基于潮位互补累积分布函数的潮滩地形遥感反演方法:以乐清湾为例

张昭源; 张华国; 曹雯婷; 厉冬玲

doi:10.3969/j.issn.1001-909X.2025.02.004

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海洋学研究 ›› 2025, Vol. 43 ›› Issue (2) : 30-38. DOI: 10.3969/j.issn.1001-909X.2025.02.004

研究论文

基于潮位互补累积分布函数的潮滩地形遥感反演方法:以乐清湾为例

张昭源 ¹^,²^,³ ,
张华国 ¹^,²^,³^,^* ,
曹雯婷 ²^,³ ,
厉冬玲 ²^,³

作者信息 +

Tidal flat topography inversion method based on tidal level complementary cumulative distribution function: A case study of Yueqing Bay

ZHANG Zhaoyuan ¹^,²^,³ ,
ZHANG Huaguo ¹^,²^,³^,^* ,
CAO Wenting ²^,³ ,
LI Dongling ²^,³

Author information +

文章历史 +

摘要

潮滩是受潮汐影响而呈现周期性淹没和出露的区域,其淹没频率可以从一定程度上反映潮滩高程。本研究利用时序合成孔径雷达(synthetic aperture radar, SAR)卫星遥感数据开展了基于潮位互补累积分布函数的潮滩地形遥感反演方法的研究,提出了基于遥感观测次数权重缩放的淹没频率校正新方法,并基于淹没频率的数学定义探究了淹没频率与潮滩高程的函数关系,构建了基于潮位互补累积分布函数的潮滩地形反演模型。以乐清湾为研究区开展方法验证,基于时序Sentinel-1卫星SAR遥感数据,成功反演了乐清湾2019—2020年与2021—2022年两期潮滩地形,并基于对应年份的ICESat-2卫星激光测高数据进行了精度验证,两期潮滩地形均方根误差分别为0.41 m与0.51 m。使用2019年的现场实测水深数据验证了2019—2020年的潮滩地形,均方根误差为0.48 m。本文提出的反演方法可以获得高精度潮滩地形,无需实测地形数据,可推广应用到潮滩地形动态监测工作中。

Abstract

Tidal flats are influenced by tides, experiencing periodic inundation and exposure, thus the inundation frequency reflects the elevation of tidal flats. This study utilizes time-series SAR satellite remote sensing data to conduct research on the remote sensing inversion method of tidal flat topography based on tidal level complementary cumulative distribution function. The key lies in proposing a new method for inundation frequency correction based on the weighting scale of remote sensing observation counts. And, based on the mathematical definition of inundation frequency, the functional relationship between inundation frequency and tidal flat elevation was explored, leading to the construction of a tidal flat topography inversion model based on the tidal level complementary cumulative distribution function. Then, the validation of the method was conducted in the Yueqing Bay. Based on the time-series Sentinel-1 satellite SAR remote sensing data, the tidal flat topographies for the periods 2019-2020 and 2021-2022 were successfully inverted. The accuracy assessment was conducted based on the corresponding period’s ICESat-2 satellite laser altimetry data. The root mean square errors (RMSE)of the tidal flat topographies for the periods 2019-2020 and 2021-2022 were 0.41 m and 0.51 m, respectively. Additionally, the RMSE of topography for the period of 2019-2020 using in-situ data was 0.48 m. The accuracy assessment suggest that the proposed method in this study can achieve high-precision tidal flat topography without field-measured topographic data. It is expected to be applicable to the monitoring of tidal flat topography in more regions.

