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InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter's experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. [ABSTRACT FROM AUTHOR]

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images. [ABSTRACT FROM AUTHOR]

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing's geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing's transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. [ABSTRACT FROM AUTHOR]

Beijing is a major city suffering from land subsidence due to long-term over-exploitation of groundwater. The South-to-North Water Diversion Project (SNWDP), however, has had a significant impact on the structure of water consumption since the end of 2014, and it is changing the status of land subsidence in Beijing. In this study, we employed Persistent Scatterers Synthetic Aperture Radar Interferometric (PS-InSAR) to investigate the decadal evolution of land subsidence in Beijing with 100 TerraSAR-X stripmap images collected from April 2010 to December 2019. The water resources, historic climate and urban construction data were compiled for the years of 2010 to 2019 to analyze changes in groundwater level, human activity, surface geology, active faults and land subsidence patterns. The results show that the changes in the water supply structure are correlated to a rise in groundwater level after 2015. These changes include an increase in the water supply from the SNWDP, a reduction in groundwater exploitation, the optimization of water consumption, replacing recycled water for environmental water and a reduction in the use of water for agriculture. Land subsidence in the study area was concentrated in the eastern regions, trending towards a decreasing velocity starting about two years after the commencement of SNWDP in 2015. Uneven subsidence in the land subsidence area was related to excavations of underground soil, and the construction of Line 6 and Line 7 led to rapid nonlinear subsidence. Our results have scientific significance for reducing subsidence hazards in the context of SNWDP and urban expansion. [ABSTRACT FROM AUTHOR]

This study was conducted in response to the Beijing–Tianjin–Hebei mega heavy rainfall event at the end of July 2023, and the severely affected and representative Jiangou village in Beijing was selected as the study area. A variety of methods were used to synthesize and analyze the situation and propose an adaptive response to heavy rainfall and flooding in the village. Based on multi-source remote sensing (RS) data, a comprehensive topographic and hydrological characterization was carried out, and the precipitation before and after the disaster was analyzed; the flood inundation area was extracted using the improved normalized water body index (MNDWI) and OTSU thresholding methods, and the changes of water bodies during the flooding period were quantitatively analyzed; and an improved convolutional-neural-network-based building identification and extraction model was constructed to extract the research distribution of buildings in the area. The sponge city construction (SPCC) method was improved to obtain a method that can mitigate flood risk and adapt to villages by constructing small artificial lakes and local topographic buffers to improve the water storage and drainage capacity of villages. The study shows that these methods are innovative in flood hazard analysis and mitigation but still need further improvement in data accuracy, simulation depth, and system evaluation. [ABSTRACT FROM AUTHOR]

Beijing, China's capital city, has experienced decades of severe land subsidence due to the long-term overexploitation of groundwater. The implementation of the South-to-North Water Diversion Project (SNWDP) and artificial ecological restoration have significantly changed Beijing's hydro-ecological and geological environment in recent years, leading to a widespread rise in groundwater levels. However, whether the related land subsidence has slowed down or reversed under these measures has not yet been effectively monitored and quantitatively analyzed in terms of time and space. Accordingly, in this study, we proposed using an improved time-series deformation method, which combines persistent scatterers and distributed scatterers, to process Sentinel-1 images from 2015 to 2022 in the Beijing Plain region. We performed a geospatial analysis to gain a better understanding of how the new hydrological conditions changed the pattern of deformation on the Beijing Plain. The results indicated that our combined PS and DS method provided more measurements both in total quantity and spatial density than conventional PSI methods. The land subsidence in the Beijing Plain area has been effectively alleviated from a subsidence region of approximately 1377 km2 in 2015 to only approximately 78 km2 in 2022. Ecological restoration areas in the northeastern part of the Plain have even rebounded over this period, at a maximum of approximately 40 mm in 2022. The overall pattern of ground deformation (subsidence and uplift) is negatively correlated with changes in the groundwater table (decline and rise). Local deformation is controlled by the thickness of the compressible layer and an active fault. The year 2015, when anthropogenic water transfers were eliminated and ecological measures to recharge groundwater were implemented, was the crucial turning point of the change in the deformation trend in the subsidence history of Beijing. Our findings carry significance, not only for China, but also for other areas where large-scale groundwater extractions are causing severe ground subsidence or rebound. [ABSTRACT FROM AUTHOR]

