Bathymetric DEM derived from Lyzenga’s algorithm (multispectral Bathymetry) using Landsat 8 images: a case study of Fort Lauderdale coast

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César Francisco de Paula
Luis Antônio de Lima
Jorge Pimentel Cintra
Henrique Cândido de Oliveira
Diogenes Cortijo Costa


Optical bathymetry can be applied to multispectral images of coastal regions with low spectral and spatial resolutions, and the results obtained are satisfactory in waters up to 15 m. However, the limitations and effectiveness may vary as they are related to physical factors such as water turbidity, specular reflection, and the presence of clouds and shadows during imagery acquisition. Some of these factors also affect the surveys conducted using green Light Detection and Ranging (LiDAR) technology. This study aimed to show that topographic representation by multispectral images is directly related to the physical factors of the water body and the atmospheric conditions during the image acquisition process, good atmospheric conditions and shallow water can generate accurate topographic representation. The experiments carried out in this study using optical bathymetry showed that Landsat 8 satellite images could be used in multispectral bathymetry with a linear regression model by using depth samples obtained from shallow water. Images taken at three different time points were used, with a certain time interval between the data collection performed via the Green LiDAR technology. The depths estimated by this model showed some differences that were possibly caused by water turbidity and temporally-occurring activities. Despite such constraints, the depths obtained by the linear regression model showed a good adaptation to the model derived from the Green LiDAR for depths up to 22 m, with an average error of -0.271 m and a root mean square error of 0.936 m.


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PAULA, C. F. de; LIMA, L. A. de; CINTRA, J. P.; OLIVEIRA, H. C. de; COSTA, D. C. Bathymetric DEM derived from Lyzenga’s algorithm (multispectral Bathymetry) using Landsat 8 images: a case study of Fort Lauderdale coast. Revista Brasileira de Cartografia, [S. l.], v. 75, 2023. DOI: 10.14393/rbcv75n0a-68742. Disponível em: Acesso em: 24 jul. 2024.
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ABRAHAM, D. A.; WILLETT, P. K. Active sonar detection in shallow water using the page test. IEEE Journal of Oceanic Engineering, v. 27, n. 1, p. 35–46, 2002. DOI. 10.1109/48.989883.

AXELSSON, P. Processing of laser scanner data - Algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, v. 54, n. 2–3, p. 138–147, 1999. DOI. 10.1016/S0924-2716(99)00011-8.

BASU, A.; MALHOTRA, S. Error detection of bathymetry data by visualization using GIS. ICES Journal of Marine Science, v. 59, n. 1, p. 226–234, 2002. DOI. 10.1006/2001.1147.

COSTANZA, R.; FARLEY, J. Ecological economics of coastal disasters: Introduction to the special issue. Ecological Economics, v. 63, n. 2–3, p. 249–253, 2007. DOI. :10.1016/2007.03.002.

DENG, Z.; JI, M.; ZHANG, Z. Mapping Bathymetry from Multi-Source Remote Sensing Images: A Case Study in the Beilun Estuary, GUANGXI , CHINA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, Vol. XXXVII. Part B8, p. 1321–1326, 2008. ISSN 1682-1750.

ELLIS, D. D. A shallow-water normal-mode reverberation model. The Journal of the Acoustical Society of America, v. 97, n. May, p. 2804–2814, 1994. DOI. 10.1121/1.411910.

FINKL, C. W.; BENEDET, L.; ANDREWS, J. L. Interpretation of Seabed Geomorphology Based on Spatial Analysis of High-Density Airborne Laser Bathymetry. Journal of Coastal Research, v. 213, n. 213, p. 501–514, 2005. DOI. 10.2112/05-756A.1.

GAO, J. Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Progress in Physical Geography, v. 33, n. 1, p. 103–116, 2009. DOI. 10.1177/0309133309105657.

GEYMAN, E. C.; MALOOF, A. C. A. Simple Method for Extracting Water Depth From Multispectral Satellite Imagery in Regions of Variable Bottom Type. Earth and Space Science, v. 6, n. 3, p. 527–537, 2019. DOI. 10.1029/2018EA000539.

