  Knowing where livestock are located enables optimized management and
mustering. However, Australian farms are large meaning that many of Australia's
livestock are unmonitored which impacts farm profit, animal welfare and the
environment. Effective animal localisation and counting by analysing satellite
imagery overcomes this management hurdle however, high resolution satellite
imagery is expensive. Thus, to minimise cost the lowest spatial resolution data
that enables accurate livestock detection should be selected. In our work, we
determine the association between object detector performance and spatial
degradation for cattle, sheep and dogs. Accurate ground truth was established
using high resolution drone images which were then downsampled to various
ground sample distances (GSDs). Both circular and cassegrain aperture optics
were simulated to generate point spread functions (PSFs) corresponding to
various optical qualities. By simulating the PSF, rather than approximating it
as a Gaussian, the images were accurately degraded to match the spatial
resolution and blurring structure of satellite imagery.
  Two existing datasets were combined and used to train and test a YoloV5
object detection network. Detector performance was found to drop steeply around
a GSD of 0.5m/px and was associated with PSF matrix structure within this GSD
region. Detector mAP performance fell by 52 percent when a cassegrain, rather
than circular, aperture was used at a 0.5m/px GSD. Overall blurring magnitude
also had a small impact when matched to GSD, as did the internal network
resolution. Our results here inform the selection of remote sensing data
requirements for animal detection tasks, allowing farmers and ecologists to use
more accessible medium resolution imagery with confidence.
