Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.

中文翻译：

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整合遥感和生物气候数据以预测数据贫乏地区的入侵物种分布：挑战和机遇综述

使用来自不同学科的综合数据集和专业知识进行预测和建模，极大地改善了入侵物种的管理。到目前为止，已经通过不同学科的具体努力进行了一些尝试来预测、处理和减轻外来入侵物种的影响。然而，最有说服力的方法是利用跨学科的知识和原则来更好地控制其入侵和随后的扩张。然而，这方面的信息是有限的，不同学科的专家有时很难相互理解。在这方面，本综述的重点是概述整合生物气候、遥感变量和物种分布模型 (SDM) 以预测数据贫乏地区入侵物种的挑战和机遇。使用Google Scholar搜索引擎收集2005-2020年（15年）发表的相关论文，使用关键词如SDM、入侵物种遥感、遥感在SDM中的贡献、生物气候变量、入侵物种分布数据——埃塞俄比亚的贫困地区和入侵物种分布。关于遥感和生物气候数据集对 SDM 的唯一贡献、主要挑战和两个数据集整合机会的信息被系统地收集、分析和以表格和图形格式讨论。确定了几个主要挑战，例如遥感数据的质量及其糟糕的解释、不适当的方法、变量和模型的选择不当。除了，具有高空间和时间分辨率的地球观测 (EO) 数据的可用性及其以合理成本覆盖大面积和难以进入的区域的能力，以及遥感数据集成技术和分析的进展都是机会之一。此外，重要的传感器特性（如空间和时间分辨率）的影响对未来的研究前景至关重要。同样重要的是分析植被和土地利用模式的年际变化对侵入性 SDM 的影响的研究。迫切需要明确定义的工作原则，用于选择变量和最合适的 SDM。重要的传感器特性（如空间和时间分辨率）的影响对未来的研究前景至关重要。同样重要的是分析植被和土地利用模式的年际变化对侵入性 SDM 的影响的研究。迫切需要明确定义的工作原则，用于选择变量和最合适的 SDM。重要的传感器特性（如空间和时间分辨率）的影响对未来的研究前景至关重要。同样重要的是分析植被和土地利用模式的年际变化对侵入性 SDM 的影响的研究。迫切需要明确定义的工作原则，用于选择变量和最合适的 SDM。