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Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is a widely used technique for local ground deformation estimation due to its millimetric accuracy and full-resolution results. However, with the enhancement of data acquisition capability of the SAR sensors, larger coverage and higher temporal-spatial resolution of SAR images can cause explosive increase and uneven distribution of PS points. PS network constructed between PSs with different qualities may cause spatial-error propagation. Large PS points list with large amount of data also have high requirements on computing performance and storage space. Those problems can bring big challenge for PS-InSAR technique in processing capacity, deformation monitoring accuracy and algorithm efficiency. In order to effectively overcome those limitations, this paper proposed a novel block PS-InSAR method that uses the approach of partitioning a study area into regular blocks with overlapping regions, selecting a high-quality reference point for each block and eliminating the discontinuity of the parameter calculating results in adjacent blocks by the weighted least square method. We used this approach to analyze ground subsidence in over 13,000 km2 of southern California, and it performed successfully when 69 Sentinel-1 images were used and 21,029,968 PSs were selected. Only approximately 10 h was needed for this experiment. Comparing the deformation rate and time series of the InSAR results with 29 GPS observations, the mean and standard deviation of difference of deformation rate were − 0.63 mm/yr and 1.53 mm/yr, respectively, and the average root mean squared error of deformation time series was 3.7 mm. The experiment demonstrated that this method could achieve high accuracy results without spatial discontinuity and increase the computational efficiency significantly. [ABSTRACT FROM AUTHOR]

Point sources in spatially referenced data can impact outcomes in surrounding locations (e.g., a factory that emits air pollution). Previous statistical methods have sought to describe the non-stationary correlation induced by the presence of a point source, with fewer attempting to quantify its overall impact. We introduce directionally varying change points (DVCP), a model that aims to estimate the magnitude of the impact of a point source as well as its range of influence across the spatial domain. DVCP allows for a unique change point parameter, describing the range of influence of a point source, at every angle extending from the source and uses a Gaussian process with directionally defined correlation function to facilitate estimation of the parameters. The Gaussian predictive process approximation is used for fitting the model to large datasets. Through simulation, we show that DVCP can easily accommodate a wide range of shapes defining the range of influence. We apply DVCP to better understand spatial patterns of ambient PM 2.5 concentrations and issues related to environmental inequity in California and Colorado. The method is available in the R package DVCP. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]