气候变化领域集成服务门户(CCPortal): Deep learning to extract the meteorological by-catch of wildlife cameras

CCPortal

DOI	10.1111/gcb.17078
	Deep learning to extract the meteorological by-catch of wildlife cameras
	Alison, Jamie; Payne, Stephanie; Alexander, Jake M.; Bjorkman, Anne D.; Clark, Vincent Ralph; Gwate, Onalenna; Huntsaar, Maria; Iseli, Evelin; Lenoir, Jonathan; Mann, Hjalte Mads Rosenstand; Steenhuisen, Sandy-Lynn; Hye, Toke Thomas
发表日期	2024
ISSN	1354-1013
EISSN	1365-2486
起始页码	30
结束页码	1
卷号	30 期号:1
英文摘要	Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.
英文关键词	alpine ecology; automated monitoring; bees; micrometeorology; proximal sensing; snow melt; time-lapse photography; Trifolium pratense
语种	英语
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
WOS类目	Biodiversity Conservation ; Ecology ; Environmental Sciences
WOS记录号	WOS:001151213000092
来源期刊	GLOBAL CHANGE BIOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302687
作者单位	Aarhus University; University of the Free State; University of the Free State; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Gothenburg; University of Gothenburg; University of the Free State; University of the Free State; University Centre Svalbard (UNIS); UiT The Arctic University of Tromso; Universite de Picardie Jules Verne (UPJV); Aarhus University
推荐引用方式 GB/T 7714	Alison, Jamie,Payne, Stephanie,Alexander, Jake M.,et al. Deep learning to extract the meteorological by-catch of wildlife cameras[J],2024,30(1).
APA	Alison, Jamie.,Payne, Stephanie.,Alexander, Jake M..,Bjorkman, Anne D..,Clark, Vincent Ralph.,...&Hye, Toke Thomas.(2024).Deep learning to extract the meteorological by-catch of wildlife cameras.GLOBAL CHANGE BIOLOGY,30(1).
MLA	Alison, Jamie,et al."Deep learning to extract the meteorological by-catch of wildlife cameras".GLOBAL CHANGE BIOLOGY 30.1(2024).