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1.武汉大学测绘遥感信息工程全国重点实验室,湖北 武汉,430079
2.湖北珞珈实验室,湖北 武汉,430079
Received:01 August 2024,
Published:05 November 2025
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田婧怡,周屈,王欣,等.SD-BSI:一种改进的多光谱卫星遥感影像盐湖卤虫带探测方法[J].武汉大学学报(信息科学版),2025,50(11):2236-2246.
TIAN Jingyi,ZHOU Qu,WANG Xin,et al.SD-BSI: An Improved Method for Brine Shrimp Slick Detection in Salt Lakes Using Multispectral Satellite Remote Sensing Imagery[J].Geomatics and Information Science of Wuhan University,2025,50(11):2236-2246.
田婧怡,周屈,王欣,等.SD-BSI:一种改进的多光谱卫星遥感影像盐湖卤虫带探测方法[J].武汉大学学报(信息科学版),2025,50(11):2236-2246. DOI:10.13203/j.whugis20240283
TIAN Jingyi,ZHOU Qu,WANG Xin,et al.SD-BSI: An Improved Method for Brine Shrimp Slick Detection in Salt Lakes Using Multispectral Satellite Remote Sensing Imagery[J].Geomatics and Information Science of Wuhan University,2025,50(11):2236-2246. DOI:10.13203/j.whugis20240283
卤虫是一种全球广泛分布的小型浮游动物,能够在水面聚集形成卤虫带,容易使用遥感影像进行观测与提取。传统卤虫遥感提取指数(brine shrimp index,BSI)方法未考虑临近水体像元的光谱信息,在复杂的盐湖水体背景中常导致大量中、低密度卤虫带像元提取失败,从而限制了BSI的整体适用性和提取准确性。针对该问题,提出一种顾及临近水体像元的光谱差异卤虫带提取方法(spectral difference BSI method,SD-BSI);在不同盐湖使用Landsat 8卫星陆地成像仪(operational land imager,OLI)数据进行实验,充分分析传感器中不同水体背景要素及不同密度卤虫带的差异光谱特征,从而设计出合适的提取参数;并与传统方法进行精度比较,在多种复杂水体场景下开展鲁棒性分析。结果表明,SD-BSI探测的平均准确率达到0.951,与改进前模型BSI相比提升了25.0%,极大程度消除了在盐湖卤虫带遥感影像中占比较高的中、低及极低密度卤虫带的提取失败,召回率显著提升35.0%,识别效果较好。此外,SD-BSI有效克服了太阳耀光、薄云、高浑浊、高叶绿素浓度等复杂盐湖水体背景对卤虫带提取的干扰,更适用于实际应用场景,在不同盐湖的卤虫带探测上都有着均衡稳定的表现,为盐湖卤虫资源监测提供参考。
To address the limitations of the traditional brine shrimp index (BSI) in detecting brine shrimp slicks in salt lakes
this paper proposes an improved detection method— the spectral difference BSI method (SD-BSI).
Objectives
2
This study enhanced the accuracy and robustness of brine shrimp slick detection in salt lakes by incorporating spectral differences from adjacent water pixels
which are overlooked by the traditional BSI method.
Methods
2
The SD-BSI method was developed by adapting the traditional BSI to include spectral data from adjacent water pixels. This study utilized Landsat 8 operational land imager data across different salt lakes to conduct experiments. The performance of SD-BSI was compared with the traditional methods through accuracy assessment and robustness analysis under complex water backgrounds.
Results
2
The experimental results demonstrated that SD-BSI achieved an average accuracy of 0.951
which is a 25.0% improvement over the traditional BSI method. The recall rate was significantly enhanced by 35.0%
effectively addressing the extraction failures in slicks of medium to very low density brine shrimp that are prevalent with traditional BSI. Furthermore
SD-BSI effectively mitigated disturbances from sunglints
thin clouds
and water with high turbidity and high chlorophyll concentration in complex salt lake environments.
Conclusions
2
The SD-BSI method offers a significant improvement in detecting brine shrimp slicks across different salt lakes. This method not only enhances the precision and robustness of detection under various water conditions but also demonstrates balanced and stable performance
and could serve as a valuable tool for ecological monitoring and sustainable management of brine shrimp resources in salt lakes.
ASEM A , SUN S C . Morphological Differentiation of Seven Parthenogenetic Artemia (Crustacea: Branchiopoda) Populations from China, with Special Emphasis on Ploidy Degrees [J]. Microscopy Research and Technique , 2016 , 79 ( 4 ): 258 - 266 .
任慕莲 , 郭焱 , 王基琳 , 等 . 中国西北部盐湖卤虫生态及资源 [M]. 哈尔滨 : 黑龙江科学技术出版社 , 1996 .
