SEMANTIC SEGMENTATION OF ALGAL BLOOMS ON THE OCEAN SURFACE USING SENTINEL 3 CHL_NN BAND IMAGERY

Venkatesh BHANDAGE

venkatesh.bhandage@manipal.edu
Manipal Institute of Technology MAHE Manipal (India)
https://orcid.org/0000-0002-9503-8196

Manohara PAI M. M.


Manipal Institute of Technology MAHE Manipal (India)
https://orcid.org/0000-0003-2164-2945

Abstract

Satellite imagery plays an important role in detecting algal blooms because of its ability to cover larger geographical regions. Excess growth of Sea surface algae, characterized by the presence of Chlorophyll-a (Chl-a), is considered to be harmful. The detection of algal growth at an earlier stage may prevent hazardous effects on the aquatic environment. Semantic segmentation of algal blooms is helpful in the quantization of algal blooms. A rule-based semantic segmentation approach for the segregation of sea surface algal blooms is proposed. Bloom concentrations are classified into three different concentrations, namely, low, medium, and high. The chl_nn band in the Sentinel-3 satellite images is used for experimentation. The chl_nn band has exclusive details of the presence of chlorophyll concentrations. A dataset is proposed for the semantic segmentation of algal blooms. The devised rule-based semantic segmentation approach has produced an average accuracy of 98%. A set of 100 images is randomly selected for testing. The tests are repeated on 5 different image sets. The results are validated by the pixel comparison method. The proposed work is compared with other relevant works. The Arabian Sea near the coastal districts of Udupi and Mangaluru has been considered as the area of study. The methodology can be adapted to monitor the life cycle of blooms and their hazardous effects on aquatic life.


Keywords:

Sentinel-3, algal bloom, remote sensing, chlorophyll-a, semantic segmentation, rule-based classifier

Al-Nawashi, M. M., Al-Hazaimeh, O. M., & Khazaaleh, M. Kh. (2024). New approach for breast cancer detection based on machine learning techniques. Applied Computer Science, 20(1), 1-16. https://doi.org/10.35784/acs-2024-01
DOI: https://doi.org/10.35784/acs-2024-01   Google Scholar

Anilkumar, P., & Venugopal, P. (2022). Research contribution and comprehensive review towards the semantic segmentation of aerial images using Deep Learning techniques. Security and Communication Networks, 2022(1), 6010912. https://doi.org/10.1155/2022/6010912
DOI: https://doi.org/10.1155/2022/6010912   Google Scholar

Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
DOI: https://doi.org/10.1109/TPAMI.2016.2644615   Google Scholar

Baran, K. (2024). Application of thermal imaging cameras for smartphone: Seek Thermal Compact Pro and FLIR One Pro for human stress detection – Comparison and study. Applied Computer Science, 20(1), 122–138. https://doi.org/10.35784/acs-2024-08
DOI: https://doi.org/10.35784/acs-2024-08   Google Scholar

Cui, B., Zhang, H., Jing, W., Liu, H., and Cui, J. (2022). SRSe-Net: Super-Resolution-Based semantic segmentation network for green tide extraction. Remote Sensing. 14(3), 710. https://doi.org/10.3390/rs14030710
DOI: https://doi.org/10.3390/rs14030710   Google Scholar

Elbahri, M., Taleb, N., Ardjoun, S. A. E. M., & Zouaoui, C. M. A. (2024). Few-shot learning with pre-trained layers integration applied to hand gesture recognition for disabled people. Applied Computer Science, 20(2), 1-23. https://doi.org/10.35784/acs-2024-13
DOI: https://doi.org/10.35784/acs-2024-13   Google Scholar

EUMETSAT. (2024). OLCI Level-2 Water Full Resolution. http://coda.eumetsat.int/#/home
  Google Scholar

