Research on Key Technologies of Precise InSAR Surveying and Mapping Applications Using Automatic SAR Imaging

Precise interferometric synthetic aperture radar (InSAR) is a new intelligent photogrammetric technology that uses automatic imaging and processing means. Precise InSAR has become the most efficient satellite surveying and mapping (SASM) method that uses the interferometric phase to create a global digital elevation model (DEM) with high precision. In this paper, we propose the application of systematic InSAR technologies to SASM. Three key technologies are proposed: calibration technology, data processing technology and post-processing technology. First, we need to calibrate the geometric and interferometric parameters including the azimuth time delay, range time delay, and atmospheric delay, as well as baseline errors. Second, we use the calibrated parameters to create a precise DEM. One of the important procedures in data processing is the determination of phase ambiguities. Finally, we improve the DEM quality through the joint use of the block adjustment method, long and short baseline combination method and descending and ascending data merge method. We use 6 sets of TanDEM-X data covering Shanxi to conduct the experiment. The root mean square error of the final DEM is 5.07 m in the mountainous regions. In addition, the low coherence area is 0.8 km 2. The result meets the China domestic SASM accuracy standard at both the 1∶50 000 and 1∶25 000 measurement scales.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Influence of higher-order ionospheric delay correction on GPS precise orbit determination and precise positioning

At present, Global Navigation Satellite Systems（GNSS） users usually eliminate the influence of ionospheric delay of the first order items by dual-frequency ionosphere-free combination. But there is still residual ionospheric delay error of higher order term. The influence of the higher-order ionospheric corrections on both GPS precision orbit determination and static Precise Point Positioning（PPP） are studied in this paper. The influence of higher-order corrections on GPS precision orbit determination, GPS observations and static PPP are analyzed by neglecting or considering the higher-order ionospheric corrections by using a globally distributed network which is composed of International GNSS Service（IGS） tracking stations. Numerical experimental results show that, the root mean square（RMS） in three dimensions of satellite orbit is 36.6 mme35.5 mm. The maximal second-order ionospheric correction is 9 cm, and the maximal third-order ionospheric correction is 1 cm. Higher-order corrections are influenced by latitude and station distribution. PPP is within 3 mm in the directions of east and up. Furthermore, the impact is mainly visible in the direction of north, showing a southward migration trend, especially at the lower latitudes where the influence value is likely to be bigger than 3 mm.

An iterative algorithm of NWTLS-EC for three dimensional-datum transformation with large rotation angle

The Gauss-Markov （GM） model and the Errors-in-Variables （EIV） model are frequently used to perform 3D coordinate transformations in geodesy and engineering surveys. In these applications, because the observation errors in original coordinates system are also taken into account, the latter is more accurate and reasonable than the former. Although the Weighted Total Least Squares （WTLS） technique has been intro- duced into coordinate transformations as the measured points are heteroscedastic and correlated, the Variance- Covariance Matrix （VCM） of observations is restricted by a particular structure, namely, only the correlations of each points are taken into account. Because the 3D datum transformation with large rotation angle is a non- linear problem, the WTLS is no longer suitable in this ease. In this contribution, we suggested the nonlinear WTLS adjustments with equality constraints （NWTLS-EC） for 3D datum transformation with large rotation an- gle, which removed the particular structure restriction on the VCM. The Least Squares adjustment with Equality （LSE） constraints is employed to solve NWTLS-EC as the nonlinear model has been linearized, and an iterative algorithm is proposed with the LSE solution. A simulation study of 3D datum transformation with large rotation angle is given to insight into the feasibility of our algorithm at last.

Properties of L-band differential InSAR for monitoring mining-induced subsidence in coalfield of Jining, Northern China

The properties and feasibility of L-band differential InSAR for detecting and monitoring mining-induced subsidence were systematically analyzed and demonstrated. The largest monitored subsidence gradient of 7.9×10-3 and magnitude of 91 cm were firstly derived by theoretical derivation. Then, the stronger phase maintaining capacity and weaker sensitivity to minor land subsidence compared with C-band DInSAR were illustrated by phase simulation of the actual mine subsidence. Finally, the data processing procedure of two-pass DInSAR was further refined to accurately observe subsidence of a coalfield of Jining in Northern China using 7 ALOS PALSAR images. The largest monitored subsidence magnitude of 39.22 cm and other properties were better investigated by testing results interpretation and subsidence analysis, and the absolute difference varying from 0.5 mm to 17.9 mm was obtained by comparison with leveling-measured subsidence. All of results show that L-band DInSAR technique can investigate the location, amount, area and other detailed subsidence information with relatively higher accuracy.

