Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimating Plant Pasture Biomass and Quality from UAV Imaging Across Queensland’s Rangelands

Version 1 : Received: 27 September 2020 / Approved: 29 September 2020 / Online: 29 September 2020 (09:07:02 CEST)

How to cite: Barnetson, J.; Phinn, S.; Scarth, P. Estimating Plant Pasture Biomass and Quality from UAV Imaging Across Queensland’s Rangelands. Preprints 2020, 2020090697 (doi: 10.20944/preprints202009.0697.v1). Barnetson, J.; Phinn, S.; Scarth, P. Estimating Plant Pasture Biomass and Quality from UAV Imaging Across Queensland’s Rangelands. Preprints 2020, 2020090697 (doi: 10.20944/preprints202009.0697.v1).

Abstract

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV) to map pasture biomass yield and nutrient status, 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 predictive models of both pasture yield and pasture nutrient status. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements of pasture yields, to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict pasture 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 pasture 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.

Subject Areas

UAV; Structure from Motion; photogrammetry; crude protein; acid detergent fibre; hyperspectral sensing

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