Analysis of spatiotemporal variations and driving forces of NDVI based on random forest and Geodetector: A case study of the Heihe river basin in China

IF 8.7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Ecological Indicators Pub Date : 2026-03-01 Epub Date: 2026-02-27 DOI:10.1016/j.ecolind.2026.114710
Huiliang Wang , Fan Wang , Hong Lv , Zhilei Yu
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Abstract

Against the backdrop of global climate change, ecosystems in arid inland river basins are experiencing increasing environmental pressures. Long-term, high-resolution monitoring of vegetation dynamics is therefore essential for assessing ecological conditions and identifying potential risks. Based on the annual maximum NDVI time series from 1990 to 2024 in the Heihe River Basin, this study systematically characterizes the spatiotemporal patterns of vegetation change by integrating trend analysis, the Geodetector model, residual diagnostics, and time-lag zoning methods. Furthermore, it quantitatively examines the relative influences of climatic factors and human activities on vegetation dynamics and their phased adjustment characteristics. The results reveal a significant overall greening trend across the Heihe River Basin during the study period, with NDVI exhibiting a clear spatial gradient characterized by higher values in the upstream regions and lower values downstream. Geodetector analysis indicates that hydrological factors—particularly precipitation and soil moisture—consistently exert strong explanatory power on vegetation change, with q values generally exceeding 0.59 throughout the study period. Meanwhile, the explanatory power of aridity indices for NDVI shows a long-term declining trend, suggesting phased adjustments in the statistical relationship between vegetation dynamics and aridity conditions. Residual diagnostics further demonstrate marked differences in the relative contributions of non-climatic factors to vegetation change around the year 2000. Hurst index analysis suggests that approximately 96% of the basin exhibits stable or persistently improving vegetation trends, while potential degradation risks are mainly concentrated in desert fringe areas. Overall, this study elucidates the pronounced spatiotemporal heterogeneity of vegetation dynamics and their driving mechanisms in the Heihe River Basin.
基于随机森林和地理探测器的NDVI时空变化及驱动力分析——以黑河流域为例
在全球气候变化的大背景下,干旱内陆河流域生态系统面临着越来越大的环境压力。因此,长期、高分辨率的植被动态监测对于评估生态条件和识别潜在风险至关重要。基于黑河流域1990—2024年NDVI年最大值时间序列,采用趋势分析、Geodetector模型、残差诊断和滞后区划等方法,系统表征了黑河流域植被变化的时空格局。定量分析了气候因子和人类活动对植被动态的相对影响及其阶段性调整特征。结果表明,研究期间黑河流域整体呈现明显的绿化趋势,NDVI呈现明显的上游高、下游低的空间梯度。地理探测器分析表明,水文因子(尤其是降水和土壤湿度)对植被变化的解释力一直很强,在整个研究期间,q值普遍超过0.59。同时,干旱指数对NDVI的解释能力呈长期下降趋势,表明植被动态与干旱条件的统计关系存在阶段性调整。残差诊断进一步表明,2000年前后非气候因子对植被变化的相对贡献存在显著差异。Hurst指数分析表明,流域约96%的植被呈现稳定或持续改善的趋势,而潜在的退化风险主要集中在沙漠边缘地区。总体而言,本研究阐明了黑河流域植被动态的明显时空异质性及其驱动机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
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