Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR.

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB.

Background noise reduction for airborne bathymetric full waveforms by creating trend models using Optech CZMIL in the Yellow Sea of China.

Raw full waveforms of green lasers used in airborne LiDAR bathymetry (ALB) are contaminated by background and random noise related to the environment and ALB devices. Traditional thresholding methods have been widely used to reduce background noise in raw full waveforms on the basis of the assumption of constant background noise. However, background noise that is mainly related to background solar radiation and detector dark current changes over time. Thresholding methods perform poorly on the full waveforms with a wide variation range of background noise. A background noise reduction method considering its wide variation is proposed to decrease the background noise by creating trend models. First, each green full waveform is divided into two parts: pulse- and non-pulse-return waveforms. Second, a linear interpolation is conducted on the non-pulse-return waveform to impute the missing noise. Third, a low-pass filter is used to filter the random noise with high frequency in the imputed non-pulse-return waveform and obtain the trend model of background noise of the full waveform. Finally, the derived background noise model is used to decrease the background noise in the pulse-return waveform. The proposed method is applied to decrease the background noise in raw green full waveforms collected by the Optech coastal zone mapping and imaging LiDAR (CZMIL). The mean and standard deviation of residual noise in the CZMIL waveform reduced by the trend model of background noise are -0.03 and 3.5 digitizer counts, respectively. The proposed background noise reduction method is easy to apply and can reduce the background noise to a significantly low level. This method is recommended for preprocessing the raw full waveforms of green lasers collected by Optech CZMIL for ALB.

Exploring the dynamics of Cu transported from the Yangtze River to the East China Sea.

We used a section-segment method to determine the copper (Cu) fluxes from the Yangtze River to the East China Sea during 2012-2016 in this study, including the maximum, minimum and mean annual fluxes of dissolved and suspended Cu. The Cu fluxes exhibited a pronounced inter-annual variability. June was characterized with the highest monthly dissolved and suspended Cu fluxes, while the lowest monthly Cu fluxes was in February. The monthly Cu concentration and Cu fluxes both increased from January to June and decreased from July to December. The monthly Cu fluxes showed a positive correlation with the water flux and monthly mean Cu concentration; however, it had a negative correlation with the ratio of tidal flux to river runoff flux. Both natural (53%) and anthropogenic inputs (32.8%) were the main sources of Cu. The discharges of industrial and domestic sewage wastewater were the main anthropogenic factors affecting the entry of Cu.

Changes in nutrient transport from the Yangtze River to the East China Sea linked to the Three-Gorges Dam and water transfer project.

River-sea transition plays a key role in global geochemical cycles. The Yangtze River Estuary of China was selected as the research area, and the Section-Segmented Method was applied to determine the nutrient discharge from the Yangtze River to the East China Sea. A 3-D numerical model for the estuary was established and validated against the field investigated data. By numerical experiments the dynamics of hydrology and nutrient from 1950 to 2016 were simulated under four varied schemes. The individual and combined impacts on the nutrient flux induced by the Three-Gorges Dam (TGD) and the South-to-North Water Transfer Project (SNWTP) were explored. The following results were observed: (1) During the Pre-TGD period, the Yangtze River delivered the loads of 1.32 Tg/yr and 0.08 Tg/yr for TN and TP, respectively. July and Feb. were characterized by the highest and lowest monthly flux, respectively. (2) TGD played a significant role in regulating the temporal nutrient deliveries. After the closing of TGD, the discharges of TN and TP in the dry season respectively went up to 0.55 Tg and 0.032 Tg, with a mean increase of 28.3%. (3) SNWTP reduced the nutrient transport at a relatively stable level, and the total loads of 40.66 Gg and 2.4 Gg were reduced per year for TN and TP, respectively. (4) The combined impacts of TGD and SNWTP varied with seasons. October was characterized by the greatest cumulative effects. In dry seasons, the reduction caused by SNWTP was leveled by TGD-induced increase, limiting the flux variation linked to project operations.