Fernández-Tejedor, M., Velasco, J. E., & Angelats, E. (2022). Accurate estimation of chlorophyll-a concentration in the coastal areas of the ebro delta (NW Mediterranean) using Sentinel-2 and its application in the selection of areas for mussel aquaculture. Remote Sensing, 14(20), 5235. https://doi.org/10.3390/rs14205235
DOI: https://doi.org/10.3390/rs14205235   Google Scholar

Fogg, G. E. (2022). Harmful algae - A perspective. Harmful Algae, 1(1), 1-4. https://doi.org/10.1016/S1568-9883(02)00002-1
DOI: https://doi.org/10.1016/S1568-9883(02)00002-1   Google Scholar

Girisha, S., Pai, M. M. M., Verma, U., & Pai, R. M. (2021a). Semantic segmentation with enhanced temporal smoothness using CRF in aerial videos. IEEE Madras Section Conference (MASCON) (pp. 1-5). IEEE. https://doi.org/10.1109/MASCON51689.2021.9563599
DOI: https://doi.org/10.1109/MASCON51689.2021.9563599   Google Scholar

Girisha, S., Verma, U., Manohara Pai, M. M., & Pai., R. M. (2021b). UVid-Net: Enhanced semantic segmentation of UAV aerial videos by embedding temporal information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4115-4127. https://doi.org/10.1109/JSTARS.2021.3069909
DOI: https://doi.org/10.1109/JSTARS.2021.3069909   Google Scholar

Haji Gholizadeh, M., Melesse, A. M., & Reddi, L. (2016). Spaceborne and airborne sensors in water quality assessment. International Journal of Remote Sensing, 37(14), 3143-3180. https://doi.org/10.1080/01431161.2016.1190477
DOI: https://doi.org/10.1080/01431161.2016.1190477   Google Scholar

Ho, J. C., Michalak, A. M., & Pahlevan, N. (2019). Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574, 667-670. https://doi.org/10.1038/s41586-019-1648-7
DOI: https://doi.org/10.1038/s41586-019-1648-7   Google Scholar

Jaiganesh, S. N. N., Sarangi, R. K., & Shukla, S. (2021). Satellite-based observation of ocean productivity in southeast Arabian Sea using chlorophyll, sea surface temperature and wind datasets. Journal of Earth System Science, 130, 5. https://doi.org/10.1007/s12040-020-01512-y
DOI: https://doi.org/10.1007/s12040-020-01512-y   Google Scholar

Kamath, R., Balachandra, M., Vardhan, A., & Maheshwari, U. (2022). Classification of paddy crop and weeds using semantic segmentation. Cogent Engineering, 9(1), 2018791. https://doi.org/10.1080/23311916.2021.2018791
DOI: https://doi.org/10.1080/23311916.2021.2018791   Google Scholar

Kinane Daouadji, A., & Bendella, F. (2024). Improving e-learning by facial expression analysis. Applied Computer Science, 20(2), 126-137. https://doi.org/10.35784/acs-2024-20
DOI: https://doi.org/10.35784/acs-2024-20   Google Scholar

Kotaridis, I., & Lazaridou, M. (2022). Semantic segmentation using a UNet architecture on Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 119-126. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-119-2022
DOI: https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-119-2022   Google Scholar

Kutser, T. (2009). Passive optical remote sensing of cyanobacteria and other Intense phytoplankton blooms in coastal and inland waters. International Journal of Remote Sensing, 30(17), 4401-4425. https://doi.org/10.1080/01431160802562305
DOI: https://doi.org/10.1080/01431160802562305   Google Scholar

Li, Z., & Demir, I. (2023). U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding. Science of the Total Environment, 869, 161757. https://doi.org/10.1016/j.scitotenv.2023.161757
DOI: https://doi.org/10.1016/j.scitotenv.2023.161757   Google Scholar

Lilay, M. Y., & Taye, G. D. (2023). Semantic segmentation model for land cover classification from satellite images in Gambella National Park, Ethiopia. SN Applied Sciences, 5, 76. https://doi.org/10.1007/s42452-023-05280-4
DOI: https://doi.org/10.1007/s42452-023-05280-4   Google Scholar

