无人机摄影测量在砂质海岸生态系统调查中的应用

doi:10.13474/j.cnki.11-2246.2025.0325

测绘通报 ›› 2025, Vol. 0 ›› Issue (3): 144-149.doi: 10.13474/j.cnki.11-2246.2025.0325

• 技术交流 • 上一篇

无人机摄影测量在砂质海岸生态系统调查中的应用

余威^1,2,3, 刘敏聪⁴, 王孟^1,2,3, 刘悦⁴, 李济坤^1,2,3, 阳杰^1,2,3, 李团结^1,2,3

1. 自然资源部南海生态中心, 广东广州 510300;
2. 自然资源部海洋环境探测技术与应用重点实验室, 广东广州 510300;
3. 海南南沙珊瑚礁生态系统国家野外科学观测研究站, 广东广州 510300;
4. 深圳海洋发展研究促进中心, 广东深圳 518052

收稿日期:2024-05-31 发布日期:2025-04-03
作者简介:余威(1987—),男,硕士,高级工程师,主要研究方向为沉积动力地貌学。E-mail:sharawei@foxmail.com
基金资助:
科技基础资源调查专项(2022FY202403);国家海洋局南海分局海洋科学技术局长基金(180208);广东省平台基地及科技基础条件建设项目(2021B1212050025)

Application of UAV photogrammetry in the survey of sandy coast ecosystem

YU Wei^1,2,3, LIU Mincong⁴, WANG Meng^1,2,3, LIU Yue⁴, LI Jikun^1,2,3, YANG Jie^1,2,3, LI Tuanjie^1,2,3

1. South China Sea Ecological Center, Ministry of Natural Resources, Guangzhou 510300, China;
2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China;
3. Nansha Islands Coral Reef Ecosystem National observation and Research Station, Guangzhou 510300, China;
4. Marine Development Research and Promotion Center of Shenzhen, Shenzhen 518052, China

Received:2024-05-31 Published:2025-04-03

摘要/Abstract

摘要： 砂质海岸是非常优质的旅游资源,具有很高的旅游悠闲价值和美学价值。应用无人机摄影测量技术生成砂质海岸正射影像的平面精度达厘米级,数字高程模型的高程精度达到亚米级别。本文基于无人机正射影像,分别应用绿叶指数(GLI)、绿红植被指数(GRVI)和红绿蓝植被指数(RGBVI)识别沙滩后滨植被面积,并与GIS数字化结果进行对比,误差分别为24.4%、7.4%和25.2%,绿红植被指数(GRVI)识别的结果与GIS数字化的结果最接近。大梅沙沙滩后滨植被种类有66种,多为园林绿化植被,原生植被较少。沙滩面积为16.3 hm²,其中干滩面积为8.1 hm²,潮间带面积为8.2 hm²。无人机未接入RTK信号,根据生成的DEM提取高程数据与地面RTK实测高程平均差值为0.598 m,无人机实测的高程低于RTK实测结果。采用五镜头相机获取真实地物信息,构建了沙滩实景三维模型,生成了沙滩雕塑和沙滩后方局部建筑物实景三维模型。

关键词: 无人机, 摄影测量, 砂质海岸生态系统, 三维模型

Abstract: The sandy coast is a very high-quality tourism resource with high tourism leisure value and aesthetic value. The plane accuracy of using drones to generate orthophoto images of sandy coasts reaches the centimeter level, and the elevation accuracy of digital elevation models reaches the sub meter level. Based on drone orthophoto images, the green leaf index(GLI), green red vegetation index(GRVI), and red green blue vegetation index(RGBVI) are applied to identify the vegetation area of backshore. Compared with the results of GIS digitization, the errors are 24.4%, 7.4%, and 25.2%, respectively. The results of green red vegetation index(GRVI) identification are closest to those of GIS digitization. There are 66 types of vegetation on the backshore of Dameisha beach, most of which are garden and green vegetation, with fewer native vegetation. The beach area is 16.3 hectares, of which the dry beach area is 8.1 hectares and the intertidal zone area is 8.2 hectares. The drone is not connected to the RTK signal, and the average difference between the elevation data extracted from the generated DEM and the ground RTK measured elevation is 0.598 m. The measured elevation of the drone is lower than the RTK measured results. We use a five lens aerial camera to obtain real land information and construct 3D model of the beach, and generate specific 3D model of the beach sculpture and local buildings behind the backshore.

Key words: unmanned aerial vehicle (UAV), photogrammetry, sandy coast ecosystem, 3D model

中图分类号:

P237

余威, 刘敏聪, 王孟, 刘悦, 李济坤, 阳杰, 李团结. 无人机摄影测量在砂质海岸生态系统调查中的应用[J]. 测绘通报, 2025, 0(3): 144-149.

YU Wei, LIU Mincong, WANG Meng, LIU Yue, LI Jikun, YANG Jie, LI Tuanjie. Application of UAV photogrammetry in the survey of sandy coast ecosystem[J]. Bulletin of Surveying and Mapping, 2025, 0(3): 144-149.