Human Settlement Analysis Based on Multi-temporal Remote Sensing Data： A Case Study of Xuzhou City, China

To evaluate urban human settlement, we propose a human settlement environment development index(HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper(TM) images from 1998 to 2010 and digital elevation model(DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km^2, covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km2 covering 52.15% of the total area; the inhabitable region was about 32.03 km^2, covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socio-economic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.

The Application of Ontology in Semantic Discovery for GeoData Web Service

GeoData Web service is an important way to achieve the integration and sharing of heterogeneous geospatial data at present. However, due to the complexity of GeoData and no sematic supporting Webservice discovery, it is very hard for data users to accurately find the GeoData WebService they really want. In order to make it easy for users to quickly and accurately find the GeoData Web Service they want in semantic level, this article firstly, constructs MetaData Ontololy, and uses MetaData Ontology to describe the related semantic information for GeoData Web Service. Then it comes up with a new way of computing the degree of semantic similarity among concepts based on Ontology. Finally, it realizes the automatic discovery for GeoData Web Service based on semantic matching. The experiment result shows that the way in this article can dramatically improve the accuracy and intelligence of GeoData Web Service discovery.

Design and Implementation of GIS Data Production System for Expressway Video and Road Infostructure

The expressway is necessary for the development of the modern transportation industry, and the level of expressway construction reflects the overall grade of national or regional economic development. In order to process the expressway road property data information, based on the current mainstream Windows operating system, this study utilizes Geographic Information System (GIS) development technology, road video processing technology, and spatial data mining method to design and develop an expressway video and road infostructure GIS data production system. The system designs a multi-layer distributed application model in accordance with the ideas and methods of GIS engineering and the characteristics of road production data. In addition, according to the characteristics and specification requirements of basic geographic data, the road production database of spatial data and attribute data integrated storage is constructed by combining database and spatial data engine. Through the development of the GIS data production system for expressway video and road infostructure, various functions such as generation of road property data, dynamic management of road infostructure, and visualization of spatial information have been realized. The system focuses on improving the production efficiency and automation level of expressway production data and meets the construction requirements for modernization, informatization, and intelligence of expressways.

Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China

The Missing Children Mobile GIS Mutual Assistance System of China (MCMAS) is a mobile service software based on mobile GIS platform software, and it is committed to providing the most convenient and efficient system of personally mutual tracing services for missing children family and society. Relying on collaborative utilization of location-based service technology, face image intelligent recognition technology, cloud computing technology, public big data sharing technology, and mobile GIS technology, the MCMAS has achieved prominent application effects since it was deployed. At present, the MCMAS is running soundly, and it has received and released the information about 1011 missing children from May 25, 2016 to May 25, 2017. In order to explore the geographical distribution features and the influencing factors of missing children, the data of missing children are used for spatial and visual analysis by the data mining and GIS technologies. At the same time, we have built the spatial thermodynamic diagram of the big data of China missing children. By comparing provinces and cities with a higher proportion of missing children, the results showed that: 1) The high proportion of missing children spatially concentrated in the eastern part of the China. 2) The number of missing children was significantly correlated with the population density and economic status of the city. Furthermore, the paper macro-levelly presents a basic basis for rescuing the missing children from two aspects: regionally spatial characteristics and influencing factors.

