Geographic Exploration of the Driving Forces of the NDVI Spatial Differentiation in the Upper Yellow River Basin from 2000 to 2020

Han, Jinxu; Zhang, Xiangyu; Wang, Jianhua; Zhang, Jiaqi

doi:10.3390/su15031922

Cited by 11 publications

(4 citation statements)

References 41 publications

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“…This involved calculating trends for each pixel in the NDVI data on an annual basis [29]. The results provide insights into the spatiotemporal characteristics of vegetation cover changes across the entire study area [30]. The formula for this analysis is as follows:…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Chai,

Guan,

et al. 2023

Applied Sciences

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Investigating the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Shanxi section of the Yellow River Basin and its coal mining areas holds significant importance for dynamic vegetation monitoring and mining area management. This study employs MODIS NDVI data and combines various analytical methods, including trend analysis and coefficient of variation analysis, to reveal the characteristics of NDVI spatiotemporal variations and their response to climatic factors in the study area. The results indicate the following: (1) The overall NDVI in the Shanxi section of the Yellow River Basin exhibits a growth trend with an annual growth rate of 1.82% and a 36% increase. Among the mining areas, the NDVI increase is most prominent in the Hebaopian mining area with a 100% growth, while the QinYuan mining area shows the lowest increase at 21%; (2) The NDVI in the Shanxi section of the Yellow River Basin displays high fluctuations, with areas of moderate and high fluctuations accounting for 54.39% of the total. The Hebaopian mining area has a substantial portion of high-fluctuation areas at 38.85%; (3) According to the Hurst index analysis, future vegetation changes in the Shanxi section of the Yellow River Basin are uncertain, with approximately 9.77% of areas expected to continue improving; (4) The variations in the NDVI and climatic factors across the Shanxi section of the Yellow River Basin display spatial heterogeneity. The NDVI exhibits a positive correlation with both temperature and precipitation, with the correlation with precipitation being more pronounced than that with temperature. Precipitation exerts a more significant influence on the NDVI than temperature. These findings not only provide scientific guidance for vegetation restoration and area management in the Shanxi section of the Yellow River Basin and its mining areas but also serve as a scientific basis for decision making regarding vegetation management under the influence of climate change and human activities.

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Section: Trend Analysis Methodsmentioning

confidence: 99%

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Chai,

Guan,

et al. 2023

Applied Sciences

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“…In recent years, with the advancement of technology, improvement of production tools, and further expansion of human activities, the impact capacity has been enhanced. The negative driving effect of human activity factors on the NDVI was particularly significant [57,58].…”

Section: Main Control Factors Of Vegetation Indexmentioning

confidence: 97%

Using PLE-SEM to Quantify the Impacts of Natural and Human Factors on Vegetation Change: A Case Study of the Jialing River Basin

Guo,

Wang,

et al. 2023

Sustainability

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Vegetation cover is an important indicator reflecting changes in terrestrial ecosystems and plays an important role in regulating and maintaining ecosystem stability. To investigate the spatial and temporal variations in the NDVI (normalized difference vegetation index) and their intrinsic driving influences, this paper uses trend analysis and a barycentric model to study the temporal and spatial variation characteristics of vegetation in the Jialing River Basin from 2000 to 2020, constructs PLS-SEMs (partial least squares structural equation models), analyzes the indirect and direct effects of latent and observable variables of surface, human activities, and climate on vegetation growth, and explores the driving processes of different levels of NDVI. The vegetation center gradually migrates northwards. The impact of surface factors on the NDVI is mainly direct and positive. The impact of human activities on the NDVI is mainly direct and negative. The impact of climate factors on the NDVI is mainly positive. The driving mechanisms of low and medium NDVI are relatively similar but tend to be opposite to those of high NDVI. Medium and high NDVI values are more influenced by observable variables. The research on vegetation change and its driving factors, through indirect and direct paths, illustrates the driving processes of different latent and observable variables of the NDVI in more detail and provides a theoretical basis for the implementation of ecological restoration projects and construction of ecological civilizations in the future.

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“…In driving force analysis, linear regression has been widely used as a practical and common method [51][52][53]. To further investigate the positive or negative effects of each factor on FVC, we conducted a supplementary analysis using the linear regression method.…”

Section: Linear Regressionmentioning

confidence: 99%

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

Huang

Qiu²,

Cao

et al. 2023

Sustainability

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The Xiong’an New Area was officially established in 2018 to construct a new, intelligent, and efficient urban area to alleviate Beijing’s non-capital functions. Using Landsat satellite images, we employed the dimidiate pixel model, band operation, and transition matrix to analyze the temporal and spatial variations in FVC (Fractional Vegetation Coverage) within the Xiong’an New Area in 2005, 2013, and 2019, respectively. Urbanization rate, precipitation, temperature, and population were considered potential driving forces, which we analyzed using grey relational analysis and linear regression to explore the correlation between FVC and these factors. The findings are as follows: from 2005 to 2019, overall improvement and significant degradation have been observed. In Baiyangdian, a part of the national key ecological area, water bodies and FVC have increased. Grey relational analysis revealed that precipitation had the highest grey relational value of 0.76. The average correlation among natural factors was 0.67, while that among human factors was 0.60. Generally, the Xiong’an New Area vegetation exhibited instability, while Baiyangdian demonstrated relatively stable FVC. Grey relational analysis indicates a strong potential for social and economic development in the Xiong’an New Area.

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Geographic Exploration of the Driving Forces of the NDVI Spatial Differentiation in the Upper Yellow River Basin from 2000 to 2020

Cited by 11 publications

References 41 publications

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Using PLE-SEM to Quantify the Impacts of Natural and Human Factors on Vegetation Change: A Case Study of the Jialing River Basin

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

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