Study on the driving mechanism of spatio-temporal non-stationarity of vegetation dynamics in the Taihangshan-Yanshan Region

• kNDVI is effectively capable of reflecting the vegetation changes in the TYR. • 70% of regions increase in kNDVI and will continue to grow throughout the year. • Substantial disparities exist in the driving mechanisms between Taihang and Yan Mountains. • ET dominates the changes in vegetation cover and shows a positive correlation. As an important ecological barrier of the Beijing-Tianjin-Hebei region, the Taihang-Yanshan Region (TYR) plays a crucial role in maintaining the sustainable stability of the ecosystem. This study aims to investigate the spatio-temporal evolution of vegetation dynamics and elucidate the various driving forces behind these changes, and results indicate that: (1) kernel Normalised Vegetation Index (kNDVI) is effectively capable of reflecting the vegetation changes in the TYR. From 2001 to 2020, kNDVI in spring, summer and autumn showed a slow upward trend and a downward trend in winter. In terms of space, vegetation coverage in Yanshan region is generally higher than that in Taihang Mountain with significant spatial heterogeneity.(2) Theil-Sen Median trend and Hurst index analysis shows that about 70% of regions have shown an increasing trend in kNDVI throughout the year and will continue to increase in the future. (3) The analysis results of Optimal Parameter Geodetector Detectors (OPGD) show that evapotranspiration (ET) is the dominant driving factor of kNDVI, and the interactive enhancement benefits with other factors is significant. Population density and land surface temperature were negatively correlated with kNDVI. (4) Based on the analysis results of multi-scale geographic weighted regression (MGWR) kNDVI is positively correlated with annual cumulative precipitation and ET, and negatively correlated with annual mean temperature and altitude, and the other variables show spatial heterogeneity. Research results provide important references for the formulation of vegetation ecosystem protection and restoration policies in this region.

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Publication Details

Journal
Ecological Indicators
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ecolind.2025.113084
Citations
12
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
13.26
Controls
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article

Study on the driving mechanism of spatio-temporal non-stationarity of vegetation dynamics in the Taihangshan-Yanshan Region

Huicong Zhang, Yanrui Ding, Liyao Dong, Meiqing Wang et al.
12 citations
Ecological Indicators
Land Use and Ecosystem Services
13.26
article

Study on the driving mechanism of spatio-temporal non-stationarity of vegetation dynamics in the Taihangshan-Yanshan Region

Huicong Zhang, Yanrui Ding, Liyao Dong, Meiqing Wang, Zhenzhou Zhu, Jiarui Li, Jiao Pang, Yan Feng
article en
12 citations

Abstract

• kNDVI is effectively capable of reflecting the vegetation changes in the TYR. • 70% of regions increase in kNDVI and will continue to grow throughout the year. • Substantial disparities exist in the driving mechanisms between Taihang and Yan Mountains. • ET dominates the changes in vegetation cover and shows a positive correlation. As an important ecological barrier of the Beijing-Tianjin-Hebei region, the Taihang-Yanshan Region (TYR) plays a crucial role in maintaining the sustainable stability of the ecosystem. This study aims to investigate the spatio-temporal evolution of vegetation dynamics and elucidate the various driving forces behind these changes, and results indicate that: (1) kernel Normalised Vegetation Index (kNDVI) is effectively capable of reflecting the vegetation changes in the TYR. From 2001 to 2020, kNDVI in spring, summer and autumn showed a slow upward trend and a downward trend in winter. In terms of space, vegetation coverage in Yanshan region is generally higher than that in Taihang Mountain with significant spatial heterogeneity.(2) Theil-Sen Median trend and Hurst index analysis shows that about 70% of regions have shown an increasing trend in kNDVI throughout the year and will continue to increase in the future. (3) The analysis results of Optimal Parameter Geodetector Detectors (OPGD) show that evapotranspiration (ET) is the dominant driving factor of kNDVI, and the interactive enhancement benefits with other factors is significant. Population density and land surface temperature were negatively correlated with kNDVI. (4) Based on the analysis results of multi-scale geographic weighted regression (MGWR) kNDVI is positively correlated with annual cumulative precipitation and ET, and negatively correlated with annual mean temperature and altitude, and the other variables show spatial heterogeneity. Research results provide important references for the formulation of vegetation ecosystem protection and restoration policies in this region.

Ecological IndicatorsVol. 170
Hebei Agricultural University (CN), China Geological Survey (CN), China University of Geosciences (Beijing) (CN)
Life in Land
Openalex Percentile: Top 1%
Land Use and Ecosystem Services
13.26
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