A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique

doi:10.6046/zrzyyg.2021007

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Abstract

Conventional methods for regional bathymetry mainly use shipborne acoustic detection technologies. However, since the hull cannot access the coastal shallow waters and the areas with dense islands and coral reefs, there is no available data of near-coastal areas. These problems can be effectively solved with the emergence and development of airborne lidar bathymetric technology, which has become a fast and efficient method for water-depth and submarine topographic exploration. Based on the airborne laser sounder CZMIL Nova, this paper introduces the characteristics and influencing factors of the land-sea integrated technologic surveying and its preliminary application in the land-sea integrated topographic surveying of islands.

Keywords airborne laser bathymetry CZMIL remote sensing coastal zone topography

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TP79

Issue Date: 23 December 2021

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	Fang WU
	Dingjian JIN
	Zonggui ZHANG
	Xinyang JI
	Tianqi LI
	Yu GAO

Cite this article:

Fang WU,Dingjian JIN,Zonggui ZHANG, et al. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4): 173-180.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021007 OR https://www.gtzyyg.com/EN/Y2021/V33/I4/173

类别	技术指标	CZMIL Nova	Hawk Eye III	VQ-880G	LADS MK III
一般指标	工作航高/m	400 ~1 000	400 ~600	600 ~1 600	400 ~1 000
一般指标	飞行速度/kts	140 ~175	140 ~175	140 ~175	125 ~175
测量指标	激光扫描频率/kHz	水深70/10地形 80	水深35/10地形 500	550	512/1.5
	最大测深	水深70/10地形 80	水深35/10地形 500	1.5 secchi	标称3.0 secchi 最深可达80 m
		浅水2.0/ $K d$	浅水2.2/ $K d$
		深水4.2/ $K d$	深水4.0/ $K d$
		$0.32 + 0.013 d 2$ m, 2σ	$0.32 + 0.013 d 2$ m, 2σ
	测深精度	$0.32 + 0.013 d 2$ m, 2σ	$0.32 + 0.013 d 2$ m, 2σ	0.025 m	0.5 m
	测深精度	水深 0.8 m×0.8 m 陆地 0.3 m×0.3 m	水深 0.8 m×0.8 m 陆地 0.1 m×0.1 m	0.025 m	0.5 m
	测点密度	水深 0.8 m×0.8 m 陆地 0.3 m×0.3 m	水深 0.8 m×0.8 m 陆地 0.1 m×0.1 m	标称69 pts/m²	2 m×2 m

Tab.1 Main technical specifications of typical ALB system

Fig.1 Schematic diagram of bathymetric survey^[10]

Fig.2-1 Profile analysis of ALB system based on topographic survey

Fig.2-2 Profile analysis of ALB system based on topographic survey

Fig.3 Land and sea integrated topographic survey(CZMIL Nova)

Fig.4 Bottom reflectivity curves (taken from test report of AGRS)

Tab.2 Statistics of the maximum detection depth(Optech,2013)

Fig.5 Technology flow of ALB survey

Fig.6 Calibration result of laser

Tab.3 Statistics of laser measurement accuracy in test area(m)

