Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic: A Review of Satellite and UAV Remote Sensing, Machine Learning, and Sensor Fusion

Arthur Platel, Juan Sandino, Justine Shaw, Barbara Bollard, Felipe Gonzalez

Polar vegetation is a critical component of global biodiversity and ecosystem health but is vulnerable to climate change and environmental disturbances. Analysing the spatial distribution, regional variations, and temporal dynamics of this vegetation is essential for implementing conservation efforts in these unique environments. However, polar regions pose distinct challenges for remote sensing, including sparse vegetation, extreme weather, and frequent cloud cover. Advances in remote sensing technologies, including satellite platforms, uncrewed aerial vehicles (UAVs), and sensor fusion techniques, have improved vegetation monitoring capabilities. This review explores applications—including land cover mapping, vegetation health assessment, biomass estimation, and temporal monitoring—and the methods developed to address these needs. We also examine the role of spatial, spectral, and temporal resolution in improving monitoring accuracy and addressing polar-specific challenges. Sensors such as Red, Green, and Blue (RGB), multispectral, hyperspectral, Synthetic Aperture Radar (SAR), light detection and ranging (LiDAR), and thermal, as well as UAV and satellite platforms, are analysed for their roles in low-stature polar vegetation monitoring. We highlight the potential of sensor fusion and advanced machine learning techniques in overcoming traditional barriers, offering a path forward for enhanced monitoring. This paper highlights how advances in remote sensing enhance polar vegetation research and inform adaptive management strategies.