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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 169-181     DOI: 10.6046/zrzyyg.2024372
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Exploring the spatiotemporal differentiation and driving factors of vegetation dynamics in the Loess Plateau using the optimal parameter-based geographical detector model
SUN Yinsuo(), FANG Xiao, ZHOU Dongmao, XUE Hongwen, SU Junwu
The Third Geological Exploration Institute of China metallurgical Geology Bureau, Taiyuan 030002, China
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Abstract  

The Loess Plateau is recognized as a typical climate-sensitive and ecologically vulnerable region in China. Understanding the spatiotemporal characteristics and potential driving factors of vegetation dynamics in different dry/wet climate zones within the Loess Plateau holds critical significance for the conservation and management of regional ecosystems. Based on the kernel normalized difference vegetation indices (kNDVIs) of the Loess Plateau from 2000 to 2022, this study investigated the spatiotemporal patterns of vegetation dynamics in different dry/wet climate zones within the Loess Plateau using the coefficient of variation and trend analysis. Employing the optimal parameter-based geographical detector model, this study accurately and scientifically identified the driving factors and ranges of vegetation dynamics under the spatial scale and zoning effect, effectively addressing the challenge of spatial heterogeneity. The results indicate that the average kNDVI of the Loess Plateau presented a spatial distribution pattern characterized by low values in the northwest and high values in the southeast. In terms of vegetation dynamics, 91.57% of the Loess Plateau showed an upward trend, with the semi-arid climate zone accounting for the highest proportion (60.41%). Different driving factors in the Loess Plateau corresponded to varying optimal dispersion methods and optimal interval breakpoints. Under the optimal zoning effect, low temperature and high rainfall were identified as the primary conditions for vegetation growth. The different ranges and types of driving factors exerted different effects on the spatial distribution of vegetation dynamics. The optimal parameter-based geographical detector model demonstrates that rainfall and land use type constituted the principal driving factors of the Loess Plateau, accounting for 65.45% of the total explanatory power. The q value (0.69) of the interaction between the two driving factors was higher than the q values of interactions between other factors. This study provides a comprehensive insight into the response mechanisms of vegetation dynamics under natural and human factors, thereby guiding the sustainable development of regional ecosystems.
Keywords vegetation dynamics      kernel normalized vegetation index (kNDVI)      dry-wet climate zones      geographic detector model with optimal parameters      Loess Plateau     
ZTFLH:  Q948  
  TP79  
Issue Date: 31 December 2025
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Yinsuo SUN
Xiao FANG
Dongmao ZHOU
Hongwen XUE
Junwu SU
Cite this article:   
Yinsuo SUN,Xiao FANG,Dongmao ZHOU, et al. Exploring the spatiotemporal differentiation and driving factors of vegetation dynamics in the Loess Plateau using the optimal parameter-based geographical detector model[J]. Remote Sensing for Natural Resources, 2025, 37(6): 169-181.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024372     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/169
Fig.1  Overview of the Loess Plateau research area
数据介绍 英文名称与缩写 数据来源
30 m分辨率GDEMV2数据集 Slope(SLO) 地理空间数据云平台(http://www.gscloud.cn/)
Slope Aspect(SA)
Digital Elevation Model(DEM)
1 km分辨率逐月地表太阳辐射均值
数据集		 Solar Radiation(SR) 地理遥感生态网(www.gisrs.cn)
1 km分辨率月平均气温 Temperature(TEM) 国家青藏高原数据中心(https://data.tpdc.ac.cn/)
1 km分辨率年降水量 Precipitation(PRE)
1 km分辨率国内生产总值格网 Gross Domestic Product(GDP) 资源环境科学数据平台(https://www.resdc.cn/)
DMSP/OLS夜间灯光数据集 Nighttime Light(NTL)
30 m分辨率一级地类土地覆盖 Land Use(LU) 国家地球系统科学数据中心(https://www.geodata.cn/)
Tab.1  Data types and sources
Fig.2  Dynamic spatial pattern of vegetation and spatial distribution of kNDVI fluctuation intensity in the Loess Plateau during from 2000 to 2022
CVkNDVI 波动程度 面积百分比/%
CVkNDVI <0.18 弱波动性 25.98
0.18 ≤ CVkNDVI <0.34 较弱波动性 36.78
0.34 ≤ CVkNDVI <0.51 中等波动性 28.93
0.51 ≤ CVkNDVI <1.00 较强波动性 8.12
CVkNDVI ≥ 1.00 强波动性 0.19
Tab.2  Coefficient of variation of kNDVI in the Loess Plateau
Fig.3  Change line chart of kNDVI in dry and wet regions with different climates in the Loess Plateau from 2000 to 2022
Fig.4  kNDVI interannual change rate and significance in the Loess Plateau from 2000 to 2022
Fig.5  Spatial pattern of kNDVI interannual change trend in the Loess Plateau from 2000 to 2022
Fig.6  The proportion of dynamic vegetation changes in different zones of the Loess Plateau from 2000 to 2022
Fig.7  Discrete partition effects of the explained variables based on the geographic detector model with optimal parameters
Fig.8  Spatial distribution of dynamic vegetation change in the Loess Plateau within the driving force range
Fig.9  Single factor detection of vegetation change effect based on optimal parameter geographic detector model
Fig.10  Interaction detection of vegetation change effects based on the optimal parameter geographic detector model
Fig.11  The type of effect of vegetation change influence based on the optimal parameter geographic detector
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