Grasslands cover between 30 and 40% of the world’s land surface and, despite providing numerous ecosystem services and being rich in biodiversity, are increasingly under threat and shrinking in coverage. As such, the development and application of monitoring techniques are of vital importance. The use of remotely sensed imagery for the monitoring of both biodiversity and functional traits in grassland ecosystems has increased substantially in the last few decades. More recently, uncrewed aerial vehicles (UAVs) have begun to play an increasingly important role, acting as a bridge between the decameter satellite imagery and the point scale data collected on the ground. The use of UAV-mounted hyperspectral sensors, covering up to hundreds of spectral bands, has become particularly popular as the senor sizes have reduced, and UAV technology has improved. Here, we provide a review of the latest remotely sensed monitoring methods for both biodiversity and functional traits using multispectral and hyperspectral sensors. We highlight the key innovations that have occurred (e.g., use of point cloud data, identification of error sources), the bottlenecks to and opportunities for further development. UAV surveys show particular promise for monitoring functional traits. We conclude that UAV methods offer the opportunity to scale surveys from individual sites to regional areas, and can aid in refining satellite-based observations to improve the monitoring of grassland ecosystems at national and global scales.

Retrogressive Thaw Slumps (RTSs), a highly dynamic form of mass wasting, are accelerating geomorphic change across ice-cored permafrost terrain, yet the main controls on their activity are poorly constrained. Questions over the spatial variability of environmentally sensitive buried massive ice (MI) bodies and a paucity of high-spatial and temporal resolution topographic data have limited our ability to project their development and wider impacts. This research addresses these key problems by investigating RTS processes on Peninsula Point — the type site for intra-sedimental MI in the Western Canadian Arctic. Utilizing high-resolution topographic data from drone surveys in 2016, 2017 and 2018 we (1) measure the temporal and spatial variations in headwall properties and retreat rates, (2) determine the spatial pattern of subsurface layering using passive seismic monitoring and (3) combine these to analyse and contextualise the factors controlling headwall retreat rates. We find that headwall properties, namely MI thickness and overburden thickness, are significant controls over rates of headwall retreat. Where persistent ice exposures are present and overburden thickness remains < 4 m, headwall retreat is typically more than double that of other headwalls. Furthermore, a 3D site model was created by combining photogrammetric and passive seismic data, highlighting the variability in internal layering, demonstrating the limitations of extrapolations based on headwall exposures, and improving predictions of headwall retreat rates compared to long term averages and extrapolations from the previous year. These results provide fresh insights into the controls on headwall retreat rates and new approaches to improve their predictability.