Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector

Li, Jie; Wang, Jinliang; Zhang, Jun; Liu, Chenli; He, Suling; Liu, Lanfang

doi:10.1016/j.ecolind.2022.108620

Cited by 53 publications

(34 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we mainly used factor detection and interaction detection. The expressions are as follows [ 54 ]:

where h takes values from 1 to L , indicating the stratification of the explanatory variable or influencing factor.

and N denote the number of cells in the whole area of stratum h .…”

Section: Methodsmentioning

confidence: 99%

Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China

Zhang

Ren

2022

IJERPH

View full text Add to dashboard Cite

In the context of continuing to promote the construction of an ecological civilization, it is of great significance to explore green development performance. However, most of the literature is based on a single perspective of level or efficiency, lacking a comprehensive examination of both. It is not scientific to explore how to promote green development only from a single perspective, which may be a new advancement by breaking the conventional thinking focusing only on level or efficiency. On this basis, we first established evaluation index systems of green development performance based on a theoretical framework. Furthermore, green development performance was measured with the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) and super-EBM models, and finally, we analyzed the spatial and temporal evolution patterns of green development performance using the ESDA method and examined its influencing factors with a geographic detector (GD) and econometric models. The main results were as follows: (1) The trend of the green development level in the Yangtze River Economic Belt from 2004 to 2017 had an inverted “N” shape, while the overall average green development efficiency continuously increased. (2) In terms of spatial and temporal patterns, both the green development level and green development efficiency showed “high in the east and low in the west” spatial divergence characteristics. In terms of the spatial and temporal evolution pattern of the green development level, the L-L clusters were mainly distributed in the western region. However, for green development efficiency, the L-L clusters were mostly distributed around the H-H clusters. (3) The results of the influencing factor analysis indicated that industrial structure and people’s welfare are still important factors of the green development level. The improvement of green development efficiency was mainly driven by economic development, and the inhibiting effect of energy consumption is significant. In addition, the effect of opening up has not yet changed from a “pollution paradise” to a “pollution halo”.

show abstract

“…In this study, we mainly used factor detection and interaction detection. The expressions are as follows [ 54 ]:

where h takes values from 1 to L , indicating the stratification of the explanatory variable or influencing factor.

and N denote the number of cells in the whole area of stratum h .…”

Section: Methodsmentioning

confidence: 99%

Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China

Zhang

Ren

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The most important factor influencing the distribution of NDVI was LULC, with a contribution of 79.4%. For vegetation distribution, LULC, as the most important influencing factor, has been confirmed by existing studies [67]. Different land-use patterns result in significant differences in the cover of surface vegetation and thus produce significant NDVI differences.…”

Section: Discussionmentioning

confidence: 55%

Changes in Vegetation Greenness and Their Influencing Factors in Southern China

Zhao

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Since the 21st century, China has experienced rapid development, and the spatial and temporal changes in vegetation cover have become increasingly significant. Southern China is a representative region for human activities, climate change, and vegetation change, but the current human understanding of the interactions between vegetation and its influencing factors is still very limited. In our study, we use NDVI as the vegetation greenness data, land cover data, temperature, precipitation, downgradient shortwave radiation, and CO2 data to investigate the interrelationship among vegetation, climate change, and human activities in southern China. The changes and their consistency were studied by trend analysis and Hurst exponent analysis. Then, the contribution of each influencing factor from 2001 to 2020 was quantified by random forest. The results showed that the vegetation in southern China showed an overall rising trend, and areas with a continuous changing trend were concentrated in the Pearl River Delta, western Guangdong, and eastern Guangdong, with a growth rate of 0.02∼0.04%. The vegetation in northern Guangdong did not change significantly. The main factor of NDVI spatial variation in southern China is the land-use factor, accounting for 79.4% of the variation, while climate factors produce further differences. The contributions and lagged effects of NDVI factors on different land-use types and the lagged effects of different climate factors are different and are related to the climate and vegetation background in Sourthern China. Our study is useful in estimating the contribution of NDVI change by each considered factor and formulating environmentally friendly regional development strategies and promoting human–land harmony.

show abstract

“…The former was mainly located at the artificial construction regions, including the Guanzhong Basin, Taiyuan Basin, Luoyang Basin, and Jinan Basin. The vegetation degradation in construction regions indicated that the vegetation growth was obviously influenced by the negative human activities, which mainly reflected the encroachment of woodland and grassland by industrial activities and urbanization process in large scale (Li et al, 2022).…”

Section: Spatial Heterogeneity Of the Effects Of Driving Factors On Fvcmentioning

confidence: 99%

“…These NDVI data have certain differences, such as sensor parameters and resolutions. Among them, the MODIS NDVI was widely employed to detect vegetation changes at large scales due to its simplicity and relatively high spatial and temporal resolutions (Li et al, 2022). For example, Ghorbanian et al (2022) Based on satellite-derived observations, many studies have observed an unprecedented vegetation greening at different spatial scales since the early 1980s (Chen et al, 2019;Yue et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution of growing‐season vegetation coverage and its natural‐human drivers in the Yellow River Basin, China

Zang,

Yu,

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

The Yellow River Basin (YRB) is an important ecological security barrier in northern China, where vegetation has undergone rapid changes under the high‐intensity anthropological activities and ongoing climate change in recent decades. This study focused on the spatiotemporal evolution of fractional vegetation cover (FVC) and the driving factors during the growing season in different sub‐basins of the YRB between 2000 and 2020. The trend analysis was employed to examine the FVC spatiotemporal changes, and the geographical detector model (GDM) and the geographically weighted regression (GWR) model were utilized to explore spatial heterogeneity of natural‐human drivers on FVC. The 21‐year average FVC was 0.5780 in the growing season in the YRB, and the southeast region was greater than the northwest region. The FVC exhibited a fluctuating and rising trend of 0.44% yr−1 in the whole YRB from 2000 to 2020, and areas with improved FVC were 36.40% more than those with degraded FVC. The dominant factors influencing the growing‐season FVC were precipitation and temperature, followed by elevation and human activity intensity. The influence of natural factors on FVC showed a decreasing trend, whereas human factors exhibited an increasing trend. Natural factors promoted vegetation growth in most area of the YRB, especially in the northwest, whereas human factors had more negative effects, except for the middle parts of the basin. The spatial differentiation analyses of FVC and driving factors provide scientific guidance for ecological restoration and sustainable development in the YRB.

show abstract

Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector

Cited by 53 publications

References 44 publications

Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China

Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China

Changes in Vegetation Greenness and Their Influencing Factors in Southern China

Spatiotemporal evolution of growing‐season vegetation coverage and its natural‐human drivers in the Yellow River Basin, China

Contact Info

Product

Resources

About