REN Mulian , GUO Yan , WANG Jilin , et al . Survey of Artemia Ecology and Resources in Iinland Salt Lakes in Northwest China [M]. Harbin : Heilongjiang Science and Technology Press , 1996 .
Introduction , Biology and Ecology of Artemia [EB/OL]. ( 2024-07-09 )[ 2024-06-21 ]. https://www.fao.org/4/w3732e/w3732e0m.htm https://www.fao.org/4/w3732e/w3732e0m.htm .
杨劲树 , 毕宁 , 杨卫军 . 卤虫的生殖方式及其调控机制研究进展 [J]. 生命科学 , 2024 , 36 ( 6 ): 831 - 841 .
YANG Jinshu , BI Ning , YANG Weijun . Progress of Studies on Reproductive Modes and Mechanisms of Regulation in Artemia [J]. Chinese Bulletin of Life Sciences , 2024 , 36 ( 6 ): 831 - 841 .
LI K , WANG Y D , DU G R , et al . Effects of Temperature and Particle Size on the Filter-Feeding Rate of Brine Shrimp Artemia Franciscana at Different Growth Stages and Stocking Densities [J]. Aquaculture International , 2024 , 32 ( 6 ): 7355 - 7367 .
LENORMAND T , NOUGUÉ O , JABBOUR-ZAHAB R , et al . Resurrection Ecology in Artemia [J]. Evolutionary Applications , 2018 , 11 ( 1 ): 76 - 87 .
MARINHO-SORIANO E , AZEVEDO C A A , TRIGUEIRO T G , et al . Bioremediation of Aquaculture Wastewater Using Macroalgae and Artemia [J]. International Biodeterioration & Biodegradation , 2011 , 65 ( 1 ): 253 - 257 .
REDÓN S , AMAT F , HONTORIA F , et al . Participation of Metanauplii and Juvenile Individuals of Artemia Parthenogenetica (Branchiopoda) in the Circulation of Avian Cestodes [J]. Parasitology Research , 2011 , 108 ( 4 ): 905 - 912 .
GREEN A J , SÁNCHEZ M I , AMAT F , et al . Dispersal of Invasive and Native Brine Shrimps Artemia (Anostraca) via Waterbirds [J]. Limnology and Oceanography , 2005 , 50 ( 2 ): 737 - 742 .
MUÑOZ J , AMAT F , GREEN A J , et al . Bird Migratory Flyways Influence the Phylogeography of the Invasive Brine Shrimp Artemia Franciscana in Its Native American Range [J]. PeerJ , 2013 , 1 : e200 .
SORGELOOS P , DHERT P , CANDREVA P . Use of the Brine Shrimp, Artemia SPP., in Marine Fish Larviculture [J]. Aquaculture , 2001 , 200 ( 1/2 ): 147 - 159 .
BELOVSKY G E , PERSCHON W C . A Management Case Study for a New Commercial Fishery: Brine Shrimp Harvesting in Great Salt Lake, Utah, USA [J]. Ecological Applications , 2019 , 29 ( 3 ): e01864 .
VAN STAPPEN G , SUI L Y , HOA V N , et al . Review on Integrated Production of the Brine Shrimp Artemia in Solar Salt Ponds [J]. Reviews in Aquaculture , 2020 , 12 ( 2 ): 1054 - 1071 .
汤秋鸿 , 张学君 , 戚友存 , 等 . 遥感陆地水循环的进展与展望 [J]. 武汉大学学报(信息科学版) , 2018 , 43 ( 12 ): 1872 - 1884 .
TANG Qiuhong , ZHANG Xuejun , QI Youcun , et al . Remote Sensing of the Terrestrial Water Cycle:Progress and Perspectives [J]. Geomatics and Information Science of Wuhan University , 2018 , 43 ( 12 ): 1872 - 1884 .
张良培 , 张乐飞 , 袁强强 . 遥感大模型:进展与前瞻 [J]. 武汉大学学报(信息科学版) , 2023 , 48 ( 10 ): 1574 - 1581 .
ZHANG Liangpei , ZHANG Lefei , YUAN Qiangqiang . Large Remote Sensing Model: Progress and Prospects [J]. Geomatics and Information Science of Wuhan University , 2023 , 48 ( 10 ): 1574 - 1581 .
沈亚峰 , 刘建强 , 丁静 , 等 . 海洋一号C星光学载荷对海面溢油的识别能力分析 [J]. 遥感学报 , 2020 , 24 ( 8 ): 933 - 944 .
SHEN Yafeng , LIU Jianqiang , DING Jing , et al . HY-1C COCTS and CZI Observation of Marine Oil Spills in the South China Sea [J]. Journal of Remote Sensing , 2020 , 24 ( 8 ): 933 - 944 .
HU C M . A Novel Ocean Color Index to Detect Floating Algae in the Global Oceans [J]. Remote Sensing of Environment , 2009 , 113 ( 10 ): 2118 - 2129 .