Ma, J., Zhou, W., Lei, J., & Yu, L. (2023). Adjacent bi-hierarchical network for scene parsing of remote sensing images. IEEE Geoscience and Remote Sensing Letters, 20, 3000705. https://doi.org/10.1109/LGRS.2023.3241648
DOI: https://doi.org/10.1109/LGRS.2023.3241648   Google Scholar

Maiyanti, S. I., Desiani, A., Lamin, S., Puspitashati., Arhami, M., Gofar, N., & Cahyana, D. (2023) Rotation-gamma correction augmentation on CNN-dense block for soil image classification. Applied Computer Science, 19(3), 96-115. https://doi.org/10.35784/acs-2023-27
DOI: https://doi.org/10.35784/acs-2023-27   Google Scholar

Makhlouf, Z., Meraoumia, A., Lakhdar, L., & Haouam, M. Y. (2024). Enhancing medical data security in e-health systems using biometric-based watermarking. Applied Computer Science, 20(1), 28-55. https://doi.org/10.35784/acs-2024-03
DOI: https://doi.org/10.35784/acs-2024-03   Google Scholar

Nallapareddy, A., (2022). Detection and classification of vegetation areas from red and near infrared bands of Landsat-8 optical satellite image. Applied Computer Science, 18(1), 45-55. https://doi.org/10.35784/acs-2022-4
DOI: https://doi.org/10.35784/acs-2022-4   Google Scholar

Nayak, R. K., Swapna, M., Manche, S. S., Mohanty, P. C., Sheshasai, M. V. R., Dadhwal, V. K., & Kumar, R. (2023). Assessment of chlorophyll-a seasonal cycle in the North Indian Ocean using observations from OCM2, MODIS, and SeaWiFS. Journal of the Indian Society of Remote Sensing, 51, 229-246. https://doi.org/10.1007/s12524-022-01642-4
DOI: https://doi.org/10.1007/s12524-022-01642-4   Google Scholar

Ogashawara, I. (2019). The use of Sentinel-3 imagery to monitor cyanobacterial blooms. Environments, 6(6), 60. https://doi.org/10.3390/environments6060060
DOI: https://doi.org/10.3390/environments6060060   Google Scholar

Randolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D. L., & Soyeux, E. (2008) Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112(11), 4009-4019. https://doi.org/10.1016/j.rse.2008.06.002
DOI: https://doi.org/10.1016/j.rse.2008.06.002   Google Scholar

Ravishankar, T., Anil, T. C., Verma, U., Pai, M. M. M., & Pai. R. (2022). MartiNet: An efficient approach for river segmentation in SAR images. IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONECCT55679.2022.9865830
DOI: https://doi.org/10.1109/CONECCT55679.2022.9865830   Google Scholar

Rodríguez-Benito, C. V., Navarro, G., & Caballero, I. (2020). Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown. Marine Pollution Bulletin, 161(Part A), 111722. https://doi.org/10.1016/j.marpolbul.2020.111722
DOI: https://doi.org/10.1016/j.marpolbul.2020.111722   Google Scholar

Roelke, D., & Buyukates, Y. (2001). The Diversity of harmful algal bloom-triggering mechanisms and the complexity of bloom initiation. Human and Ecological Risk Assessment: An International Journal, 7(5), 1347-1362. https://doi.org/10.1080/20018091095041
DOI: https://doi.org/10.1080/20018091095041   Google Scholar

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Seg-mentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Vol. 9351, pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28
DOI: https://doi.org/10.1007/978-3-319-24574-4_28   Google Scholar

Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
DOI: https://doi.org/10.1109/TPAMI.2016.2572683   Google Scholar

Singh, N. J., & Nongmeikapam, K. (2023). Semantic segmentation of satellite images using Deep-Unet. Arabian Journal for Science and Engineering, 48, 1193–1205. https://doi.org/10.1007/s13369-022-06734-4
DOI: https://doi.org/10.1007/s13369-022-06734-4   Google Scholar

