Semi-natural grasslands provide many ecosystem services with biomass production for livestock being of major importance. Both plant species diversity (i.e. taxonomic diversity at the species level) and functional diversity can affect grassland productivity and ecosystem services. However, previous research has revealed positive, negative or unimodal diversity–productivity relationships in grasslands. This research focused on three different Irish semi-natural grassland habitats: dry calcareous and neutral grassland (GS1), dry-humid acid grassland (GS3) and wet grassland (GS4). Comparison of functional diversity revealed that GS1 and GS4 grasslands had higher functional richness than GS3. Classification of strategies from leaf traits according to the competitor, stress tolerator, ruderal (CSR) system showed that GS4 had the highest values for competitiveness (C) and GS3 the lowest. GS3 had higher values for stress tolerance (S) than the other two habitats, but ruderality (R) did not differ among the habitats. Based on differences found between the habitats, we tested the hypothesis that diversity-productivity relationships depend on grassland habitat type. For all habitats combined, functional diversity was positively correlated with species richness, Simpson diversity and Simpson evenness, but not for the generally species-rich GS1 grasslands. Relationships between productivity (NDVI derived from UAV surveys) and diversity were overall negative, with the most strongly negative diversity-productivity relationship found in the GS3 grassland habitat. Community-weighted means of leaf dry matter content (CWM–LDMC) and community S strategy were also negatively correlated with productivity in GS3 grasslands, but no relationship between CWM-LA or C strategy was found in any of the grassland habitats. Differences in diversity-productivity relationships in different habitats suggest that management or soil type influence the nature of these relationships, especially in stressful acid grasslands.

The use of remotely sensed imagery for the monitoring of both plant 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, providing repeatable very high-resolution data, acting as a bridge between the decameter satellite imagery and the point scale data collected on the ground. At the same time, machine learning approaches are rapidly expanding, adding new analysis and modelling tools to the plethora of UAV, aircraft and satellite observational data. Here, we provide a review of remotely sensed monitoring methods for grassland plant biodiversity and functional traits (Leaf Dry Matter Content, Crude Protein, Potassium, Phosphorous, Nitrogen and Leaf Area Index) between 2018 and 2024. We highlight the key innovations that have occurred, sources of error identified, new analysis methods presented and identify the bottlenecks to and opportunities for further development. We emphasise the need for (1) the integration of observations across spatial and temporal scales, (2) a more systematic identification and examination of sources or error and uncertainty (3) more widespread use of hyperspectral satellite data and (4) greater focus on the development of grassland global spectra, species and traits data base, from multi- and hyper-spectral instruments, to accelerate the creation of more robust, scalable and generalisable remote sensing based grassland models.

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.