张杰 , 吕恒 , 潘洪洲 , 等 . 内陆湖泊颗粒有机碳反演及日变化初步研究 [J]. 武汉大学学报(信息科学版) , 2015 , 40 ( 12 ): 1618 - 1624 .
ZHANG Jie , LÜ Heng , PAN Hongzhou , et al . Quantitative Estimation of Particulate Organic Carbon and Diurnal Variation in Inland Eutrophic Lake [J]. Geomatics and Information Science of Wuhan University , 2015 , 40 ( 12 ): 1618 - 1624 .
QI L , HU C M , MIKELSONS K , et al . In Search of Floating Algae and Other Organisms in Global Oceans and Lakes [J]. Remote Sensing of Environment , 2020 , 239 : 111659 .
王欣 , 刘建强 , 丁静 , 等 . HY-1C卫星CZI影像卤虫条带提取研究: 以艾比湖为例 [J]. 遥感学报 , 2023 , 27 ( 1 ): 104 - 115 .
WANG Xin , LIU Jianqiang , DING Jing , et al . Extraction of Artemia Slicks From HY-1C CZI Images: Taking Ebinur Lake as an Example [J]. National Remote Sensing Bulletin , 2023 , 27 ( 1 ): 104 - 115 .
WANG X , TIAN J Y , MA L , et al . Remote Detection of Artemia Slicks Using Multisatellite Observations: Spectral, Spatial and Temporal Considerations [J]. International Journal of Digital Earth , 2024 , 17 ( 1 ): 2301433 .
李微 , 刘伟男 , 贾越平 , 等 . 基于面向对象法艾比湖卤虫信息提取 [J]. 国土资源遥感 , 2018 , 30 ( 4 ): 176 - 181 .
LI Wei , LIU Weinan , JIA Yueping , et al . Information Extraction of the Ebinur Lake Artemia Based on Object-Oriented Method [J]. Remote Sensing for Land & Resources , 2018 , 30 ( 4 ): 176 - 181 .
QI L , YAO Y , ENGLISH D E , et al . Remote Sensing of Brine Shrimp Cysts in Salt Lakes [J]. Remote Sensing of Environment , 2021 , 266 : 112695 .
TIAN L Q , TIAN J Y , WANG J R , et al . A Novel Remote Sensing Index for Brine Shrimp (Artemia) Slick Detection in Salt Lakes [J]. Remote Sensing of Environment , 2023 , 286 : 113428 .
徐金传 , 易翀 . 艾比湖卤虫在渔业中的应用 [J]. 科学养鱼 , 2011 ( 2 ): 67 - 68 .
XU Jinchuan , YI Chong . Application of Artemia in Ebinur Lake in Fisheries [J]. Scientific Fish Farming , 2011 ( 2 ): 67 - 68 .
FAO Expert Workshop on Sustainable Use and Management of Artemia Resources in Asia [EB/OL]. 2016 -
11 - 07 . https://www.fao.org/fileadmin/user_uplo https://www.fao.org/fileadmin/user_uplo
ad/affris/docs/PowerPoint_Presentatitions . pdf .
LIU C J , DUAN P , ZHANG F , et al . Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter Under the Missing-Data Scenario [J]. Remote Sensing , 2021 , 13 ( 19 ): 3952 .
HOU X J , FENG L , DAI Y H , et al . Global Mapping Reveals Increase in Lacustrine Algal Blooms over the Past Decade [J]. Nature Geoscience , 2022 , 15 ( 2 ): 130 - 134 .
PEKEL J F , COTTAM A , GORELICK N , et al . High-Resolution Mapping of Global Surface Water and Its Long-Term Changes [J]. Nature , 2016 , 540 ( 7633 ): 418 - 422 .
GOWER J , HU C M , BORSTAD G , et al . Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico [J]. IEEE Transactions on Geoscience and Remote Sensing , 2006 , 44 ( 12 ): 3619 - 3625 .
QI L , HU C M . To What Extent Can Ulva and Sargassum Be Detected and Separated in Satellite Imagery? [J]. Harmful Algae , 2021 , 103 : 102001 .
HU C M . Remote Detection of Marine Debris Using Satellite Observations in the Visible and near Infrared Spectral Range: Challenges and Potentials [J]. Remote Sensing of Environment , 2021 , 259 : 112414 .
张海明 , 王明常 , 陈学业 , 等 . 领域知识优化深度置信网络的遥感变化检测 [J]. 武汉大学学报(信息科学版) , 2022 , 47 ( 5 ): 762 - 768 .
ZHANG Haiming , WANG Mingchang , CHEN Xueye , et al . Remote Sensing Change Detection Based on Deep Belief Networks Optimized by Domain Knowledge [J]. Geomatics and Information Science of Wuhan University , 2022 , 47 ( 5 ): 762 - 768 .
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