2016 Vol. 41, No. 2
National geographical conditions are an important part of the basic national conditions, including spatial distribution and relationships among natural elements, cultural elements, and socio-economic information. The first national geographical census in China has recently been completed and geographical conditions monitoring has entered a phase of organization, summarization, and statistical analysis of census data. In the future, routine geographic conditions monitoring will be carried out. In this paper, some issues about contents, scales, units, models,and frequency of geographical conditions monitoring are discussed. Firstly, in addition to the existing census contents, impervious surface and socio-economic information necessary to obtain, a comprehensive national geographical conditions should be paid much attention. Secondly, suitable scales, units of monitoring, and statistics must be determined to make monitoring results more accurate. Thirdly, based on different requirements, corresponding monitoring models must be constructed for statistical data analysis. Finally, scientific monitoring plans ought to be made on the basis of characteristics and change rules of monitoring elements.
Current methods for retrieving surface temperature using remote sensing data and point data from ground temperature sensor networks yield low temperature inversion precision. To solve this problem, collaborative inversion methods with ground temperature sensor network(GSN) data and remote sensing inversion data fusion were explored four solutions for combination ground sensor network technology and remote sensing based on HJ-1 data, which were proposed to retrieve ground temperature. Experimental results shown that root mean square error of four solutions respectively decreased from 0.8848℃ to 0.6562℃, 0.4288℃, 0.4535℃ and 0.4261℃, and the correlation coefficients increased from the initial 0.6195 to 0.6343, 0.8629, 0.8507 and 0.8629. Moreover, the temperature error of solution four was below 0.45℃ and correlation coefficients were above 0.85 in the case of increasing pixel intervals. The results were validated using different images and GSN data. A comparison of the results and analysis of the models shown that the new model combining brightness temperature with classification results increased the accuracy of the initial retrieved results.
The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) introduces uncertainties and results in sub-optimal model state estimates. A modified EnKF method, the deterministic ensemble Kalman filter (DEnKF), can approach the analysis error covariance matrix without perturbing observations. As a forecast operator, the common land model (CoLM) is advantageous for sub-grid heterogeneity analysis. To reduce some errors stemming from the uncertainty in snow data assimilation, a new DEnKF-based snow data assimilation method is proposed for considering model sub-grid heterogeneity. The proposed method was used to assimilate the MODIS-derived snow cover products into CoLM for improving simulated snow depth. The daily snow depth of five meteorological stations from November 2007 to April 2008 in Altay is used for validation. The experimental results show that the DEnKF-based assimilation method can improve the simulated snow depth effectively. The improved snow depth does not only show the consistent time trends with in-situ snow depth but also reflects time-varying characteristics for different seasons.
Taking the growing processing results as angles, an ordinary Voronoi diagram and weighted Voronoi diagram are produced at uniform speed on an ideal Euclidean plane. However, based on analysis, modeling is not always sound in that a Voronoi diagram progresses at varying velocities on a non-ideal plane. The anisotropic non-ideal plane is depicted by weight distance and the growth velocity is formalized in a form conforming to the time derivative of weight distance. Therefore, a new Voronoi diagram, namely Gradient Voronoi Diagram (GVD) was defined in this paper. Taking the gradient caused by changes in elevation as an example, a typical construction model for GVD was propounded with the help of the dilation operator for mathematical morphology in raster space. An analysis shows that GVD has better guided significance and practical application value in the expression of influence regions and the Voronoi adjacency relationship.
Identifying corresponding objects is crucial in the process of heterogeneous road network matching. This paper proposed a road network matching method based on a multiple logistic regression algorithm. First, three dissimilar characteristics integrating both spatial and non-spatial features were used to describe the difference of the corresponding pairs of road objects;the minimum angle of the orientation, the mixed median Hausdorff distance, and semantic discrepancy. Using these three characteristics as variables of multiple logistic regression, we built a basic multiple logistic regression matching model. Samples to train the final road matching model were acquired to obtain matching results by predicting probability of each candidate road matching pair. Experimental results show that this method needs no exact feature weights and thresholds, and can solve the matching result problems stemming from over-reliance on single variable. This method has good adaptability, with higher precision and recall rates.
