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
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.
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SUN Yinsuo, FANG Xiao, ZHOU Dongmao, XUE Hongwen, SU Junwu. Exploring the spatiotemporal differentiation and driving factors of vegetation dynamics in the Loess Plateau using the optimal parameter-based geographical detector model. Remote Sensing for Natural Resources, 2025, 37(6): 169-181.
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2025, Vol. 37 
), 方霄, 周东茂, 薛洪文, 苏俊武
