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

文献类型：期刊论文

作者Xing, Qianguo1,2,3; Liu, Hailong1,2,3; Li, Jinghu1,2,3; Hou, Yingzhuo1,2,3; Meng, Miaomiao1,2,3; Liu, Chunli4
刊名WATER
出版日期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
DOI10.3390/w15173080
通讯作者Xing, Qianguo(qgxing@yic.ac.cn)
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.
WOS关键词UNMANNED AERIAL VEHICLE ; YELLOW SEA ; SARGASSUM ; BLOOMS ; VEGETATION ; RESOLUTION ; COVERAGE ; SEAWEED ; BIOMASS ; IMAGES
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001064133600001
资助机构The authors are thankful to the anonymous reviewers for their useful suggestions.
源URL[http://ir.yic.ac.cn/handle/133337/36760]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_海岸带信息集成与综合管理实验室
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.

入库方式： OAI收割

来源：烟台海岸带研究所

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