Remote sensing information extraction of sargassum from the in-shore shallow sea in Daya Bay

doi:10.13474/j.cnki.11-2246.2024.0519

Abstract

Abstract: Sargassum plays an important role in healthy coastal ecosystems. Remote sensing monitoring mostly studies of floating sargassum in the open sea, and less research on shallow seabed sargassum; technical methods are mostly based on empirical models and rely on a large amount of measured data. This paper establishes a sargassum remote sensing information extraction model based on the radiation transfer process of water bodies. The results of three experimental areas in Daya Bay show that:①The sargassum abundance distribution is essentially consistent with the field survey data. ②Sargassum in Daya Bay grows better and has a higher abundance when the water is highly open, the water exchange conditions are good, the water is affected by wind and waves, the bottom is sand-covered rocks or reefs, and the water depth is less than 1.6m.③Water depth and waves are important environmental factors that affect the growth and distribution of sargassum. The method in this paper has positive significance for the protection of sargassum resources and the construction of coastal biodiversity.

Key words: sargassum, marine benthic habitat, abundance information, in-shore waters, mixed pixel unmixing

CLC Number:

P237

LI Jun, HE Yingqing, DENG Ruru, XIONG Longhai, ZHANG Ruihao. Remote sensing information extraction of sargassum from the in-shore shallow sea in Daya Bay[J]. Bulletin of Surveying and Mapping, 2024, 0(5): 108-114.

References

[1] HEDLEY J D,ROELFSEMA C M,PHINN S R,et al. Environmental and sensor limitations in optical remote sensing of coral reefs:implications for monitoring and sensor design[J].Remote Sensing,2012,4(1):271-302.
[2] VISSER F,WALLIS C,SINNOTT A M. Optical remote sensing of submerged aquatic vegetation: opportunities for shallow clearwater streams[J]. Limnologica,2013,43(5): 388-398.
[3] QI L,HU C. To what extent can Ulva and Sargassum be detected and separated in satellite imagery？[J]. Harmful Algae,2021,103: 102001.
[4] PENUELAS J,GAMON J A,GRIFFIN K L,et al. Assessing community type,plant biomass,pigment composition,and photosynthetic efficiency of aquatic vegetation from spectral reflectance[J]. Remote Sensing of Environment,1993,46(2): 110-118.
[5] 黄超华. 中国黄、东海马尾藻属(Sargassum)的分类学修订[D].青岛:中国科学院大学(中国科学院海洋研究所),2017.
[6] HUANG X,WANG D,BAO M,et al. Spectral characteristics of Sargassum horneri in seawater[C]//Proceedings of 2018 International symposium on optoelectronic technology and application. [S.l.]:SPIE,2018.
[7] LASQUITES J J,BLANCO A C,TAMONDONG A.Mapping of sargassum distribution in the eastern coast of southern leyte using Sentinel-2 satellite imagery[J].The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2019,42:289-295.
[8] CUEVAS E,URIBE-MARTÍNEZ A,LICEAGA-CORREA M D L Á.A satellite remote-sensing multi-index approach to discriminate pelagic Sargassum in the waters of the Yucatan Peninsula,Mexico[J]. International Journal of Remote Sensing,2018,39(11):3608-3627.
[9] HU C. A novel ocean color index to detect floating algae in the global oceans[J]. Remote Sensing of Environment,2009,113(10):2118-2129.
[10] 张正龙. 我国黄、东海浒苔和马尾藻的遥感鉴别及绿潮发生过程研究[D].上海:华东师范大学,2014.
[11] 金松,韩震,刘瑜. 一种区分浒苔和马尾藻的遥感方法[J]. 遥感信息,2016,31(2): 44-48.
[12] SETYAWIDATI N,KAIMUDDIN A H,WATI I P,et al. Percentage cover,biomass,distribution,and potential habitat mapping of natural macroalgae,based on high-resolution satellite data and in situ monitoring,at Libukang Island,Malasoro Bay,Indonesia[J]. Journal of Applied Phycology,2018,30(1):159-171.
[13] HOANG T C,COLE A J,FOTEDAR R K,et al. Seasonal changes in water quality and Sargassum biomass in southwest Australia[J]. Marine Ecology Progress Series,2016,551:63-79.
[14] BALADO J,OLABARRIA C,MARTÍNEZ-SÁNCHEZ J,et al. Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning[J]. International Journal of Remote Sensing,2021,42(5):1785-1800.
[15] VENTURA D,BONIFAZI A,GRAVINA M F,et al. Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)[J]. Remote Sensing,2018,10(9):1331.
[16] ZHENG Y,WU J,WANG A,et al.Object-and pixel-based classifications of macroalgae farming area with high spatial resolution imagery[J].Geocarto International,2018,33(10):1048-1063.
[17] STUMPF R P,DAVIS T W,WYNNE T T,et al. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria[J]. Harmful Algae,2016,54(1): 160-173.
[18] 蒋福康,李庆欣,林坚士. 大亚湾的马尾藻资源研究[J]. 热带海洋,1996,15(1): 85-90.
[19] 于杰,黄洪辉,舒黎明,等. 马尾藻遥感信息提取[J]. 遥感信息,2013,28(2): 93-100.
[20] 李俊,张文志,邓孺孺,等. 高分一号WFV影像的深圳市水库CODMn浓度时空分布特征[J]. 遥感学报,2022,26(8): 1562-1574.
[21] MCKINNA L I W,FEARN P R C,WEEK S J,et al. A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization[J]. Journal of Geophysical Research: Oceans,2015,120(3):1741-1770.
[22] 邓孺孺,何颖清,秦雁,等. 分离悬浮质影响的光学波段(400-900nm)水吸收系数测量[J]. 遥感学报,2012,16(1): 174-191.
[23] DOGLIOTTI A I,RUDDICK K,GUERRERO R. Seasonal and inter-annual turbidity variability in the Río de la Plata from 15 years of MODIS: El Niño dilution effect[J]. Estuarine,Coastal and Shelf Science,2016,182(Part A): 27-39.
[24] XU S,XU S,ZHOU Y,et al. Single beam sonar reveals the distribution of the eelgrass Zostera marina L and threats from the green tide algae Chaetomorpha linum K in Swan-Lake lagoon (China)[J]. Marine Pollution Bulletin,2019,145: 611-623.