Multi-dimensional spatiotemporal monitoring and analysis of tidal flats in the Maowei Sea using integrated optical remote sensing and SAR

doi:10.11947/j.AGCS.2026.20250412

Abstract

Abstract:

Tidal flats are transitional areas where land meets the sea and play a crucial role in maintaining the health of coastal ecosystems and in sequestering blue carbon. With the challenges posed by global sea-level rise, increased human activities, and frequent natural disasters, there is an urgent need for effective monitoring and analysis of the spatial and temporal distribution, as well as the surface deformation, of tidal flats. This study focuses on the coastal zone of Maowei Sea in the Beibu Gulf, China. By integrating Sentinel-2 imagery from 2015 to 2023 with low-tide Sentinel-1 imagery and various other data sources, including sea-level changes, precipitation, and hydrological variations, the following investigations were conducted: ① Employing the Google Earth Engine (GEE) cloud platform, this study used time-series Sentinel-2 imagery along with the enhanced mangrove vegetation index (EMVI), normalized difference water index (NDWI), the maximum spectral index composite (MSIC) algorithm, and Otsu's method (OTSU). This approach successfully identified intertidal flats within the study area. Results demonstrated an overall accuracy of 96.6% and a Kappa coefficient of 0.92, confirming the reliability of this identification method. The tidal flat area in Maowei Sea decreased from 19.98 km² in 2016 to 15.80 km² in 2023, a 20.9% reduction. Land use types shifted from tidal flats to mangrove forests, construction land, and other categories. ② A deformation extraction method termed “PS+SBAS-InSAR” was developed, incorporating partial permanent scatterers (PS) into small baseline subset synthetic interferometric aperture radar (SBAS-InSAR) technology to monitor tidal flat deformation. Results show that between 2015 and 2023, the surface deformation rates on the tidal flats ranged from－54.72 mm/a to 41.71 mm/a. During this period, 98.77% of the area experienced deformation rates between－20 mm/a and 20 mm/a, and 84.62% of the area had rates between－10 mm/a and 10 mm/a, indicating overall stable ground deformation. The most significant subsidence was observed at Mangrove Bay, with a cumulative total of－308.21 mm, while the highest uplift occurred at Kangxi Ridge, with a cumulative total of 311.90 mm. The tidal flats of Maowei Sea show uneven deformation patterns, mainly characterized by slight uplift. Specifically, subsidence occurs along both banks of the Jianshan River and in Hongshu Bay, while uplift is observed at Kangxi Ridge and in the northwestern part of Jianshan. Irregular uplift and subsidence patterns are evident along both banks of Longmen Qishierjing. ③ Comprehensive analysis of multiple data sources shows that precipitation, hydrological movement, sea-level rise, and mangrove changes are the main factors affecting tidal flat surface deformation. Sea-level rise is negatively related to this deformation. Precipitation and extreme-weather patterns are seasonal, while mangrove dynamics, sea-level rise, and coastal aquaculture have long-term effects.

Key words: tidal flats, remote sensing, deformation monitoring, key drivers, Maowei Sea

CLC Number:

P237

Ertao GAO, Jing LIU, Shujin LI, Guoqing ZHOU, Bolin FU, Shuxian LI. Multi-dimensional spatiotemporal monitoring and analysis of tidal flats in the Maowei Sea using integrated optical remote sensing and SAR[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(4): 632-646.

