| 黄河主要流域植被动态及其驱动因素分析 |
| 摘要点击 576 全文点击 16 投稿时间：2025-06-11 修订日期：2025-10-15 |
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| 中文关键词 归一化植被指数（NDVI） Geodetector模型 时空演变 驱动因素 黄河主要流域（YRB） |
| 英文关键词 normalized difference vegetation index （NDVI） Geodetector model spatial-temporal variation driving factors main basins of Yellow River （YRB） |
| DOI 10.13227/j.hjkx.202506134 |
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| 中文摘要 |
| 植被动态是生态环境变化的双重指示器，揭示区域尺度上植被动态的变化特征及其驱动机制，是理解陆地表层生态系统响应气候变化与人类活动的关键科学问题. 基于2000~2022年8个黄河主要流域归一化植被指数（NDVI）数据，分析植被动态时空变化特征，结合小波相干分析和Geodetector模型等方法，系统解析各环境影响因素对NDVI的驱动机制. 结果表明：①在时间尺度上，黄河主要流域NDVI变化均呈显著上升趋势（P<0.05），空间变化率介于0.001 7~0.01 a-1之间；在空间尺度上，NDVI呈自西北向东南递增的特征，植被以改善趋势为主，占比为71.08%，且各植被等级间的流动以向更高等级为主. ②综合多种突变检验方法，得出2011年和2017年为关键转折点，有序聚类法和滑动T检验法更适用于黄河流域NDVI和气象因素的突变检测. NDVI对降水和气温的响应具有不均匀性和滞后性，相比之下，降水对NDVI的影响要强于温度. ③驱动因素多以气候因素（年降水量和日照时数）为主导，土地利用类型与海拔在局部区域产生协同增强效应. NDVI与日照时数呈显著负相关（P<0.05），与年降水量和土地利用类型呈显著正相关关系. 研究结果可为黄河主要流域生态环境保护政策的制定提供理论指导. |
| 英文摘要 |
| Vegetation dynamics serve as a dual indicator of climate and ecological changes. Quantifying the characteristics and drivers of vegetation dynamics at the regional scale is a key scientific issue for understanding how terrestrial ecosystems respond to climate change and human activities. Based on normalized difference vegetation index （NDVI） data from 2000 to 2022 across eight major basins of the Yellow River （YRB）， this study quantified the spatiotemporal dynamics of vegetation and， by integrating wavelet coherence analysis with Geodetector， provided a mechanistic disentanglement of how individual environmental drivers govern NDVI variability. The results show that： ① Over time， there was a stalky upward tendency in its change across all regions from 2000 to 2022 （P<0.05）， and the spatial change rate was between 0.001 7-0.01 a-1. It tended to increase from northwest to southeast at the spatial scale， with 71.08% of the total area showing significant improvement， mainly shifting to higher coverage levels. ② Integrating multiple change-point detection algorithms， we identified 2011 and 2017 as the two dominant breakpoints in the NDVI series. Ordinal clustering and the moving T-test proved the most robust for identifying abrupt shifts in both NDVI and meteorological variables in YRB. NDVI exhibited heterogeneous and lagged responses to precipitation and temperature， with precipitation exerting a markedly stronger control than temperature. ③ Climate factors （annual precipitation and sunshine duration） were primary drivers， while land-use types and elevation exerted localized synergistic effects. NDVI correlated negatively with sunshine duration （P<0.05） but positively with precipitation and land-use intensity. These findings provide mechanistic insights for optimizing ecological conservation strategies in YRB. |
