Abstract
Uncrewed aerial vehicles (UAVs) have become essential for remote sensing in extreme environments like Antarctica, but detecting moss and lichen using conventional red, green, blue (RGB) and multispectral sensors remains challenging. This study investigates the potential of hyperspectral imaging (HSI) for mapping cryptogamic vegetation and presents a workflow combining UAVs, ground observations, and machine learning (ML) classifiers. Data collected during a 2023 summer expedition to Antarctic Specially Protected Area 135, East Antarctica, were used to evaluate 12 configurations derived from five ML models, including gradient boosting (XGBoost, CatBoost) and convolutional neural networks (CNNs) (G2C-Conv2D, G2C-Conv3D, and UNet), tested with full and light input feature sets. The results show that common indices like normalised difference vegetation index (NDVI) are inadequate for moss and lichen detection, while novel spectral indices are more effective. Full models achieved high performance, with CatBoost and UNet reaching 98.3% and 99.7% weighted average accuracy, respectively. Light models using eight key wavelengths (i.e., 404, 480, 560, 655, 678, 740, 888, and 920 nm) performed well, with CatBoost at 95.5% and UNet at 99.8%, demonstrating suitability for preliminary monitoring of moss health and lichen. These findings underscore the importance of key spectral bands for large-scale HSI monitoring using UAVs and satellites in Antarctica, especially in geographic regions with limited spectral range. Hyperspectral images were captured using a Headwall Nano-Hyperspec VNIR payload mounted on the DJI M300 RTK drone.