GORDON, H. R. Ocean remote sensing using laser. US National Oceanic & Atmospheric Administration, Seattle, Washington, Pacific Marine Environmental Laboratory Technical Memorandum, , n. NOAA-TM-ERL-PMEL-18, 1980.

GUENTHER, G. C. Airborne Laser Hydrography: System Design and Performance Factors. 1 Ed. ed. Springfield, 1985.

GUENTHER, G. C.; CUNNINGHAM, A. G.; LAROCQUE, P. E. Meeting the Accuracy Challenge in Airborne Lidar Bathymetry. EARSeL eProceedings, v. 1, n. 1, p. 1–27, 2000. DOI. 10.1590/s1982-21702018000300025.

HEALD, G. J.; PACE, N. G. Implications of a bi-static treatment for the second echo from a normal incident sonar. Proceedings of 3rd European Conference on Underwater Acoustics, p. 649–554, 1996.

HIGGINS, S. A.; JAFFE, B. E.; FULLER, C. C. Reconstructing sediment age profiles from historical bathymetry changes in San Pablo Bay, California. Estuarine, Coastal and Shelf Science, v. 73, n. 1–2, p. 165–174, 2007. DOI. 10.1016/2006.12.018.

HUFF, LLOYD C., NOLL, G. T. Sonar. Digital Elevation Model Technologies and Applications: The DEM Users Manual. Second Edi ed., p.321–350, 2007. Maryland.

IRISH, J. L.; WHITE, T. E. Coastal engineering applications of high-resolution lidar bathymetry. Coastal Engineering, v. 35, n. 1–2, p. 47–71, 1998. DOI.10.1016/S0378-3839(98)00022-2.

JAGALINGAM, P.; AKSHAYA, B. J.; HEGDE, A. V. Bathymetry Mapping Using Landsat 8 Satellite Imagery. Procedia Engineering, v. 116, n. Apac, p. 560–566, 2015. Elsevier B.V. DOI: 10.1016/2015.08.326.

KIBELE, J.; SHEARS, N. T. Nonparametric Empirical Depth Regression for Bathymetric Mapping in Coastal Waters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, p. 1–9, 2016. DOI.10.1109/2016.2598152.

KOURGLI, A.; OUKIL, Y. Very High Resolution Satellite Images Filtering. 2013 Eighth International Conference on Broadband, Wireless Computing, Communication and Applications, Compiegne – France, 465-470, 2013/28-30. DOI. 10.1109/BWCCA.2013.81.

LYZENGA, D. R. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and landsat data. International Journal of Remote Sensing, v. 2, n. 1, p. 71–82, 1981. DOI.10.1080/01431168108948342.

LYZENGA, D. R. Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing, v. 6, n. 1, p. 115–125, 1985. DOI. 10.1080/01431168508948428.

LYZENGA, D. R.; MALINAS, N. P.; TANIS, F. J. Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, v. 44, n. 8, p. 2251–2259, 2006. DOI. 10.1109/TGRS.2006.872909.

MA, R. DEM Generation and Building Detection from Lidar Data. American Society for Photogrammetry and Remote Sensing, v. 71, n. 7, p. 847–854, 2005. DOI. 0099-1112/05/7107–0847.


MANDLBURGER, G.; HAUER, C.; WIESER, M.; PFEIFER, N. Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats—A Case Study at the Pielach River. Remote Sensing Journal, n.4 May 2014, p. 6160–6195, 2015. DOI. 10.3390/rs70506160.

MCFEETERS, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, v. 17, n. 7, p. 1425–1432, 1996. DOI.10.1080/01431169608948714.

NISHIDA, T.; MOHRI, M.; ITOH, K.; NAKAGOME, J. Study of bathymetry effects on the nominal hooking rates of yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus) exploited by the Japanese. IOTC Proceedings, v. 4, n. 4, p. 191–206, 2001. Disponível em:

NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOOA). U. S. Departament of Commerce. São Paulo, 2020. Disponível em: Acesso em: 30 abr. 2020.