Seasonal and diurnal characteristics of the vertical profiles of aerosol properties are essential for detecting the regional transport and the climatic radiative effects of aerosol particles. We have studied the seasonal and diurnal characteristics of the vertical distribution of aerosols in urban Beijing from 2017 to 2021 based on long-term Raman–Mie LiDAR observations. The influence of the vertical distribution of aerosols, the meteorological conditions within the boundary layer, the optical–radiometric properties of aerosols, and their interconnections, were investigated during a heavy haze pollution event in Beijing from 8 to 15 February 2020 using both meteorological and sun photometer data. The aerosol extinction coefficient was highest in summer (0.4 km−1), followed by winter (0.35 km−1), and roughly equal in spring and autumn (0.3 km−1). The aerosol extinction coefficient showed clear daily variations and was different in different seasons as a result of the variation in the height of the boundary layer. During the haze pollution event, the particulate matter mainly consisted of scattered spherical fine particles and the accumulation time of pollutants measured via the AOD440nm and PM2.5 mass concentration was different as a result of the hygroscopic growth of the aerosol particles. This growth increased scattering and led to an increase in the aerosol optical depth. The vertical transport of particulate matter also contributed to the increase in the aerosol optical depth. [ABSTRACT FROM AUTHOR]

Identifying the patterns of rainstorms is essential for improving the precision and accuracy of flood forecasts and constructing flood disaster prevention systems. In this study, we used a manifold learning algorithm method of machine learning to analyze rainstorm patterns. We analyzed the spatial–temporal characteristics of heavy rain in Beijing and Shenzhen. The results showed a strong correlation between the spatial–temporal pattern of rainstorms and underlying topography in Beijing. However, in Shenzhen, the spatial–temporal distribution characteristics of rainstorms were more closely related to the source of water vapor causing the rainfall, and the variation in characteristics was more complex and diverse. This method may be used to quantitatively describe the development and dynamic spatial–temporal patterns of rainfall. In this study, we found that spatial–temporal rainfall distribution characteristics, extracted by machine learning technology could be explained by physical mechanisms consistent with the climatic characteristics and topographic conditions of the region. [ABSTRACT FROM AUTHOR]

Accurate spatial population distribution information, especially for metropolises, is of significant value and is fundamental to many application areas such as public health, urban development planning and disaster assessment management. Random forest is the most widely used model in population spatialization studies. However, a reliable model for accurately mapping the spatial distribution of metropolitan populations is still lacking due to the inherent limitations of the random forest model and the complexity of the population spatialization problem. In this study, we integrate gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and support vector regression (SVR) through ensemble learning algorithm stacking to construct a novel population spatialization model we name GXLS-Stacking. We integrate socioeconomic data that enhance the characterization of the population's spatial distribution (e.g., point-of-interest data, building outline data with height, artificial impervious surface data, etc.) and natural environmental data with a combination of census data to train the model to generate a high-precision gridded population density map with a 100 m spatial resolution for Beijing in 2020. Finally, the generated gridded population density map is validated at the pixel level using the highest resolution validation data (i.e., community household registration data) in the current study. The results show that the GXLS-Stacking model can predict the population's spatial distribution with high precision (R2 = 0.8004, MAE = 34.67 persons/hectare, RMSE = 54.92 persons/hectare), and its overall performance is not only better than the four individual models but also better than the random forest model. Compared to the natural environmental features, a city's socioeconomic features are more capable in characterizing the spatial distribution of the population and the intensity of human activities. In addition, the gridded population density map obtained by the GXLS-Stacking model can provide highly accurate information on the population's spatial distribution and can be used to analyze the spatial patterns of metropolitan population density. Moreover, the GXLS-Stacking model has the ability to be generalized to metropolises with comprehensive and high-quality data, whether in China or in other countries. Furthermore, for small and medium-sized cities, our modeling process can still provide an effective reference for their population spatialization methods. [ABSTRACT FROM AUTHOR]

