06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access A MATLAB toolbox for the Scintrex CG-5 gravimeter at GIS(2017) Gu, SiyunThis thesis is about a MATLAB toolbox for the Scintrex CG-5 gravimeter. The aim of this toolbox is to offer a basic data process for gravity measurement, which is compatible for most applications in geodesy. In particular, the toolbox covers: 1. data selection, 2. adjustment, 3. gravity gradient computation, 4. gravity visualization, 5. calibration factor estimation. A graphical user interface enables users without deeper programming knowledge to operate this toolbox and obtain the results like adjusted values or figures.Item Open Access Exploring the performances of SAR altimetry and improvements offered by fully focused SAR(2021) Wu, YuweiWith the development of the altimetry techniques, the measurement principle has been changed from the conventional pulse-limited principle to the delay-Doppler principle since CryoSat-2. The delay-Doppler altimetry presents scientists with the chance to develop new processing schemes and improve products that maximize the benefits of the measurements. Nevertheless, one of the challenges for delay-Doppler Altimetry lies in the complexity of the post-processing, especially the Delay-Doppler processing. The focus of this thesis is to better understand delay-Doppler and fully focused SAR altimetry. This thesis compares the retrieved waveforms and resultant water level time series with different altimetry principles, processing options and retracking methods. By using platform SARvatore for delay-Doppler altimetry and SMAP for fully focused SAR altimetry, different processing options (data posting rate, Hamming window and zero padding) and different retrackers (SAMOSA family for SARvatore, PTR for SMAP) can be applied and compared. Our results reveal that the waveforms generated by different configurations have different peaks for SARvatore. For SMAP, with or without zero padding or Hamming window had very little impact, with more differences mainly coming from the different retracking methods. Our results also show that fully focused SAR does not bring a significant improvement when applied to Sentinel-3 data. In summary, different configurations and retracking methods can significantly affect the shape of waveforms and their derived ranges. According to this thesis's experiments, the configuration with 80 Hz data posting rate, Hamming window, zero padding, extended receiving window and retracker SAMOSA++ offers the best performance.Item Open Access Monitoring inland surface water level from Sentinel-3 data(2019) Wang, BoInland surface water bodies (e.g. lakes and rivers) are very important to the nature and human society. To monitor the water level of inland water bodies, gauge stations were built since 19th century, but the amount of the stations is declining since the 1970s because of lack of maintenance. An accurate and continuous monitoring of lakes and rivers is available because of the satellite altimetry missions launched, e.g. Jason-2 and ENVISAT. These satellites can provide water level with proper spatial and temporal resolution. In the recent past, researchers have used different satellite mission observations to generate time series of inland water level in order for monitoring the water bodies. In this thesis, we use the new designed satellite mission Sentinel-3, which carries different sensors, to generate the water level time series of Dongting Lake and Poyang Lake in China. Initially, we combine the altimetry measurements with satellite images to determine virtual station. We choose Sentinel-3 Ku band data and on-board Ocean tracker to generate the water level time series. Afterwards, we apply different waveform retracking algorithms (5β-parameter and OCOG) to compare the results with on-board tracker. We also validate the results with the other database, then investigate the waveforms of each sampling date. The comparisons show the three tracking methods we used are capable to Quasi-Specular waveforms, and OCOG shows the best result to flat patch waveforms. Furthermore, some suggestions for improvements are also discussed in the last chapter.Item Open Access The GRACE event calendar(2012) Vishwakarma, Bramha DuttGRACE mission is a joint venture of NASA and GFZ. This mission was launched to provide with unprecedented accuracy, estimates of the global high resolution models of the Earth’s gravity field. The study of time-variability of Earth’s gravity field is very helpful in climate sciences and earth’s sciences studies. People have done a lot of work to demonstrate the effect of many natural phenomenon on gravity. Gravity estimates from GRACE are used for estimating mass redistribution at continental scale. So, we can observe hydrology, seismology and glaciology potential areas where GRACE can be useful. This research work focuses on identifying the hydrological events such as floods and drought, seismic events such as earthquakes and volcanic activity and also the glacier melting in the GRACE time-series. The work includes the development of strategy for the analysis of these events keeping in mind their behaviour and GRACE limitations of spatial resolution and sensitivity. Further in this work we would produce a