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基于机载激光雷达测深技术的海底底质分类研究进展
Research Progress in Seabed Sediment Classification Based on Airborne Lidar Bathymetry Technology

DOI: 10.12677/AG.2023.135047, PP. 495-505

Keywords: 机载激光雷达,测深技术,底质分类
Airborne Lidar
, Bathymetric Technology, Seabed Sediment Classification

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Abstract:

机载激光雷达测深技术是一种非常适合沿海地区测绘的技术。机载激光雷达测深系统不仅可以获得水深数据,还能同时获得含有底质信息的激光脉冲回波数据。随着机载激光雷达测深技术的革新和应用,其底质分类功能得到不断的开发,基于机载激光雷达测深波形数据,并结合水深衍生数据的海底底质分类效率不断提高,通过与多源遥感信息的结合,海底底质分类的精度也有所提升。本文主要论述了机载激光雷达测深技术在海底底质分类研究的现状,介绍了机载激光雷达测深技术的基本原理,分析了该领域存在的主要问题,展望了基于机载激光雷达测深技术的底质分类研究的发展趋势。
Airborne lidar bathymetric technology is a very suitable technology for coastal area surveying and mapping. Airborne lidar bathymetric systems can not only obtain water bathymetry data, but also obtain laser pulse waveform data containing bottom material information. With the innovation and application of airborne lidar bathymetry technology, its seabed sediment classification function has been continuously developed. The efficiency of seabed sediment classification based on airborne lidar bathymetry waveform data and combined with water bathymetry derived data has been continuously improved. Through the combination of multi-source remote sensing information, the accuracy of seabed sediment classification has also been improved. This article mainly discusses the current situation of airborne lidar bathymetry technology in seabed sediment classification, introduces the basic principles of airborne lidar bathymetry technology, analyzes the main problems in this field, and looks forward to the development trend of research on seabed sediment classification based on airborne lidar bathymetry technology.

