Satellite-derived bathymetry (SDB): an approach to bathymetric cartography with multispectral images in shallow waters of Bahía Solano, Colombia

  • Mauricio Alejandro Perea-Ardila Centro de Investigaciones Oceanográficas e Hidrográficas del Pacífico – CCCP. Área de Manejo Integrado de Zona Costera. Capitanía de puerto de Tumaco. Tumaco, Colombia.
  • Fernando Oviedo-Barrero Centro de Investigaciones Oceanográficas e Hidrográficas del Pacífico – CCCP. Área de Manejo Integrado de Zona Costera. Capitanía de puerto de Tumaco. Tumaco, Colombia.
Keywords: Bathymetry, Colombian Pacific, Landsat 8, Satellite, GIS


Ocean depth measurement plays a fundamental role to plan and manage marine resources and safe boat navigation. Satellite-Derived Bathymetry (SDB) is presented as a complementary technique to determine coastal water depth through remote sensing tools and Geographic Information Systems (GIS). The goal of this study was to determine the applicability of the SDB method in shallow waters in the Punta Luna sector in Bahía Solano, northern Colombian Pacific coast, using Landsat 8 satellite images from January 2017 and in situ bathymetric survey data from November 2016. The main result obtained in this study was a depth estimate of up to ± 7 m with R2 = 0.80, as well as an RMSE and an MAE equivalent to 1.49 and 2.22 m, respectively. Depth estimates obtained using SDB meet 51.17% of the Total Vertical Uncertainty (TVU) for the Special Order category, regarding the Standards for Hydrographic Surveys from the International Hydrographic Organization (IHO). Results obtained will serve as a reference to calculate depth using multispectral images and a benchmark for hydrographic officials and academics interested in coastal and marine research in the region.