event calendar for such events stating whether gravity changes caused by such events are visible to GRACE. Calendars are generated for hydrological events, floods and droughts separately and also for earthquake events. For rest of the phenomenon we have not generated calendars since these events are very few in numbers. This work is a qualitative analysis, so we could observe whether GRACE signal is able to observe these events or not. Hydrological events are observed by searching outliers in the grace observed time-series. The large floods such as 2009 Amazon floods can be seen when we take whole catchment, but the small floods affecting smaller region such as Sao Paulo flood is not visible in catchment time-series, so we have to go for selected area time-series generation. The factors such as time period for floods and droughts are very important factors when we want to observe them by GRACE. Earthquakes visibility depends on range rate amplitude, and also the quality of ΔC20, we have discussed these aspects while analysing earthquakes occurred in last decade from GRACE. We have given the possible explanation for the events not visible, and those visible have helped in the development of a methodology for analysis of a particular event. The volcanic activity in Caldera and Bolivia are pushing earth upward so we can expect some signal, but the spatial extent of these areas is small with caldera area greater than that of Bolivia, only caldera showed a trend. We also did trend analysis for 2 Asian glaciers and a part of Greenland for observing the melting of these ice masses. The work finally produces a series of events which we were able to observe by GRACE and we also get the methodology suitable for analysis of an event.Item Open Access Analysis of water volume change of the lakes and reservoirs in the Mississippi River basin using Landsat imagery and satellite altimetry(2021) Wang, LingkeIn recent years, the demand for freshwater has been steadily increasing owing to population growth and economic expansion. Surface waters such as lakes and reservoirs function as a dominant factor in mankind's freshwater provision. Analysis of changes in their water storage is consequently vital for understanding of the global water cycle and water resources. However, the water volume changes in lakes or reservoirs cannot be measured directly from space, but can be inferred from lake areas and lake water levels. Lake area can be measured globally from space but lake water level is not easy to be obtained globally. Because the number of in situ stations is few, and in situ data are only accessible for some lakes with few measurement epochs, despite in situ stations can measure lake water level and provide high accuracy observations. Although the altimetry technique can generate the time series of the water level for the majority of lakes, they are not global coverage due to the distance between satellite tracks and the gap between different missions. Therefore, in situ data and satellite altimetry measurements of water levels of lakes and reservoirs are not always available. For example, there are only 22 lakes or reservoirs in this study covered by satellite altimetry or in situ stations out of 90 research cases in Mississippi River Basin. Then, in case of unavailable in situ data or altimetry measurements, this research proposes an alternative method to estimate the water level through Digital Elevation Model (DEM). Because satellite imagery offers global coverage and DEM is the global digital representation of the land surface elevation with respect to any reference datum, this study allows for the evaluation of global water volume changes by acquiring lake area data from space and lake height data from DEM. Therefore, the objective of this study is that changes in water volume in lakes or reservoirs can be successfully monitored even when in situ data and satellite altimetry measurements are not available for lakes or reservoirs. Hereby, we investigate 90 lakes and reservoirs in the Mississippi River Basin and develop an alternative remote sensing technique to monitor the water volume changes by combining the improved water mask with DEM. Meanwhile, we propose practical methods to detect the shoreline pixels of the water body from improved water mask. Given the assumption that all pixels in the shoreline should have the same height, four water level estimation models are developed, including water level estimation model based on statistical analysis, frequency maps, change pixels and pixel pair analysis. To this end, the study estimates the time series of lake height from water level estimation model and obtains the time series of lake surface area from HydroSat. Subsequently, this study builds the unique function between the lake water level and the lake surface area and then develops the function between the lake water volume change and the lake surface area. Finally, this study analyses the water volume changes of lakes and reservoirs in the Mississippi River Basin using this alternative remote sensing method. Four water level estimation models are proposed and evaluated. They are respectively based on statistical analysis, frequency maps, change pixels and pixel pair analysis. As a result of their