References

[1]  唐秋华, 纪雪, 丁继胜, 等. 多波束声学底质分类研究进展与展望[J]. 海洋科学进展, 2019, 37(1): 1-10.
[2]  王润田. 海底声学探测与底质识别技术的新进展[J]. 声学技术, 2002, 21(1): 96-98.
[3]  Collin, A., Archambault, P. and Long, B. (2008) Mapping the Shallow Water Seabed Habitat with the SHOALS. IEEE Transactions on Geoscience and Remote Sensing, 46, 2947-2955.
https://doi.org/10.1109/TGRS.2008.920020
[4]  翟国君, 吴太旗, 欧阳永忠, 等. 机载激光测深技术研究进展[J]. 海洋测绘, 2012, 32(2): 67-71.
[5]  Guenther, G.C., Cunningham, A.G., Larocque, P.E., et al. (2000) Meeting the Accuracy Challenge in Airborne Lidar Bathymetry. Proceedings of EARSeL-SIG-Workshop LIDAR, Dresden, 16-17 June 2000 Dresden, 1-27.
[6]  Hickman, G.D. and Hogg, J.E. (1969) Application of an Airborne Pulsed Laser for Near Shore Bathymetric Measurements. Remote Sensing of Environment, 1, 47-58.
https://doi.org/10.1016/S0034-4257(69)90088-1
[7]  Wang, C., Li, Q., Liu, Y., et al. (2015) A Comparison of Waveform Processing Algorithms for Single-Wavelength LiDAR Bathymetry. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 22-35.
https://doi.org/10.1016/j.isprsjprs.2014.11.005
[8]  Steinvall, O.K., Koppari, K.R. and Karlsson, U.C. (1994) Airborne Laser Depth Sounding: System Aspects and Performance. Proceedings of the SPIE, 2258, 392-412.
https://doi.org/10.1117/12.190082
[9]  叶修松. 机载激光水深探测技术基础及数据处理方法研究[D]: [博士学位论文]. 郑州: 解放军信息工程大学, 2010.
[10]  Stoyanov, T., Magnusson, M., Andreasson, H. and Lilienthal, A.J. (2012) Fast and Accurate Scan Registration through Minimization of the Distance between Compact 3D NDT Representations. The International Journal of Robotics Research, 31, 1377-1393.
https://doi.org/10.1177/0278364912460895
[11]  Kumari, P., Shrestha, R. and Carter, B. (2009) Registration of LiDAR Data through Stable Surface Matching. Proceedings of the 2009 17th International Conference on Geoinformatics, Fairfax, 12-14 August 2009, 1-5.
https://doi.org/10.1109/GEOINFORMATICS.2009.5293421
[12]  金鼎坚, 吴芳, 于坤, 等. 机载激光雷达测深系统大规模应用测试与评估——以中国海岸带为例[J]. 红外与激光工程, 2020, 49(Z2): 1-15.
[13]  刘永明, 邓孺孺, 秦雁, 等. 机载激光雷达测深数据处理与应用[J]. 遥感学报, 2017, 21(6): 982-995.
[14]  张熠星. 机载测深激光雷达的海底回波提取技术[D]: [硕士学位论文]. 上海: 东华大学, 2018.
[15]  Guenther, G.C. and Mesick, H.C. (1988) Analysis of Air-borne Laser Hydrography Waveforms. Proceedings of the SPIE, 925, 232-241.
https://doi.org/10.1117/12.945729
[16]  Abdallah, H., Baghdadi, N., Bailly, J.S., et al. (2012) Wa-LiD: A New LiDAR Simulator for Waters. IEEE Geoscience and Remote Sensing Letters, 9, 744-748.
https://doi.org/10.1109/LGRS.2011.2180506
[17]  Kumpum?ki, T., Ruusuvuori, P., Kangasniemi, V. and Lipping, T. (2015) Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis. Remote Sensing, 7, 13390-133409.
https://doi.org/10.3390/rs71013390
[18]  Eren, F., Pe’eri, S., Rzhanov, Y. and Ward, L. (2018) Bottom Characteriza-tion by Using Airborne Lidar Bathymetry (ALB) Waveform Features Obtained from Bottom Return Residual Analysis. Remote Sensing of Environment, 206, 260-274.
https://doi.org/10.1016/j.rse.2017.12.035
[19]  Letard, M., Collin, A., Corpetti, T., et al. (2022) Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds. Remote Sensing, 14, Article 341.
https://doi.org/10.3390/rs14020341
[20]  Tulldahl, H.M., Pappalardo, G., Vahlberg, C., et al. (2007) Sea Floor Classification from Airborne Lidar Data. Proceedings of the SPIE, 6750, 675003-675014.
https://doi.org/10.1117/12.737922
[21]  Tulldahl, H.M. and Wikstr?m, S.A. (2012) Classification of Aquatic Macrovegetation and Substrates with Airborne Lidar. Remote Sensing of Environment, 121, 347-357.
https://doi.org/10.1016/j.rse.2012.02.004
[22]  Hou, W.W., Arnone, R.A., Eren, F., et al. (2016) Airborne Lidar Bathymetry (ALB) Waveform Analysis for Bottom Return Characteristics. Proceedings of the SPIE, 9827, 98270H.
[23]  Wilson, N., Parrish, C.E., Battista, T., et al. (2019) Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms. Estuaries and Coasts, 45, 923-937.
https://doi.org/10.1007/s12237-019-00652-9
[24]  Amani, M., Macdonald, C., Salehi, A., Mahdavi, S. and Gullage, M. (2022) Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland. Water, 14, Article 3809.
https://doi.org/10.3390/w14233809
[25]  马洪超, 李奇. 改进的EM模型及其在激光雷达全波形数据分解中的应用[J]. 遥感学报, 2009, 13(1): 35-41.
[26]  Vierling, K.T., Vierling, L.A., Gould, W.A., Martinuzzi, S. and Clawges, R.M. (2008) Lidar: Shedding New Light on Habitat Characterization and Modeling. Frontiers in Ecology and the Environment, 6, 90-98.
https://doi.org/10.1890/070001
