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With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm–convolutional neural network–long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning. [ABSTRACT FROM AUTHOR]