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

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posted on 24.05.2020, 00:00 by Zhan HuZhan Hu, J. Zhou, C. Wang, H. Wang, Z. He, Y. Peng, P. Zheng, F. Cozzoli, T.J. (Tjeerd) Bouma
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.

Funding

EPSRC, EP/R024537/1

NSFC, 51761135022

NWO, ALWSD.2016.026

Sustainable Deltas

History

Publisher

4TU.Centre for Research Data

Time coverage

2018/2019

Geolocation

National mangrove park in Hailing island, Yangjiang city, Guangdong province, China

Geolocation Longitude

111.967418

Geolocation Latitude

21.649590

Format

media types: application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/x-matlab-data, application/zip, text/plain

Organizations

Department of Geography, Trinity College Dublin; Department of Geography, University of Cambridge; Department of Physical Geography, Utrecht University; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente; Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, and School of Marine Science, Sun Yat-sen University; HKV Consultants; Institute of Environmental and Ecological engineering, Guangdong University of Technology; NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems and Utrecht University; Research Institute for Nature and Forest (INBO); Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, and State Environmental Protection Key Laboratory of Satellite Remote Sensing; School of Marine Engineering and Technology, Sun Yat-Sen University, Guangzhou; Southern Marine Science and Engineering Guangdong Laboratory; TU Delft, Faculty of Civil Engineering and Geosciences; University of Antwerp, Ecosystem Management Research Group; Water Engineering and Management, University of Twente