actions, the first model based on statistical analysis, with an average correlation of 0.62 and an average RMSE of 0.91 meters, functions in the majority of situations and demonstrates excessive outlier removal in some cases. The second model based on frequency maps is more general than the first, with an average correlation of 0.66 and an average RMSE of 1.11 meters. The average correlation for the third model based on change pixels is 0.71, and the average RMSE is 0.99 meters. The resulting model based on pixel pair analysis obtains a mean correlation of 0.67 and a mean RMSE of 1.00 meters. Finally, these models behave differently in different seasons, so they exhibit distinct monthly behaviour. To conclude, the above validation results show that this alternative method can be used in different lakes and reservoirs in case of absence of water level observation data, and achieve to monitor the water volume changes during a long period.Item Open Access Use of the autocorrelation function in EOF analysis of GRACE data(2014) Goswami, SujataThe GRACE mission launched with a pair of satellites, orbiting in a near-polar orbit, at a distance of about 220 km from eachother, to map the time-variable earth’s gravity field, since 2002. The study of time-variable gravity field has been proved to be very helpful in climate science studies. The gravity variations in the GRACE observations are mass variations inside the earth, exchanges between glaciers and oceans, changes due to surface and deep currents in the ocean. Monthly maps are used to study these gravity variations. The raw gravity field data obtained, is too much noisy and the main source of this noise is north-south stripes which is due to polar orbit of satellites. Due to these noisy stripes, filtering of GRACE time-variable gravity field is required. In this thesis, Empirical Orthogonal Function (EOF) analysis is used to filter and analyse the GRACE gravity data. The method is used to extract the dominant variations by reducing the dimensionality of a dataset, among a group of time-series data. This dimensionality reduction and extraction of dominant variance is achieved by linear coordinate transformation to a new set of basis vectors via singular value decomposition. The decomposition gives spatial and temporal components along with variance values. Temporal components are analysed by the dominant variance rule, Kolmogorov-Smirnov rule and autocorrelation function respectively, in order to recover signal from noise. Dataset recovered by dominant variance rule, reduces the striping but it may remove the signals as well, especially the signals from ocean. The level of signal reduction is less in Kolmogorov-Smirnov rule, whereas autocorrelation performs well in comparison to both. Geophysical signal reduction is very less in using the autocorrelation function for filtered data analysis and the results are even much better if both Kolmogorov-Smirnov rule and autocorrelation results are combined together. Thus, autocorrelation can be a better approach to select the signal components from the noisy ones. EOF anlaysis is explained with its theoretical background and then its application on the GRACE data. The focus is on the use of autocorrelation function and its performance in the filtered data analysis. Here, the entire procedure is applied in spatial domain on the processed equivalent water height values. In future, autocorrelation function can be used for data analysis in spectral domain for more better results. Its performance can be evaluated on regional analysis basis.Item Open Access Assessment of altimetric river water level time series densification methods(2018) Xia, ZhugeNowadays, collecting and analysing water level time series recorded by gauging stations or by satellite altimetry is crucial for the geodetic and environmental purposes, such as modelling ocean circulation and monitoring climate change. Since the 1970s, a large number of gauging stations has been removed. This has made altimetry increasing more important. However, data collected by individual altimetric satellites are limited, i.e., the temporal resolution is limited to the repeat cycle of satellites, and the spatial resolution is constrained to the distribution of virtual stations. In order to overcome these limitations, methods have been developed to combine all available altimetric satellite missions along a river to construct a new densified time series. This is referred to as densification. To our knowledge, there are only two proven densification methods applied to the river for now. The first is a hydraulic statistic densification method developed by Tourian et al. (2016). The other is the kriging densification method published by Boergens et al. (2017). However, each of them is realized under different circumstances, which makes them incomparable with each other. In this work, we implement the two densification methods and apply them under similar conditions. The various densified water level time series are compared and analysed both visually and statistically. Results reveal different characteristics of the two densification methods.Item Open Access The optimal regularization and its application in extreme learning machine for regression analysis and multi-class