Data provenance is an important reference factor of data reliability evaluation and important research content of geospatial data ontology. Taking consideration of provenance, an important research object of geospatial data, we constructed a geospatial data provenance conceptual model based on systemic analysis of the meaning of geospatial data provenance. Based on it, we put forward geospatial data Provenance-Ontology concepts system and the formalization method for constructing geospatial data Provenance-Ontology. Finally, we take the data materials in “special work of the science and technology basic work” as an example. Based on Provenance-Ontology library, using RDF to link geospatial data and D3.js to achieve the data provenance visualization. The result shows that data linking based on Provenance-Ontology can effectively solve the problem of the nonstandardization in the description of data provenance information. It can support geospatial data semantic retrieval, intelligent recommendation and other applications. It also provides new ideas for geodata sharing and data linking.
Parameter optimization is an effective means for the accurate estimation of ecosystem model parameters and the reduction of the uncertainty in model predictions. We proposed a method for parameter optimization of the ecosystem model, which is based on the Bayesian machine learning and called No-U-Turn-Sampler (NUTS). As an efficient means of parameter optimization, NUTS uses a recursive algorithm to build a set of candidate points to obtain the posterior information of the parameters. If the constraint condition of “Non-U-Turn” is met, subtrees will be built to update parameters. Otherwise, “the optimal” set of parameters from current sample will be recorded, and then the next sampling begin to run until enough samples are taken. This algorithm avoids sampling redundancy caused by random walk and thus improves the efficiency of parameter optimization. Taking the carbon flux simulations of the Qianyanzhou subtropical coniferous plantation as an example, we implemented the parameter inversion of the carbon flux (Net Ecosystem Exchange, NEE) model using the NUTS method based on the Pymc3 framework. The comparison between the inversion results of NUTS and Metropolis-Hastings (MH) shows that the sampling frequency reduces about 85%, and the optimization efficiency increases about 3 times when the parameter values of the NUTS algorithm reaches convergence. The uncertainties of the seven parameters estimated by NUTS in the two NEE models are reduced by 10%-53% compared to MH. The NEE simulation improved significantly, with the R2 between the simulated values and the observed values increased by 23% and 17%, respectively and the RMSE decreased by 3% and 4%, respectively. In sum, the NUTS parameter optimization method proposed in this paper provides an efficient approach for the parameter optimization in ecosystem modeling.
The problem of urban traffic congestion has become a serious problem in the development of many cities in the world. To solve this problem, pan-spatial information system provides a new way of solving urban traffic congestion by multi-granularity abstracting, multi-scale modeling and multi-level comprehensive analysis of dynamic and complex traffic jam processes. In reality, the process of traffic congestion is usually accompanied by the dissemination of traffic warning information. Accordingly, when the competition occurs, which is generated by traffic congestion and the spreading of warning information in different network layers, the interplay between traffic congestion and warning information plays an important role. Thus, in order to study the interplay between information spreading and traffic congestion spreading, we constructed a multiplex network with road intersections or sites to analyze the interplay between information spreading and traffic congestion spreading. Firstly, we considered the effect of the surrounding nodes and proposed an improved SIS model. Then, based on the improved SIS model, we used the method of state transition probability to study the competing spreading processes of multiplex network. Finally, using the Monte Carlo method, we analyzed and simulated the traffic congestion threshold in both homogeneous network and heterogeneous network. This study indicates that the process of traffic congestion depends on dynamics of warning information spreading through transport network.
With the sustained and rapid growth of car ownership, the parking problem in urban areas has become increasingly serious. Estimating parking spaces and parking potential in such areas is of great importance for government departments to make parking planning and ease parking contradiction. However, the current survey method of parking spaces and parking potential under the framework of statistical data collection and field investigation is difficult to reflect the supply situation of parking facilities in a timely and comprehensive way. Using GIS technologies, a method of estimating parking spaces and parking potential in urban areas is proposed based on four kinds of data sources: the monitoring data of national geographical conditions, the thematic data from the department of transport and geomatics, and the aerial remote sensing image with high resolution. Firstly, ground parking lots and roads, impervious surface in residential districts, government agencies, enterprises, and public institutions are extracted by using monitoring data of national geographic conditions. Then, the estimation model of the ground parking spaces is constructed with parking lot shape indices, and combining with the survey data of accessory parking space, the total number of parking spaces is estimated. Finally, the estimation model of the curb parking potential is defined with constraint conditions of the road width. Also, the estimation method of the impervious surface parking potential based on shape indices is designed. Taking urban built-up areas in Wuhan as a case, this study estimates and evaluates the actual and potential supply capacity of parking spaces, obtains the basic parking gap, and presents suggestions for easing parking contradiction. The experiment results show that this method has a preferable estimation accuracy which is greater than 82.6% over 15 typical parking lots sampled in the studied area, and is suitable for urban parking spaces and parking potential estimation. Overall, this study can provide an effective method of applying monitoring data of national geographic conditions to estimate urban parking resources and make parking planning in a scientific way.
