Application of Simple Refraction Correction Method for Shallow Coastal Bathymetric Mapping Based on UAV-Photogrammetry
Abstract
Shallow coastal bathymetric data has an important role in various applications, especially in maritime and coastal management fields. Various survey techniques to produce such data have been carried out by several researchers, one technique that is cheap, covers the wide area, flexible, and produces high-resolution bathymetric data is UAV-photogrammetry. However, this technique is affected by refraction effects that cause the estimated depth of underwater objects to be shallower than fact, thus reducing the accuracy of the bathymetric model. Therefore, the objective of this study is to present a simple refraction correction method based on the Least-Square method. To test the reliability of the method, the accuracy of the model is compared with two other methods (without correction and using a correction factor = 1.34). In addition, the effectiveness of the method was also tested through its application in various survey conditions. Overall, the proposed method outperforms the two existing methods. It is also very effective in reducing the error value during high tide conditions by up to 70%. The height of the UAV does not significantly affect the accuracy of the correction model, so in this case it is recommended to use an altitude of 100 m for survey efficiency.
References
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Harwin, S., & Lucieer, A. (2012). Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6), 1573-1599. https://doi.org/10.3390/rs4061573.
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James, M. R., & Robson, S. (2014). Mitigating systematic error in topographic models derived from UAV and ground‐based image networks. Earth Surface Processes and Landforms, 39(10), 1413-1420. https://doi.org/10.1002/esp.3609.
Kanari, M., Tibor, G., Hall, J. K., Ketter, T., Lang, G., & Schattner, U. (2020). Sediment transport mechanisms revealed by quantitative analyses of seafloor morphology: New evidence from multibeam bathymetry of the Israel exclusive economic zone. Marine and Petroleum Geology, 114, 104224. https://doi.org/10.1016/j.marpetgeo.2020.104224.
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Kholil, M., Ismanto, I., & Fu’Ad, M. (2021). 3D reconstruction using structure from motion (SFM) algorithm and multi view stereo (MVS) based on computer vision. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Kinzel, P. J., Legleiter, C. J., & Nelson, J. M. (2013). Mapping river bathymetry with a small footprint green LiDAR: applications and challenges 1. JAWRA Journal of the American Water Resources Association, 49(1), 183-204. https://doi.org/10.1111/jawr.12008.
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Lane, S. N., Widdison, P. E., Thomas, R. E., Ashworth, P. J., Best, J. L., Lunt, I. A., ... & Simpson, C. J. (2010). Quantification of braided river channel change using archival digital image analysis. Earth Surface Processes and Landforms, 35(8), 971-985. https://doi.org/10.1002/esp.2015.
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Marcus, W. A. (2012). Remote sensing of the hydraulic environment in gravel‐bed rivers. Gravel‐bed rivers: Processes, tools, environments, 259-285. https://doi.org/10.1002/9781119952497.ch21.
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. https://doi.org/10.3390/rs12132069
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Partama, I. G. Y., Kanno, A., Akamatsu, Y., Inui, R., Goto, M., & Sekine, M. (2017). A simple and empirical refraction correction method for UAV-based shallow-water photogrammetry. International Journal of Geological Environmental Engineering, 11(4), 295-302.
Partama, I. G. Y., Sumantra, I. K., WP, A. S., & Yogiswara, A. S. (2020). Validation of high-resolution and simple river monitoring technique using UAV-SFM Method. International Journal of Recent Technology and Engineering, 8(5), 5409-5414.
Sepúlveda, I., Tozer, B., Haase, J. S., Liu, P. L. F., & Grigoriu, M. (2020). Modeling uncertainties of bathymetry predicted with satellite altimetry data and application to tsunami hazard assessments. Journal of Geophysical Research: Solid Earth, 125(9), e2020JB019735. https://doi.org/10.1029/2020JB019735.
Shintani, C., & Fonstad, M. A. (2017). Comparing remote-sensing techniques collecting bathymetric data from a gravel-bed river. International journal of remote sensing, 38(8-10), 2883-2902. https://doi.org/10.1080/01431161.2017.1280636.
