Invasive Alien Plants (IAPs) pose major threats to biodiversity, ecosystem functioning and services. The availability of moderate resolution satellite data (e.g. Sentinel‐2 Multispectral Instrument and Landsat‐8 Operational Land Imager) offers an opportunity to map and monitor the occurrence and spatial distribution of IAPs. The use of two multispectral remote sensing data sets to map and monitor IAPs in the Heuningnes Catchment, South Africa, was therefore investigated using the maximum likelihood classification algorithm. It was possible to identify areas infested with IAPs using remote sensing data. Specifically, IAPs were mapped with a higher overall accuracy of 71%, using Sentinel‐2 MSI as compared to using Landsat 8 OLI, which produced 63% accuracy. However, both sensors showed similar patterns in the spatial distribution of IAPs within the hillslopes and riparian zones of the catchment. This work demonstrates the utility of the two multispectral data sets in mapping and monitoring the occurrence and distribution of IAPs, which contributes to improved ecological modelling and thus to improved management of invasions and biodiversity in the catchment.

中文翻译：

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利用多光谱卫星数据集来提高对缺水集水区中外来入侵植物分布的生态理解，南非

外来入侵植物（IAP）对生物多样性，生态系统功能和服务构成重大威胁。中分辨率卫星数据（例如Sentinel‐2多光谱仪器和Landsat‐8操作性陆地成像仪）的可用性为绘制和监测IAP的发生和空间分布提供了机会。因此，使用最大似然分类算法研究了使用两个多光谱遥感数据集来映射和监测南非Heuningnes集水区的IAP。可以使用遥感数据确定感染了IAP的区域。具体来说，与使用Landsat 8 OLI相比，使用Sentinel-2 MSI进行定位的IAP的总体准确度更高，达到71％。然而，两种传感器在集水区的山坡和河岸带内的IAP的空间分布中都显示出相似的模式。这项工作证明了这两个多光谱数据集在制图和监测IAPs的发生和分布方面的实用性，这有助于改善生态模型，从而改善流域内入侵和生物多样性的管理。