Winter sea ice albedo variations in the Bohai Sea of China

Sea ice conditions in the Bohai Sea of China are sensitive to large-scale climatic variations. On the basis of CLARA-A1-SAL data, the albedo variations are examined in space and time in the winter（December, January and February） from 1992 to 2008 in the Bohai Sea sea ice region. Time series data of the sea ice concentration（SIC）, the sea ice extent（SIE） and the sea surface temperature（SST） are used to analyze their relationship with the albedo. The sea ice albedo changed in volatility appears along with time, the trend is not obvious and increases very slightly during the study period at a rate of 0.388% per decade over the Bohai Sea sea ice region.The interannual variation is between 9.93% and 14.50%, and the average albedo is 11.79%. The sea ice albedo in years with heavy sea ice coverage, 1999, 2000 and 2005, is significantly higher than that in other years; in years with light sea ice coverage, 1994, 1998, 2001 and 2006, has low values. For the monthly albedo, the increasing trend（at a rate of 0.988% per decade） in December is distinctly higher than that in January and February. The mean albedo in January（12.90%） is also distinctly higher than that in the other two months. The albedo is significantly positively correlated with the SIC and is significantly negatively correlated with the SST（significance level 90%）.

Investigation of Lushan earthquake ionosphere VTEC anomalies based on GPS data

Vertical total electron content （VTEC） time series were obtained from 22 GPS stations near the epicenter of the Lushan earthquake. In this paper, we have adopted a sliding average method to detect and analyze anomalous VTEC associated with the earthquake. The results show that signif- icant, negative ionosphere VTEC anomalies appeared over the 5 days before the earthquake, and on the day when earthquake occurred. The maximum value of VTEC anoma- lies that exceeded the lower bound reached 20 TECU. The spatial distribution of VTEC anomalies showed a conjugate structure, which shifted to the magnetic equator, and subse- quently moved westwards.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition&#174. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

The Spatial Distribution Pattern Analysis of City Infrastructure Cases of Urban Management

The effective disposal of daily city infrastructure cases is an important issue for urban management. To more effectively utilize a large amount of historical cases data collected and accumulated in the urban grid management system, and to analyze their spatial distribution pattern information for city managers, this study used the comparative kernel density analysis method in two types of cases (i.e. power facilities and traffic guardrail) in Xicheng District, Beijing for the year 2016 and 2017. This research analyzes them at different scales (300 m, 600 m, 1,200 m), and the experiment results show that the method of comparative kernel density analysis is able to provide an intuitively spatial visualization distribution analysis of city infrastructure related cases. The quantitative information of spatial agglomeration degree is helpful for city managers making decision.

Analysis of the merged quasi-geoid of neighbouring areas

Employing the merged quasi-geoid, we analyses the causes of systematic errors in modelling of quasigeoid of neighbouring areas in the paper, and the efficient method is introduced to improve the accuracy of quasi-geoid. First, the systematic error is weakened with the moving window method applied to established quasi-geoids in two adjacent regions, and the accuracy of the merged quasi-geoid in the stitching region is checked using the measured GPS benchmark data; Second, the whole quasi-geoid is recomputed with data obtained from two adjacent regions if the accuracy of the quasi-geoid obtained from the first step in the stitching region is low; Finally, the quasi-geoids in two adjacent regions are recomputed respectively using GPS benchmark data of own region and adjacent region with the same solution if the accuracy of whole quasi-geoid obtained from the second step also is low. Actual data sets from Sichuan Province and Chongqing City are employed to test the moving window method. It is shown that the quasi-geoid models with high resolution and accuracy were obtained.

Seismic Strain Energy Release of Active Faults in the Southeastern Margin of the Qinghai-Tibetan Plateau

Using the methods of the Gutenberg magnitude energy empirical formula and the Benioff seismic strain energy release curve,we make a systematic study on seismic strain energy release of historical earthquakes in the southeastern margin of the Qinghai-Tibetan Plateau since 1500.This paper provides a periodic table of the earthquake strain energy release in the fault zones and the fault block areas.The study shows that seismic strain energy release is strong in the east and south,and weak in the west and north.The overall seismic strain energy release of the Yushu-Xianshuihe-Xiaojiang fault system is consistent with the quasi-periodic pattern.The seismic cycle of some fault zones and fault block areas shows synchronization to a certain extent.The risk cannot be ignored in the current large release period of seismic strain energy in the southeastern margin of the Qinghai-Tibetan plateau.Local seismic risk analysis shows that seismic risk is very high on the Anninghe-Zemuhe and Xiaojiang fault zones.These dangerous zones need follow-up research.In future,it is necessary to combine different research methods to improve the reliability of seismic risk assessment.

