气候变化领域集成服务门户(CCPortal): Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

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DOI	10.1038/s41598-023-50308-9
	Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach
	Torresani, Michele; Rocchini, Duccio; Ceola, Giada; de Vries, Jan Peter Reinier; Feilhauer, Hannes; Moudry, Vitezslav; Bartholomeus, Harm; Perrone, Michela; Anderle, Matteo; Gamper, Hannes Andres; Chieffallo, Ludovico; Guatelli, Enrico; Gatti, Roberto Cazzolla; Kleijn, David
发表日期	2024
ISSN	2045-2322
起始页码	14
结束页码	1
卷号	14 期号:1
英文摘要	The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique Structure from Motion (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an R-2 of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry.
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001138677500061
来源期刊	SCIENTIFIC REPORTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305484
作者单位	Free University of Bozen-Bolzano; University of Bologna; Czech University of Life Sciences Prague; Wageningen University & Research; Leipzig University; Helmholtz Association; Helmholtz Center for Environmental Research (UFZ); Wageningen University & Research; European Academy of Bozen-Bolzano; University of Milan
推荐引用方式 GB/T 7714	Torresani, Michele,Rocchini, Duccio,Ceola, Giada,et al. Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach[J],2024,14(1).
APA	Torresani, Michele.,Rocchini, Duccio.,Ceola, Giada.,de Vries, Jan Peter Reinier.,Feilhauer, Hannes.,...&Kleijn, David.(2024).Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach.SCIENTIFIC REPORTS,14(1).
MLA	Torresani, Michele,et al."Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach".SCIENTIFIC REPORTS 14.1(2024).