Slocum, R. K., Wright, W., Parrish, C., Costa, B., Sharr, M., & Battista, T. A. (2019). Guidelines for bathymetric mapping and orthoimage generation using sUAS and SfM, an approach for conducting nearshore coastal mapping. National Oceanic and Atmospheric Administration. https://doi.org/10.25923/07mx-1f93
Specht, M., Szostak, B., Lewicka, O., Stateczny, A., & Specht, C. (2023). Method for determining of shallow water depths based on data recorded by UAV/USV vehicles and processed using the SVR algorithm. Measurement, 221, 113437. https://doi.org/10.1016/j.measurement.2023.113437.
Westaway, R. M., Lane, S. N., Hicks, D. M. J. P. E., & Sensing, R. (2001). Remote sensing of clear-water, shallow, gravel-bed rivers using digital photogrammetry. Photogrammetric engineering remote sensing, 67(11), 1271-1282.
Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth surface processes and landforms, 40(1), 47-64. https://doi.org/10.1002/esp.3613.
Zhang, C., Sun, A. R., Hassan, M. A., & Qin, C. (2022). Assessing through-water structure-from-motion photogrammetry in gravel-bed Rivers under controlled conditions. Remote Sensing, 14(21), 5351. https://doi.org/10.3390/rs14215351.
Agrafiotis, P., Skarlatos, D., Georgopoulos, A., & Karantzalos, K. (2019). Shallow water bathymetry mapping from UAV imagery based on machine learning. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2(W10).
Agrafiotis, P., Karantzalos, K., Georgopoulos, A., & Skarlatos, D. (2020). Correcting image refraction: Towards accurate aerial image-based bathymetry mapping in shallow waters. Remote Sensing, 12(2), 322. https://doi.org/10.3390/rs12020322.
Alho, P., Vaaja, M., Kukko, A., Kasvi, E., Kurkela, M., Hyyppä, J., . . . Kaartinen, H. (2011). Mobile laser scanning in fluvial geomorphology: Mapping and change detection of point bars. J Zeitschrift fur Geomorphologie-Supplementband, 55(2), 31.
Bangen, S. G., Wheaton, J. M., Bouwes, N., Bouwes, B., & Jordan, C. (2014). A methodological intercomparison of topographic survey techniques for characterizing wadeable streams and rivers. Geomorphology, 206, 343-361. https://doi.org/10.1016/j.geomorph.2013.10.010.
Burns, J. H. R., Delparte, D., Gates, R. D., & Takabayashi, M. (2015). Integrating structure-from-motion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs. PeerJ, 3, e1077. https://doi.org/10.7717/peerj.1077.
Butler, J., Lane, S., Chandler, J., & Porfiri, E. (2002). Through‐water close range digital photogrammetry in flume and field environments. The Photogrammetric Record, 17(99), 419-439. https://doi.org/10.1111/0031-868X.00196.
Cao, B., Deng, R., & Zhu, S. (2020). Universal algorithm for water depth refraction correction in through-water stereo remote sensing. International Journal of Applied Earth Observation and Geoinformation, 91, 102108. https://doi.org/10.1016/j.jag.2020.102108.
Carbonneau, P. E., & Dietrich, J. T. (2017). Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth surface processes and landforms, 42(3), 473-486. https://doi.org/10.1002/esp.4012.
Casella, E., Collin, A., Harris, D., Ferse, S., Bejarano, S., Parravicini, V., ... & Rovere, A. (2017). Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs, 36, 269-275. https://doi.org/10.1007/s00338-016-1522-0.
Costa, B. M., Battista, T. A., & Pittman, S. J. (2009). Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment, 113(5), 1082-1100. https://doi.org/10.1016/j.rse.2009.01.015.
Del Savio, A. A., Luna Torres, A., Vergara Olivera, M. A., Llimpe Rojas, S. R., Urday Ibarra, G. T., & Neckel, A. (2023). Using UAVs and Photogrammetry in Bathymetric Surveys in Shallow Waters. Applied Sciences, 13(6), 3420. https://doi.org/10.3390/app13063420.