High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data

An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2(HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks;spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.

A Vector-based Cellular Automata Model for Simulating Urban Land Use Change

Cellular Automata(CA) is widely used for the simulation of land use changes. This study applied a vector-based CA model to simulate land use change in order to minimize or eliminate the scale sensitivity in traditional raster-based CA model. The cells of vector-based CA model are presented according to the shapes and attributes of geographic entities, and the transition rules of vector-based CA model are improved by taking spatial variables of the study area into consideration. The vector-based CA model is applied to simulate land use changes in downtown of Qidong City, Jiangsu Province, China and its validation is confirmed by the methods of visual assessment and spatial accuracy. The simulation result of vector-based CA model reveals that nearly 75% of newly increased urban cells are located in the northwest and southwest parts of the study area from 2002 to 2007, which is in consistent with real land use map. In addition, the simulation results of the vector-based and raster-based CA models are compared to real land use data and their spatial accuracies are found to be 84.0% and 81.9%, respectively. In conclusion, results from this study indicate that the vector-based CA model is a practical and applicable method for the simulation of urbanization processes.

Tropospheric Wet Refractivity Tomography Based on the BeiDou Satellite System

This paper presents a novel approach for assessing the precision of the wet refractivity field using BDS （BeiDou navigation satellite system） simulations only,GPS,and BDS＋GPS for the Shenzhen and Hongkong GNSS network.The simulations are carried out by adding artificial noise to a real observation dataset.Instead of using the δ and σ parameters computed from slant wet delay,as in previous studies,we employ the Bias and RMS parameters,computed from the tomography results of total voxels,in order to obtain a more direct and comprehensive evaluation of the precision of the refractivity field determination.The results show that：（1） the precision of tropospheric wet refractivity estimated using BDS alone （only 9 satellites used） is basically comparable to that of GPS; （2） BDS＋GPS （as of current operation） may not be able to significantly improve the data＇s spatial density for the application of refractivity tomography; and （3） any slight increase in the precision of refractivity tomography,particularly in the lower atmosphere,bears great significance for any applications dependent on the Chinese operational meteorological service.

Early-season crop type mapping using 30-m reference time series

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction,but the lack of ground-surveyed training samples is the main challenge for crop type identification.Although reference time series based method(RBM)has been proposed to identify crop types without the use of ground-surveyed training samples,the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution.As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series,we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI)time series at 30-m resolution(30-m RBM)using both Landsat and Sentinel-2 data,then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season.As a test case,we tried to use the 30-m RBM to identify major crop types in Hengshui,China at early season of 2018,the results showed that when the time series of the entire growing season were used for classification,overall classification accuracies of the 30-m RBM were higher than 95%,which were similar to the accuracies acquired using the ground-surveyed training samples.In addition,cotton,spring maize and summer maize distribution could be accurately generated 8,6 and 8 weeks before their harvest using the 30-m RBM;but winter wheat can only be accurately identified around the harvest time phase.Finally,NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases.Comparing with the previous RBM,advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop casification;while,samples collected from multiple years should be futher used so that the reference time series could contain more crop growth conditions.

MHSS ARAIM Algorithm Combined with Gross Error Detection

Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets with the larger computational load,a method combining MHSS ARAIM with gross error detection is proposed in this paper.The gross error detection method is used to identify and eliminate the gross data in the original data first,then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection.Therefore,this makes up for the weakness of the MHSS ARAIM algorithm.With the data processing and analysis from several international GNSS service(IGS)and international GNSS monitoring and assessment system(iGMAS)stations,the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service(LPV-200)when using GPS and BDS measure data.Under the assumption of a single-faulty satellite,the effective monitoring threshold(EMT)is improved about 22.47%and 9.63%,and the vertical protection level(VPL)is improved about 32.28%and 12.98%for GPS and BDS observations,respectively.Moreover,under the assumption of double-faulty satellites,the EMT is improved about 80.85%and 29.88%,and the VPL is improved about 49.66%and 18.24%for GPS and BDS observations,respectively.