Invasive Lantana camara (L. camara) is one of the key drivers of social-ecological and environmental change. Understanding its distribution is critical in determining its impact on the environment and livelihoods and in developing sustainable remediation and rehabilitation strategies. In this study we demonstrate the first comparative assessment of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) satellite data in detecting and mapping of invasive L. camara from other land cover types (i.e., built up, fields/bare patches, grassland, and shrub) in semiarid rangeland ecosystems of South Africa. Discriminant analysis (DA) classification technique was used to detect and characterize the spatial distribution of L. camara using Landsat 8 OLI and Sentinel-2 derivatives (i.e., spectral bands, indices, and combined variables). Comparatively, the results show that Sentinel-2 data were able to detect and map L. camara with a high overall accuracy (78.4%) than Landsat 8 OLI, which yielded an accuracy of 65.5%. Further, Student’s t-test statistical analysis results showed that Sentinel-2 outperformed Landsat 8 (P < 0.05, Student’s t < 0.233) in mapping L. camara from other land cover types. High performance from Sentinel-2 data indicates the relevance and potential of characterizing and profiling invasive species with the new-generation sensors, a previously daunting task, with broadband multispectral sensors.

侵入性马Lan丹（L. camara）是社会生态和环境变化的主要驱动力之一。了解其分布对于确定其对环境和生计的影响以及制定可持续的补救和恢复战略至关重要。在这项研究中，我们证明陆地卫星的第一比较评估在检测和侵入性的映射8运算陆地成像仪（OLI）和Sentinel-2多光谱仪（MSI）的卫星数据马缨丹从其他土地覆盖类型（即，建立起来的，字段/裸露的草地，草地和灌木丛中）。判别分析（DA）分类技术用于检测和表征Camara的空间分布使用Landsat 8 OLI和Sentinel-2导数（即光谱带，指数和组合变量）。相比之下，结果表明，与Landsat 8 OLI相比，Sentinel-2数据能够以较高的总体准确度（78.4％）检测和绘制Camara L. camara，后者的准确度为65.5％。此外，Student's t检验的统计分析结果表明，在从其他土地覆盖类型中绘制L. camara时，Sentinel-2优于Landsat 8（P <0.05，Student's t <0.233）。Sentinel-2数据的高性能表明，利用宽带多光谱传感器，利用以前难以实现的新一代传感器，对入侵物种进行表征和特征分析具有相关性和潜力。