PACHECO, A.; HORTA, J.; LOUREIRO, C.; FERREIRA, Ó. Remote Sensing of Environment Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sensing of Environment, 2014. Elsevier Inc. DOI: 10.1016/j.rse.2014.12.004.

PAN, Z.; GLENNIE, C.; HARTZELL, P.; HARTZEL, P.; DIAZ, J. C. F; LEGLEITER, C.; OVERSTREET, B. Performance assessment of high resolution airborne full waveform LiDAR for shallow river bathymetry. Remote Sensing, v. 7, n. 5, p. 5133–5159, 2015. DOI.10.3390/rs70505133.

PHILPOT, W. D. Bathymetric mapping with passive multispectral imagery. Applied Optics, v. 28, n. 8, p. 1569, 1989. DOI.10.1364/AO.28.001569.

PICKRILL, R. A.; TODD, B. J. The multiple roles of acoustic mapping in integrated ocean management, Canadian Atlantic continental margin. Ocean and Coastal Management, v. 46, n. 6–7, p. 601–614, 2003. DOI. 10.1016/S0964-5691(03)00037-1.

PRANDLE, D. Dynamical controls on estuarine bathymetry: Assessment against UK database. Estuarine, Coastal and Shelf Science, v. 68, n. 1, p. 282–288, 2006. DOI.10.1016/j.ecss.2006.02.009.

PUSHPARAJ, J.; HEGDE, A. V. Estimation of bathymetry along the coast of Mangaluru using Landsat-8 imagery. International Journal of Ocean and Climate Systems, 2017. DOI.10.1177/17593131166796.

ROSSI, L.; MAMMI, I.; PELLICCIA, F. UAV multispectral images for bathymetry estimation. IMEKO TC-19 – International Workshop on Metrology for the Sea, Genoa, p. 119–124, 2019. DOI. 10.21014/actaimeko.v12i3.1684.

SÁNCHEZ-CARNERO, N.; ACEÑA, S.; RODRÍGUEZ-PÉREZ, D.; COUÑAGO, E.; FRAILE, P.; FREIRE, J. Fast and low-cost method for VBES bathymetry generation in coastal areas. Estuarine, Coastal and Shelf Science, v. 114, n. December, p. 175–182, 2012. Elsevier Ltd. DOI: 10.1016/j.ecss.2012.08.018.

STUMPF, R. P.; HOLDERIED, K.; SPRING, S.; SINCLAIR, M. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, v. 48, p. 547–556, 2003. DOI.10.4319/Io.2003.48.1.

SUTHERLAND, J.; WALSTRA, D. J. R.; CHESHER, T. J.; VAN RIJN, L. C.; SOUTHGATE, H. N. Evaluation of coastal area modelling systems at an estuary mouth. Coastal Engineering, v. 51, n. 2, p. 119–142, 2004. DOI.10.1016/2003.12.003.

WEHR, A.; LOHR, U. Airborne laser scanning - An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, v. 54, n. 2–3, p. 68–82, 1999. DOI.10.1016/S0924-2716(99)00011-8.

WESTFELD, P.; RICHTER, K.; MAAS, H. G.; WEISS, R. Analysis of the effect ofwave patterns on refraction in airborne lidar bathymetry. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 2016-Janua, n. July, p. 133–139, 2016. DOI.10.5194/133-2016.

ZHAO, J.; ZHAO, X.; ZHANG, H.; ZHOU, F. Shallow water measurements using a single green laser corrected by building a near water surface penetration model. Remote Sensing, v. 9, n. 5, p. 1–18, 2017a. DOI.10.3390/rs9050426.

ZHAO, J.; ZHAO, X.; ZHANG, H.; ZHOU, F. Improved model for depth bias correction in airborne LiDAR bathymetry systems. Remote Sensing, v. 9, n. 7, p. 1–16, 2017b. DOI.10.3390/rs9070710.

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