Geodesy and Geodynamics最新文献

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model (MAM error model), we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model. The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation. The experimental results show that based on the weighted least squares criterion, the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation. The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods, which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines. To solve these problems, in this study, we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR, and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region. The results showed that the relative error of tree height extraction using UAV optical images was less than 5%, and the lowest relative error was 0.11%. The determination coefficient R² between the optical image tree height extraction results and the measured tree height was 0.97, thus indicating a high correlation for both. In the field of tree barrier monitoring, the determination coefficient R² of tree height extracted using airborne LiDAR point cloud, and canopy height model (CHM) and of the measured tree height were 0.947 and 0.931, respectively. The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91% and 0.2%, respectively, with an extraction accuracy of over 95%. The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately, which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.

The Global Navigation Satellite System (GNSS) positioning method has been significantly developed in geodetic surveying. However, the height obtained through GNSS observations is given in a geodetic height system that needs to be converted to orthometric height for engineering applications. Information on geoid height, which can be calculated using the global geopotential mode, is required to convert such GNSS observations into orthometric height. However, its accuracy is still insufficient for most engineering purposes. Therefore, a reliable geoid model is essential, especially in areas growing fast, e.g., the central part of Java, Indonesia. In this study, we modeled the local geoid model in the central part of Java, Indonesia, using terrestrial-based gravity observations. The Stokes' formula with the second Helmert's condensation method under the Remove-Compute-Restore approach was implemented to model the geoid. The comparison between our best-performing geoid model and GNSS/leveling observations showed that the standard deviation of the geoid height differences was estimated to be 4.4 cm. This geoid result outperformed the commonly adopted global model of EGM2008 with the estimated standard deviation of geoid height differences of 10.7 cm.

This research presents the variation of the gravity field and associated gravity field components over the continental area of Nigeria to provide data for geoscience research, geodetic and engineering works, aerodynamic studies and deep crustal inferences. Accurate positions and elevations were observed at 58 of the 59 base stations of the Primary Gravity Network of Nigeria (PGNN), whose absolute gravity values had been accurately determined. The absolute gravity values were plotted against their respective positions to reveal the distribution pattern and strength of the gravity field within the study area. Theoretical gravity values at each base station were generated using the Somigliana's equation. The free-air gravity and free-air anomaly gravity values were generated with respect to the World Geodetic System 1984 (WGS84) ellipsoid using GPS-derived elevation data. Then, the perturbing potential, free-air gravity with respect to the geoid, and the indirect effects were evaluated. The average of the indirect effects was used to adjust the WGS84 gravity formula to produce a gravity formula that better approximates the geoid across the continental area of Nigeria, compatible with the heights measured relative to the geoid, which can serve as a reference for establishing a vertical height control. The Bouguer gravity and Bouguer gravity anomalies across Nigeria revealed a “trans-southern gravity high strip” interpreted to be associated with mantle upwelling. Two new major mega-lineaments related to mantle upwelling were mapped. A batholith province trending NW–SE was delineated, occurring from north central Nigeria to the north western region and containing closures of “Bouguer gravity lows” interpreted as batholiths. A separate closure of “Bouguer gravity low” was detected at Azare, north eastern Nigeria, which may be due to the presence of intrusive granitic body. It is recommended that the mantle structure beneath “the trans-southern gravity high strip”, “delineated batholith province” and “isolated gravity closures” around the northeast of Nigeria should be studied from seismic shear wave splitting analysis for better understanding of the deep lithospheric structures and moho relief.

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography. Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions. The Synthetic Aperture Radar (SAR) altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution. In this study, we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry, where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated. Furthermore, we study the effects on bathymetry modeling by using different scale factor calculation approaches, where a partition-wise scheme is implemented. The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SAR-based altimetric gravity data improves local coastal bathymetry modeling, compared with the model calculated without SAR altimetry data by a magnitude of $3.55$ m within 10 km of offshore areas. Moreover, by using the partition-wise scheme for scale factor calculation, the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method. These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios I and II, respectively). The results showed that the position estimation in Scenario II was better than in Scenario I, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.

