High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data

Qiuhua TANG; Ningning LI; Yujie ZHANG; Zhipeng DONG; Yongling ZHENG; Jingjing BAO; Jingyu ZHANG

doi:10.1007/s00343-025-5002-7

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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data

Geology | Updated：2026-03-26

- High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data
- Journal of Oceanology and Limnology Vol. 44, Issue 1, Pages: 99-108(2026)
- Affiliations：
  
  1.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China
  3.College of Mapping and Spatial Information, Shandong University of Science and Technology, Qingdao 266590, China
  4.Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
- Author bio：
  
  tangqiuhua@fio.org.cn
  529142251@qq.com
- Funds：
- DOI：10.1007/s00343-025-5002-7
  CLC：
- Received：01 January 2025，
  
  Accepted：31 March 2025，
  
  Online First：29 April 2025，
  
  Published：01 January 2026
- Accepted：
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TANG Qiuhua,LI Ningning,ZHANG Yujie,et al.High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data[J].Journal of Oceanology and Limnology,2026,44(01):99-108.
DOI：

TANG Qiuhua,LI Ningning,ZHANG Yujie,et al.High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data[J].Journal of Oceanology and Limnology,2026,44(01):99-108. DOI： 10.1007/s00343-025-5002-7.

摘要

Abstract

Benthic habitat mapping is an emerging discipline in the international marine field in recent years

providing an effective tool for marine spatial planning

marine ecological management

and decision-making applications. Seabed sediment classification is one of the main contents of seabed habitat mapping. In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range

where a single data source does not fully reflect the substrate type

we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources. Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data

constructed a random forests (RF) classifier with optimal feature selection. A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island

Hainan

South China. Different seabed sediment types

such as sand

seagrass

and coral reefs were effectively identified

with an overall classification accuracy of 92%. Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources

which improved the accuracy of seabed sediment classification. Therefore

the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.

关键词

Keywords

references

Angnuureng D B , Brempong K E , Jayson-Quashigah P N et al . 2022 . Satellite, drone and video camera multi-platform monitoring of coastal erosion at an engineered pocket beach: a showcase for coastal management at Elmina Bay, Ghana (West Africa). Regional Studies in Marine Science , 53 : 102437 , https://doi.org/10.1016/j.rsma.2022.102437 https://doi.org/10.1016/j.rsma.2022.102437 .

Breiman L . 1996 . Bagging predictors . Machine Learning , 24 ( 2 ): 123 - 140 , https://doi.org/10.1007/BF00058655 https://doi.org/10.1007/BF00058655 .

Breiman L . 2001 . Random forests . Machine Learning , 45 ( 1 ): 5 - 32 , https://doi.org/10.1023/A:1010933404324 https://doi.org/10.1023/A:1010933404324 .

Brown C J , Hewer A J , Limpenny D S et al . 2004 . Mapping seabed biotopes using sidescan sonar in regions of heterogeneous substrata: case study east of the Isle of Wight, English Channel . Underwater Technology , 26 ( 1 ): 27 - 36 , https://doi.org/10.3723/175605404783101558 https://doi.org/10.3723/175605404783101558 .

Brown C J , Smith S J , Lawton P et al . 2011 . Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques . Estuarine, Coastal and Shelf Science , 92 ( 3 ): 502 - 520 , https://doi.org/10.1016/j.ecss.2011.02.007 https://doi.org/10.1016/j.ecss.2011.02.007 .

Chakraborty B , Kaustubha R , Hegde A et al . 2001 . Acoustic seafloor sediment classification using self-organizing feature maps . IEEE Transactions on Geoscience and Remote Sensing , 39 ( 12 ): 2722 - 2725 , https://doi.org/10.1109/36.975006 https://doi.org/10.1109/36.975006 .

Chakraborty B , Kodagali V , Baracho J . 2003 . Sea-floor classification using multibeam echo-sounding angular backscatter data: a real-time approach employing hybrid neural network architecture . IEEE Journal of Oceanic Engineering , 28 ( 1 ): 121 - 128 , https://doi.org/10.1109/JOE.2002.808211 https://doi.org/10.1109/JOE.2002.808211 .

Cui X D , Xing Z , Yang F L et al . 2020 . A method for multibeam seafloor terrain classification based on self-adaptive geographic classification unit. Applied Acoustics , 157 : 107029 , https://doi.org/10.1016/j.apacoust.2019.107029 https://doi.org/10.1016/j.apacoust.2019.107029 .

Ho T K . 1998 . The random subspace method for constructing decision forests . IEEE Transactions on Pattern Analysis and Machine Intelligence , 20 ( 8 ): 832 - 844 , https://doi.org/10.1109/34.709601 https://doi.org/10.1109/34.709601 .

Ji X , Yang B S , Wei Z et al . 2024 . Benthic habitat sediments mapping in coral reef area using amalgamation of multi-source and multi-modal remote sensing data. Remote Sensing of Environment , 304 : 114032 , https://doi.org/10.1016/j.rse.2024.114032 https://doi.org/10.1016/j.rse.2024.114032 .

Kabiri K , Kazeminezhad M H . 2025 . Depth estimation from multispectral satellite imagery: a comparison of conventional and machine learning-based approaches—case study: Kish Island, Persian Gulf . Earth Science Informatics , 18 ( 1 ): 68 , https://doi.org/10.1007/s12145-024-01542-w https://doi.org/10.1007/s12145-024-01542-w .

