Beijing is severely affected by land subsidence, and rapid urbanisation and building construction might accelerate the land subsidence process. Based on 39 Envisat Advanced Synthetic Aperture Radar (ASAR) images acquired between 2003–2010, 55 TerraSAR-X images acquired between 2010–2016, and urban building information, we analysed the relationship between land subsidence and buildings at the regional, block, and building scales. The results show that the surface displacement rate in the Beijing urban area ranged from −109 mm/year to +13 mm/year between 2003–2010, and from −151 mm/year to +19 mm/year between 2010–2016; two subsidence bowls were mainly distributed in the eastern part of the Chaoyang District. The displacement rate agreed well with the levelling measurements, with an average bias of less than six mm/year. At the regional scale, the spatial pattern of land subsidence was mainly controlled by groundwater extraction, compressible layer thickness, and geological faults. Subsidence centres were located in the area around ground water funnels with a compressible layer depth of 50–70 m. The block-scale analysis demonstrated a clear correlation between the block construction age and the spatial unevenness of subsidence. The blocks constructed between 1998–2005 and after 2005 showed considerably more subsidence unevenness and temporal instability than the blocks constructed before 1998 during both time periods. The examination of the new blocks showed that the spatial unevenness increased with building volume variability. For the 16 blocks with a high building volume, variability, and subsidence unevenness, the building-scale analysis showed a positive relationship between building volume and settlement in most blocks, although the R² was lower than 0.5. The results indicate that intense building construction in urban areas could cause differential settlement at the block scale in Beijing, while the settlement of single buildings could be influenced by the integrated effects of building volume, foundation structures, and the hydrogeological background.

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.

Over-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R² over 0.96. Although the maximum annual displacement rate reached −159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R² > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.

In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the city of Beijing. In this study, we proposed a method for the detection of seasonal deformation of highway overpasses using the integration of persistent scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) techniques and seasonal indices, i.e., deformation concentration degree (DCD) and deformation concentration period (DCP) indices. Taking eastern Beijing urban area as a case study area, we first used the PS-InSAR technique to derive time series surface deformation based on 55 TerraSAR-X images during 2010–2016. Then, we proposed DCD and DCP indices to characterize seasonal deformation of 25 highway overpasses in the study area, with DCD representing to what degree the annual deformation is distributed in a year, and DCP representing the period on which deformation concentrates in the year. Our results showed that the maximum annual deformation rate reached −141.3 mm/year in Beijing urban area, and the PS-InSAR measurements agreed well with levelling measurements (R² > 0.97). For PS pixels with DCD ≥ 0.3, the monthly deformation showed obvious seasonal patterns with deformation values during some months greater than those during the other months. DCP revealed that the settlement during autumn and winter was more serious than that in spring and summer. The seasonal patterns seemed to be related to the location, structure, and construction age of the overpasses. The upper-level overpasses, the newly constructed overpasses, and those located in the subsidence area (rate < −40 mm/year) tended to show a greater seasonal pattern. The seasonal deformation variations were also affected by groundwater-level fluctuation, temperature, and compressible layer.

Accurate remote-sensed precipitation data are crucial to the effective monitoring and analysis of floods and climate change. The Global Precipitation Measurement (GPM) satellite product offers new options for the global study of precipitation. This paper evaluates the applicability of GPM IMERG products at different time resolutions in comparison to ground-measured data. Based on precipitation data from 107 meteorological stations in the Beijing-Tianjin-Hebei region, GPM products were analysed at three timescales: half-hourly (GPM-HH), daily (GPM-D), and monthly (GPM-M). We use a cumulative distribution function (CDF) model to correct GPM-D and GPM-M products to analyse temporal and spatial distributions of precipitation. We came to the following conclusions: (1) The GPM-M product is strongly correlated with ground station data. Based on five evaluation indexes, NRMSE (Normalized Root Mean Square Error), NSE (Nash-Sutcliffe), FAR (False Alarm Ratio), UR (Underreporting Rate), and CSI (Critical Success Index), the monthly GPM products showed the best performance, better than GPM-HH products and GPM-D products. (2) The performance of GPM products in summer and autumn was better than in winter and spring. However, the GPM satellite’s precision in undulating terrain was poor, which could easily lead to serious errors. (3) CDF models were successfully used to modify GPM-D and GPM-M products and improve their accuracy. (4) The range of 0–100 mm precipitation could be corrected best, but the GPM-M products were underestimated. Corrected GPM-M data in the range >100 mm were overestimated. According to this analysis, the GPM IMERG Final Run products at daily and monthly timescales have good detection ability and can provide data support for long-time series analyses in the Beijing-Tianjin-Hebei region.

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.