Satellite Derived Bathymetry on Shallow Reef Platform: A Preliminary Result from Semak Daun, Seribu Islands, Java Sea, Indonesia


Derivation of the bathymetric model from satellite imaging for non-navigable coastal waters has been developed. It is the purpose of this presented paper to assess the depth accuracy of the bathymetric model derived from such optical satellite imagery. The study domain is situated in the Semak Daun reef platform, Java Sea, Indonesia. The area represents shallow sub- and inter-tidal water with various benthic covers. Satellite imagery used here is retrieved from the European Space Agency Sentinel-2 satellite observation system. Two methods in deriving bathymetry from optical imagery are used. The first one is the empirical band ratio transform algorithm and the second one is the analytical approach. Coefficients involved in both models are obtained from means of calibration against sounding data from a single-beam echo-sounding survey. About 9% of sounding data are used for the calibration, while the rests are used to validate the resulting bathymetric models. It is found that both methods can successfully be applied at depth of up to 10 m. The root mean square errors indicated by both models are comparable. Accuracy measures in the order of 1.9 m are obtained with a coefficient of determination of 0.7. The results presented here confirm the applicability of satellite-derived bathymetry for mapping shallow seabed complying to the category zone of confidence C as of the International Hydrographic Organization standard. It should be bear in mind that such an assessment is typical for the environmental condition considered in this study.

[1] Said, N. M., et al. (2017). Satellite-Derived Bathymetry: Accuracy Assessment on Depths Derivation Algorithm for Shallow Water Area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Kuala Lumpur, Malaysia.

[2] Green, E. P., et al. (2000). Remote sensing handbook for tropical coastal management, in Coastal Management Sourcebooks 3, edited by A. J. Edwards (UNESCO, Paris, 2000).

[3] Mavraeidopoulos, A.K., et al. (2017). Satellite Derived Bathymetry (SDB) and Safety of Navigation. International Hydrographic Review.

[4] Chybicki, A. (2017). Mapping South Baltic Near-Shore Bathymetry Using Sentinel Observation, (Police Maritime Research 24, 2017), pp. 15–25.

[5] Stumpf, R. P., et al. (2003). Determination of Water Depth with High-Resolution Satellite Imagery over Variable Bottom Types. Limnology and Oceanography 48 (1part2), pp. 547–56. doi:10.4319/lo.2003.48.1_part_2.0547

[6] Lyzenga, D. R., et al. (2006). Multispectral Bathymetry Using a Simple Physically Based Algorithm. IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 8, pp. 2251–59. doi:10.1109/TGRS.2006.872909.

[7] Su, H., et al. (2008). Automated Derivation of Bathymetric Information from Multi-Spectral Satellite Imagery Using a Non-Linear Inversion Model. Marine Geodesy, vol. 31, pp. 281–298.

[8] Poerbandono, et al. (2006). Assessment of Coral Reef Environment using Hydro-acoustic Data, Aerial photos and Satellite Images. Case Study: Semak Daun Island, Java Sea, Indonesia. Environmental Technology and Management Conference.

[9] Ashphaq, M. (2018). Bathymetry Estimation in Turbid Water Using Sentinel-2 Image, in INCA International Congress. Hyderabad, India.