Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model

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.

Unveiling the driving factors of urban land subsidence in Beijing, China

Land subsidence, an insidious and gradual geological phenomenon, presents a latent threat to future urban development and socio-economic progress. Beijing City, renowned for its high population density, has encountered significant challenges associated with land subsidence. In this study, we leverage time-series interferometric synthetic aperture radar (time-series InSAR) method to analyze the spatio-temporal patterns of land subsidence in Beijing. Furthermore, we quantify the contributions of natural and anthropogenic factors to land subsidence. Our findings reveal that land subsidence primarily occurs in the plain area of Beijing, exhibiting an average rate of -5.6 mm/year (Positive values indicate uplift, while negative values indicate subsidence.). Notably, several large-scale subsidence centers are identified, with the maximum subsidence rate reaching an alarming -232.7 mm/year. The assessments indicate that geological factors, specifically fault activity, account for 33 % of the observed land subsidence, while human activities contribute to the remaining 67 %, with groundwater overexploitation playing a prominent role. The insights gained from this study provide a foundation for understanding the causative factors behind urban land subsidence and can aid in the formulation of effective intervention policies targeting this critical issue.

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents' estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China

Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literature, there are only a few methods that can effectively monitor GD. Microwave remote sensing data such as interferometric synthetic aperture radar (InSAR) are mostly adopted to assess GD. These data can reveal the surface deforming areas with great precision, mapping GD results at a large scale. In this study, the effects of GD and the influencing factors, such as the building area, the water level, the cumulative precipitation, and the cumulative temperature, are modeled in the Erhai region with small baseline subset interferometric SAR (SBAS-InSAR) data that are applied using machine learning (ML) methods. The ML methods, namely, multiple linear regression (MLR), multilayer perceptron backpropagation (MLP-BP), least squares support vector machine (LSSVM), and particle swarm optimization (PSO)-LSSVM, are used to predict GD, and the results are compared. Particularly, the PSO-LSSVM method has obtained the least root mean square error (RMSE) and mean relative error (MRE) of 11.448 and 0.112, respectively. Therefore, the results have proven that the proposed PSO-LSSVM is very efficient in analyzing GD.

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

The November 2019 Seismic Sequence in Albania: Geodetic Constraints and Fault Interaction

The seismic sequence of November 2019 in Albania culminating with the Mw = 6.4 event of 26 November 2019 was examined from the geodetic (InSAR and GNSS), structural, and historical viewpoints, with some ideas on possible areas of greater hazard. We present accurate estimates of the coseismic displacements using permanent GNSS stations active before and after the sequence, as well as SAR interferograms with Sentinel-1 in ascending and descending mode. When compared with the displacements predicted by a dislocation model on an elastic half space using the moment tensor information of a reverse fault mechanism, the InSAR and GNSS data fit at the mm level provided the hypocentral depth is set to 8 ± 2 km. Next, we examined the elastic stress generated by the Mw = 7.2 Montenegro earthquake of 1979, with the Albania 2019 event as receiver fault, to conclude that the Coulomb stress transfer, at least for the elastic component, was too small to have influenced the 2019 Albania event. A somewhat different picture emerges from the combined elastic deformation resulting after the two (1979 and 2019) events: we investigated the fault geometries where the Coulomb stress is maximized and concluded that the geometry with highest induced Coulomb stress, of the order of ca. 2–3 bar (0.2–0.3 MPa), is that of a vertical, dextral strike slip fault, striking SW to NE. This optimal receiver fault is located between the faults activated in 1979 and 2019, and very closely resembles the Lezhe fault, which marks the transition between the Dinarides and the Albanides.

Analysis of Land Surface Deformation in Chagan Lake Region Using TCPInSAR

Due to earthquakes and large-scale exploitation of oil, gas, groundwater, and coal energy, large-scope surface deformation has occurred in Songyuan City, Jilin Province, China, and it is posing a serious threat to sustainable development, including urban development, energy utilization, environmental protection, and construction to improve saline–alkali land. In this study, we selected the Chagan Lake region, which is located in Songyuan City, as our research area. Using temporarily coherent point synthetic aperture radar interferometry (TCPInSAR), we obtained a time series of land surface deformation and the deformation rate in this area from 20 ALOS PALSAR images from 2006 to 2010. The results showed that the deformation rate in the Chagan Lake region ranged from −46.7 mm/year to 41.7 mm/year during the monitoring period. In three typical land cover areas of the Chagan Lake region, the subsidence in the wetland area was larger than that in the saline–alkali area, while the highway experienced a small uplift. In addition, surface deformation in lakeside areas with or without dykes was different; however, as this was mainly affected by soil freeze–thaw cycles and changes in groundwater level, the deformation showed a negative correlation with temperature and precipitation. By monitoring and analyzing surface deformation, we can provide a data reference and scientific basis for sustainable ecological and economic development in the Chagan Lake region and adjacent areas.