Species Distribution Modelling (SDM) coupled with freely available multispectral imagery from Sentinel-2 (S2) satellite provides an immense contribution in monitoring invasive species. However, attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices (S2-RIs) and biophysical variables, in particular, were limited. Hence, this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin, Ethiopia. Thirty-five variables were computed from Sentinel-2B level-2A, and out of the variables, twelve significant variables were selected using Variable Inflation Factor (VIF). A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software “sdm” package. The performance of the models was evaluated using sensitivity, specificity, True Skill Statistics (TSS), kappa coefficient, area under the curve (AUC), and correlation. Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction. Among the tested models, Random Forest (RF) performed better with 93% TSS and 99% AUC followed by Boosted Regression Trees (BRT), ensemble, Generalized Additive Model (GAM), Support Vector Machine (SVM), and Generalized Linear Model (GLM) in decreasing order. The relative influence of vegetation indices was the highest followed by soil indices, biophysical variables, and water indices in decreasing order. According to RF prediction, 16.14% (1553.5 km2) of the study area was invaded by the alien species. Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution. Besides, the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.

中文翻译：

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埃塞俄比亚下阿瓦什河流域的入侵物种Prosopis juliflora的物种分布建模性能及其对Sentinel-2预报的意义

物种分布建模（SDM）与Sentinel-2（S2）卫星免费提供的多光谱图像相结合，为监测入侵物种提供了巨大的帮助。但是，尝试使用S2谱带和S2辐射指数（S2-RIs）以及生物物理变量来评估SDM的性能的尝试尤其受到限制。因此，本研究旨在评估六个常用的SDM和一个基于S2的变量的整体模型在建模埃塞俄比亚较低的阿瓦什河流域的朱Pro Prosopis juliflora当前分布方面的性能。从Sentinel-2B 2A级计算了35个变量，使用变量通胀因子（VIF）从变量中选择了12个重要变量。使用R软件“ sdm”包中的十倍引导复制方法，收集了总共680个存在和不存在的数据，以训练和验证变量。使用敏感性，特异性，真实技能统计（TSS），kappa系数，曲线下面积（AUC）和相关性来评估模型的性能。我们的发现表明，除了bioclim以外，所有机器学习和回归模型都提供了成功的预测。在测试的模型中，随机森林（RF）的性能更好，TSS为93％，AUC为99％，其次是增强回归树（BRT），整体，广义加性模型（GAM），支持向量机（SVM）和广义线性模型（ （GLM）降序排列。植被指数的相对影响最大，其次是土壤指数，生物物理变量，和水指数按降序排列。根据RF预测，外来物种入侵了研究区域的16.14％（1553.5 km2）。我们的研究结果突出表明，S2-RI和生物物理变量与机器学习和回归模型相结合，具有更高的建模入侵物种分布的能力。此外，使用机器学习算法（例如RF算法）对于基于遥感的侵入式SDM至关重要。