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Mapping the intertidal wetlands of Fujian Province based on tidal dynamics and vegetational phonology

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

  • Email：zczhichao@163.com
  • Introduction：智超，1997年生，女，硕士研究生，研究方向为海岸带遥感。E-mail： zczhichao@163.com
ZHI Chao,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

  • Email：wuwt@fzu.edu.cn
  • Introduction：吴文挺，1990年生，男，助理研究员，研究方向为海岸带遥感。E-mail： wuwt@fzu.edu.cn
WU Wenting*,  
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

SU Hua

Resümee

Intertidal wetlands are the transitional zone between terrestrial and marine ecosystems, and they are of ecological and economic importance. However, intertidal wetlands are severely damaged due to natural causes (e.g., climate change and sea-level rise) and anthropogenic causes (e.g., coastal reclamation and excessive tourism development). Therefore, tracking the spatiotemporal changes of intertidal wetlands is important for scientific management and high-quality development of coastal areas. Compared with traditional surveying methods, remote sensing has better capacity in monitoring intertidal wetlands dynamically on a large scale. Acquiring complete information of intertidal wetland from a single-phase remote sensing image is difficult owing to the influences of cloudy weather and tidal periodic submergence. The problem of extracting the information of the intertidal zone under the influences of dynamic tidal submerge should be solved for the application of remote sensing in coastal areas.In this study, we proposed a combined method using the time-series remote sensing indices and the geographic characteristics in the subtropical intertidal wetland of Fujian Province, China on the basis of the GEE platform. Three main types of intertidal wetlands including high marsh, low marsh, and tidal flat were classified by the following steps. First, water and vegetation indices were utilized to extract water bodies and vegetation from every single image. Second, the water and vegetation frequencies derived from time-series images were used to distinguish the high marsh, low marsh, and tidal flat according to the tidal dynamics and vegetational phonology. Finally, the accuracy of the results was verified by the high-resolution image on Google Earth Pro and in situ data. The results were compared with similar datasets to assess the reliability and robustness of the proposed method.The overall classification accuracy was 97.47%, and the Kappa coefficient was 0.96. The verifications showed misclassifications in the transitional area. The total area of intertidal wetlands in Fujian Province was 1061.3 km2, and the areas of high marsh, low marsh, and tidal flat were 18.1, 137.3, and 905.8 km2, respectively. Intertidal wetlands were concentrated in estuaries and bays. The area of tidal flat decreased from north to south along the coast, but a converse trend of the area of high marsh was observed. The vegetation was mainly distributed in the southern Quanzhou Bay, Jiulongjiang Estuary, and Zhangjiang Estuary, and it was less in northern Fujian. Comparing the results of this study with similar datasets showed that our study improved classification accuracy in the Fujian Province. However, some objective factors such as mixed pixels and clouds could affect the accuracy of the classification.This research developed a method based on the GEE platform and time-series remote sensing indices to classify intertidal wetlands for overcoming the dilemma faced by single-phase remote sensing images in the intertidal zone information extraction. The results showed certain superiority compared with similar datasets during the same period. The method reduced the impact of the year-round cloudy and rainy weather in the subtropical coastal zone and tidal dynamics effectively. The present datasets will provide important basic data and technical supports for the sustainable management and utilization of coastal resources of the region.

Schlüsselwort

Time-series remote sensing data;intertidal wetlands;GEE;Fujian province;phenology;frequency-based adgorithm

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