Abstract:
Burrowing animals are important ecosystem engineers affecting soil properties, the redis-tribution of nutrients and soil carbon sequestration through their burrowing activity. The magnitude of these effects depends on the spatial density and depth of the burrows, but a method to derive this type of spatial explicit data of is still missing. Here, we test the poten-tial of consumer-oriented UAV RGB imagery to determine density and depth of holes cre-ated by burrowing animals at four study sites along a climate gradient in Chile by com-bining UAV data with empirical field plot observations and machine learning. We used the trained models for area wide predictions of density and depth of holes created by burrowing animals across 8 hillsides along a climate gradient in Chile.