classification(2018) Qian, KunExtreme Learning Machine (ELM) proposed by Huang et al. (2006) is a newly developed single layer feed-forward neural network (SLFN). It is attractive for its high training efficiency and satisfactory performance, especially when dealing with a large amount of data, which are often in high-dimensional space. However, current ELM cannot solve the over-fitting problem among other several problems. While minimizing residuals of output errors for the training data, it tends to generate an over-fitting model, whose generalization ability is relatively weak. Even if the model fits the training data perfectly, it performs unsatisfactory for the testing data. In training process, we aim to minimize residuals of output errors of training data. It tends to generate an over-fitting model, which has poor generalization ability. The model maybe fit the training data perfectly, but performs badly in testing data. Furthermore, in order to improve accuracy, the traditional way is increasing the number of hidden-layer neurons, but excessive hidden-layer neurons result in an ill-posed normal matrix and a model which is over sensitive to the change of the training data. In such case, the performance of ELM is significantly affected by the outliers in the training data. In order to overcome these problems, we apply the regularization to the original ELM. In this study, the A-optimal design regularization is performed to improve the generalization ability and stability of ELM. The performance of ELM with the A-optimal design regularization will be evaluated through two main applications, respectively, regression analysis and satellite image multi-class classification.Item Open Access GPS time-variable seasonal signals modeling(2015) Chen, QiangSeasonal signals (annual plus semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, i.e. ice and hydrology. Conventionally these signals (annual and semi-annual) are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e. they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. In this study, a non-parametric approach, singular spectrum analysis (SSA) is introduced. It uses time domain data to extract information from short and noisy time series without prior knowledge of the dynamics affecting the time series. A prominent benefit is that obtained trends are not necessarily linear and extracted oscillations can be amplitude and phase modulated. In this work, the capability of SSA for analyzing time-variable seasonal signals from GPS time series is investigated. We also compare SSA-based results to two model-based results, i.e. least-squares analysis and Kalman filtering. Our results show that singular spectrum analysis could be a viable and complementary tool for exploring modulated oscillations from GPS time series. Based on the SSA-derived seasonal signals, we look into the effects of the input noise variances in the framework of Kalman filtering. Two Kalman filtering based approaches with different process noise models are compared over 79 GPS sites. We find that the basic Kalman filtering technique with the input noise model suggested by Davis et al. (2012) turns out to be optimal.Item Open Access Height systems calculations at Swabian Alb test area(2014) Wanthong, PrapasThis project aims at the consolidation of the data from integrated fieldwork in Swabian Alb test area since 1996 to 2013 as well as the height systems computations. Reliable data were checked by height differences and gravity values, after that they were grouped into 5 closed loops with 56 out of 121 observed points. Potential differences were computed from height differences which acquired from spirit levelling and gravity value, then least square adjustment was adopted. Observation equation (A-matrix) and condition equation (B-matrix) were applied in the adjustment, a weight matrix was also assigned in the adjustment. Geopotential differences were computed based on the Helmert orthometric height at point 580, then geopotenial numbers of the other points were computed by adding the geopotential number with the adjusted potential differences, then height systems could be determined as well as height corrections. In the closed loop adjustment especially adjustment of several loops with many data, condition equation adjustment is preferred because of the smaller size of design matrix compared to the observation equations and the advantage of condition equations over observation equations is that loop misclosures can be determined by condition equation. The results from both equations are the same. The difference of the geopotential numbers between unweighted and weighted adjustment is up to 0.0129 m2/s2 and the difference of height systems between unweighted and weighted adjustment is up to 0.0013 meter or 1.3 millimeter, so the height system computations were not significantly affected by the assigned weight. The difference of height corrections between unweighted and weighted adjustment is up to 10 -8 m so the assigned weights did not affect height correction results. Normal corrections give the smallest values while dynamic corrections give the largest values because the test area is located at latitude 48.485° instead of latitude 45° so the correction values are quite large.