Aerosols can affect vertical thermal structure during heavily polluted episodes (HPEs). Here, we selected four typical HPEs in 2018, which were further subdivided into dust and haze events. The vertical distribution of aerosols extinction coefficient (EC) and variations in columnar optical properties were investigated based on sun-photometer and Lidar observation at an urban site in Beijing. The vertical characteristics in shortwave radiative heating rate (HR) of aerosols were studied using NASA/Goddard radiative transfer model along with observational data. In the haze episode, EC layer is less than 1.5 km and shows strong scattering, with single-scattering albedo (SSA440nm) of ~0.97. The heating effects are observed at the middle and upper atmosphere, and slight heating effects are found at the lower layer. The mean HR within 1.5 km can be up to 16.3 K day−1 with EC of 1.27 km−1, whereas the HR within 0.5 km is only 1.3 K day−1. In the dust episode, dust aerosols present the absorption with SSA440nm of ~0.88, which would heat the lower atmosphere to promote vertical turbulence, and the height of EC layer can be up to 2.0–3.5 km. In addition, the strong heating effects of dust layer produced cooling effects near the surface. Therefore, the accurate measurement of aerosols optical properties in HPEs is of great significance for modeling aerosols direct radiative effects. [ABSTRACT FROM AUTHOR]

Simple Summary: Airflow is very important for the long-distance migration of O. decorus asiaticus, and wind shear, in particular, is the main factor related to forced landing. Analyzing the weather records, we found that the northwest wind prevailed when the population invaded. Specifically, from July to August, a large number of emerging adults appeared in the source areas of Mongolia, and the large-scale northwest wind was the decisive condition for the successful long-distance migration of O. decorus asiaticus. The species has a strong migratory ability, flying along the airflow for several nights. If the northwest air current meets the southwest warm current going north, a large number of O. decorus asiaticus will drop due to wind shear, and then a major outbreak will occur. Analysis of the source of the insects shows that the O. decorus asiaticus break outs in China may have originated from Mongolia. They were brought into China by the southerly airflow at night, and they likely made a forced landing in Beijing due to wind shear, sinking airflow, rainfall and other reasons. In summary, through analysis of the insect's prevalence and the meteorological conditions in Mongolia, we can provide a basis for predicting the occurrence of O. decorus asiaticus in China. Oedaleus decorus asiaticus (Bey-Bienko) is a destructive pest in grasslands and adjacent farmland in northern China, Mongolia, and other countries in Asia. It has been supposed that this insect pest can migrate a long distance and then induce huge damages, however, the migration mechanism is still unrevealed. The current study uses insect light trap data from Yanqing (Beijing), together with regional meteorological data to determine how air flow contributes to the long-distance migration of O. decorus asiaticus. Our results indicate that sinking airflow is the main factor leading to the insects' forced landing, and the prevailing northwest wind was associated with O. decorus asiaticus taking off in the northwest and moving southward with the airflow from July to September. Meanwhile, the insects have a strong migratory ability, flying along the airflow for several nights. Thus, when the airflow from the northwest met the northward-moving warm current from the southwest, a large number of insects were dropped due to sinking airflow, resulting in a large outbreak. Our simulations suggest that the source of the grasshoppers involved in these outbreaks during early 2000s in northern China probably is in Mongolia, and all evidence indicates that there are two important immigrant routes for O. decorus asiaticus migration from Mongolia to Beijing. These findings improves our understanding of the factors guiding O. decorus asiaticus migration, providing valuable information to reduce outbreaks in China that have origins from outside the country. [ABSTRACT FROM AUTHOR]