Data presented in the paper: A Novel Instrument for Bed Dynamics Observation Supports: Machine Learning Applications in Mangrove Biogeomorphic Processes

  • Zhan Hu (Creator)
  • J. Zhou (Creator)
  • ChunQing Wang (Creator)
  • H. Wang (Creator)
  • Z. He (Creator)
  • Y. Peng (Creator)
  • P. Zheng (Creator)
  • F Cozzoli (Creator)
  • T.J. (Tjeerd) Bouma (Creator)



Short-term bed level dynamics on the intertidal flats plays a critical role in long-term coastal wetland dynamics. High-frequency observation techniques are crucial for better understanding of intertidal biogeomorphic evolutions. Here, we introduce an innovative instrument for bed dynamics observation, i.e. LSED-sensor (Laster based Surface Elevation Dynamics sensor). LSED-sensors inherit the merits of the previously-introduced optical SED-sensors as it enables continuous long-term monitoring with relatively low cost of labor and acquisition. By adapting Laster-ranging technique, LSED-sensors avoid touching the measuring object (i.e. bed surface) and they do not rely on daylights, as it is for the optical SED-sensors. Furthermore, the new LSED-sensors are equipped with a real-time data transmission function, enabling creating automatic observation networks covering multiple (remote) sites. During a 21-days field survey in a mangrove wetland, good agreement (R2=0.7) has been obtained between the automatic LSED-sensor measurement and an accurate ground-truth measurement method, i.e. Sedimentation Erosion Bars. The obtained LSED-sensor data was subsequently used to develop machine learning predictors, which revealed the main drivers of the accumulative and daily bed level changes. We expect that the LSED-sensors can further support machine learning applications to extract new knowledge on coastal biogeomorphic processes.
Date made available24 May 2020
PublisherTU Delft - 4TU.ResearchData
Date of data production2018 - 2019
Geographical coverageNational mangrove park in Hailing island, Yangjiang city, Guangdong province, China

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