An improved positioning model of deep-seafloor datum point at large incidence angle

The inhomogeneous sound speed in seawater causes refraction of sound waves,and the elimination of the refraction effect is essential to the accuracy of underwater acoustic positioning.The raytracing method is an indispensable tool for effectively handling problems.However,this method has a conflict between localization accuracy and computational quantity.The equivalent sound speed profile(ESSP)method uses a simple sound speed profile(SSP)instead of the actual complex SSP,which can improve positioning precision but with residual error.The residual error is especially non-negligible in deep water and at large beam incidence angles.By analyzing the residual error of the ESSP method through a simulation,an empirical formula of error is presented.The data collected in the sailing circle mode(large incidence angle)of the South China Sea are used for verification.The experiments show that compared to the ESSP method,the improved algorithm has higher positioning precision and is more efficient than the ray-tracing method.

Automatic segmentation of gas plumes from multibeam water column images using a U-shape network

Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold seeps.However,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective segmentation.Therefore,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes.Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods.The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference.The segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.

Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China

Accurate monitoring of forest aboveground biomass(AGB)is vital for sustainable forest management.Generally,the AGB is estimated by combining satellite images andfield measurements.Nevertheless,field plots are inaccessible in complex forest areas.Due to the limitations related to either optical or SAR data alone,combining the two data types is indispensable.Here,we explored the LiDAR-derived biomass as training/testing samples for the combination of yearly time-series of Sentinel-1 and Sentinel-2 images to estimate AGB in north-eastern Conghua,Guangdong province,China.The AGB reference map derived from airborne LiDAR data andfield plots could provide more samples for satellite-based AGB estimation with a limited amount of sampling plots.We designed four groups of experiments based on diverse Sentinel-1 and Sentinel-2 variables,including backscatter,backscatter indices,texture features,spectral bands,vegetation indices,and biophysical features.Four different prediction methods(support vector regression,multi-layer perceptron neural network,K-nearest neighbour,and random forest(RF))were separately used to estimate AGB.Results showed that the RF model achieved the best accuracy for AGB mapping.All Sentinel-1 and Sentinel-2 experiment(R^(2):0.72,RMSEr:17.65%)performed better than the monthly complementary experiment(R^(2):0.66,RMSEr:19.01%).Additionally,the arid period images were observed to be sensitive for estimating AGB.The most contributing Sentinels predictors were determined via a sequential forward selection.Consequently,the proposed methodology has great potential for low-cost,large-scale,and high-precision AGB estimation.

Automatic calibration for wobble errors in shallow water multibeam bathymetries

The wobble errors caused by the imperfect integration of motion sensors and transducers in multibeam echo-sounder systems(MBES)manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed micro-topography under a dynamic marine environment.There are many types of wobble errors with certain coupling among them.However,those current calibration methods ignore the coupling and are mainly manual adjustments.Therefore,we proposed an automatic calibration method with the coupling.First,given the independence of the transmitter and the receiver,the traditional georeferenced model is modified to improve the accuracy of footprint reduction.Secondly,based on the improved georeferenced model,the calibration model associated with motion scale,time delay,yaw misalignment,lever arm errors,and soundings is constructed.Finally,the genetic algorithm(GA)is used to search dynamically for the optimal estimation of the corresponding error parameters to realize the automatic calibration of wobble errors.The simulated data show that the accuracy of the calibrated data can be controlled within 0.2%of the water depth.The measured data show that after calibration,the maximum standard deviation of the depth is reduced by about 5.9%,and the mean standard deviation of the depth is reduced by about 11.2%.The proposed method has significance in the precise calibration of dynamic errors in shallow water multibeam bathymetrie s.