导出引用

张昭源, 张华国, 曹雯婷, 等. 基于潮位互补累积分布函数的潮滩地形遥感反演方法:以乐清湾为例[J]. 海洋学研究. 2025, 43(2): 30-38 https://doi.org/10.3969/j.issn.1001-909X.2025.02.004

ZHANG Zhaoyuan, ZHANG Huaguo, CAO Wenting, et al. Tidal flat topography inversion method based on tidal level complementary cumulative distribution function: A case study of Yueqing Bay[J]. Journal of Marine Sciences. 2025, 43(2): 30-38 https://doi.org/10.3969/j.issn.1001-909X.2025.02.004

中图分类号： P237;P714.7

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	ALLEN J R L. Morphodynamics of Holocene salt marshes: A review sketch from the Atlantic and southern north sea coasts of Europe[J]. Quaternary Science Reviews, 2000, 19(12): 1155-1231. 本文引用 [1]

[2]	PATERSON D M, FORTUNE I, ASPDEN R J, et al. Intertidal flats[M]// Coastal wetlands. Amsterdam: Elsevier, 2019: 383-406. 本文引用 [1]

[3]	ALONGI D M. Carbon sequestration in mangrove forests[J]. Carbon Management, 2012, 3(3): 313-322. 本文引用 [1]

[4]	MURRAY N J, PHINN S R, DEWITT M, et al. The global distribution and trajectory of tidal flats[J]. Nature, 2019, 565(7738): 222-225. 本文引用 [1]

[5]	JUNG R, ADOLPH W, EHLERS M, et al. A multi-sensor approach for detecting the different land covers of tidal flats in the German Wadden Sea: A case study at Norderney[J]. Remote Sensing of Environment, 2015, 170: 188-202. 本文引用 [1]

[6]	ZHANG H F, WANG L, ZHAO Y F, et al. Application of airborne LiDAR measurements to the topographic survey of the tidal flats of the northern Jiangsu radial sand ridges in the southern Yellow Sea[J]. Frontiers in Marine Science, 2022, 9: 871156. 本文引用 [1]

[7]	RYU J H, CHOI J K, LEE Y K. Potential of remote sensing in management of tidal flats: A case study of thematic mapping in the Korean tidal flats[J]. Ocean & Coastal Management, 2014, 102: 458-470. 本文引用 [1]

[8]	SALAMEH E, FRAPPART F, ALMAR R, et al. Monitoring beach topography and nearshore bathymetry using spaceborne remote sensing: A review[J]. Remote Sensing, 2019, 11(19): 2212. 本文引用 [1]

[9]	MASON D C, DAVENPORT I J, ROBINSON G J, et al. Construction of an inter-tidal digital elevation model by the ‘Water-Line’ Method[J]. Geophysical Research Letters, 1995, 22(23): 3187-3190. 本文引用 [1]

[10]	穆敬, 吴迪, 朱穆华, 等. 基于BJ-1影像和高分辨率潮汐网格的潮滩DEM构建[J]. 海洋测绘, 2018, 38(1):39-42. MU J, WU D, ZHU M H, et al. Tidal land digital elevation model establishment based on Beijing-1 small satellite image and high-resolution tidal level grid[J]. Hydrographic Surveying and Charting, 2018, 38(1): 39-42. 本文引用 [2]

[11]	周永, 张东, 邓慧丽, 等. 岸外沙洲潮间带地形的增强型遥感构建方法[J]. 海洋学报, 2021, 43(12):133-143. ZHOU Y, ZHANG D, DENG H L, et al. The enhanced construction method for intertidal terrain of offshore sandbanks by remote sensing[J]. Haiyang Xuebao, 2021, 43(12): 133-143. 本文引用 [2]

[12]

王子赫, 康彦彦, 王敏京. ICESat-2与多源光学影像的潮滩地形反演方法[J]. 测绘通报, 2023(5):51-55,61.