It is important to acquire the amount and the spatial distribution features of permanent population accurately, which can be used to clarify the development of social state. Thus, it would enhance the capacity of population management. Currently, population census data is mainly collected in administrative regions, making it difficult to describe the spatial distribution features of population in cities. Moreover, the precision decreases when using night light data to regress population, and it is clearly affected by roads, public service facilities and the lights of the cities. Therefore, it is necessary to improve the precision of population regression. This study takes Shanghai as the study area because it is one of the national center cities and faced with huge population pressure along with the rapid urbanization processes. Two types of data sources are involved in the study, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP -VIIRS) night light data and township-level permanent population census data. We extracted the night light data in commercial and residential land in order to mitigate the influence of roads and the lights of the city. Results showed that the correlation coefficient between summation of night light data and amount of permanent population was improved from 0.7032 to 0.8026. Further, we used a spatial regression model to derive the permanent population of Shanghai in 2013, and found that the relative error is 10.57%. Finally, we corrected the results in partition. Experimental results of high precision can be achieved when spatial regression model was used to regress permanent population. Moreover, the gridding results of permanent population can make up the shortcoming of low spatial resolution of traditional statistical data, and describe the circle feature and real distribution of permanent population with more details.
Now, there are still many land border territory disputes causing local wars or unstableness between countries. Under the current international situation, negotiation is the most effective method of solving territory disputes and it needs lots of demarcation technology. Current demarcation methods cannot fully safeguard unilateral resource interest and it costs lots of time. Thus, we proposed a new solution using neighborhood expansion method (NEM). Firstly, the disputed area was split using hexagon where related information such as resource reserve was mapped, and benefit density (BD), which is the comprehensive evaluation value of related resources in each grid, was calculated and disposed. Secondly, the disputed area was initially divided using NEM under the guide of regional integrity, BD and bilateral agreed area ratio, where most hexagons with higher BD were assigned to the related country. Thirdly, the single-source optimal path algorithm based on hexagon was improved to solve the optimal path from non-enclave to enclave caused during the initial segmentation of disputed area. The ascriptions of all the enclaves were determined once again based on some rules. Finally, the integrity of unilateral region was optimized, the gap between the unilateral area and the agreed area was reduced to the extent smaller than the area of single complete grid using NEM. The disputed zone was split accurately according to the agreed area ratio. Tests were made to compare our method with the other one using genetic algorithm based on simulated data, different hexagon sizes and agreed area ratios. The results indicated that our method owns the following characteristics: (1) it can correctly assign bilateral agreed never-lost regions and impenetrable areas like ethnic settlements; (2) the disputed area can be divided fast and precisely according to agreed area ratio; (3) it can fully safeguard unilateral resource interest. These features indicate that our method is effective and reliable and it can provide important reference and guide for one-side delimitation.
In recent years, the frequent occurrence of waterlogging in China has been one of the serious urban diseases. Spatial pattern of urban impervious surface density is an important factor affecting the waterlogging. This paper aims to provide a new method to optimize the spatial pattern of impervious surface in order to reduce urban waterlogging by the integration of SCS-CN model and the Ant colony algorithm. Firstly, the density of urban impervious surface was estimated by remote sensing images through the method of linear spectral mixing modeling. Secondly,the CN value was corrected by using the Williams formula. Then, the modified SCS-CN model was used to calculate the surface runoff. Thirdly,according to the goal of minimizing runoff coefficient, the spatial pattern of impervious surface of 18 runoff plots was optimized by Ant colony algorithm. Fourthly, landscape pattern indices were used to analyze the spatial pattern of impervious surface. The results show that: in rainfall reappearing periods of 1 year, 5 years, 10 years, 20 years, 50 years and 100 years, the optimized impervious surface pattern could reduce the runoff coefficient by 21.19%, 19.58%, 19.38%, 18.93%, 18.41% and 17.25%, respectively. Based on the experimental results above, this research puts forward three suggestions for the optimization of urban renewal. ① Increase the area of grassland, garden, trees and other vegetation types to reduce the high impervious surface area which can be further divided into patches of lower levels of impervious surface. ② Gather low and medium-to-low types of impervious surface to increase the connectivity and the medium-to-high levels of impervious surface. ③ Increase the quantity and density of patches in each runoff plot and reduce the degree of spread and aggregation.
