A case study of Chongqing, China

Spatiotemporal Variation in Vegetation and Its Driving Mechanisms in the Southwest Alpine Canyon Area of China

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