|A novel instrument for bed dynamics observation supports machine learning applications in mangrove biogeomorphic processes|Hu, Z.; Zhou, J.; Wang, C.; Wang, H.; He, Z.; Peng, Y.; Zheng, P.; Cozzoli, F.; Bouma, T.J. (2020). A novel instrument for bed dynamics observation supports machine learning applications in mangrove biogeomorphic processes. Water Resour. Res. 56(7). https://dx.doi.org/10.1029/2020wr027257
In: Water Resources Research: a Journal of the Sciences of Water. American Geophysical Union: Washington etc.. ISSN 0043-1397; e-ISSN 1944-7973, meer
bed dynamics observation; machine learning; mangroves; biogeomorphic processes
|Auteurs|| || Top |
- Zheng, P.
- Bouma, T.J., meer
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 (R2 = 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.