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Dr. Reda Fekry Abdlekawy KHALIEL :: Theses :

Title Detecting Earth's Surface Change by Integrating DInSAR and GPS Data
Type MSc
Supervisors Abdallah Saad; Ayman Elshehaby; Mervat Ameen
Year 2017
Abstract The main goal of this research is studying the crustal movement via GPS and Synthetic Aperture Radar (SAR) data integration. The followed methodology is called “SISTEM”, an abbreviation of (Simultaneous and Integrated Strain Tensor Estimation from satellite and geodetic deformation Measurements). This study is applied on the area of Aswan geodetic network around Lake Nasser and Aswan High dam due to its dynamic nature and the availability of various data sources to achieve the research goals. The size of the study area is taken as the swath of ASAR data (100 km×100 km). GPS measures the three dimensional position displacements at sparse locations (Receiver dependent observations) with high temporal resolution and low spatial resolution. On the other hand, DInSAR measures only the terrain movement along the radar Line Of Sight (LOS) in one dimension with low temporal resolution depending on the revisit time of the satellite and with high spatial resolution from meter to several meters according to the radar sensor resolution. The previous methods known in literature for studying the crustal movement in Aswan (e.g. Shaker et al., 2010 and M. S. Abdel-Monem et al., 2012) used only GPS data to determine the position displacements at the GPS stations of Aswan GPS network and to compute the strain field yielding from these displacements with the help of seismic data. Here, the GPS and DInSAR data are used simultaneously to map the deformation in the area taking the advantages of both datasets. The GPS (Global Positioning System) observations for eleven permanent stations and ASAR (Advanced Synthetic Aperture Radar) ENVISAT (ENVIronmental SATellite) images were available for the case study over the period from 2006 to 2010. These data were used to produce the displacement maps in the three directions and to also obtain a complete description of the strain and the rotation tensor over all the area in the time epoch of the study. To study all the probabilities from the available datasets, the crustal movement is investigated from these data in six epochs (2006/2008), (2006/2009), (2006/2010), (2008/2009), (2008/2010), and (2009/2010) individually using only GPS data, DInSAR data alone and via GPS and DInSAR integration. The GPS results proved that the study area has a dynamic nature and the average annual rate of displacement in E, N and Up is 4.1±0.8, 3.7±0.6 and 3.8±4.4 mm per year respectively. The resulting displacements from InSAR (Interferometric Synthetic Aperture Radar) data and GPS data are integrated simultaneously and three displacement maps with 30m × 30m spatial resolution are generated for each time epoch along with complete strain tensor information of the study area. Because the two datasets are not temporally consistent, one of them should be scaled to the time of the other. Two cases of scaling are implemented. First, GPS data are scaled, and the second case is performed by scaling DInSAR LOS data. The average annual rates of displacements from integration in the two cases were 8.7±2.6, 3.6±3.9 and 8.3±4.4 mm per year in E, N and Up respectively. The integration results are validated by three permanent GPS stations in each epoch and the validation differences were:  For first case (GPS data scaling): 13.2±8.8, 8.8±5.4 and 15.2±11.1 mm in E, N and Up directions respectively.  For second case (DInSAR data scaling): 12.4±8.7, 7.9±4.6 and 14±10 mm in E, N and Up directions respectively. A two dimensional strain analysis has been made for the area from the resulting movements and the computed values were dilation (Δ), total shear strain (γ) and the direction of maximum principal strain (α). The analysis of these strain components resulted in mean values of -0.03, 0.28 microstrains per year for dilation and total shear respectively in the NE-SW direction by an angle of 33.9° which indicates that:  The nature of the strain in the area is the lowest class according to Fuji’s classification.  The area has the ability to retain its shape as the values of strain from 2006 to 2010 are negative for some periods and positive for the others with opposite directions.