Dietrich, J. T. (2017). Bathymetric structure‐from‐motion: Extracting shallow stream bathymetry from multi‐view stereo photogrammetry. Earth Surface Processes and Landforms, 42(2), 355-364. https://doi.org/10.1002/esp.4060.
DS, A. A., & Subardjo, P. (2017). Bathymetry mapping study as a consideration in determining shipping channel in pramuka island waters, seribu islands, DKI Jakarta. International Journal of Marine Aquatic Resource Conservation Co-existence, 2(1), 12-17. https://doi.org/10.14710/gt.v%25vi%25i.16876.
Eker, R., Aydın, A., & Hübl, J. (2018). Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study. Environmental monitoring and assessment, 190, 1-14. https://doi.org/10.1007/s10661-017-6402-8.
Fryer, J. G., & Kniest, H. T. (1985). Errors in depth determination caused by waves in through‐water photogrammetry. The Photogrammetric Record, 11(66), 745-753. https://doi.org/10.1111/j.1477-9730.1985.tb01326.x.
Grenzdörffer, G. J., & Naumann, M. (2016). Investigations on the Possibilities of Monitoring Coastal Changes Including Shallow Under Water Areas With Uas Photo Bathmetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 843-850.
Harwin, S., & Lucieer, A. (2012). Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6), 1573-1599. https://doi.org/10.3390/rs4061573.
Hsu, P. H., & Wang, C. K. (2011, December). Acquiring underwater DSM using close-range photogrammetry. In 32nd Asian Conference on Remote Sensing 2011, ACRS 2011 (pp. 267-272).
James, M. R., & Robson, S. (2014). Mitigating systematic error in topographic models derived from UAV and ground‐based image networks. Earth Surface Processes and Landforms, 39(10), 1413-1420. https://doi.org/10.1002/esp.3609.
Kanari, M., Tibor, G., Hall, J. K., Ketter, T., Lang, G., & Schattner, U. (2020). Sediment transport mechanisms revealed by quantitative analyses of seafloor morphology: New evidence from multibeam bathymetry of the Israel exclusive economic zone. Marine and Petroleum Geology, 114, 104224. https://doi.org/10.1016/j.marpetgeo.2020.104224.
Kanno, A., Yonehara, C., Partama, I.G.Y., Komuro, T., Inui, R., Goto, M., Akamatsu, Y. (2018). A Study on Water Surface Refraction Correction Coefficients for Photogrammetric Survey of Flooded Riverbeds Using UAV and SfM-MVS. Journal of River Engineering, 24, 19-24.
Kholil, M., Ismanto, I., & Fu’Ad, M. (2021). 3D reconstruction using structure from motion (SFM) algorithm and multi view stereo (MVS) based on computer vision. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Kinzel, P. J., Legleiter, C. J., & Nelson, J. M. (2013). Mapping river bathymetry with a small footprint green LiDAR: applications and challenges 1. JAWRA Journal of the American Water Resources Association, 49(1), 183-204. https://doi.org/10.1111/jawr.12008.
Landuci, F. S., Rodrigues, D. F., Fernandes, A. M., Scott, P. C., & Poersch, L. H. D. S. (2020). Geographic Information System as an instrument to determine suitable areas and identify suitable zones to the development of emerging marine finfish farming in Brazil. Aquaculture Research, 51(8), 3305-3322. https://doi.org/10.1111/are.14666
Lane, S. N., Widdison, P. E., Thomas, R. E., Ashworth, P. J., Best, J. L., Lunt, I. A., ... & Simpson, C. J. (2010). Quantification of braided river channel change using archival digital image analysis. Earth Surface Processes and Landforms, 35(8), 971-985. https://doi.org/10.1002/esp.2015.
Lingua, A. M., Maschio, P., Spadaro, A., Vezza, P., & Negro, G. (2023). Iterative Refraction-Correction Method on Mvs-Sfm for Shallow Stream Bathymetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 249-255.
