Spatially Heterogeneous Land Surface Deformation Data Fusion Method Based on an Enhanced Spatio-Temporal Random Effect Model

Shi, Qiang; Dai, Wujiao; Santerre, Rock; Li, Zhiwei; Liu, Ning

doi:10.3390/rs11091084

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…In STRE, the spatial model constructed by the spatial basis reduced the order of the spatio-temporal covariance matrix. Shi 8 considered the complex spatial heterogeneity existing in the monitoring area based on Liu’s research, thus adding an additional spatial basis as spatial heterogeneity constraints. Ji combined the strain model with the Kalman filter (SM-Kalman).…”

Section: Introductionmentioning

confidence: 99%

Settlement monitoring data fusion approach for high-speed railways based on GNSS and InSAR

Qiu,

Wang,

Zhang

et al. 2023

J. Appl. Rem. Sens.

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.The high-precision and high spatio-temporal resolution settlement time series data generated by integrating Global Navigation Satellite System (GNSS) and Interferometric Synthetic-Aperture Radar (InSAR) data are of great value for the safe operation of high-speed railways. The GNSS monitoring stations and InSAR monitoring area present zonal distribution along the high-speed railways, which causes the spatial matrix of the GNSS and InSAR data fusion model to be an ill-conditioned matrix. The inverse of the ill-conditioned space matrix is unstable, and it is difficult to estimate the optimal parameter value of the fusion model, which leads to low accuracy and large fluctuations in the fusion of GNSS and InSAR data. We propose a spatio-temporal filter fusion (STFF) method to solve the influence of the ill-conditioned space matrix on the fusion of GNSS and InSAR data, which realizes the high-precision fusion of GNSS and InSAR data along the high-speed railway. First, we propose the kriging model of the adaptive spectral correction method to construct the spatial model of GNSS and InSAR data, avoiding the generation of the ill-conditioned space matrix. Second, iterative almost unbiased estimation is used to obtain the weights of GNSS and InSAR data, avoiding the occurrence of negative variance components. The experimental data are fused using STFF, the spatio-temporal Kalman filter (STKF) method, and the spatio-temporal random effect (STRE) fusion method, respectively. The research results show that the comprehensive performance of STFF is significantly better than that of STKF and STRE. In the spatial domain, the root mean square error (RMSE) reduced by 47% and 46% compared with STKF and STRE, respectively, and the structural similarity index increased by 22% and 16% compared with STKF and STRE, respectively. In the time domain, the RMSE reduced by 29% and 34% compared with STKF and STRE, respectively.

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Section: Introductionmentioning

confidence: 99%

Settlement monitoring data fusion approach for high-speed railways based on GNSS and InSAR

Qiu,

Wang,

Zhang

et al. 2023

J. Appl. Rem. Sens.

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“…Liu et al [6] applied the spatiotemporal random effects (STRE) model to the fusion of GNSS and InSAR deformation data, but they did not consider the spatial heterogeneity of the deformation data, resulting in regional spatial clustering errors. Shi et al [7] added spatial heterogeneity constraints to the STRE model, improving the accuracy of results of data fusion, but relied on external prior information to obtain the extent of spatially heterogeneous regions. Yan et al [8] established a method to automatically determine the optimal location of the spatial bases, which improved the reliability and applicability of the STRE model, but was less effective for deformation data fusion with obvious spatial heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

et al. 2022

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The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

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“…Earth system model data (ESMD), which comprehensively characterize the spatiotemporal changes of the Earth system with multiple variables, are presented as multidimensional arrays of floating-point numbers (Kuhn et al, 2016;Simmons et al, 2016). With the rapid development of Earth system models in finer computational grids and growing ensembles of multi-scenario simulation experiments, ESMD have shown an exponential increase in data volume (Nielsen et al, 2017;Sudmanns et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)

Zhang

et al. 2021

Geosci. Model Dev.

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Abstract. Lossy compression has been applied to the data compression of large-scale Earth system model data (ESMD) due to its advantages of a high compression ratio. However, few lossy compression methods consider both global and local multidimensional coupling correlations, which could lead to information loss in data approximation of lossy compression. Here, an adaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on the foundation of a stream compression method for geospatial data called blocked hierarchical geospatial field data representation (Blocked-HGFDR). In addition, the original Blocked-HGFDR method is also improved from the following perspectives. Firstly, the original data are divided into a series of data blocks of a more balanced size to reduce the effect of the dimensional unbalance of ESMD. Following this, based on the mathematical relationship between the compression parameter and compression error in Blocked-HGFDR, the control mechanism is developed to determine the optimal compression parameter for the given compression error. By assigning each data block an independent compression parameter, Adaptive-HGFDR can capture the local variation of multidimensional coupling correlations to improve the approximation accuracy. Experiments are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions compared with ZFP and Blocked-HGFDR. For the compression results among 22 climate variables, Adaptive-HGFDR can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity and fast changing rate. This study provides a new potential method for the lossy compression of the large-scale Earth system model data.

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Spatially Heterogeneous Land Surface Deformation Data Fusion Method Based on an Enhanced Spatio-Temporal Random Effect Model

Cited by 9 publications

References 23 publications

Settlement monitoring data fusion approach for high-speed railways based on GNSS and InSAR

Settlement monitoring data fusion approach for high-speed railways based on GNSS and InSAR

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)

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