Fig.7 Application of land and sea integrated topographic products

[1]	徐广袖, 翟国君, 吴太旗, 等. 机载激光测深作业的关键技术问题[J]. 海洋测绘, 2019,39(2):45-49.
[1]	Xu G X, Zhai G J, Wu T Q, et al. The key technical issues on airborne LiDAR bathymetry operation[J]. Hydrographic Surveying and Charting, 2019,39(2):45-49.
[2]	刘焱雄, 郭锴, 何秀风, 等. 机载激光测深技术及其研究进展[J]. 武汉大学学报(信息科学版), 2017,42(9):1185-1194.
[2]	Liu Y X, Guo K, He X F, et al. Research progress of airborne laser bathymetry technology[J]. Geomatics and Information Science of Wuhan University, 2017,42(9):1185-1194.
[3]	翟国君, 王克平, 刘玉红. 机载激光测深技术[J]. 海洋测绘, 2014,34(2):72-75.
[3]	Zhai G J, Wang K P, Liu Y H. Technology of airborne laser bathymetry[J]. Hydrographic Surveying and Charting, 2014,34(2):72-75.
[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.
[4]	Zhai G J, Wu T Q, Ouyang Y Z, et al. The development of airborne laser bathymetry[J]. Hydrographic Surveying and Charting, 2012,32(2):67-71.
[5]	刘智敏, 杨安秀, 阳凡林, 等. 机载LiDAR测深在海洋测绘中应用的可行性分析[J]. 海洋测绘, 2018,38(4):43-47.
[5]	Liu Z M, Yang A X, Yang F L, et al. The feasibility analysis for the airborne LiDAR bathymetry application in marine surveying and charting[J]. Hydrographic Surveying and Charting, 2018,38(4):43-47.
[6]	CZMIL HydroFusion Manual & CZMIL Operation Manual[Z]. Canada:Teledyne Optech Inc. 2017.
[7]	贺岩, 胡善江, 陈卫标, 等. 国产机载双频激光雷达探测技术研究进展[J]. 激光与光电子学进展, 2018,55(8):082801.
[7]	He Y, Hu S J, Chen W B, et al. Research progress of domestic airborne dual-frequency LiDAR detection technology[J]. Laser & Optoelectronics Progress, 2018,55(8):082801.
[8]	Tuell G, Barbor K, Wozencraft J. Overview of the coastal zone mapping and imaging LiDAR(CZMIL):A new multi-sensor airborne mapping system for the U.S. army corps of engineers[C]//Proceedings of SPIE-The International Society for Optical Engineering, 2010.
[9]	毕世普, 别君, 张勇. 机载LiDAR在海岸带地形测量中的应用[J]. 海洋地质前沿, 2012,28(11):59-63.
[9]	Bi S P, Bie J, Zhang Y. Application of airborne LiDAR to coastial topographic survey[J]. Marine Geology Letters, 2012,28(11):59-63.
[10]	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]. 郑州:解放军信息工程大学, 2010.
[10]	Ye X S. Research on principle and data processing methods of airborne laser bathymetric technique[D]. Zhengzhou:Information Engineering University, 2010.
[11]	Fuchs E, Tuell G. Conceptual design of the CZMIL data acquisition system (DAS):Integrating a new bathymetric lidar with a commercial spectrometer and metric camera for coastal mapping applications[C]//Proceedings of SPIE-The International Society for Optical Engineering, 2010.
[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(s2):9-23.
[12]	Jin D J, Wu F, Yu K, et al. Large-scale application test and evaluation of an airborne lidar bathymetry system——A case study in China’s coastal zone[J]. Infrared and Laser Engineering, 2020,49(s2):9-23.
[13]	Tuell G, Barbor K, Wozencraft J. Overview of the coastal zone mapping and imaging LiDAR (CZMIL):A new multi-sensor airborne mapping system for the U.S. Army Corps of Engineers[C]//SPIE, 2010,7695:76950R.
[14]	王宗伟, 朱士才, 卢刚, 等. CZMIL机载激光测深系统及其在骆马湖测深试验评估[J]. 海洋学研究, 2017,35(3):20-26.
[14]	Wang Z W, Zhu S C, Lu G, et al. CZMIL airborne laser sounding system and its testing evaluation in Luoma Lake[J]. Journal of Marine Sciences, 2017,35(3):20-26.
[15]	CZMIL Calibration Survey Requirements[Z]. Canada:Teledyne Optech Inc. 2017.
[16]	亓发庆, 黎广钊, 孙永福, 等. 北部湾涠洲岛地貌的基本特征[J]. 海洋科学进展, 2003,21(1):41-50.
[16]	Qi F Q, Ni G Z, Sun Y F, et al. Basic geomorphologic features of the Weizhou Island of the Beibu Bay[J]. Advances in Marine Science, 2003,21(1):41-50.

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