https://doi.org/10.13474/j.cnki.11-2246.2023.0135

摘要

针对潮滩地形监测存在的操作难度大、价格高、精度低等问题，本文基于ICESat-2卫星激光点云剖面数据，提出了结合多源光学影像的潮滩地形反演方法。以中国黄海辐射沙脊群的两大沙洲系统——条子泥与高泥为研究对象，利用低通滤波降噪技术提取地形高度值，并与潮汐水位数据相结合赋值，实现了从二维到三维的转换，提高了目前主流的水边线潮位赋值法的反演精度。该模型展现了更多滩面地形起伏变化的细节，高度差异明显，可以观测到清晰的潮沟地形。对比实测数据，相关系数为0.89，均方根误差为0.34 m。研究结果验证了ICESat-2数据对于潮滩地形反演的重要作用，为构建人类活动影响下的潮滩演化理论奠定了数据基础。

WANG

Z H

, KANG

Y Y

, WANG

M J

. ICESat-2 and multivariate optical image for tidal flat topography inversion methods[J]. Bulletin of Surveying and Mapping, 2023(5): 51-55, 61.

https://doi.org/10.13474/j.cnki.11-2246.2023.0135

本文引用 [2] 摘要

In response to the problems of large operational difficulties, high price and low accuracy of tidal flat topography monitoring, based on ICESat-2 satellite laser point cloud profile data, this paper proposes a digital elevation model inversion method combining multivariate optical images. The two major sandbar systems of the Tiaozini and Gaoni in the Yellow Sea of China are used as research objects. Using low-pass filtering techniques to extract topographic height values and combine them with tidal water level data to assign values, the conversion from 2D to 3D is achieved, and improves the inversion accuracy of the current mainstream waterline tide level assignment method. The model shows more detail of the topographic variation of the tidal flat surface, with significant differences in height, allowing the presence of clear tidal trench topography to be observed. Comparing the measured data, the correlation coefficient is 0.89 with a root mean square error of 0.34 m. The results of this study demonstrate the significance of ICESat-2 data for the inversion of beach topography and lay the data foundation for the construction of the theory of tidal flats evolution under the influence of human activities.

[13]	CAO W T, ZHOU Y Y, LI R, et al. Mapping changes in coastlines and tidal flats in developing islands using the full time series of Landsat images[J]. Remote Sensing of Environment, 2020, 239: 111665. 本文引用 [1]

[14]	XU N, MA Y, YANG J, et al. Deriving tidal flat topography using ICESat-2 laser altimetry and Sentinel-2 imagery[J]. Geophysical Research Letters, 2022, 49(2): e2021GL096813. 本文引用 [6]

[15]	CHEN C P, ZHANG C, TIAN B, et al. Tide2Topo: A new method for mapping intertidal topography accurately in complex estuaries and bays with time-series Sentinel-2 images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 55-72. 本文引用 [5]

[16]	ZHU M H, XIA X M, CHEN Y N, et al. Spatiotemporal variation of tidal wetlands affected by human activities during the past 50 years: A case study of Yueqing bay in Eastern China[J]. Land, 2023, 12(4): 851. 本文引用 [1]

[17]	TORRES R, SNOEIJ P, GEUDTNER D, et al. GMES Sentinel-1 mission[J]. Remote Sensing of Environment, 2012, 120: 9-24. 本文引用 [1]

[18]	ACHANTA R, SÜSSTRUNK S. Superpixels and polygons using simple non-iterative clustering[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 4895-4904. 本文引用 [1]

[19]	EGBERT G D, BENNETT A F, FOREMAN M G G. TOPEX/POSEIDON tides estimated using a global inverse model[J]. Journal of Geophysical Research: Oceans, 1994, 99(C12): 24821-24852. 本文引用 [1]

[20]	STAMMER D, RAY R D, ANDERSEN O B, et al. Accuracy assessment of global barotropic ocean tide models[J]. Reviews of Geophysics, 2014, 52(3): 243-282. 本文引用 [1]

[21]	MARKUS T, NEUMANN T, MARTINO A, et al. The ice, cloud, and land elevation satellite-2 (ICESat-2): Science requirements, concept, and implementation[J]. Remote Sensing of Environment, 2017, 190: 260-273. 本文引用 [1]

[22]	OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. 本文引用 [1]