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Aguilar, H., Mora, R. & Vargas, C. (2015). Metodología para la corrección atmosférica deimágenes Aster, Rapideye, Spot 2 y Landsat 8 con el módulo Flaash del software Envi. Rev. Geogr. Am. Cent., 2(53), 39-59.
Ariza, A. (2013). Descripción y corrección de productos Landsat 8 LDCM (Landsat Data Continuity Mission) Versión 1.0. Centro de Investigación y Desarrollo - CIAF. Instituto Geográfico Agustín Codazzi.
Ariza, A. & Ramírez, H. M. (2014). Modelo batimétrico derivado de imágenes Landsat ETM + en zonas de arrecifes tropicales. Rev. Cartogr., 90, 43-58.
Ashphaq, M. (2018). Bathymetry estimation in turbid water using SENTINEL 2 image.
Benny, A. H. & Dawson, G. J. (1983). Satellite Imagery as an Aid to Bathymetric Charting in the Red Sea. CARTOGR J., 20(1), 5-16.
Bramante, J. F., Raju, D. K. & Sin, T. M. (2013). Multispectral derivation of bathymetry in Singapore’s shallow, turbid waters. Int. J. Remote Sens., 34(6), 2070-2088.
Caballero, I. & Stumpf, R. P. (2020). Atmospheric correction for satellite-derived bathymetry in the Caribbean waters : from a single image to multi-temporal approaches using Sentinel-2A/B. Opt. Express, 28(8), 11742-11766.
CIOH. Centro de Investigaciones Oceanográficas e Hidrográficas del Caribe. (2017). Carta Hidrográfica 710, bahía interior de Solano Escala 1:10.000.
Chéiner, R., Faucher, M. A. & Ahola, R. (2018). Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts. ISPRS Int. J. Geo-Inf., 7(306), 2-15.
Chybicki, A. (2017). Mapping south baltic near-shore bathymetry using sentinel-2 observations. Pol. Marit. Res., 24(3), 15-25.
Deng, Z., Ji, M. & Zhang, Z. (2008). Mapping bathymetry from multi-source remote sensing images: a case study in the beilun estuary, guangxi, China. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 37, 1321-1326.
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. Limnol. Oceanogr., 48, 444-455.
Ekpa, A. U. & Ojinnaka, O. C. (2018). Estimating bathymetry of cross river in Nigeria using remote sensing technique. SSRG Int. J. Geoinf. Geol. Sci., 5(3), 1-15.
ESRI. (2018). ArcGIS Desktop: Release 10.6. EE. UU.: Environmental Systems Research Institute.
Evagorou, E., Mettas, C. Agapiou, A. Themistocleous, K. & Hadjimitsis, D. (2019). Bathymetric maps from multi-temporal analysis of Sentinel-2 data: the case study of Limassol, Cyprus. Adv. Geosci., 45, 397-407.
Gao, J. (2009). Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Prog. Phys. Geogr., 33(1), 103-116.
Goodrich, K. (2018). Machine Learning Applications for Satellite Derived Bathymetry. Colombia: GEBCO.
Hedley, J., Harborne, A. & Mumby, P. (2005). Simple and robust removal of sun glint for mapping shallow-water benthos Simple and robust removal of sun glint for mapping shallow-water. Int. J. Remote Sens., 26(10), 2107-2112.
Ibrahim, M. & Cracknell, A. P. (1990). Cover Bathymetry using Landsat MSS data of Penang Island in Malaysia. Int. J. Remote Sens., 11(4), 557-559.
IHO-IOC. International Hydrographic Organization - Intergovernmental Oceanographic Commission (2019). The IHO-IOC GEBCO CookBook. Francia: OHI Publication B-11, IOC Manuals and Guides 63. International Hydrographic Organization, Intergovernmental Oceanographic Commission.
Jagalingam, P., Akshaya, B. J. & Hegde, A. V. (2015). Bathymetry mapping using landsat 8 satellite imagery. Procedia Eng., 116(1), 560-566.
Jégat, V., Pe´eri, S. Freire, R. Klemm, A. & Nyberg, J. (2016, mayo). Satellite-Derived Bathymetry: Performance and Production. Canadian Hydrographic Conference, Halifax, NS.
Jonas, M. (2018, junio). Why is satellite derived bathymetry needed? SDB Day, User and Technology Forum, Munich, Alemania.
Kabiri, K. (2017). Accuracy assessment of near-shore bathymetry information retrieved from Landsat-8 imagery. Earth Sci. Inform., 10, 235-245.
Kimeli, A., Thoya, P. Ngisiang, N. Ong, H. & Magori, C. (2018). Satellite-derived bathymetry: A case study of Mombasa Port Channel and its approaches, Kenya. West. Indian Ocean J. Mar. Sci., 17(2), 93-102.
Knudby, A., Ahmad, S. K. & Ilori, C. (2016). The Potential for Landsat-Based Bathymetry in Canada. Can. J. Remote. Sens., 42, 367-378.
Lyzenga, D. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Appl. Opt., 17(3), 379-383.
Lyzenga, D. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. Int. J. Remote Sens., 6(1), 115-125.
Mavraeidopoulos, A., Navy, H. Pallikaris, A. Academy, H. N. & Oikonomou, E. (2017). Satellite derived bathymetry (SDB) and safety of navigation. Int. Hydrogra. Rev., 7-19.
Najhan, S., Mohd, M. & Rozaimi, H. (2017). Satellite-Derived Bathymetry: Accuracy assessment on depths derivation algorithm for shallow water area. Int. Arch. Photogramm., Remote Sens. Spat. Inform. Sci., 42, 159-164.
OIH. Organización Hidrográfica Internacional. (2008). Normas de la OHI para los Levantamientos Hidrográficos. S-44 (5th ed.). Francia. Bureau Hidrográfico Internacional.
Pacheco, A., Horta, J. Loureiro, C. & Ferreira, Ó. (2016). Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sens. Environ, 159, 102-116.
Pe’eri, S., Parrish, C. Azuike, C. & Armstrong, A. (2014). Satellite remote sensing as a reconnaissance tool for assessing nautical chart adequacy and completeness. Mar. Geod., 37(3), 37-41.
Philpot, W. (1989). Bathymetric mapping with passive multispectral imagery. Appl. Opt., 28(8), 1569-1578.
Pierini, O. & Rodríguez, A. (2014). Caracterización oceanográfica de la bahía de Solano. Boletín Científico CIOH., 32, 223-256.
Polcyn, F. C., Brown, W. L. & Sattinger, I. J. (1970). The Measurement of Water Depth by Remote Sensing Techniques. EE. UU.: The Institute of Science and Technology, The University of Michigan.
Politi, E., Paterson, S. K. Scarrott, R. Tuohy, E. Mahony, C. O. & Cámaro-garcía, W. C. A. (2019). Earth observation applications for coastal sustainability: potential and challenges for implementation. Anthropocene Coasts., 2(1), 306-329.
Pushparaj, J. & Hegde, A. V. (2016). Estimation of bathymetry along the coast of Mangaluru using Landsat-8 imagery. Int. J. Ocean Climate Syst., 8(2), 71-83.
Putri, J. C. A., Fuad, M. A. Z. & Asa´di, M. A. (2018). Bathymetry mapping using Landsat 8 multyspectral data of bangsring coastal area. Omni-Akuatika, 14(1), 54-61.
Rocchio, L. E. P. (2016). Avoiding Rock Bottom: How Landsat Aids Nautical Charting. Landsat Sci., 71-77.
Setiawan, K., Adawiah, S. Marini, Y. & Winarso, G. (2017). Bathymetry data extraction analysis using Landsat 8 data. Int. J. Remote Sens. Earth Sci., 13(2), 79-86.
Stumpf, R. P., Holderied, K. & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr, 48, 54-556.
Tang, K. K. W. & Mahmud, M. R. (2018). Imagery-derived bathymetry in Strait of Johor’s turbid waters using multispectral images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 42, 139-145.
USGS. U. S. Geological Survey. (2017). Imágen Multiespectral Landsat 8 OLI, ID LC80100562017023LGN01. EE. UU.: Landsat Data Continuity Mission.
USGS. U. S. Geological Survey. (2018). Landsat 8 (L8) Data Users Handbook - Version 3.0. EE. UU.: USGS.
Vinayaraj, P., Raghavan, V. & Masumoto, S. (2016). Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model. Mar. Geod., 39(6), 458-478.
Vivas-Aguas, L. J., Espinosa, J. Sánchez, B. Cadavid, P. Bautista, M. Quintero, J…& Espinosa. (2012). Diagnóstico y Evaluación de la Calidad Ambiental Marina en el Caribe y Pacífico colombiano. Red de vigilancia para la conservación y protección de las aguas marinas y costeras de Colombia –REDCAM. Informe técnico 2011. Santa Marta: INVEMAR.
Warne, D. K. (1978). Landsat as an Aid in the Preparation of Hydrographic Charts. Photogramm Eng Remote Sensin., 44(8), 1011-1016.
How to Cite
Perea-Ardila, M., & Oviedo-Barrero, F. (2020). Satellite-derived bathymetry (SDB): an approach to bathymetric cartography with multispectral images in shallow waters of Bahía Solano, Colombia. Journal of Marine and Coastal Sciences, 12(1), 117.134.

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