[27]  Collin, A., Long, B. and Archambault, P. (2011) Benthic Classifications Using Bathymetric LIDAR Waveforms and Integration of Local Spatial Statistics and Textural Features. Journal of Coastal Research, 62, 86-98.
https://doi.org/10.2112/SI_62_9
[28]  Collin, A., Archambault, P. and Long, B. (2011) Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners. PLOS ONE, 6, e21265.
https://doi.org/10.1371/journal.pone.0021265
[29]  Kogut, T. and Weistock, M. (2019) Classifying Airborne Bathymetry Data Using the Random Forest Algorithm. Remote Sensing Letters, 10, 874-882.
https://doi.org/10.1080/2150704X.2019.1629710
[30]  Su, D., Yang, F., Ma, Y., et al. (2019) Classification of Coral Reefs in the South China Sea by Combining Airborne LiDAR Bathymetry Bottom Waveforms and Bathymetric Features. IEEE Transactions on Geoscience and Remote Sensing, 57, 815-828.
https://doi.org/10.1109/TGRS.2018.2860931
[31]  Letard, M., Collin, A., Lague, D., Rattray, A. and Monk, J. (2021) Towards 3D Mapping of Seagrass Meadows with Topo-Bathymetric Lidar Full Waveform Processing. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, 11-16 July 2021, 8069-8072.
https://doi.org/10.1109/IGARSS47720.2021.9554262
[32]  Zavalas, R., Ierodiaconou, D., Ryan, D., et al. (2014) Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR. Remote Sensing, 6, 2154-2175.
https://doi.org/10.3390/rs6032154
[33]  Sun, Y.D. and Shyue, S.W. (2017) A Hybrid Seabed Classification Method Using Airborne Laser Bathymetric Data. Journal of Marine Science & Technology, 25, Article 12.
[34]  Subarno, T., Siregar, V.P., Agus, S.B. and Sunuddin, A. (2016) Modelling Complex Terrain of Reef Geomorphological Structures in Harapan-Kelapa Island, Kepulauan Seribu. Procedia Environmental Sciences, 33, 478-486.
https://doi.org/10.1016/j.proenv.2016.03.100
[35]  Wilson, M.F.J., O’connell, B., Brown, C., et al. (2007) Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Marine Geodesy, 30, 3-35.
https://doi.org/10.1080/01490410701295962
[36]  Smith, G., Yesilnacar, E., Jiang, J. and Taylor, C. (2015) Marine Habitat Mapping Incorporating Both Derivatives of LiDAR Data and Hydrodynamic Conditions. Journal of Marine Science and Engineering, 3, 492-508.
https://doi.org/10.3390/jmse3030492
[37]  Lundblad, E.R., Wright, D.J., Miller, J., et al. (2006) A Benthic Terrain Classification Scheme for American Samoa. Marine Geodesy, 29, 89-111.
https://doi.org/10.1080/01490410600738021
[38]  Schmidt, J., Ian, E.S. and Brinkmann, J. (2003) Comparison of Polynomial Models for Land Surface Curvature Calculation. International Journal of Geographical Information Science, 17, 797-814.
https://doi.org/10.1080/13658810310001596058
[39]  Hossain, M.S., Bujang, J.S., Zakaria, M.H. and Hashim, M. (2015) The Application of Remote Sensing to Seagrass Ecosystems: An Overview and Future Research Prospects. International Journal of Remote Sensing, 36, 61-114.
https://doi.org/10.1080/01431161.2014.990649
[40]  Kamerman, G.W., Tuell, G.H. and Park, J.Y. (2004) Use of SHOALS Bottom Reflectance Images to Constrain the Inversion of a Hyperspectral Radiative Transfer Model. Proceedings of the SPIE, 5412, 185-193.
[41]  Tuell, G., Park, J.Y., Aitken, J., et al. (2005) SHOALS-Enabled 3D Benthic Mapping. Proceedings of the SPIE, 5806, 816-826.
https://doi.org/10.1117/12.607010
[42]  Hou, W.W., Tulldahl, H.M., Philipson, P., et al. (2013) Sea Floor Classification with Satellite Data and Airborne Lidar Bathymetry. Proceedings of the SPIE, 8724, Article ID: 87240B.
https://doi.org/10.1117/12.2015727
[43]  Torres-Madronero, M.C., Velez-Reyes, M. and Goodman, J.A. (2014) Subsurface Unmixing for Benthic Habitat Mapping Using Hyperspectral Imagery and Lidar-Derived Bathymetry. Proceedings of the SPIE, 9088, 90880M.
https://doi.org/10.1117/12.2053491
[44]  Zhang, C. (2019) Combining Ikonos and Bathymetric LiDAR Data to Improve Reef Habitat Mapping in the Florida Keys. Papers in Applied Geography, 5, 256-271.
https://doi.org/10.1080/23754931.2019.1694967
[45]  Jalali, A., Young, M., Huang, Z., et al. (2018) Modelling Current and Future Abundances of Benthic Invertebrates Using Bathymetric LiDAR and Oceanographic Variables. Fisheries Oceanography, 27, 587-601.
https://doi.org/10.1111/fog.12280
[46]  Wang, M., Wu, Z., Yang, F., et al. (2018) Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea. Sensors, 18, Article 3828.
https://doi.org/10.3390/s18113828

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