Area-wide prediction of vertebrate and invertebrate hole density and depth across a climate gradient in chile based on uav and machine learning

Paulina Grigusova*, Annegret Larsen, Sebastian Achilles, Alexander Klug, Robin Fischer, Diana Kraus, Kirstin Übernickel, Leandro Paulino, Patricio Pliscoff, Roland Brandl, Nina Farwig, Jörg Bendix

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The mag-nitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.

Original languageEnglish
Article number86
JournalDrones
Volume5
Issue number3
DOIs
StatePublished - Sep 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authorsLicensee MDPI, Basel, Switzerland.

Keywords

  • Burrowing animals
  • Chile
  • Climate gradient
  • Heterogeneity
  • Machine learning
  • UAV
  • Vegetation patterns

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