湖泊科学2026，Vol.38Issue(1)：129-141,中插14-中插15,15.DOI:10.18307/2026.0115
基于无人机影像和深度学习技术的青海湖刚毛藻水华提取研究
Extraction of Cladophora blooms in Lake Qinghai based on unmanned aerial vehicle(UAV)imagery and deep learning techniques
摘要
Abstract
Frequent outbreaks of Cladophora blooms in the newly formed littoral zone of Lake Qinghai have been observed due to the warming and humidification of the Qinghai-Tibet Plateau climate.Previous studies on the extraction of Cladophora blooms mainly relied on multi-source satellite remote sensing imagery.However,the limitations of image spatial resolution and mixed-pixel effects hindered the accurate identification of the true distribution and detailed features of the blooms.This study utilized low-alti-tude UAV imagery combined with the Attention DeepLab V3+deep learning model to automatically extract Cladophora bloom fea-tures in Lake Qinghai.A comparative analysis was conducted with results derived from spectral indices and machine learning meth-ods,and the differences between UAV imagery and optical satellite remote sensing imagery in extracting Cladophora blooms were explored.The subsequent results are outlined below:(1)It has been demonstrated that Attention DeepLab V3+is capable of ac-curately detecting Cladophora blooms without the necessity of prior thresholds,achieving a kappa coefficient,precision,recall,and F1 score of 0.985,0.969,0.983,and 0.976,respectively.(2)In comparison with both the random forest model and the red-green-blue floating algae index,the model demonstrated a marked improvement in both the kappa coefficient and the F1 score,with increases of 4.47％and 6.35％,respectively.This is particularly noteworthy in terms of its superior adaptability to complex bloom distribution patterns,as evidenced by its ability to capture boundary details and differentiate between voids.(3)Optical satellite remote sensing imagery tends to overestimate Cladophora blooms in Lake Qinghai,with mean relative error values ranging from 5.5％to 323.47％.This study utilized the high-resolution capabilities of UAV imagery to provide technical support for accu-rately assessing the true distribution of Cladophora blooms in Lake Qinghai,thereby establishing a foundation for the monitoring and tracking of algal bloom features in other water bodies.关键词
青海湖/刚毛藻水华/Attention DeepLab V3+/无人机影像Key words
Lake Qinghai/Cladophora blooms/Attention DeepLab V3+/UAV imagery引用本文复制引用
Zhang Juan,Yao Xiaojun,Chen Jinxuan,Zhang Yuxuan,Han Shengli,Dou Haomin..基于无人机影像和深度学习技术的青海湖刚毛藻水华提取研究[J].湖泊科学,2026,38(1):129-141,中插14-中插15,15.基金项目
国家自然科学基金项目(42571163)和西北师范大学绿洲科学科研成果突破行动计划(NWNU-LZKX-202301)联合资助. （42571163）
