Abstract Expansion of Antarctic vegetation in ice-free areas underlines the need for effective remote sensing techniques to properly monitor the changes. Detection and mapping of vegetation remains limited in the Antarctic environment given the complexity of its surface coverage. Some cryptogamic species exhibit low reflectance in the near-infrared region and are not easily detected by vegetation indices, such as the normalized difference vegetation index (NDVI). In addition, spectral reflectance of Antarctic vegetation is highly variable according to seasonal conditions, which may influence NDVI results. As ultra-high resolution aerial imagery allows for a detailed analysis of vegetation and enables the validation of satellite imagery, in this study we assess the ability of the NDVI from unmanned aerial vehicle (UAV), Sentinel-2, and Landsat 8 to identify vegetated areas in the ice-free environment of Hope Bay, Antarctic Peninsula. NDVI classification with class ranges set by statistical parameters (i.e., mean and standard deviation) is performed. The results show that different sensors provide different NDVI values for the same vegetation class. NDVI classification enabled the identification of areas showing vegetation cover, which are in accordance with the manually mapped areas in the UAV image. Correspondence in vegetation distribution and classes can be observed across all classifications, demonstrating that aerial and satellite imagery may be used for Antarctic vegetation monitoring. A close association between NDVI classes and Antarctic vegetation type is identified, where lichens are generally classified in lower probability classes, and algae and moss in higher probability classes. This article shows the potential of NDVI applied to Antarctic vegetation and the significance of data statistical parameters in the selection of thresholds, reducing the need for ground-truth information in remote areas.

中文翻译：

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评估无人机和卫星衍生的 NDVI 以绘制南极海洋植被图

摘要 南极植被在无冰区的扩张强调需要有效的遥感技术来正确监测变化。鉴于其表面覆盖的复杂性，对植被的探测和测绘在南极环境中仍然有限。一些隐花种在近红外区域表现出低反射率，不易被植被指数检测到，如归一化差异植被指数（NDVI）。此外，南极植被的光谱反射率根据季节条件变化很大，这可能会影响 NDVI 结果。由于超高分辨率航空影像允许对植被进行详细分析并能够验证卫星影像，因此在本研究中，我们评估了来自无人机 (UAV) Sentinel-2 的 NDVI 的能力，和 Landsat 8 确定南极半岛希望湾无冰环境中的植被区域。使用由统计参数（即均值和标准差）设置的类范围进行 NDVI 分类。结果表明，不同的传感器为同一植被类别提供不同的 NDVI 值。NDVI 分类能够识别显示植被覆盖的区域，这些区域与无人机图像中的手动映射区域一致。在所有分类中都可以观察到植被分布和类别的对应关系，这表明航空和卫星图像可用于南极植被监测。确定了 NDVI 类别和南极植被类型之间的密切关联，其中地衣通常被归类为较低概率类别，以及更高概率类别的藻类和苔藓。本文展示了 NDVI 应用于南极植被的潜力以及数据统计参数在阈值选择中的意义，减少了偏远地区对地面实况信息的需求。