We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning.
- Authors
Xing, Qianguo; Liu, Hailong; Li, Jinghu; Hou, Yingzhuo; Meng, Miaomiao; Liu, Chunli
- Abstract
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.
- Subjects
DEEP learning; DRONE aircraft; ULVA; STANDARD deviations; REMOTE-sensing images; REMOTE sensing
- Publication
Water (20734441), 2023, Vol 15, Issue 17, p3080
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w15173080