As a preparatory section for urban development, urban fringe area is not only a transitional area for both urban and rural areas, but also the most unstable region for urban development. In general, the primary task of urban fringe region study is to execute the spatial recognition and boundary division of urban fringe area. With the increased development of Geographic Information Systems (GIS) and satellite imagery technique, the definition of urban fringe area is becoming increasingly convenient and feasible. Owing to the existing one-fold or relatively complicated problems in extracting research indexes, it is of great significance to select better discrimination parameters in demarcating urban fringe. Based on SPOT-5 and Landsat-5 TM remotely sensed images and other corresponding auxiliary data, this study firstly constructed an evaluation framework and the index system of urban fringe area from the perspectives of physics, landscape and population, using the impervious surface coverage and the degree of landscape disturbance as primary indicators, and the population density data as auxiliary indexes. Then, the spatial range of the urban fringe of Xi'an city in 2010 was further quantified in this study by performing the computation of information entropy and mutation detection methods. The final results obtained are as follows: (1) The selected indexes show distinctive spatial signatures. The extraction model of urban fringe, which is based on the variability characteristics of both urban and rural areas, is practicable. Besides, the selected indicators are more scientific and accurate, which is of great significance in this paper. (2)Xi'an city takes on a clear ring and wedged structure of urban core area, urban fringe area, and rural hinterland. The extension of urban fringe area is primarily promoted by traffic facilities and policy orientation. This study can provide theoretical support and scientific basis for other relevant researches on urban fringe area.
Coastline change detection is critical for analyzing the rise of sea levels, coastal erosion, harbor siltation, wetland ecological resources, and the offshore environment. Satellite remote sensing technology has a wide application and plays an indispensable role in coastline monitoring. The Pearl River Estuary is one of city groups with the high density population and the most developed economy in China. With the consistent increase of the reclamation and coastal zone exploitation, the coastline changes in the Pearl River Estuary are dramatic. In this paper, a set of Landsat images from 1973 to 2015 were collected to detect the coastline evolutions in the Pearl River Estuary. Firstly, the coastlines were divided into 8 categories and extracted with the aid of remote sensing and geographic information system (GIS) technologies. In addition, the spatiotemporal evolution characteristics of coastline length, categories, and spatial changes were analyzed during the study period. A coastline utilization index was proposed to determine the impact of human activities. Finally, the driving factors of coastline changes were discussed. The results are as follows: ① The total length of coastlines in the Pearl River Estuary increased by 135.46 km, which was equivalent to a growth of 3.15 km per year. The artificial coastline increased significantly, with a net increase of 315.94 km in length. The natural coastline constantly declined, with the most decrease in mud coastline. The change intensity of the coastline length showed remarkable periodicity. It was slow before 1990, peaked from 1990 to 2000, and then weakened after 2000. ② The coastline category was changed from natural coastline to artificial coastline in the study period. The natural coastline was the main coastline category before 1990, but the artificial coastline took the lead position thereafter. Among all coastline categories, the proportion of the construction coastline changed most dynamically, which increased from 7.09% in 1973 to 46.49% in 2015. ③ During the period of 1973-2015, the coastline showed a prevailing trend of advancing seaward, reaching an annual rate of 39.10 m. The seaward extension rate had significant difference in different area. The greatest extension speed appeared on the coastline between Jiti outlet and Hutiao outlet. The seaward extensions of the coastlines between Modao outlet and Jiti outlet, and between Jiao outlet and Hongqi outlet, were remarkable, too. Other regions had an advancing seaward but with a small magnitude. ④ In the past 40 years, the coastline utilization index grew stably. The growth rate increased markedly from 1973 to 1995 and changed gently after 1995. The coastline utilization index in the east coast of the Lingding Sea occupied the largest increasing extent because more and more natural coastline had been artificialized. ⑤ The coastlines in the Pearl River Estuary are affected mainly by human activities, such as outlet renovation, coastal zone construction, and sea farming. Environmental conditions, demographic and economic growth, as well as policies are important driving forces of coastline changes. This study will provide scientific support for the coastline change detection, coastal zone management and sustainable development in the coastal area.
