Geodetector model-based quantitative analysis of vegetation change characteristics and driving forces: A case study in the Yongding River basin in China

• Published in: International journal of applied earth observation and geoinformation Vol. 132; p. 104027
• Main Authors: Guo, Yujing, Cheng, Lirong, Ding, Aizhong, Yuan, Yumin, Li, Zhengyan, Hou, YiZhe, Ren, Liangsuo, Zhang, Shurong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024; Elsevier
• Subjects: Driving forces; Geographical detector model; Hurst exponent; NDVI; Driving forces; Hurst exponent; Geographical detector model; NDVI
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2024.104027

[Display omitted] •Characterization of spatiotemporal changes in NDVI and future trend.•Geographic detector model is used to quantify drivers of vegetation changes.•Land use type is the main factor influencing NDVI change.•Groundwater depth contributed 4.1% to the explanation of vegetation change.•A combination of anthropogenic and natural factors dominated vegetation change. Vegetation is one of the most crucial components of terrestrial ecosystems, and monitoring vegetation change as well as studying the factors that drive its formation provide significant guidance for restoring ecological biodiversity. The choice of driving indicators for vegetation change in previous studies has not been comprehensive enough, and particularly groundwater depth has not been considered. Therefore, 10 natural factors and 5 human factors were chosen for our study. We adopted the normalized difference vegetation index (NDVI) to measure vegetation growth. In this study, we utilized trend analysis, the Mann-Kendall test, and the Hurst index to investigate the spatiotemporal variance of NDVI in the YDRB. The geographical detector model (Geodetector) was employed to examine vegetation change attributed to human and natural variables. As a result of the study, we found that over the past 22 years, the NDVI in the basin increased from 0.62 to 0.70, with an increase of +0.0040/yr. Land use type is the most significant driver affecting NDVI changes. The interaction of two factors has a greater effect on vegetation change more than a single factor. The relationship between land use type and annual mean precipitation explained 34.5 % of the change in vegetation. Groundwater depth contributed 4.1 % to the explanation of vegetation change. Furthermore, we have determined the optimal range of specific variables conducive to vegetation growth. The results help us further understand the potential driving mechanism of vegetation cover change in the YDRB and provide a theoretical reference for relevant managers to formulate the ecological restoration measures in the basin.