Characterization of Deltaic Channel Morphodynamics From Imagery Time Series Using the Channelized Response Variance

Abstract

River deltas are complex, dynamic systems whose channel networks evolve in response to internal and external forcings. To capture these changes, methods to extract and analyze deltaic morphodynamics automatically using available remotely sensed imagery and experimental observations are needed. Here, we apply a promising method for the automatic extraction of channel presence called RivaMap, on both synthetic and experimental data sets, to investigate the changes experienced by the system in response to five changes in forcings. RivaMap is an automated method to extract nonbinarized channel locations from imagery based on a singularity index that combines the multiscale first and second derivatives of the image intensity to favor the identification of curvilinear features and suppress edges. We quantify how the channelization varies by computing the channelized response variance (CRV), which we define as the variance of each pixel's singularity index response through time. We find that increasing magnitudes of sediment inflow (Qs) and water inflow (Qw) result in corresponding increases in the maximum CRV. We find that increasing the ratio of Qs to Qw results in increased number of channelized areas. We see that adding cohesion to the exposed sediment surface of the experimental delta results in decreased magnitude and decreased number of channelized areas in the CRV. Finally, by observing changes to the CRV over time, we are able to quantify the timescale of internal channel reorganization events as the experimental delta evolves under constant forcings.