The Accuracy of Satellite Derived Bathymetry in Coastal and Shallow Water Zone
DOI:
https://doi.org/10.11113/ijbes.v8.n3.681Keywords:
Imagery derived bathymetry, Multispectral images, Bathymetric surveyingAbstract
Precise and accurate bathymetric measurements are conventionally acquired by means of ship-based acoustic equipment. Nevertheless, recent multispectral satellite imagery has been utilised as a substitute source to map the seabed topography which indicates new revolution in hydrographic surveying. This study assesses the satellite bathymetric depth’s accuracy based on the vertical uncertainty as stated in the Standards for Hydrographic Surveys issued by the International Hydrographic Organization. Two empirical algorithms, namely, Dierssen’s and Stumpf’s approaches have been adopted to model the seafloor topography over the coastal and shallow water at Tanjung Kupang, Malaysia. The outcomes demonstrate a decent correlation between the derived water depths and the sounding values acquired from a ship-based acoustic survey. For instance, a total of 1,215 out of the 1,367 generated water depths by Stumpf’s model have hit the minimum standard of survey in S-44. Similarly, out of the 1,367 samples from Diessen’s model, 1,211 samples have met the minimum requirement listed in the survey standard. The results demonstrate both imageries derived bathymetry models convey promising results which can be ultilised for bathymetric mapping application. Therefore, this imagery derived bathymetry can be considered as an alternative bathymetric surveying technique to supply cost-effective solution and survey data to support the Blue Economy and Sustainable Development Goals 14.
References
Bramante, J. F., Raju, D. K. & Sin, T. M. (2013). Multispectral Derivation of Bathymetry In Singapore's Shallow, Turbid Waters. International Journal of Remote Sensing, 34(6): 2070-2088.
Darama, Y., Selek, Z. & Selek, B. (2019). "Determination of sediment deposition of Hasanlar Dam using bathymetric and remote sensing studies." Natural Hazards, 97(1): 211-227.
Dierssen, H. M., Zimmerman, R. C., Leathers, R. A., Downes, T. V., & Davis, C. O. (2003). Ocean Color Remote Sensing of Seagrass and Bathymetry in The Bahamas Banks By High‐Resolution Airborne Imagery. Limnology and Oceanography, 48(1part2): 444-455.
Gao, J. (2009). Bathymetric Mapping by Means of Remote Sensing: Methods, Accuracy and Limitations. Physical Geography, 33(1): 103-116.
Hamylton, S. M., Hedley, J. D., & Beaman, R. J. (2015). Derivation of High-Resolution Bathymetry from Multispectral Satellite Imagery: A Comparison of Empirical and Optimisation Methods Through Geographical Error Analysis. Remote Sensing, 7(12): 16257-16273.
International Hydrographc Organization (2020), IHO Standards for Hydrographic Surveys (6th Edition). Monaco: IHO Publication S-44.
Jawak, S. D., Vadlamani, S. S., & Luis, A. J. (2015). A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons. Advances in Remote Sensing, 4(02): 147.
Jégat, V., Pe’eri, S., Freire, R., Klemm, A., & Nyberg, J. (2016). Satellite-Derived Bathymetry: Performance and Production. In Canadian Hydrographic Conference, May 16-19.
Lyzenga, D. R., Malinas, N. R., & Tanis, F. J. (2006). Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2251-2259. doi:10.1109/Tgrs.2006.872909
Mateo-Pérez, V., Corral-Bobadilla, M., Ortega-Fernández, F. & Vergara-González, E. P. (2020). "Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery." Remote Sensing 12(13): 2069.
Mavraeidopoulos, A. K., Pallikaris, A., & Oikonomou, E. (2017). Satellite Derived Bathymetry (SDB) and Safety of Navigation. The International Hydrographic Review (17).
Pacheco, A., Horta, J., Loureiro, C., & Ferreira, Ó. (2015). Retrieval of Nearshore Bathymetry from Landsat 8 Images: A Tool for Coastal Monitoring In Shallow Waters. Remote Sensing of Environment, 159: 102-116.
Pe'eri, S., Parrish, C., Azuike, C., Alexander, L., & Armstrong, A. (2014). Satellite Remote Sensing as A Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness. Marine Geodesy, 37(3): 293-314.
Said, N. M., Mahmud, M. R., & Hasan, R. C. (2017). Satellite-Derived Bathymetry: Accuracy Assessment on Depths Derivation Algorithm for Shallow Water Area. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-4/W5: 159-164.
Said, N. M., Mahmud, M. R., & Hasan, R. C. (2018). Evaluating Satellite-Derived Bathymetry Accuracy from Sentinel 2A High-Resolution Multispectral Imageries for Shallow Water Hydrographic Mapping. Paper presented in 9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing, Kuala Lumpur, Malaysia.
Sánchez-Carnero, N., Ojeda-Zujar, J., Rodríguez-Pérez, D., & Marquez-Perez, J. (2014). Assessment of Different Models For Bathymetry Calculation Using SPOT Multispectral Images In A High-Turbidity Area: The Mouth Of The Guadiana Estuary. International Journal of Remote Sensing, 35(2): 493-514.
Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of Water Depth with High-Resolution Satellite Imagery Over Variable Bottom Types. Limnology and Oceanography, 48(1): 547-556.
Su, H. B., Liu, H. X., & Heyman, W. D. (2008). Automated Derivation of Bathymetric Information from Multi-Spectral Satellite Imagery Using A Non-Linear Inversion Model. Marine Geodesy, 31(4): 281-298.
Syvitski, J. P., Vörösmarty, C. J., Kettner, A. J., & Green, P. (2005). Impact of Humans on The Flux of Terrestrial Sediment To The Global Coastal Ocean. Science, 308(5720): 376-380.
Tsolakidis, I. and M. Vafiadis (2019). "Comparison of Hydrographic Survey and Satellite Bathymetry in Monitoring Kerkini Reservoir Storage." Environmental Processes, 6(4): 1031-1049.
Zheng, G., Chen, F., & Shen, Y. (2017). Detecting the Water Depth of The South China Sea Reef Area from Worldview-2 Satellite Imagery. Earth Science Informatics, 10(3): 331-337.
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