干旱区生态环境知识资源中心(Arid): Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

Arid

DOI	10.3390/drones5030086
	Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning
	Grigusova, Paulina; Larsen, Annegret; Achilles, Sebastian; Klug, Alexander; Fischer, Robin; Kraus, Diana; Uebernickel, Kirstin; Paulino, Leandro; Pliscoff, Patricio; Brandl, Roland; Farwig, Nina; Bendix, Joerg
通讯作者	Grigusova, P (corresponding author), Univ Marburg, Dept Geog, Lab Climatol & Remote Sensing, D-35037 Marburg, Germany.
来源期刊	DRONES
EISSN	2504-446X
出版年	2021
卷号	5 期号:3
英文摘要	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 (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.
英文关键词	UAV machine learning burrowing animals climate gradient Chile vegetation patterns heterogeneity
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000699399000001
WOS关键词	SMALL MAMMALS ; SPECIES-DIVERSITY ; IMAGE TEXTURE ; FOREST SOIL ; BIOTURBATION ; VEGETATION ; MODELS ; RICHNESS ; PATTERNS ; RODENTS
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368463
作者单位	[Grigusova, Paulina; Achilles, Sebastian; Klug, Alexander; Fischer, Robin; Bendix, Joerg] Univ Marburg, Dept Geog, Lab Climatol & Remote Sensing, D-35037 Marburg, Germany; [Larsen, Annegret] Wageningen Univ & Res, Dept Environm Sci, Soil Geog & Landscape, NL-6700 AA Wageningen, Netherlands; [Kraus, Diana; Farwig, Nina] Univ Marburg, Dept Biol, Conservat Ecol, D-35032 Marburg, Germany; [Uebernickel, Kirstin] Univ Tubingen, Dept Geosci, Earth Syst Dynam, D-72076 Tubingen, Germany; [Paulino, Leandro] Univ Concepcion, Fac Agron, Chillan 3780000, Chile; [Pliscoff, Patricio] Pontificia Univ Catolica Chile, Inst Geog, Fac Hist Geog & Ciencia Polit, Santiago 7820436, Chile; [Pliscoff, Patricio] Pontificia Univ Catolica Chile, Fac Ciencias Biol, Dept Ecol, Santiago 8331150, Chile; [Pliscoff, Patricio] Pontificia Univ Catolica Chile, Ctr Appl Ecol & Sustainabil CAPES, Santiago 8331150, Chile; [Brandl, Roland] Univ Marburg, Dept Biol, Anim Ecol, D-35032 Marburg, Germany
推荐引用方式 GB/T 7714	Grigusova, Paulina,Larsen, Annegret,Achilles, Sebastian,et al. Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning[J],2021,5(3).
APA	Grigusova, Paulina.,Larsen, Annegret.,Achilles, Sebastian.,Klug, Alexander.,Fischer, Robin.,...&Bendix, Joerg.(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(3).
MLA	Grigusova, Paulina,et al."Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning".DRONES 5.3(2021).