The calibration of the sea surface height (SSH) measured by satellite altimeters is essential to understand altimeter biases. Many factors affects the construction and maintenance of a permanent calibration site. In order to calibrate Chinese satellite altimetry missions, the feasibility of maintaining a calibration site based on the Qianliyan islet in Yellow Sea of China is taken into account. The related calibration facilities, such as the permanent tide gauge, GNSS reference station and meteorological station, were already operated by the Ministry of Natural Resources of China. The data could be fully used for satellite altimeter calibration with small fiscal expenditure. In addition, the location and marine environments of Qianliyan were discussed. Finally, we used the Jason-3 mission to check the possibility of calibration works. The result indicates that the brightness temperatures of three channels measured by microwave radiometer (MWR) and the derived wet tropospheric correction varies smoothly, which means the land contamination to MWR could be ignored. The high frequency waveforms at the Qianliyan site present no obvious difference from the normal waveforms received by satellite radar altimeter over the open ocean. In conclusion, the Qianliyan islet will not influence satellite altimetry observation. Following these analyses, a possible layout and mechanism of the Qianliyan calibration site are proposed.

The Yangtze River Basin (YRB) is an important region for China's economic development. However, it has a complex terrain layout, most of which is affected by monsoon weather, and the geographical and temporal distribution of water resources is severely unbalanced. Therefore, the detailed analysis of spatio-temporal water mass changes is helpful to the development and rational utilization of water resources in the YRB. In this study, the variation of terrestrial water storage (TWS) is monitored by Gravity Recovery and Climate Experiment (GRACE) satellite gravity. We find that the University of Texas Center for Space Research (CSR) solution shows a notable difference with the Jet Propulsion Laboratory (JPL) in space, but the general trend is consistent in time series. Then the GRACE inferred water mass variation reveals that the YRB has experienced several drought and flood events over the past two decades. Global Land Data Assimilation System (GLDAS) results are similar to GRACE. Furthermore, the overall precipitation trend tends to be stable in space, but it is greatly influenced by the strong El Niño-Southern Oscillation (ENSO), which is the response to global climate change. The upper YRB is less affected by ENSO and shows a more stable water storage signal with respect to the lower YRB.

At present, one of the methods used to determine the height of points on the Earth's surface is Global Navigation Satellite System (GNSS) leveling. It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available. This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08, EIGEN-6C4, GECO, and XGM2019e_2159. This methodology was tested in different areas of the research field, covering various relief forms. The dependence of the change in corrected height accuracy on the input data was analyzed, and the correction was also conducted for model heights in three tidal systems: “tide free”, “mean tide”, and “zero tide”. The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of $1°\times 1°,2°\times 2°,and3°\times 3°$. The EGM08 model presents an almost identical result. The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the $1°\times 1°$ area. The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor, which has fuzziness in terms of numerical fluctuation.

This research investigates the capability of artificial neural networks to predict vertical total electron content (VTEC) over central Anatolia in Turkey. The VTEC dataset was derived from the 19 permanent Global Positioning System (GPS) stations belonging to the Turkish National Permanent GPS Network-Active (TUSAGA-Aktif) and International Global Navigation Satellite System Service (IGS) networks. The study area is located at 32.6°E-37.5°E and 36.0°N-42.0°N. Considering the factors inducing VTEC variations in the ionosphere, an artificial neural network (NN) with seven input neurons in a multi-layer perceptron model is proposed. The KURU and ANMU GPS stations from the TUSAGA-Aktif network are selected to implement the proposed neural network model. Based on the root mean square error (RMSE) results from 50 simulation tests, the hidden layer in the NN model is designed with 41 neurons since the lowest RMSE is achieved in this attempt. According to the correlation coefficients, absolute and relative errors, the NN VTEC provides better predictions for hourly and quarterly GPS VTEC. In addition, this paper demonstrates that the NN VTEC model shows better performance than the global IRI2016 model. Regarding the spatial contribution of the GPS network to TEC prediction, the KURU station performs better than ANMU station in fitting with the proposed NN model in the station-based comparison.