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Abstract
The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.
Highlights
Rangelands occupy over 80% of the state of Queensland Australia and are extensively grazed by cattle, sheep and goats, estimates in excess of 20 million exist [1]
Two locations across the region of South East Queensland in Eastern Australia were chosen for the repeated collection of both Remotely Piloted Aircraft Systems (RPAS) imagery and manual plant pasture yield sampling
The average minimum quadrat height showed the strongest agreement between the terrestrial laser scan and the RPAS photogrammetric height models for the grassland pasture, followed by the mean
Summary
Rangelands occupy over 80% of the state of Queensland Australia and are extensively grazed by cattle, sheep and goats, estimates in excess of 20 million exist [1]. The antecedent climatic conditions, including knowledge of the existing pasture growth state, are important in the matching of livestock numbers to the initial and ongoing available pasture resource, requiring continual observation and fine-tuning of herbivore density [1,3]. This estimation of stocking rate depends primarily on the assessment of the available and on-going pasture yield and as described by [4] and others [5,6] was traditionally carried out through manual field-based sampling and simulation modelling.
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