globalchange  > 气候变化与战略
DOI: 10.3390/rs12030489
论文题名:
Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach
作者: González-Rivero M.; Beijbom O.; Rodriguez-Ramirez A.; Bryant D.E.P.; Ganase A.; Gonzalez-Marrero Y.; Herrera-Reveles A.; Kennedy E.V.; Kim C.J.S.; Lopez-Marcano S.; Markey K.; Neal B.P.; Osborne K.; Reyes-Nivia C.; Sampayo E.M.; Stolberg K.; Taylor A.; Vercelloni J.; Wyatt M.; Hoegh-Guldberg O.
刊名: Remote Sensing
ISSN: 20724292
出版年: 2020
卷: 12, 期:3
语种: 英语
英文关键词: Artificial intelligence ; Automated image analysis ; Coral reefs ; Monitoring
Scopus关键词: Artificial intelligence ; Automation ; Convolutional neural networks ; Cost effectiveness ; Data integration ; Deep learning ; Image analysis ; Image enhancement ; Image recognition ; Monitoring ; Reefs ; Abundance estimation ; Automated estimation ; Automated image analysis ; Automated image annotations ; Benthic communities ; Coral reef ; Cost-effective approach ; Monitoring programs ; Cost benefit analysis
英文摘要: Ecosystemmonitoring is central to effectivemanagement, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection formonitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reefmonitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery inmonitoring with automated image annotation can dramatically improve how wemeasure andmonitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and acrossmanagement areas. © 2020 by the authors.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159921
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


作者单位: Australian Institute of Marine Science, Cape Cleveland, QLD 4810, Australia; Global Change Institute, University of Queensland, St Lucia, QLD 4072, Australia; School of Biological Sciences and ARC CoE for Coral Reef Studies, University of Queensland, St Lucia, QLD 4072, Australia; Berkeley Artificial Intelligence Research, University of California, Berkeley, CA 94720, United States; Instituto de Ecologia y Zoologia Tropical, Universidad Central de Venezuela, Caracas, Miranda, 1051, Venezuela; Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, United States; ARC Centre of Mathematical and Statistical Frontiers, Queensland University of Technology, School of Mathematical Sciences, Brisbane, QLD 4000, Australia

Recommended Citation:
González-Rivero M.,Beijbom O.,Rodriguez-Ramirez A.,et al. Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach[J]. Remote Sensing,2020-01-01,12(3)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[González-Rivero M.]'s Articles
[Beijbom O.]'s Articles
[Rodriguez-Ramirez A.]'s Articles
百度学术
Similar articles in Baidu Scholar
[González-Rivero M.]'s Articles
[Beijbom O.]'s Articles
[Rodriguez-Ramirez A.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[González-Rivero M.]‘s Articles
[Beijbom O.]‘s Articles
[Rodriguez-Ramirez A.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.