Marcus, W. A. (2012). Remote sensing of the hydraulic environment in gravel‐bed rivers. Gravel‐bed rivers: Processes, tools, environments, 259-285. https://doi.org/10.1002/9781119952497.ch21.
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. https://doi.org/10.3390/rs12132069
Murase, T., Tanaka, M., Tani, T., Miyashita, Y., Ohkawa, N., Ishiguro, S., . . . Yamano, H. (2008). A photogrammetric correction procedure for light refraction effects at a two-medium boundary. Photogrammetric engineering remote sensing, 74(9), 1129-1136. https://doi.org/10.14358/PERS.74.9.1129.
Nugroho, C., Manik, H., Gultom, D., & Firdaus, M. (2022). Implementasi multibeam echosounder untuk pengukuran dan analisis data kedalaman Perairan Teluk Jakarta berdasarkan Standar International Hydrographic Organization. POSITRON, 12(1), 60-71. http://dx.doi.org/10.26418/positron.v12i1.51833.
Padró, J. C., Muñoz, F. J., Planas, J., & Pons, X. (2019). Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms. International journal of applied earth observation and geoinformation, 75, 130-140. https://doi.org/10.1016/j.jag.2018.10.018.
Partama, I. G. Y., Kanno, A., Akamatsu, Y., Inui, R., Goto, M., & Sekine, M. (2017). A simple and empirical refraction correction method for UAV-based shallow-water photogrammetry. International Journal of Geological Environmental Engineering, 11(4), 295-302.
Partama, I. G. Y., Sumantra, I. K., WP, A. S., & Yogiswara, A. S. (2020). Validation of high-resolution and simple river monitoring technique using UAV-SFM Method. International Journal of Recent Technology and Engineering, 8(5), 5409-5414.
Sepúlveda, I., Tozer, B., Haase, J. S., Liu, P. L. F., & Grigoriu, M. (2020). Modeling uncertainties of bathymetry predicted with satellite altimetry data and application to tsunami hazard assessments. Journal of Geophysical Research: Solid Earth, 125(9), e2020JB019735. https://doi.org/10.1029/2020JB019735.
Shintani, C., & Fonstad, M. A. (2017). Comparing remote-sensing techniques collecting bathymetric data from a gravel-bed river. International journal of remote sensing, 38(8-10), 2883-2902. https://doi.org/10.1080/01431161.2017.1280636.
Slocum, R. K., Wright, W., Parrish, C., Costa, B., Sharr, M., & Battista, T. A. (2019). Guidelines for bathymetric mapping and orthoimage generation using sUAS and SfM, an approach for conducting nearshore coastal mapping. National Oceanic and Atmospheric Administration. https://doi.org/10.25923/07mx-1f93
Specht, M., Szostak, B., Lewicka, O., Stateczny, A., & Specht, C. (2023). Method for determining of shallow water depths based on data recorded by UAV/USV vehicles and processed using the SVR algorithm. Measurement, 221, 113437. https://doi.org/10.1016/j.measurement.2023.113437.
Westaway, R. M., Lane, S. N., Hicks, D. M. J. P. E., & Sensing, R. (2001). Remote sensing of clear-water, shallow, gravel-bed rivers using digital photogrammetry. Photogrammetric engineering remote sensing, 67(11), 1271-1282.
Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth surface processes and landforms, 40(1), 47-64. https://doi.org/10.1002/esp.3613.
Zhang, C., Sun, A. R., Hassan, M. A., & Qin, C. (2022). Assessing through-water structure-from-motion photogrammetry in gravel-bed Rivers under controlled conditions. Remote Sensing, 14(21), 5351. https://doi.org/10.3390/rs14215351.
Published
2023-12-21
How to Cite
PARTAMA, I GD Yudha et al.
Application of Simple Refraction Correction Method for Shallow Coastal Bathymetric Mapping Based on UAV-Photogrammetry.
Geosfera Indonesia, [S.l.], v. 8, n. 3, p. 245-261, dec. 2023.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/43360>. Date accessed: 22 nov. 2024.
doi: https://doi.org/10.19184/geosi.v8i3.43360.
Section
Original Research Articles
