1. Plants like mosses can be sensitive stress markers of subtle shifts in Arctic and Antarctic environmental conditions, including climate change. Traditional ground-based monitoring of fragile polar vegetation is, however, invasive, labour intensive and physically demanding. High-resolution multispectral satellite observations are an alternative, but even their recent highest achievable spatial resolution is still inadequate, resulting in a significant underestimation of plant health due to spectral mixing and associated reflectance impurities.
2. To resolve these obstacles, we have developed a new method that uses low-altitude unmanned aircraft system (UAS) hyperspectral images of sub-decimeter spatial resolution. Machine-learning support vector regressions (SVR) were employed to infer Antarctic moss vigour from quantitative remote sensing maps of plant canopy chlorophyll content and leaf density. The same maps were derived for comparison purposes from the WorldView-2 high spatial resolution (2.2 m) multispectral satellite data.
3. We found SVR algorithms to be highly efficient in estimating plant health indicators with acceptable root mean square errors (RMSE). The systematic RMSEs for chlorophyll content and leaf density were 3.5–6.0 and 1.3–2.0 times smaller, respectively, than the unsystematic errors. However, application of correctly trained SVR machines on space-borne multispectral images considerably underestimated moss chlorophyll content, while stress indicators retrieved from UAS data were found to be comparable with independent field measurements, providing statistically significant regression coefficients of determination (median r2 = .50, pt test = .0072).
4. This study demonstrates the superior performance of a cost-efficient UAS mapping platform, which can be deployed even under the continuous cloud cover that often obscures optical high-altitude airborne and satellite observations. Antarctic moss vigour maps of appropriate resolution could provide timely and spatially explicit warnings of environmental stress events, including those triggered by climate change. Since our polar vegetation health assessment method is based on physical principles of quantitative spectroscopy, it could be adapted to other short-stature and fragmented plant communities (e.g. tundra grasslands), including alpine and desert regions. It therefore shows potential to become an operational component of any ecological monitoring sensor network.