Complex network theory is an important topological analytic for networks. Current work applies complex network theory to address the entire topology of an urban road network but lacks capability to measure the influence of each road segment in maintaining the entire road network topology connectivity. In this article, a hierarchical representation model of road network topology intensity is proposed. The importance of the role each road segment plays in maintaining the topology connectivity of the entire network, topology intensity, is determined. Street networks are represented as a hierarchical model based on topology intensity. Experiments were conducted with the street networks of Changsha city. The rationality and the effectiveness of the hierarchical representation model were validated from an analysis of the experimental results.
In this paper we propose a raster line vectorization method based on Delaunay triangulation network. This method realizes the skeleton line extraction of line elements by spatiak subdivision of the raster line element using Delaunay triangulation network. This paper discusses raster map preprocessing, line element recognition, and the generation of edge point sets for line elements. A Delaunay triangular network is generated from edge point sets, and skeleton line extraction of line elements is based on tracing the midpoint of a Delaunay triangle public edge as the main technical line. We introduce implementation details of this vectorization process, and present the results of several experiments to validate the accuracy and timeliness of the method presented in this paper.
This article presents a new cloud detection method combining regularized least squares algorithm and threshold method based on the characteristics of Chinese ZY-3 multispectral imagses. In the process of the new method, second extraction of clouds using a regularized least squares algorithm is done based on a first extraction of clouds using the threshold method, which overcomes confusion of clouds, roads, and buildings. Compared to existing cloud detection methods, the accuracy of the new method is subjectively visibly higher than the threshold method and the K-means clustering combined with threshold method, achieving the same level of accuracy as a support vector machine combined with the threshold method for higher efficiency. Using the new method on different scenes collected at different time, the overall accuracy of the proposed cloud detection method is higher than 97% and the Kappa coefficient is higher than 0.9. These results show that the new method can detect cloud effectively in the case of different underlying surfaces. It is anticipated that this method will be popularized and further applied to imagery from other satellite systems.
A clustered threshold method for conveniently and efficiently extracting urban built-up areas has been proposed in this study. This method has broken through the limitations created by single pixel analysis and the administrative boundaries by using built-up objects identified by the recursive connected-region labeling algorithm as basic spatial units. It classifies urban development levels using spatial clustering on the basis of the size of the objects and the DN value of the geographic center of objects and extracts built-up areas based on the optimal threshold sequence as determined from statistical data. It optimizes the geometric morphology of built-up objects by removing small scraps, stuffing internal holes, and smoothing the jagged edges. Extraction results were analyzed and compared to the statistical data found in the China Statistical Yearbook over the years and images derived from Google Earth. These results show that the clustered threshold method can effectively obtain the total acreage and spatial pattern of urban built-up areas, with high validity and reliability in both quantity scale and spatial pattern.
The optimization of beneficial management practices (or beneficial management practices, BMPs) is a typical case of complex geo-computation; a computation-intensive search for optimal solutions of watershed BMPs through many iterative watershed model simulations. This paper presents a parallelization of the epsilon non-dominated sorted genetic algorithm (ε-NSGA-Ⅱ), an increasingly widely-used algorithm for BMPs optimization. The proposed parallel optimization algorithm was designed based on a master-slave parallelization strategy and implemented using the message passing interface (MPI). A case study executed on an IBM cluster for the Meichuan Jiang watershed (about 6366 km2) in the Lake Poyang basin shows that the proposed parallel BMPs optimization algorithm performs well. When the count of cores used in the case study increased (8~512 cores), the proposed parallel optimization algorithm delivered a higher speedup ratio. The speedup ratio reached 310 when 512 cores were used. In this case study, the parallel efficiency of the proposed parallel BMPs optimization algorithm decreased with an increase of the count of cores. The parallel efficiency ranged from 0.61 to 0.91, demonstrating that the proposed algorithm achieves good parallel performance.
Aimed at the resolving problems in selecting shallow sounding features by manual operations, such as low efficiency and easy omission, a new method for selecting features based on the slope-relationship from soundings in digital charts is put forward in the paper. By constructing a weighted Delaunay triangular network for soundings, the slope-relationship between different soundings can be confirmed by the spatial rule, and the latent lines from the soundings can be distilled, thus enabling automatic shallow sounding feature selection. Experiemental results show that this new method is an effective way to improve efficiency when distilling feature shallow soundings and avoid the omissions.