Image mosaicking is an important part of remote sensing image processing. It plays a vital role in the analysis of trans-regional remote sensing images. In order to solve the problems of low utilization rates of the nodes and frequent data I/O in the traditional parallel algorithms of remote sensing images, we proposed a parallel mosaicking algorithms based on self-defined RDD (Resilient Distributed Datasets), in which the Spark distributed memory computing framework has been used. In this paper, we take full advantage of the Spark, which is conducive to the processing of iterative data, and build remote sensing images parallel mosaic processing model through the operation of the Spark RDD. Firstly, according to the logical separability and data independence of the Fourier transform and inverse Fourier transform in the phase correlation method, we improved the traditional phase correlation method by executing a single instruction on multiple nodes, which are executed parallel in the cluster. We did so to improve the image overlapping region estimation multi-node parallel computation in the algorithm. Then, we override the compute and getPartitions methods in RDD and self-define the RDD for remote sensing image processing. Meanwhile, we used the three key steps of the image mosaicking, including overlapping region estimation, image registration and image fusion, which are the transformation-type operators of the self-defined RDD. These transformation-type operators do not perform calculations in the process of parallel mosaicking, until the final mosaicking image is required to be written to disk or file system. Thus, reducing the time consumption in the process of image parallel mosaicking. Finally, the parallel processing of image mosaicking is realized by calling the operators of self-defined RDD with the method of implicit conversion, compared with the parallel mosaicking algorithm based on MPI. The experimental results show that the parallel mosaicking algorithm of massive remote sensing image based on self-defined RDD can effectively improve the image mosaicking efficiency of large data volume on the basis of guaranteeing the image mosaicking effects.
Detecting breakpoints plays an important role in plotting and analyzing time series of the changing characteristics such as firing, logging, diseases and insect pests in vegetation. It is a useful technique of extracting the significant information in time series data. We focused on the method of Detecting Breakpoints and Estimating Segments in Trend (DBEST). We studied the detection of vegetation breakpoints by using vegetation fractional coverage (VFC) data which is derived from MODIS NDVI remote sensing images ( 250 m) from 2000 to 2015 in Changting County of Fujian Province. In order to determine if the results of breakpoints detection are reasonable, the primary experiment is to test the applicability of DBEST method by using the VFC data of various changing types in time series. We select several samples of time series data which covered the key water and soil erosion conservation area. The vegetation changes more frequently in this area for conducting the break-points detection experiments. We make an accuracy evaluation of changing time and changing types by using the temporal trajectories and Landsat remote sensing images of every point. We find that DBEST is suitable for VFC time series data of Changting, by using the default first and second level-shift-thresholds (θ1 = 0.1, θ2 = 0.2) which indicated that DBEST could define the changing level of VFC, but the duration-thresholdφ should be adjusted according to the study area and the type of time series data (we setφ=3). Those parameters have weak influences on the accuracy of breakpoints positions, but have more effects on the changing types of breakpoints. On the whole, the excessive intervention is not necessary for detecting vegetation in DBEST. However, through a lot of experiments we believe that the threshold of the changing magnitude can be modified by our own need to gain a satisfying results. Finally, we set β = 0.2 to fit our own research targets. The precision of the changing time is 92%, greater than the changing types (80%), indicating that DBEST method works well in extracting the important changing information for VFC time series. Meanwhile, the experimental results are broadly consistent with the varying conditions of the local vegetation.
