ABSTRACT Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12th and 28th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment Graphical abstract

中文翻译：

---

基于多源空间数据的津巴布韦独脚金（Striga asiatica）入侵风险建模

摘要 对独脚金属等破坏性入侵杂草的监测需要准确、近乎实时的预测和综合评估技术，以实现更好的监测和一致的评估计划。因此，在本研究中，我们预测了津巴布韦独脚金（Striga asiatica）杂草的潜在生态位，以识别和了解其传播并绘制潜在的脆弱种植区图。来自遥感、生物气候和其他环境变量（即种植系统、土壤、地表温度和地形）的植被物候学被用作预测因子。评估了六种机器学习建模技术和集成模型对预测当前和未来独脚金分布模式的适用性。上述预测变量（n = 40）被整合到六个模型中，这些模型具有“仅存在”的训练和评估数据，这些数据是在 2018 年 3 月 12 日至 28 日期间在津巴布韦收集的。 曲线下面积 (AUC) 和真实技能统计量 (TSS) 用于衡量 Striga 建模框架的性能。结果表明，与其他建模算法相比，集成模型具有最强的独脚金发生预测能力（AUC = 0.98；TSS = 0.93）。温度季节性（Bio4）、最暖月的最高温度（Bio5）和降水季节性（Bio15）被确定为影响独脚金发生的最主要的生物气候变量。“增强植被指数基值”的“季节开始”和“季节最小值”是最相关的遥感变量。根据预测的气候变化情景，该研究表明，到 2050 年，津巴布韦独脚金的适宜繁殖面积将增加约 0.73%。目前的工作证明了整合多源数据在预测由于杂草繁殖可能导致的作物生产限制方面的重要性。结果可以加强国家准备和管理战略，特别是如果可以确定当前和未来的风险领域进行早期干预和遏制图形摘要 目前的工作证明了整合多源数据在预测由于杂草繁殖可能导致的作物生产限制方面的重要性。结果可以加强国家准备和管理战略，特别是如果可以确定当前和未来的风险领域进行早期干预和遏制图形摘要 目前的工作证明了整合多源数据在预测由于杂草繁殖可能导致的作物生产限制方面的重要性。结果可以加强国家准备和管理战略，特别是如果可以确定当前和未来的风险领域进行早期干预和遏制图形摘要