Inversion of intertidal zone topography based on optimized random forest regression characteristic parameters

It is a fundamental task to monitor the topography and understand the changes of intertidal zone for rational utilization and sustainable development. A new method is proposed for identifying the terrain of the intertidal zone, using ICESat-2 data to replace a large amount of on-site observation data, thereby reducing costs and improving efficiency. Based on pre-experiments and correlation analysis, time phase index, water index, water transparency index and suspended sediment concentration index are added as features for the random forest (RF). Compared with using only the original band as the model input, the RMSE is reduced by 0.08 m. The results show that the inverted terrain has an RMSE of 0.45 m compared with handheld RTK data, and the RMSE at the mudflat from UAV data is 0.20 m. Based on the analysis of terrain changes over the four-year period, the trend towards sedimentation closer to land becomes more pronounced.