Retrieval of Remotely Sensed Sediment Grain Size Evolution Characteristics along the Southwest Coast of Laizhou Bay Based on Support Vector Machine Learning

Abstract

Grain size is the basic property of intertidal zone sediment. Grain size acts as an indicator of sedimentary processes and geomorphological evolution under human and nature interactions. The remote sensing technique provides an alternative for sediment grain-size parameter monitoring with the advantages of wide coverage and real-time surveying. This paper attempted to map the distributions of three sediment grain size contents and the mean grain size with multitemporal Landsat images along the southwestern coast of Laizhou Bay, China, from 1989 to 2015. Considering the low correlations between the measured reflectance and grain-size parameters, we used a support vector machine (SVM) to develop a nonlinear calibration model by taking several band indices as input variables. Then, the performance of the back propagation neural network (BPNN) was determined and discussed with that of the SVM. The SVM performed better than the BPNN in calibrating the four grain-size parameters based on a comparison of R2 and the root-mean-square error (RMSE). Moreover, an atmospheric correction algorithm originally proposed for case II water enabled the TM\ETM+ images to be precisely atmospherically corrected in this study. The SVM-mapped spatial-temporal grain-size variation showed a coarsening trend, which agreed with that obtained during in situ measurements in a former study. The changes in Yellow River discharge and precipitation associated with the coarsening trend were further analyzed. The yielded results showed that the coarsening trend and reduction in tidal flat area might be aggravated with overutilization. More reasonable planning would be necessary in this case.

Coastal Sediment Grain Size Estimates on Gravel Beaches Using Satellite Synthetic Aperture Radar (SAR)

“…Additionally, due to the dynamic nature of coastal regions, it may be necessary to carry out frequent surveys, which further increases the associated time and costs. For this reason, in recent years, there has been increased interest in developing the use of remote sensing techniques to monitor coastal environments (e.g., [1,4,5]). Considering that more than 99% of beaches are not monitored, the use of remote sensing could offer significant promise in supporting the monitoring and protection of coastal areas and local communities from the ever increasing threat of sea level rise and changes due to climate change [6].…”

“…Previous studies that have used remote sensing techniques to characterise sediment grain size and their properties in inter-tidal areas largely focused on using data derived from the visible range of the electromagnetic spectrum (e.g., [4,[7][8][9]). For example, Yu et al [4] initially found low correlations between the reflectance from multi-temporal Landsat data and grain size, but incorporated the use of a support vector machine (SVM) to find a coarsening trend in grain size, which agreed with the field measurements. However, a major limitation of optical satellite approaches is the requirement for near cloud-free conditions, which can be problematic for monitoring a large proportion of coastal areas across the world.…”