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References 35

[1]	HE Qiang, LI Zu'ang, DALEO P, et al. Coastal wetland resilience through local, regional and global conservation[J]. Nature Reviews Biodiversity, 2025, 1(1): 50-67.
[2]	GAO Ertao, ZHOU Guoqing, LI Shuxian, et al. Spatio-temporal evolution monitoring and analysis of tidal flats in Beibu Gulf from 1987 to 2021 using multisource remote sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 6099-6114.
[3]	WU Wenting, ZHANG Min, CHEN Chunpeng, et al. Coastal reclamation shaped narrower and steeper tidal flats in Fujian, China: evidence from time-series satellite data[J]. Ocean & Coastal Management, 2024, 247: 106933.
[4]	TANG Pengfei, GUO Shanchuan, ZHANG Peng, et al. A highly efficient index for robust mapping of tidal flats from sentinel-2 images directly[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 218: 742-760.
[5]	TSAI Y S, TSENG K H. Monitoring multidecadal coastline change and reconstructing tidal flat topography[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 118: 103260.
[6]	邓开元, 任超. 多光谱光学遥感影像水体提取模型[J]. 测绘学报, 2021, 50(10): 1370-1379. DOI: . doi: 10.11947/j.AGCS.2021.20200482
	DENG Kaiyuan, REN Chao. Water extraction model of multispectral optical remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1370-1379. DOI: . doi: 10.11947/j.AGCS.2021.20200482
[7]	ZHANG Xuhui, ZUO Liqin, LU Yongjun, et al. An improved approach for retrieval of tidal flat elevation based on inundation frequency[J]. Estuarine, Coastal and Shelf Science, 2025, 313: 109061.
[8]	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.
[9]	GORELICK N, HANCHER M, DIXON M, et al. Google Earth Engine: planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18-27.
[10]	MURRAY N J, PHINN S P, FULLER R A, et al. High-resolution global maps of tidal flat ecosystems from 1984 to 2019[J]. Scientific Data, 2022, 9: 542.
[11]	WANG Xinxin, XIAO Xiangming, ZOU Zhenhua, et al. Tracking annual changes of coastal tidal flats in China during 1986-2016 through analyses of Landsat images with Google Earth Engine[J]. Remote Sensing of Environment, 2020, 238: 110987.
[12]	JIA Mingming, WANG Zongming, MAO Dehua, et al. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine[J]. Remote Sensing of Environment, 2021, 255: 112285.
[13]	仇传银, 李行, 刘淑安, 等. 长江三角洲滩涂信息的遥感提取及时空变化[J]. 地球信息科学学报, 2019, 21(2): 269-278.
	QIU Chuanyin, LI Xing, LIU Shu'an, et al. Monitoring tidal flats in the Yangtze River delta using landsat images[J]. Journal of Geo-Information Science, 2019, 21(2): 269-278.
[14]	王霞迎. InSAR在地表形变监测中的关键技术及应用[J]. 测绘学报, 2022, 51(10): 2244. DOI: . doi: 10.11947/j.AGCS.2022.20220001
	WANG Xiaying. Key techniques and their application of InSAR in ground deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2244. DOI: . doi: 10.11947/j.AGCS.2022.20220001
[15]	刘媛媛. 不同尺度综合地表形变InSAR时序监测与机理分析[J]. 测绘学报, 2020, 49(7): 935-935. DOI: . doi: 10.11947/j.AGCS.2020.20190339
	LIU Yuanyuan. Research on the monitoring and inversion of different-scale complex surface deformation with multi-temporal InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 935-935. DOI: . doi: 10.11947/j.AGCS.2020.20190339
[16]	XIONG Jiacheng, HE Xiufeng, Alfred STEIN, et al. Deformation monitoring of the embankments using multi-temporal InSAR: a case study of the Kangshan embankment[J]. Journal of Geodesy and Geoinformation Science, 2025, 8(1): 12-29.
[17]	FERRETTI A, PRATI C, ROCCA F. Permanent scatterers in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.
[18]	BERARDINO P, FORNARO G, LANARI R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383.
[19]	朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10): 1717-1733. DOI: . doi: 10.11947/j.AGCS.2017.20170350
	ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1717-1733. DOI: . doi: 10.11947/j.AGCS.2017.20170350
[20]	ZHOU LÜ, ZHAO Yizhan, ZHU Zilin, et al. Spatial and temporal evolution of surface subsidence in Tianjin from 2015 to 2020 based on SBAS-InSAR technology[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(1): 60-72.
