Abstract
The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.
Highlights
Queensland’s rangelands occupy over 80% of the state and are extensively grazed by sheep and cattle, estimates in excess of 20 million exist [1]
This study aims to build upon recent advancements in Unmanned Aerial Vehicles (UAV) technologies, to develop and test a method to map and monitor pasture resources across the large and heterogeneous rangelands of Queensland, Australia
A number of limitations exist in the field and UAV measurement of pasture height exist, a least-squares linear regression of both the field and UAV derived measures in this study indicated the potential of the UAV based method to replace the manual method, with enough ongoing validation across a range of pasture heights, swards and species
Summary
Queensland’s rangelands occupy over 80% of the state and are extensively grazed by sheep and cattle, estimates in excess of 20 million exist [1] The value of this industry to the state and national economy is estimated at over $5 billion per-annum [2]. The initial antecedent climatic conditions, including the existing soil, water and pasture growth state, is an often-overlooked aspect of systems thinking for managing climate variability [3]. This simple concept provides the starting point to any future projection of productivity driven by how the future seasonal climate may unfold. Describing the ambient antecedent condition of Queensland pasture resources is problematic due to the heterogeneous nature of grazing landscapes at micro and macro spatial scale over the year
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