Cite as:
Grigusova, P.; Larsen, A.; Achilles, S.; Klug, A.; Fischer, R.; Kraus, D.; &Uuml;bernickel, K.; Paulino, L.; Pliscoff, P.; Brandl, R.; Farwig, N. &amp; Bendix, J. (2021): <b>Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning</b>. <i>Drones</i> <b>5</b>(3), -.

Resource Description

Title: Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning
FOR816dw ID: 441
Publication Date: 2021-08-30
License and Usage Rights:
Resource Owner(s):
Individual: Paulina Grigusova
Individual: Annegret Larsen
Individual: Sebastian Achilles
Individual: Alexander Klug
Individual: Robin Fischer
Individual: Diana Kraus
Individual: Kirstin Übernickel
Individual: Leandro Paulino
Individual: Patricio Pliscoff
Individual: Roland Brandl
Individual: Nina Farwig
Individual: Joerg Bendix
Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude 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.
| machine learning | Chile | UAV | Burrowing animals | climate gradient | vegetation patterns | heterogeneity |
Literature type specific fields:
Journal: Drones
Volume: 5
Issue: 3
Page Range: -
Metadata Provider:
Individual: Maik Dobbermann
Online Distribution:
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