Using multi-source remote sensing data to extract impervious surface information is an important and active research direction. The present study integrated spatial and spectral information from nighttime light data and Landsat TM remote sensing images to automatically extract the coverage information of Impervious Surface Area (ISA), given that in the previous studies, manual selection of impervious surface samples was usually needed for model training. In the present method, firstly, ISA concentrated urban areas were located according to the distribution of nighttime lights. Thus, the ISA spectral characteristics of the local scale in the urban area parts were more clear and obvious compared to the whole-image scene scale. Meanwhile, for the urban exterior, there were mostly non-ISA pixels, therefore the soil samples which were easily confused with ISA were extracted from the urban exterior, and the general spectral features of these samples on this image were calculated. These features could be utilized to distinguish ISA pixels from urban areas. Thus, highly reliable ISA and non-ISA samples were automatically selected from urban area and urban exterior, respectively. Secondly, ISA from urban areas was extracted by an iterative classification process. For the iterative classification process, new samples from the previous extraction results were collected and then added to the following classification process, to make the features of the ISA samples more representative of different types of ISA coverage. Then, ISA samples of urban area were selected from the extraction results, combined with the non-ISA samples of the urban exterior. A sample set was formed to classify the urban exterior. Lastly, the classification results were integrated to complete the whole image. An experiment with this method was completed. DMSP/OLS nighttime light images and Landsat5 TM images of the Syracuse, USA were chosed. 84 urban areas were extracted and the detection accuracy rate was above 95% compared to the Openstreet map. Two urban areas with high and low ISA density from the detection results were selected as the test areas. Automatic selection of ISA and non-ISA samples were performed to the TC transform feature bands of the Landsat5 TM images. The overall accuracy and kappa coefficients of sample selection in urban areas were 92.45% and 0.76, respectively, and 96.52% and 0.85 in urban exterior. For the results extracted by decision tree classifier, the average overall accuracy and Kappa coefficient were 88.23% and 0.63 in the urban areas; 78.6% and 0.54 in the urban exterior. These results are superior to manual methods. This is because the approach of automatic samples selection was more capable of obtaining samples of mixed pixel types compared to manual samples selection. Moreover, the representativeness of samples in spatial distribution and spectral characteristics was better since the iterative classification process was introduced. It suggests an automated classifcaltion workflow is achieved by the proposed method, and this method is reliable and effective for both urban area and urban exterior. In further researches, it could be expected that the ISA extraction accuracy could be improved by optimizing classification characteristics (e.g. adding space features) and improving classification algorithms.
Spartina alterniflora is one of the major invasive species putting a high pressure to the native Phragmites australis in the coastal wetland in Chongming Island, Shanghai. Both species grow up together and result in extensive transitional zones. Spartina alterniflora and Phragmites australis have differences in physiology. The former prefers to live in a high-salt environment and it distributes closer to the sea. In the transitional zones, the two species mix in different proportion along the direction from the sea to the land and grow competitively with each other. Their growth in the transitional zones reflects the intensity of competition. Moreover, the change of the location of transitional zones reflects the dynamic process of the invasion of Spartina alterniflora. Thus, the transitional zone plays a key role in the study of dynamic change of wetland ecosystem. However, it is difficult to extract such information precisely by remote sensing because of the similar spectra of two species and complex composition of the transitional zone. They have similarities in both spectra and physiological and ecological characteristics because both of them are gramineous plants. In addition, the two species in transitional zones mix with different proportions in different regions, so the composition of the transitional zones is complex. There is little related research focusing on the transitional zones so far. This paper presents a comprehensive extraction method. Firstly, we combine different phenology with spectral characteristics to narrow the scope of the appropriate indicators down to reduce the workload. Secondly, we also consider location difference of the land and the sea. Analyzing the spectral characteristics along the direction from the sea to the land, it will be more intuitive for the spectral characteristics of vegetation in the transitional zones as well as the relationship between the change of mixing ratios and spectral characteristics. Finally, we determine extracting indicators and threshold by actual measured dataset. Remote sensing is an important measure for monitoring wetland ecosystem. Extracting transitional zone requires high-resolution remotely sensed images because the width of transitional zone in our research is narrow and transitional zone contains many patches which is due to many complex factors such as underwater micro-topography differences. This study selected appropriate multi-spectral remotely sensed data from GF-1 as research object through analyzing the canopy spectral differences between Spartina alterniflora and Phragmites australis in different time. Also, this study extracted transitional zone successfully in the study area which is an intertidal area located in the northeastern part of Chongming Island. The results indicate that different indicators should be used in different time. We select near-infrared band reflectance for spring and red band reflectance for autumn The near-infrared band reflectance of vegetation in transitional zone is lower than the other two pure species regions in spring while the red band reflectance is higher than the other two pure species regions in autumn. The scale of transitional zone has obvious difference in the two growing stages, the width of transitional zone in autumn is narrower than that in spring and the location moves towards the direction of Phragmites australis regions. The difference reflects the competitive situation in different seasons, objectively. Competitive edge of Spartina alterniflora is more evident in autumn.