Srichandan, S., Baliarsingh, S. K., Samanta, A. Jena, A. K., Lotliker, A. A., Nair, T. M. B., Barik, K. K., & Acharyya, T. (2022). Satellite-based characterization of phytoplankton blooms in coastal waters of the northwestern bay of bengal. Journal of the Indian Society of Remote Sensing, 50, 2221-2228. https://doi.org/10.1007/s12524-022-01597-6
DOI: https://doi.org/10.1007/s12524-022-01597-6   Google Scholar

Tendolkar, A., Choraria, M. M., Manohara Pai, S., Girisha, G., Dsouza & Adithya, K. S. (2021). Modified crop health monitoring and pesticide spraying system using NDVI and Semantic Segmentation: An AGROCOPTER based approach. IEEE International Conference on Autonomous Systems (ICAS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICAS49788.2021.9551116
DOI: https://doi.org/10.1109/ICAS49788.2021.9551116   Google Scholar

Tholkapiyan, M., Shanmugam, P., & Suresh, T. (2014). Monitoring of ocean surface algal blooms in coastal and oceanic waters around India. Environmental Monitoring and Assessment, 186, 4129–4137. https://doi.org/10.1007/s10661-014-3685-x
DOI: https://doi.org/10.1007/s10661-014-3685-x   Google Scholar

Vase, V. K., Ajay, N., Kumar, R. Jayaraman, J., & Rohit, P. (2022). Evaluation of satellite sensors to compute Chlorophyll-a concentration in the Northeastern Arabian Sea: A validation approach. Journal of the Indian Society of Remote Sensing, 50, 2209-2220. https://doi.org/10.1007/s12524-022-01598-5
DOI: https://doi.org/10.1007/s12524-022-01598-5   Google Scholar

Verma, U., Chauhan, A., Manohara, M.P., & Pai, R. (2021). DeepRivWidth: Deep Learning based semantic segmentation approach for river identification and width measurement in SAR images of coastal Karnataka. Computers & Geosciences, 154, 104805. https://doi.org/10.1016/j.cageo.2021.104805
DOI: https://doi.org/10.1016/j.cageo.2021.104805   Google Scholar

Wang, Z., Zhang, S., Zhang, C., & Wang, B. (2023). Hidden feature-guided semantic segmentation network for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-17, 5603417. https://doi.org/10.1109/TGRS.2023.3244273
DOI: https://doi.org/10.1109/TGRS.2023.3244273   Google Scholar

Yang, N., & Tang, H. (2021). Semantic segmentation of satellite images: A Deep Learning approach integrated with geospatial hash codes. Remote Sensing, 13(14), 2723. https://doi.org/10.3390/rs13142723
DOI: https://doi.org/10.3390/rs13142723   Google Scholar

Zhu, S., Wu, Y., & Ma, X. (2023). Deep Learning-based algal bloom identification method from remote sensing images - Take China’s Chaohu Lake as an example. Sustainability, 15(5), 4545. https://doi.org/10.3390/su15054545
DOI: https://doi.org/10.3390/su15054545   Google Scholar

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Published
2024-09-30

Cited by

BHANDAGE, V., & PAI M. M., M. (2024). SEMANTIC SEGMENTATION OF ALGAL BLOOMS ON THE OCEAN SURFACE USING SENTINEL 3 CHL_NN BAND IMAGERY. Applied Computer Science, 20(3), 34–50. https://doi.org/10.35784/acs-2024-27

Authors

Venkatesh BHANDAGE 
venkatesh.bhandage@manipal.edu
Manipal Institute of Technology MAHE Manipal India
https://orcid.org/0000-0002-9503-8196

Authors

Manohara PAI M. M. 

Manipal Institute of Technology MAHE Manipal India
https://orcid.org/0000-0003-2164-2945

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