Resident express as area features in large and medium scale maps. The map loading of area resident features can be up to 70%-80%. At the same time, the numerical relationship of feature name and resident feature can be 1:n. Therefore, the name placement of area resident is rather difficult. In this paper, consulting standard specifications, we propose algorithm for name placement for area resident features. Firstly, we preprocess the resident features to form annotation units used for name placement. Secondly, we divide annotation units into different groups and candidate annotations are generated with corresponding annotation generating method. Thirdly, the candidates are evaluated with annotation evaluation rules. At last, the final location of an annotation is determined by the conflict rule. Experiments with resident data at the 1:50 000 scale yielded good results.
The distribution pattern of urban facility POIs usually forms clusters (i.e. "hot spots") in local geographic space. The kernel density estimation (KDE), which has been usually utilized for expressing these spatial characteristics, is one of the most popular visualization tools. Considering the missing of quantitative statistical inference assessment in KDE, this paper proposes a novel method to detect the hot spots of urban facility POIs. First, this method computes the attribute value of geographic unit with the "distance decay effect", then by adopting the statistical index of Getis-Ord Gi*, we analysis the local spatial cluster characteristics of urban facilities. Comparing this method with the conventional spatial autocorrelation based on the Quadrat clustering, the attribute value of kernel density computing can preserve the local information of data, and the spatial cluster characteristics of urban facilities can reflect the continuity characteristics of urban services, for that the KDE considers the regional impact based on the First Law of Geography. The actual data experiment for analyzing the financial POIs' distribution patterns indicates that this approach is effective to extract the hot spots of urban facility POIs in city areas.
Geographic object-based image analysis (GEOBIA) techniques have recently seen considerable development in comparison to traditional pixel-based image analysis, representing a paradigm shift in remote sensing interpretation. The main aim is to incorporate and develop geographic-based intelligence. The random forest (RF) machine learning method is a relatively new, non-parametric, data-driven classification method that can supply intelligent means for feature selection and classification modelling. This paper presents a novel RF GEOBIA method for land-cover classification that makes full use of the advantages of GEOBIA and RF. A detailed RF GEOBIA workflow is proposed to guide the design and implementation of the method, and to guide experts during elaboration of feature selection and classification modelling. Theoretical and experimental results are compared with the support vector machine (SVM) approach, demonstrating that it is a robust and intelligent method for land-cover classification with wrapper feature selection and classification modelling. The RF GEOBIA method reduces the number of features required, computing time, and memory requirements, with no associated reduction in performance. It also provides a priori knowledge for further classification and supports large scale applications where "big data" is involved.
The identification of the rural-urban fringe is a hotspot in the rural-urban fringe research field. However, fussy indictors,threshold value methods, and identifying fringe respectively are usually used according to existing research approaches. We however, introduced the wavelet transform method to identify rural-urban fringe through detect mutation point group of LUDCI values based on modulus maxima of wavelet transform, after an analysis of the relationship between land use degree comprehensive index(LUDCI)and rural-urban fringe. Along each profile from central city to suburb,the inner boundary of a rural-urban fringe is where a mutation point group appears, while the external boundary is where mutation point group disappears. Wuhan is experiencing rapid urbanization, and therefore were chosen to implement a case study. The db3 Daubechies wavelet function was selected as the mother wavelet, and wavelet transform was scale 3. The visualization of mutation point detection results were performed using ARCGIS.These case study results suggest that the two rural-urban fringe boundaries could be identified together. Our results show that this method is a stricter and more efficient approach for identifying rural-urban fringes than other methods.
Land subsidence in the modern Yellow River Delta at high spatial and temporal resolution was deduced from SBAS time-series analysis of ERS1/2 data. The experimental results show that land subsidence in the modern Yellow River Delta is widespread and unevenly distributed with large differences. The average subsidence rate is -5.1 mm/yr, while the highest subsidence rate of -33.2 mm/yr occurring in the subsidence funnel formed by an oil field. The InSAR results are shown to be reliable, when compared with leveling survey measurements. Ground based leveling measurements included 53 leveling points were used to evaluate the accuracy of our SBAS time-series analysis results with a consistent deformation trend between the two sets of results. A comparison between leveling points and their nearest SBAS points at the same time interval showed they were in complete agreement, while the mean square error between them was at the mm level. The main influencing factors differ by region. Severe land subsidence however, is caused by oil extraction including extracting nearby shallow groundwater used for artificial water injection after oil exploitation and sediment consolidation. Oil exploitation was the main influencing factor and responsible for the rapid, patchy subsidence evident at Dongying city, Hekou district, Gudao town, Zhuangxi, and the Gudong oilfield. Groundwater extraction for making salt and oilfield water injection is likely responsible for land subsidence in the Liuhu township and at the Guangrao salt pan. Increased surface load aggravated land subsidence in the old urban district of Dongying city, and sediment consolidation might be considerable after the lobe is abandoned.
