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Article

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

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Laboratory for Climatology and Remote Sensing, Department of Geography, University of Marburg, 35037 Marburg, Germany
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Soil Geography and Landscape, Department of Environmental Sciences, Wageningen University & Research, 6700 AA Wageningen, The Netherlands
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Conservation Ecology, Department of Biology, University of Marburg, 35032 Marburg, Germany
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Earth System Dynamics, Department of Geosciences, University of Tübingen, 72076 Tübingen, Germany
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Facultad de Agronomía, Universitad de Concepción, Chillán 3780000, Chile
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Facultad de Historia, Geografía y Ciencia Política, Instituto de Geografía, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
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Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
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Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
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Animal Ecology, Department of Biology, University of Marburg, 35032 Marburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Higinio González Jorge and Luis Miguel González de Santos
Drones 2021, 5(3), 86; https://doi.org/10.3390/drones5030086
Received: 20 July 2021 / Revised: 20 August 2021 / Accepted: 25 August 2021 / Published: 30 August 2021
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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. View Full-Text
Keywords: UAV; machine learning; burrowing animals; climate gradient; Chile; vegetation patterns; heterogeneity UAV; machine learning; burrowing animals; climate gradient; Chile; vegetation patterns; heterogeneity
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MDPI and ACS Style

Grigusova, P.; Larsen, A.; Achilles, S.; Klug, A.; Fischer, R.; Kraus, D.; Übernickel, K.; Paulino, L.; Pliscoff, P.; Brandl, R.; Farwig, N.; Bendix, J. Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning. Drones 2021, 5, 86. https://doi.org/10.3390/drones5030086

AMA Style

Grigusova P, Larsen A, Achilles S, Klug A, Fischer R, Kraus D, Übernickel K, Paulino L, Pliscoff P, Brandl R, Farwig N, Bendix J. Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning. Drones. 2021; 5(3):86. https://doi.org/10.3390/drones5030086

Chicago/Turabian Style

Grigusova, Paulina, Annegret Larsen, Sebastian Achilles, Alexander Klug, Robin Fischer, Diana Kraus, Kirstin Übernickel, Leandro Paulino, Patricio Pliscoff, Roland Brandl, Nina Farwig, and Jörg Bendix. 2021. "Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning" Drones 5, no. 3: 86. https://doi.org/10.3390/drones5030086

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