Keywords DInSAR; GPS; Integration; Deformation
University Benha
Country Egypt
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Title Marker-free registration and fusion of multi-modal point clouds in forests for structural tree parameter estimation
Type PhD
Supervisors Wei YAO; Xiao-Li DING
Year 2023
Abstract LiDAR has become an important source of high-density 3D data acquisition. Depending on the data acquisition environment and application, LiDAR devices are mounted on different platforms (e.g., aircraft, vehicles, drones, etc.). As a result, LiDAR is used for a wide range of applications such as urban mapping, object recognition, 3D urban modeling, environmental monitoring, archaeology, architecture, and forest mapping. LiDAR data is acquired in strips or overlapping scans, similar to conventional photogrammetric data acquisition methods, to provide comprehensive coverage of the area of interest. In addition, the different viewing perspectives and penetration capabilities of existing LiDAR systems favor data integration for improved scene representation. For example, in forest scenarios, airborne LiDAR systems can capture tree canopy and high-canopy levels more efficiently than ground-based LiDAR systems. Ground-based LiDAR data, on the other hand, provide a more accurate description of the near-ground levels and tree trunks. Consequently, the integration of airborne and ground-based LiDAR data would provide more information than a single platform. It is expected that this integration would also improve tree representation and reconstruction, leading to more realistic estimates of structural tree attributes. However, the literature review on co-registration of LiDAR data in forests has shown that performance is limited in some cases, such as (a) non-coincidence of tree locations in windy forest areas or with different data collection perspectives (e.g., (a) mismatch of tree locations in windy forest areas or with different data collection perspectives (e.g., ground-based and airborne point clouds), especially when the stems are not present in the point cloud (e.g., airborne LiDAR data); and (b) plantation forests in which tree attributes are indistinguishable at the plot level because all trees are the same species, age, growth stage, DBH, and tree heights. QSM of trees from LiDAR data provides detailed and accurate information on tree parameters such as trunk length, height, and volume. Due to the complementarity of ground-based and airborne LiDAR data, the QSM of trees based on the fusion of ground-based and airborne LiDAR data would improve the accuracy of tree parameter estimation. Existing LiDAR data fusion methods have not used their results for further forest mapping and interpretation. In addition, accurate tree segmentation is of great importance for tree modeling. Unfortunately, the performance of existing tree segmentation methods is limited due to the following factors: (a) the 3D information loss due to point cloud projection; (b) the forest plots with mixed tree species; (c) the high computational requirements of point-based segmentation methods; and (d) the data annotation and conversion of point clouds to raster or multiple views in deep learning-based approaches. Therefore, this work consists of three parts: (a) development of a comprehensive framework for co-registration of LiDAR data in forests; (b) estimation of tree parameters from the fusion of ground-based and airborne LiDAR data using QSM; and (c) segmentation of individual trees using graph neural networks. The co-registration framework is divided into three main phases: (a) canopy clustering and keypoint extraction; (b) feature similarity and matching; and (c) transformation search. Instead of tree locations, the proposed system uses virtual keypoints based on canopy clustering and analysis. This mitigates the limitations of tree localization. Moreover, the transformation is performed by permuting all possible pair combinations of the correspondence set. The approach has great potential in matching UAV LiDAR strip adjustment and co-registering LiDAR data from multiple platforms. In the second part of this work, the co-registration framework is extended to perform a fusion of ground-based and airborne LiDAR data based on (a) the removal of noisy points and (b) the elimination of redundant points. Therefore, the structural tree parameters are determined using the QSM of the fused point cloud. The results of tree parameter estimation show that tree height, crown volume, and tree volume are among the most beneficial fusion parameters. Consequently, the combination of ground-based and airborne LiDAR data would improve the estimation of AGB, since tree height is one of the most essential variables. The last part of this research deals with the segmentation of individual trees since it is of great importance for tree modeling to determine parameters and thus for forest management. The proposed approach is motivated by the graph link prediction problem. A database is created from the point cloud and fed into a graph convolutional network that predicts the presence of a connection between the unconnected edges of the input graph. The approach is unsupervised, so no data labeling or other knowledge of forest parameters is required.
University The Hong Kong Polytechnic University
Country Hong Kong
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