Being an important part of the green space system, urban grassland has played a significant role in landscaping environment, regulating microclimate and preventing soil from erosion. Therefore, it is of great importance to monitor the health status of urban grassland timely and efficiently. Remote sensing technique has been widely used for assessing vegetation growth status for decades. Numerous studies have found that red edge indices are closely related to the important biochemical parameters of green plants. Thus, they can be regarded as important indicators for monitoring health status of vegetation. However, there is no explicit conclusion about which index is more suitable for monitoring the health status of urban grasslands among the existing red edge indices. The European Sentinel-2A satellite was successfully launched in late June 2015, aiming to replace and improve the old generation of satellite sensors of high resolution (i.e. Landsat and SPOT), with improved spectral capabilities. The multispectral instrument (MSI) of Sentinel-2 has made available a set of 13 spectral bands ranging from visible (VIS) and near infrared (NIR) to shortwave infrared (SWIR), featuring four bands at 10 m, six bands at 20 m, and three bands at 60 m of spatial resolution. In comparison to the previous sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region centered at 705, 740 and 783 nm, providing an opportunity for assessing red-edge spectral indices for monitoring the health status of urban grasslands. For this reason, the main objective of this paper is to find a red edge index that is more suitable for evaluating the growth status of urban grassland based on Sentinel-2A sensor data. Taking the urban grasslands in Fuzhou and Xiamen cities, Southeastern China, as examples, we firstly investigated the spectral responsive characteristics of grasslands in different health status using Sentinel-2A images dated on June 23, 2016 and August 22, 2016, respectively for Fuzhou and Xiamen. On this basis, six red edge indices related to grassland chlorophyll content were then selected to test their efficiency in detecting grassland health status. These are the red edge position (REP), the terrestrial chlorophyll index (MTCI), the normalized difference red edge index (NDRE1), the novel inverted red-edge chlorophyll index (IRECI), the red-edge chlorophyll index (CIred-edge) and the modified chlorophyll absorption ratio index (MCARI2). Furthermore, independent sample T test and Euclidean distance methods were employed to evaluate the performance of the selected indices in the detection of grassland health status. Results showed that the six red edge indices had different performances. They have different degrees of sensitivity to the changes of grassland health status. In general, the IRECI was the most sensitive to the grassland health status among the six indices in the two study areas. The index can reveal significant differences in the numerical range and mean values between grasslands with different health status. The overall accuracy of the index is greater than 85% with a kappa coefficient exceeding 0.8 both in Fuzhou and Xiamen cases. The NDRE1 and MCARI2 indices ranked the second and third, while the other three indices were unable to effectively detect the health status of the grasslands. Accordingly, the IRECI is the optimal red edge index for evaluating the grassland health status using Sentinel-2A imagery.
The Beijing-Tianjin high-speed railway is the first high-speed railway in China. The stability of the geological environment is crucial for the safe operation of the high-speed railway. Land subsidence especially uneven subsidence will probably cause the deformation of the roadbed and bridge, which may affect the safety of high-speed railway operation. Therefore, it has very important significance for land subsidence monitoring along the high-speed railway. Interferometric synthetic aperture radar (InSAR) is an effective way for monitoring land subsidence with high precision. Based on 45 high-resolution TerraSAR-X images acquired from 2010 to 2015, the Permanent Scatter Interferometry (PS-InSAR) is empolyed to obtain land subsidence information along Beijing-Tianjin high-speed railway in Beijing section. The results indicate that there exist different spatial distributions of the land subsidence along the high-speed railway, the annual subsidence rate from Beijing south railway station to Shilihe interval is less than 10 mm/a, and from Shilihe to Shibalidian interval the annual subsidence rate ranges from 10 to 40 mm/a. And the maximum annual subsidence rate reaches 90mm/a from Yizhuang station to the east interval. The comprehensive analysis of static-dynamic loadings and hydrogeological data can help to understand the causes of land subsidence along high-speed railway. Over-exploitation of groundwater is the main factor of land subsidence in the study area, and the combination of dynamic and static loadings have certain influence on land subsidence. To some extent, the land subsidence along the high-speed railway is controlled by the Nanyuan-Tongzhou fault and the Jiugong fault, and most parts of the land subsidence are located in the Daxing uplifted belt with thick clay layer.
