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Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for extracting the land vertical displacement rates (VDRs) in Shanghai (China) using the multi-platform SAR images collected between 2009 and 2010, i.e., the ascending/descending COSMO-SkyMed (CSK) X-band images and the descending TerraSAR (TSX) X-band images. Different from the conventional approach the proposed method estimates the VDRs by multi-temporal interferometric SAR processing (i.e., temporarily coherent point InSAR, TCPInSAR) and the localized least square (LS) solution, through which the horizontal displacement rates (HDRs) can be also derived as the by-product. The three types of displacement rates in the radar line of sight (LOS) are first estimated by TCPInSAR with use of the ascending/descending CSK and descending TSX data. Both the VDRs and HDRs are then estimated by a localized LS method on the basis of the three types of LOS displacement rates derived by TCPInSAR. For comparison purposes, the VDRs for the single-platform SAR data are also obtained through dividing the LOS displacement rates by the cosine of the radar incidence angle. The analysis with the aid of GPS data shows the horizontal motion due to the geological settings in Shanghai cannot be ignored, and the VDRs derived from the single-platform data are not accurate and reliable without taking into account the horizontal motion. The experimental results indicate the TCPInSAR-LS method can be used to efficiently improve the accuracy in the VDRs by integrating the multi-platform SAR data and considering both horizontal and vertical motion, thus overcoming the disadvantages in the conventional approach. The improved VDRs in Shanghai range between -22.8 and 9.6 mm/year, while the relative east-west motion rates range between -7.2 and 6.2 mm/year, and the relative north--south motion is subtle and can be ignored. Analysis of the improved VDRs shows that a slightly uplifting area of about 70 km² appears in the west downtown of Shanghai, and a subsiding bowl appears in the Hongkou District of Shanghai. The validation indicates that the uplifting trend can be ascribed to the measures conducted by the Shanghai municipal government, i.e., decreasing the groundwater pumping volume and increasing the groundwater recharge volume. [ABSTRACT FROM AUTHOR]

Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on systematically implementing this global land cover product in regional studies. Therefore, this paper presents an object-based extended change vector analysis (ECVA_OB) and transfer learning method to update the reginal land cover map using GlobeLand30 product. The method is designed to highlight small and subtle changes through the concept of uncertain area analysis. Updating is carried out by classifying changed objects using a change-detection-based transfer learning method. Land cover changes are analyzed and the factors affecting updating results are explored. The method was tested with data from Shanghai, China, a city that has experienced significant changes in the past decade. The experimental results show that: (1) the change detection and classification accuracy of the proposed method are 83.30% and 78.77%, respectively, which are significantly better than the values obtained for the multithreshold change vector analysis (MCVA) and the multithreshold change vector analysis and support vector machine (MCVA + SVM) methods; (2) the updated results agree well with GlobeLand30 2010, especially for cultivated land and artificial surfaces, indicating the effectiveness of the proposed method; (3) the most significant changes over the past decade in Shanghai were from cultivated land to artificial surfaces, and the total area containing artificial surfaces in Shanghai increased by about 55% from 2000 to 2011. The factors affecting the updating results are also discussed, which be attributed to the classification accuracy of the base image, extended change vector analysis, and object-based image analysis. [ABSTRACT FROM AUTHOR]