参考文献

[1] 于吉涛,陈子燊.砂质海岸侵蚀研究进展[J].热带地理,2009,29(2):112-118.
[2] 衣伟虹.我国典型地区海岸侵蚀过程及控制因素研究[D].青岛:中国海洋大学,2011.
[3] BRYANT E.Regional sea level,southern oscillation and beach change,New South Wales,Australia[J].Nature,1983,305:213-216.
[4] CAI Feng,SU Xianze,LIU Jianhui,et al.Coastal erosion in China under the condition of global climate change and measures for its prevention[J].Progress in Natural Science,2009,19(4):415-426.
[5] JOHNSON J W.Scale effects in hydraulic models involving wave motion[J].Eos,Transactions American Geophysical Union,1949,30(4):517-525.
[6] KRAUS N C,LARSON M,KRIEBEL D L.Evaluation of beach erosion and accretion predictors[C]//Proceedings of 1991 Conference on Coastal Sedimen.[S.l.]:American Society of Civil Engineers,1991.
[7] BRUUN P.Sea-level rise as a cause of shore erosion[J].Journal of the Waterways and Harbors Division,1962,88(1):117-130.
[8] 陈子燊.海滩剖面时空变化过程分析[J].海洋通报,2000,19(2):42-48.
[9] 戴志军,陈子燊,欧素英.粤东汕尾岬间海滩剖面月内日变化过程特征分析[J].热带海洋学报,2002,21(1):27-32.
[10] DONOGHUE D,THOMAS D C R,ZONG Y.Mapping and monitoring the intertidal zone of the east coast of England using remote sensing techniques and a coastal monitoring GIS[J].Marine Technology Society Journal,1994,28(2):19-29.
[11] 毋亭,侯西勇.1940s以来中国大陆岸线变化的趋势分析[J].生态科学,2017,36(1):80-88.
[12] 赵宗泽.30年来福建省大陆海岸线变迁遥感分析[D].青岛:山东科技大学,2013.
[13] 姚晓静,高义,杜云艳,等.基于遥感技术的近30a海南岛海岸线时空变化[J].自然资源学报,2013,28(1):114-125.
[14] 刘杨,张涵,冯海宽,等.无人机成像高光谱的马铃薯地上生物量估算[J].光谱学与光谱分析,2021,41(9):2657-2664.
[15] 杜蒙蒙,Ali Roshanianfard,刘颖超.可见光波段无人机遥感图像的小麦茎蘖密度定量反演[J].光谱学与光谱分析,2021,41(12):3828-3836.
[16] 李亚东,曹明兰,李长青,等.无人机森林航摄影像三维点云估测林分蓄积量研究[J].中南林业科技大学学报,2019,39(3):56-60.
[17] 王好,邢利利,黄林果,等.某石化工业园区大气中VOCs垂直分布的无人机监测研究[J].中国环境监测,2021,37(2):60-67.
[18] 高山,陈思.城市三维建模技术与标准研究[J].测绘通报,2013(3):95-97.
[19] 陈仁朋,吴熠文,余加勇,等.基于无人机影像序列的城市精细化三维模型精度评估[J].湖南大学学报(自然科学版),2019,46(11):172-180.
[20] STORLAZZI C D,DARTNELL P,HATCHER G A,et al.End of the chain？ rugosity and fine-scale bathymetry from existing underwater digital imagery using structure-from-motion (SfM) technology[J].Coral Reefs,2016,35(3):889-894.
[21] KALACSKA M,ARROYO-MORA J P,DE GEA J,et al.Videographic analysis of eriophorum vaginatum spatial coverage in an ombotrophic bog[J].Remote Sensing,2013,5(12):6501-6512.
[22] LEHMANN J,MVNCHBERGER W,KNOTH C,et al.High-resolution classification of south Patagonian peat bog microforms reveals potential gaps in up-scaled CH₄ fluxes by use of unmanned aerial system (UAS) and CIR imagery[J].Remote Sensing,2016,8(3):173.
[23] JAUD M,GRASSO F,LE DANTEC N,et al.Potential of UAVs for monitoring mudflat morphodynamics(application to the seine estuary,France)[J].ISPRS International Journal of Geo-Information,2016,5(4):50.
[24] LONG N,MILLESCAMPS B,GUILLOT B,et al.Monitoring the topography of a dynamic tidal inlet using UAV imagery[J].Remote Sensing,2016,8(5):387.
[25] HARWIN S,LUCIEER A.Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery[J].Remote Sensing,2012,4(6):1573-1599.
[26] 戴玮琦,李欢,龚政,等.无人机技术在潮滩地貌演变研究中的应用[J].水科学进展,2019,30(3):359-372.
[27] 汪尧峰,基于无人机遥感的海岸线提取与测量方法[J].测绘与空间地理信息,2022,45(10):143-146.
[28] 郑群峰,张超,胡霄,等.1954—2019年深圳高影响台风气候特征分析[J].广东气象,2020,42(3):14-18.

无人机摄影测量在砂质海岸生态系统调查中的应用

Application of UAV photogrammetry in the survey of sandy coast ecosystem

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