A Novel Instrument for Bed Dynamics Observation Supports Machine Learning Applications in Mangrove Biogeomorphic Processes. Issue 7 (17th July 2020)

• Record Type: Journal Article
• Authors: Hu, Z.; Zhou, J.; Wang, C.; Wang, H.; He, Z.; Peng, Y.; Zheng, P.; Cozzoli, F.; Bouma, T. J.
• Is Part Of: Water resources research. Volume 56:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-17
• Subjects: bed dynamics observation -- machine learning -- mangroves -- biogeomorphic processes; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR027257
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Short‐term bed level changes play a critical role in long‐term coastal wetland dynamics. High‐frequency observation techniques are crucial for better understanding of intertidal biogeomorphic evolution. Here, we introduce an innovative instrument for bed level dynamics observation, that is, LSED‐sensor (Laser based Surface Elevation Dynamics sensor). The LSED‐sensors inherit the merits of the previously introduced optical SED sensors as it enables continuous high‐frequency monitoring with relatively low cost of labor and acquisition. As an iteration of the optical SED‐sensors, the LSED‐sensors avoid touching the measuring object (i.e., bed surface), and they do not rely on daylight by adapting laser‐ranging technique. Furthermore, the new LSED‐sensors are equipped with a real‐time data transmission function, enabling automatic observation networks covering multiple (remote) sites. During a 22‐day field survey in a mangrove wetland, good agreement ( R 2 = 0.7) has been obtained between the automatic LSED‐sensor measurement and an accurate ground‐truth measurement method, that us, Sedimentation Erosion Bars. The obtained LSED‐sensor data were subsequently used to develop machine learning predictors, which revealed the effect of vegetation is a main driver in 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. Key Points: A novel automatic instrument is developed for high‐frequency intertidal bed level dynamics observation Data transmission function enables building automatic bed level observation networks to support coastal research and management Machine learning applications reveal that the presence of vegetation plays a key role in mangrove biogeomorphic processes