Data presented in the paper: A Novel Instrument for Bed Dynamics Observation Supports: Machine Learning Applications in Mangrove Biogeomorphic Processes
datasetposted on 24.05.2020 by Zhan Hu, J. Zhou, C. Wang, H. Wang, Z. He, Y. Peng, P. Zheng, F. Cozzoli, T.J. (Tjeerd) Bouma
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.