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author:

Chen, Chunpeng (Chen, Chunpeng.) [1] | Zhang, Ce (Zhang, Ce.) [2] | Tian, Bo (Tian, Bo.) [3] | Wu, Wenting (Wu, Wenting.) [4] | Zhou, Yunxuan (Zhou, Yunxuan.) [5]

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EI

Abstract:

Intertidal zones serve as a critical transition between land and ocean and are periodically inundated by tides. They are extremely important to coastal communities as it provides diverse habitats and buffers against marine hazards. However, the reduced river sediment together with sea level rise and coastal reclamation have caused the widespread loss of intertidal areas. Reliable, high-resolution, and up-to-date intertidal topography maps are key information for coastal vulnerability assessment and restoration. Existing approaches to intertidal topography reconstruction involve significant ground surveys, with limited spatial coverage, accuracy, and efficiency, thus, restricting their potential to generalize globally. To address these issues, we propose a pixel-based approach to construct intertidal topography from dense Sentinel-2 satellite time-series and limited ground truth surveys, named as Tide2Topo. Tide2Topo differs conceptually from the widely used waterline method since it considers tidal inundation frequency as a topographic indicator rather than instantaneous waterlines. Therefore, Tide2Topo does not involve manual selection of images at different tide levels. The relationship between intertidal inundation frequency and elevation is calibrated using linear and polynomial models based on a few transect measurements instead of tidal height, preventing errors introduced by inaccurate tidal data. The proposed Tide2Topo was applied and validated in the UK and China over several complicated estuaries and bays with extensive muddy flats or sandy beaches. The results demonstrated that the topography derived from intertidal inundation frequency compared to LiDAR or UAV photogrammetric observations has a root mean square error ranging from 16 to 38 cm. The large errors were found in the tidal channel areas where the inundation frequency calculation was prone to uncertainty. Dense Sentinel-2 time-series observations ensure finer sampling of the tidal cycle, thereby not only eliminating errors caused by spatial interpolation but also maintaining the accuracy of elevation estimations at pixel level. Tide2Topo is a robust, portable and rapid method that is well suited to large-scale intertidal topography reconstruction. Future work could use Sentinel-1 SAR as an alternative data source for Tide2Topo, realizing seasonal or annual monitoring of intertidal geomorphological changes. © 2023 The Author(s)

Keyword:

Errors Estuaries Floods Mean square error Optical radar Pixels Sea level Tides Time series Topography

Community:

  • [ 1 ] [Chen, Chunpeng]State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai; 200241, China
  • [ 2 ] [Chen, Chunpeng]Lancaster Environment Centre, Lancaster University, Lancaster; LA1 4YQ, United Kingdom
  • [ 3 ] [Zhang, Ce]Lancaster Environment Centre, Lancaster University, Lancaster; LA1 4YQ, United Kingdom
  • [ 4 ] [Zhang, Ce]UK Centre for Ecology & Hydrology, Library Avenue, Lancaster; LA1 4AP, United Kingdom
  • [ 5 ] [Tian, Bo]State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai; 200241, China
  • [ 6 ] [Wu, Wenting]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, China
  • [ 7 ] [Zhou, Yunxuan]State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai; 200241, China

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Source :

ISPRS Journal of Photogrammetry and Remote Sensing

ISSN: 0924-2716

Year: 2023

Volume: 200

Page: 55-72

1 0 . 6

JCR@2023

1 0 . 6 0 0

JCR@2023

ESI HC Threshold:26

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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