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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dc.contributor.authorGrigusova, Paulina
dc.contributor.authorLarsen, Annegret
dc.contributor.authorAchilles, Sebastian
dc.contributor.authorKlug, Alexander
dc.contributor.authorFischer, Robin
dc.contributor.authorKraus, Diana
dc.contributor.authorUebernickel, Kirstin
dc.contributor.authorPaulino, Leandro
dc.contributor.authorPliscoff, Patricio
dc.contributor.authorBrandl, Roland
dc.contributor.authorFarwig, Nina
dc.contributor.authorBendix, Joerg
dc.date.accessioned2024-01-10T13:10:17Z
dc.date.available2024-01-10T13:10:17Z
dc.date.issued2021
dc.description.abstractBurrowing 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 (R-2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R-2 = 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.
dc.description.funderGerman Research Foundation, DFG
dc.description.funderDFG
dc.format.extent20 páginas
dc.fuente.origenWOS
dc.identifier.doi10.3390/drones5030086
dc.identifier.eissn2504-446X
dc.identifier.urihttps://doi.org/10.3390/drones5030086
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/77824
dc.identifier.wosidWOS:000699399000001
dc.information.autorucFacultad de Historia, Geografía y Ciencia Política; Pliscoff Varas, Patricio Andres; S/I; 1435
dc.issue.numero3
dc.language.isoen
dc.nota.accesoSin adjunto
dc.publisherMDPI
dc.revistaDRONES
dc.rightsregistro bibliográfico
dc.subjectUAV
dc.subjectmachine learning
dc.subjectburrowing animals
dc.subjectclimate gradient
dc.subjectChile
dc.subjectvegetation patterns
dc.subjectheterogeneity
dc.subjectSMALL MAMMALS
dc.subjectSPECIES-DIVERSITY
dc.subjectIMAGE TEXTURE
dc.subjectFOREST SOIL
dc.subjectBIOTURBATION
dc.subjectVEGETATION
dc.subjectMODELS
dc.subjectRICHNESS
dc.subjectPATTERNS
dc.subjectRODENTS
dc.subject.ods13 Climate Action
dc.subject.ods15 Life on Land
dc.subject.odspa13 Acción por el clima
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.titleArea-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning
dc.typeartículo
dc.volumen5
sipa.codpersvinculados1435
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
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