Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

Yelong Zeng, Li Jia, M. Menenti, Min Jiang, Chaolei Zheng, A. Bennour, Yunzhe Lv
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Abstract

The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.This study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.The study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.
萨赫勒-苏丹-几内亚地区植被绿度和气候驱动因素的区域差异演变:非线性和可解释的机器学习
非洲萨赫勒-苏丹-几内亚地区是干旱区与湿润区之间最大的过渡地带之一,该地区的植被动态对了解区域生态系统变化具有重要意义。然而,基于线性假设的时变趋势对全面了解萨赫勒-苏丹-几内亚地区植被绿度的演变以及追踪生态系统对气候的复杂响应提出了挑战。本研究首先应用集合经验模式分解(EEMD)方法,基于2001-2020年该地区归一化差异植被指数(NDVI)数据,检测植被绿度的时变趋势,然后通过可解释的机器学习框架,识别NDVI趋势的主要气候驱动因素。在约 60% 的研究区域内,褐变到绿化和绿化到褐变的趋势逆转占主导地位。褐变到绿化的逆转主要出现在萨赫勒南部、赤道以北的刚果盆地和东非,断点出现在 2008 年左右;绿化到褐变的逆转主要出现在西非,断点出现在 2011 年左右。持续变绿主要发生在萨赫勒北部、中非共和国和南苏丹;而持续变褐则集中在西非中部和乌干达,主要是农田。此外,随机森林算法(RF)和SHAPLEY Additive exPlanations(SHAP)方法的结合可以建立稳健的模型,并揭示观测到的 NDVI 趋势与气候变量之间的关系。结果表明,气温和降水是控制整个萨赫勒-苏丹-几内亚地区归一化差异植被指数趋势的最重要气候驱动因素。干旱地区的归一化差异植被指数趋势更有可能与太阳辐射和水汽压差呈负相关,而在湿润地区则可能呈正相关。研究还发现,海面温度(SST)异常引起的大尺度气候变化与次大陆尺度的植被绿度趋势逆转关系密切。这些发现加深了人们对气候驱动因素对萨赫勒-苏丹-几内亚地区植被绿度演变的影响的理解。
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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