cff-version: 1.2.0
abstract: "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."
authors:
  - family-names: Hu
    given-names: Zhan
  - family-names: Zhou
    given-names: J.
  - family-names: Wang
    given-names: C.
    orcid: "https://orcid.org/0000-0003-3914-0767"
  - family-names: Wang
    given-names: H.
  - family-names: He
    given-names: Z.
  - family-names: Peng
    given-names: Y.
  - family-names: Zheng
    given-names: P.
  - family-names: Cozzoli
    given-names: F.
  - family-names: Bouma
    given-names: T.J. (Tjeerd)
    orcid: "https://orcid.org/0000-0001-7824-7546"
title: "Data presented in the paper: A Novel Instrument for Bed Dynamics Observation Supports: Machine Learning Applications in Mangrove Biogeomorphic Processes"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/uuid:3d971ec0-7a0d-46fa-be02-a2b3d4b9badd
license: CC0
date-released: 2020-05-24