Kabiri K , Rezai H , Moradi M . 2018 . Mapping of the corals around Hendorabi Island (Persian Gulf), using WorldView-2 standard imagery coupled with field observations . Marine Pollution Bulletin , 129 ( 1 ): 266 - 274 , https://doi.org/10.1016/j.marpolbul.2018.02.045 https://doi.org/10.1016/j.marpolbul.2018.02.045 .

Kabiri K , Rezai H , Moradi M . 2020 . A drone-based method for mapping the coral reefs in the shallow coastal waters—case study: Kish Island, Persian Gulf . Earth Science Informatics , 13 ( 4 ): 1265 - 1274 , https://doi.org/10.1007/s12145-020-00507-z https://doi.org/10.1007/s12145-020-00507-z .

Kabiri K . 2020 . Mapping coastal ecosystems and features using a low-cost standard drone: case study, Nayband Bay, Persian Gulf, Iran . Journal of Coastal Conservation , 24 ( 5 ): 62 , https://doi.org/10.1007/s11852-020-00780-6 https://doi.org/10.1007/s11852-020-00780-6 .

Lin Y C , Cheng Y T , Zhou T et al . 2019 . Evaluation of UAV LiDAR for mapping coastal environments . Remote Sensing , 11 ( 24 ): 2893 , https://doi.org/10.3390/rs11242893 https://doi.org/10.3390/rs11242893 .

Lyzenga D R , Malinas N P , Tanis F J . 2006 . Multispectral bathymetry using a simple physically based algorithm . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 8 ): 22512259 , https://doi.org/10.1109/TGRS.2006.872909 https://doi.org/10.1109/TGRS.2006.872909 .

Michalopoulou Z H , Alexandrou D , de Moustier C . 1995 . Application of neural and statistical classifiers to the problem of seafloor characterization . IEEE Journal of Oceanic Engineering , 20 ( 3 ): 190 - 197 , https://doi.org/10.1109/48.393074 https://doi.org/10.1109/48.393074 .

Misiuk B , Brown C J . 2024 . Benthic habitat mapping: a review of three decades of mapping biological patterns on the seafloor. Estuarine , Coastal and Shelf Science , 296 : 108599 , https://doi.org/10.1016/j.ecss.2023.108599 https://doi.org/10.1016/j.ecss.2023.108599 .

Monteiro J G , Jiménez J L , Gizzi F et al . 2021 . Novel approach to enhance coastal habitat and biotope mapping with drone aerial imagery analysis . Scientific Reports , 11 ( 1 ): 574 , https://doi.org/10.1038/s41598-020-80612-7 https://doi.org/10.1038/s41598-020-80612-7 .

Mumby P J , Green E P , Clark C D et al . 1998 . Digital analysis of multispectral airborne imagery of coral reefs . Coral Reefs , 17 ( 1 ): 59 - 69 , https://doi.org/10.1007/s003380050096 https://doi.org/10.1007/s003380050096 .

Prampolini M , Angeletti L , Castellan G et al . 2021 . Benthic habitat map of the southern Adriatic Sea (Mediterranean Sea) from object-based image analysis of multi-source acoustic backscatter data . Remote Sensing , 13 ( 15 ): 2913 , https://doi.org/10.3390/rs13152913 https://doi.org/10.3390/rs13152913 .

Reshitnyk L , Costa M , Robinson C et al . 2014 . Evaluation of WorldView-2 and acoustic remote sensing for mapping benthic habitats in temperate coastal Pacific waters . Remote Sensing of Environment , 153 : 7 - 23 , https://doi.org/10.1016/j.rse.2014.07.016 https://doi.org/10.1016/j.rse.2014.07.016 .

Su D P , Huang Y , Yang F L et al . 2023 . Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed . National Remote Sensing Bulletin , 27 ( 9 ): 2219 - 2228 , https://doi.org/10.11834/jrs.20222283. https://doi.org/10.11834/jrs.20222283. (in Chinese with English abstract)

Sullivan E , Papagiannopoulos N , Clewley D et al . 2025 . Mapping coastal marine habitats using UAV and multispectral satellite imagery in the NEOM region, northern Red Sea . Remote Sensing , 17 ( 3 ): 485 , https://doi.org/10.3390/rs17030485 https://doi.org/10.3390/rs17030485 .

Vermote E F , Tanre D , Deuze J L et al . 1997 . Second simulation of the satellite signal in the solar spectrum, 6 S: an overview . IEEE Transactions on Geoscience and Remote Sensing , 35 ( 3 ): 675 - 686 , https://doi.org/10.1109/36.581987 https://doi.org/10.1109/36.581987 .

Wan J X , Qin Z L , Cui X D et al . 2023 . Seafloor habitat mapping by combining multiple features from optic and acoustic data: a case study from Ganquan Island, South China Sea . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 16 : 7248 - 7263 , https://doi.org/10.1109/JSTARS.2023.3298472 https://doi.org/10.1109/JSTARS.2023.3298472 .

Wicaksono P , Aryaguna P A , Lazuardi W . 2019a . Benthic habitat mapping model and cross validation using machine-learning classification algorithms . Remote Sensing , 11 ( 11 ): 1279 , https://doi.org/10.3390/rs11111279 https://doi.org/10.3390/rs11111279 .

Wicaksono P , Fauzan M A , Kumara I S W et al . 2019b . Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images . International Journal of Remote Sensing , 40 ( 23 ): 8955 - 8978 , https://doi.org/10.1080/01431161.2019.1624866 https://doi.org/10.1080/01431161.2019.1624866 .

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