[21]	LIU Guang, PERSKI Z, SALVI S, et al. Land surface displacement geohazards monitoring using multi-temporal InSAR techniques[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(1): 77-87.
[22]	明小勇, 田义超, 张强, 等. 基于哨兵主被动遥感的茅尾海潮滩地表形变监测与分析[J]. 遥感学报, 2024, 28(9): 2306-2319.
	MING Xiaoyong, TIAN Yichao, ZHANG Qiang, et al. Monitoring and analysis of surface deformation of tidal flat in Maowei Sea on the basis of Sentinel active and passive remote sensing[J]. National Remote Sensing Bulletin, 2024, 28(9): 2306-2319.
[23]	李鹏, 白建博, 李振洪, 等. 融合多轨道TS-InSAR的广域海岸带地面沉降监测及成因解析：以山东省为例[J]. 测绘学报, 2025, 54(7): 1178-1191. DOI: . doi: 10.11947/j.AGCS.2025.20250061
	LI Peng, BAI Jianbo, LI Zhenhong, et al. Wide area coastal subsidence monitoring and driver analysis with multi tracks of TS-InSAR: a case study of Shandong province[J]. Acta Geodaeticaet Cartographica Sinica, 2025, 54(7): 1178-1191. DOI: . doi: 10.11947/j.AGCS.2025.20250061
[24]	朱武, 张勤, 丁晓利. 多参考点的PS-InSAR变形监测数据处理[J]. 测绘学报, 2012, 41(6): 886-890, 903.
	ZHU Wu, ZHANG Qin, DING Xiaoli. PS-InSAR deformation monitoring data processing based on multi-reference points[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6): 886-890, 903.
[25]	YANG Gang, HUANG Ke, SUN Weiwei, et al. Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 189: 236-254.
[26]	TALUKDAR S, PAL S. Effects of damming on the hydrological regime of Punarbhaba river basin wetlands[J]. Ecological Engineering, 2019, 135: 61-74.
[27]	GAO Bocai. NDWI: a normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote Sensing of Environment, 1996, 58(3): 257-266.
[28]	陆双龙. 月尺度下的茅尾海营养盐与环境因子的时空变化及空间自相关特征研究[D]. 南宁: 南宁师范大学, 2021.
	LU Shuanglong. Spatiotemporal variation and spatial autocorrelation of nutrient and environmental factors in Maowei Sea at monthly scale[D]. Nanning: Nanning Normal University, 2021.
[29]	黎树式, 黄鹄. 近50年钦江水沙变化研究[J]. 广西科学, 2018, 25(4): 409-417.
	LI Shushi, HUANG Hu. Variations of runoff and sediment in Qinjiang River in the past 50 years[J]. Guangxi Sciences, 2018, 25(4): 409-417.
[30]	LEUVEN J R F W, PIERIK H J, VAN DER VEGT M, et al. Sea-level-rise-induced threats depend on the size of tide-influenced estuaries worldwide[J]. Nature Climate Change, 2019, 9(12): 986-992.
[31]	罗新正, 孟宪伟. 基于相对海平面变化的广西茅尾海红树林面积稳定性评估[J]. 海洋环境科学, 2022, 41(6): 881-887.
	LUO Xinzheng, MENG Xianwei. Assessment of mangrove area stability based on relative sea level change in Maowei Sea, Guangxi[J]. Marine Environmental Science, 2022, 41(6): 881-887.
[32]	许珊珊, 杨夏玲, 黎树式, 等. 北部湾钦江响应极端天气的水沙变化过程[J]. 热带地理, 2023, 43(11): 2135-2145.
	XU Shanshan, YANG Xialing, LI Shushi, et al. Variations in the characteristics of water and sediment in response to extreme weather conditions in the Qinjiang River of the Beibu Gulf[J]. Tropical Geography, 2023, 43(11): 2135-2145.
[33]	江锐捷, 程鹏, 高建华, 等. 红树林对潮流底边界层动力过程的影响[J]. 海洋地质前沿, 2020, 36(4): 37-44.
	JIANG Ruijie, CHENG Peng, GAO Jianhua, et al. Impacts of mangrove on the dynamic process of bottom boundary layer[J]. Marine Geology Frontiers, 2020, 36(4): 37-44.
[34]	王日明, 苏金恒, 戴志军, 等. 钦州湾茅岭江河口红树林湿地动态变化过程[J]. 热带海洋学报, 2025, 44(6): 143-154.
	WANG Riming, SU Jinheng, DAI Zhijun, et al. Dynamic changes in mangrove wetland of the Maolingjiang Estuary, Qinzhou Gulf[J]. Journal of Tropical Oceanography, 2025, 44(6): 143-154.
[35]	焦海峰, 郑丹, 严巧娜, 等. 温度、盐度及交互作用对僧帽牡蛎(Saccostrea cucullata)呼吸排泄的影响[J]. 生态学报, 2017, 37(2): 692-699.
	JIAO Haifeng, ZHENG Dan, YAN Qiaona, et al. Effects of water temperature and salinity on oxygen consumption rate and ammonia excretion rate of Saccostrea cucullata[J]. Acta Ecologica Sinica, 2017, 37(2): 692-699.

提取类型	地物类别			用户精度
提取类型	潮滩	其他	行总计	用户精度
潮滩	143	6	149	95.97%
其他	11	340	351	96.87%
列总计	154	346	500	—
制图精度	92.86%	98.27%	—	—
	总体分类精度：96.60%		Kappa系数：0.92

速率范围	相干点数量	所占比例/（%）
速率≤－20	876	0.26
－20<速率≤－10	16 929	4.98
－10<速率≤－5	44 330	13.04
－5<速率≤0	87 536	25.74
0<速率≤5	96 187	28.28
5<速率≤10	59 724	17.56
10<速率≤20	31 171	9.17
速率>20	3312	0.97
总计	340 065	100