Low‐Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Author:

Yang Bo1ORCID,Hawthorne Timothy L.2ORCID,Aoki Lillian34ORCID,Beatty Deanna S.5ORCID,Copeland Tyler2,Domke Lia K.6ORCID,Eckert Ginny L.6ORCID,Gomes Carla P.7ORCID,Graham Olivia J.3ORCID,Harvell C. Drew3ORCID,Hovel Kevin A.8ORCID,Hessing‐Lewis Margot9ORCID,Harper Leah10,Mueller Ryan S.11ORCID,Rappazzo Brendan7,Reshitnyk Luba9,Stachowicz John J.5ORCID,Tomas Fiona12ORCID,Duffy J. Emmett10ORCID

Affiliation:

1. Department of Urban and Regional Planning San Jose State University CA San Jose USA

2. Department of Sociology and College of Sciences GIS Cluster University of Central Florida FL Orlando USA

3. Department of Ecology and Evolutionary Biology Cornell University NY Ithaca USA

4. Data Science Initiative University of Oregon OR Eugene USA

5. Department of Evolution and Ecology University of California Davis CA Davis USA

6. College of Fisheries and Ocean Sciences University of Alaska Fairbanks AK Juneau USA

7. Department of Computer Science Cornell University NY Ithaca USA

8. Department of Biology San Diego State University CA San Diego USA

9. Near‐shore Marine Ecology Hakai Institute BC Heriot Bay Canada

10. MarineGEO Program and Smithsonian Environmental Research Center MD Edgewater USA

11. Department of Microbiology Oregon State University OR Corvallis USA

12. Instituto Mediterráneo de Estudios Avanzados CSIC‐UIB Esporles Spain

Abstract

AbstractDeclines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low‐altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low‐altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions.

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3