Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine

Liu, Chenli; Li, Wenlong; Wang, Wenying; Zhou, Huakun; Liang, Tiangang; Hou, Fujiang; Xu, Jiang; Xue, Pengfei

doi:10.1016/j.catena.2021.105500

Cited by 87 publications

(64 citation statements)

References 74 publications

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“…Before analyzing the spatial autoregression relationship, we need to verify the spatial weighted relationship between different spatial units to calculate the Moran's I index of the carbon emission in China's transportation industry. According to the Liu, C., et al [29], this article uses the spatial inversed distance matrix to reflect the spatial relationship between different spatial units, and adopts the distance between different provincial capital cities to reflect the distance between different provinces. Then, we can calculate the spatial Moran's I index between different regions [30], and have:…”

Section: The Spatial Relationship Analysis Modelmentioning

confidence: 99%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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The carbon reduction effect of technological innovation in the transportation industry is conducive to China’s anticipated realization of carbon neutrality. Therefore, we evaluated carbon emission reduction effect of technological innovation in the transportation industry in China. Based on the panel data of 30 sample provinces in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2012 to 2018, using the Moran’I index and Getis-Ord Gi index, this paper analyzes the evolutionary trend and spatial autocorrelation of carbon emission in the transportation industry, and analyzes the impact of technological innovation on carbon emission levels of the transportation industry and its spatiotemporal differences by using the geographical and temporal weighted regression (GTWR) model by using ArcGIS 10.4 software. The conclusions are as follows: The carbon emission level of China’s transportation industry generally has been rising steadily, showing a spatial distribution pattern of high emissions in the east and low emissions in the west. The cold spots are concentrated in the western region, and the hot spots are situated in the central and eastern regions. Technological innovation has a carbon reduction effect on the transportation industry in the eastern and north-eastern regions, while the effect in other regions is not obvious. However, there is an obvious “inverted U-shaped” relationship between technological innovation and the transportation industry’s carbon emissions. The technological innovation in the transportation industry will have a significant carbon reduction effect after breaking through the technical pain points. This carbon reduction effect has a higher effect on the western region than on the eastern region. In addition, the economic development level, the fiscal expenditure proportion of the transportation industry, the higher education level, and the proportion of fixed asset investment in the transportation industry have played a positive role in reducing carbon in the transportation industry, but the spatial heterogeneity of this carbon reduction effect is relatively strong. Therefore, during the “14th Five-Year Plan” development period in China, it is necessary to continuously promote the low-carbon development of the transportation industry with technological innovation, while highlighting the differentiated carbon reduction governance, and consolidating the role of talents and fiscal support.

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Section: The Spatial Relationship Analysis Modelmentioning

confidence: 99%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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“…Temperature and precipitation are considered the most important climatic factors that affect vegetation [53,54]. Anthropogenic activities such as land-use type, population density, and distance to main roads can dramatically affect the space of vegetation growth [16,18]. The Geodetector software can only deal with discrete variables [38].…”

Section: Factors Selectionmentioning

confidence: 99%

“…We used the Create Fishnet function in ArcGIS to create a 3 km × 3 km regular grid to generate 18001 sample points. Then, we used the Extract Multi Values to Points function in ArcGIS to extract information of all variables based on the position of sample points to quantify the relationships between NDVI and potential driving factors [16].…”

Section: Factors Selectionmentioning

confidence: 99%

“…In contrast, MODIS NDVI datasets based on a single sensor are not affected by platform/sensor shift, which maintains data consistency. Due to high data quality and moderate spatiotemporal resolution, the MODIS NDVI datasets have been widely used in research of vegetation change [16][17][18][19][20]. Compared to these three main NDVI datasets, the Landsat datasets have a higher spatial resolution (30 m).…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation changes are also closely related to many other factors include the CO 2 fertilization effect, nitrogen deposition rate, and extreme weather events [25,34,35]. The vegetation change is a complicated process due to the interaction effect of natural and anthropogenic factors, which make it challenging to quantitatively evaluate the contribution of different factors to vegetation change [7,16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

2021

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Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions.

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Effects of soil conservation on the spatial heterogeneity of vegetation carbon sequestration in the Yellow River Basin, China

et al. 2023

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An improved understanding of the potential controls on vegetation carbon sequestration (VCS) is essential for the prediction of VCS in response to global change. Ecosystem restoration can provide remarkable contributions to VCS. However, attention to the impact of soil conservation (SC) on VCS is lacking. Therefore, the Yellow River Basin, a typical area of soil erosion in the world, was chosen as the study area. The VCS and SC trends from 2000 to 2020 were analyzed and the potential response of VCS to SC was explored by adopting correlation analysis, elastic coefficient method, geographically weighted regression model, and geographical detector model. The influence of SC drivers on the spatial heterogeneity of VCS was also revealed. Results showed the following: (1) VCS and SC had a significant upward trend, especially in the eastern monsoonal ecoregion. (2) The area with a significant positive correlation between VCS and SC accounted for 31.74% of the total, mainly concentrated in the key areas of SC, wherein a 100% increase in SC would lead to a 25%–100% increase in VCS. (3) Among the SC drivers, vegetation cover and management (C) factor and rainfall erosion force (R) factor mainly influenced spatial heterogeneity in VCS, and their interaction considerably enhances this effect. Therefore, the interaction between precipitation and vegetation should be considered when evaluating the impact of water erosion management on VCS. The results of the study have important implications for the enhancement of VCS capacity of degraded land in the ecological restoration process.

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Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine

Cited by 87 publications

References 74 publications

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Effects of soil conservation on the spatial heterogeneity of vegetation carbon sequestration in the Yellow River Basin, China

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