A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

doi:10.3390/w15173080

	A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning
	Xing, Qianguo1,2,3 ; Liu, Hailong 1,2,3; Li, Jinghu 1,2,3; Hou, Yingzhuo 1,2,3; Meng, Miaomiao 1,2,3; Liu, Chunli 4
发表期刊	WATER
	2023-09-01
卷号	15 期号:17 页码:14
关键词	Ulva pertusa U-Net deep learning remote sensing unmanned aerial vehicle
DOI	10.3390/w15173080
通讯作者	Xing, Qianguo([email protected])
英文摘要	Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available.
资助机构	The authors are thankful to the anonymous reviewers for their useful suggestions.
收录类别	SCI
语种	英语
关键词[WOS]	UNMANNED AERIAL VEHICLE ; YELLOW SEA ; SARGASSUM ; BLOOMS ; VEGETATION ; RESOLUTION ; COVERAGE ; SEAWEED ; BIOMASS ; IMAGES
研究领域[WOS]	Environmental Sciences & Ecology ; Water Resources
WOS记录号	WOS:001064133600001
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/36761
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Xing, Qianguo
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 2.Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Shandong Univ, Marine Coll, Weihai 264209, Peoples R China
推荐引用方式 GB/T 7714	Xing, Qianguo,Liu, Hailong,Li, Jinghu,et al. A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning[J]. WATER,2023,15(17):14.
APA	Xing, Qianguo,Liu, Hailong,Li, Jinghu,Hou, Yingzhuo,Meng, Miaomiao,&Liu, Chunli.(2023).A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning.WATER,15(17),14.
MLA	Xing, Qianguo,et al."A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning".WATER 15.17(2023):14.