It is difficult for the BeiDou satellite navigation system to establish a global tracking network abroad, therefore regional tracking station observations are an important tool to achieve precise orbit determination (POD). Our investigation demonstrates that ambiguity resolution and reasonable arc length are important factors for improving the accuracy of regional orbit determination. GPS observations from CMONOC were adopted to simulate regional precise orbit determination; under the condition of the same station distribution, the accuracy of POD with ambiguity resolution was better than 30% with floating solution. Furthermore, the 3D RMS of an orbit with ambiguity resolution using seven stations was about 20 cm, better than the results of an ambiguity float solution using 50 stations. In addition, we evaluated the impact of arc length on POD accuracy from the satellite constellation design and the visible coverage of regional tracking stations. This experiment confirmed that when the observation time of regional stations is more than 48 hours, a valid arc length of regional POD, not less than 24 hours, can always be selected for each GPS satellite; the best selection can achieve a 3D accuracy about 0.3 m.
Considering the situation that the weight matrix of observation vector and coefficient matrix may be inaccurate, an available algorithm is introduced in this paper, which is derived on the basis of combining the Helmert variance component estimation with a kind of fast weighted total least squares algorithm in the errors-in-variables models. And the derivative process of the fast weighted total least squares is described in detail and the comparison with three other algorithms is implemented in this paper. Using the fast weighted total least squares algorithm combining Helmert variance component estimation derived in this paper, the stochastic model and the unknown parameters of the functional model can be solved simultaneously. Three empirical examples, two straight line fitting and one linear parameter estimation, are also used to investigate the application of posteriori estimation of stochastic model on weighted total least squares problem. Results show that the algorithm is very effective.
In the process of global navigation satellite system (GNSS) signal structural assessment, multipath error envelope gives the upper bound of multipath error,but it can not effectively reflect code tracking multipath error caused by the carrier and subcarrier phase of reflected signal. To solve this problem,a code tracking multipath error non-envelope assessment method is proposed. In the proposed method, the code phase delay is mapped to carrier and subcarrier phase to get an accurate theoretical code tracking multipath error value. This approach also avoids calculation of the envelope curve as found in the multipath error envelope analysis method. The theoretical expression of the proposed method was derived and a multipath rejection performance experiment simulation was performed for BPSK, AltBOC, MBOC and BOC. The multipath error envelope and proposed method were compared. The theoretical and simulated results show that the proposed method can be used to assess code tracking multipath error more accurately, and provides effective theoretical guidance for dealing with GNSS signal multipath error.
An extended robust Kalman filter based on the chi-square test algorithm was developed to address the observation failure for position and velocity due to the fact that GNSS signals are sheltered as they interfere with integrated navigation.The algorithm was used for anti-processing the GNSS position and velocity observations allowing the use of the robust Kalman filter in situations lacking redundant observations. Finally, measured data was processed to verify the algorithm. Results show that observation outliers can be controlled effectively while the filter stability and reliability is improved by the extended robust Kalman filter based on chi-square test algorithm, even if there are no redundant observations.
In December, 2013, the Chinese lunar lander Chang'E-3 made a soft landing on the Moon successfully. Its precision positioning is a basic requirement in the analysis of scientific data. In this paper, the modeling of the precise observation equation and the statistical positioning method are first described briefly. Second, using the limited tracking data of the lander, the statistical positioning of the lander was performed and the positioning accuracy analyzed by three different methods. The results indicate that the determined altitude of the lander was approximately 4.5 m different from the latest lunar topographical model. Compared with the lander location observed from the lunar reconnaissance orbiter camera, there is a deviation less than 100 m in the three dimensions. We subsequeently analyze the positioning ability under current tracking conditions using the covariance analysis theory. These results show that the systematic error in the ranging data was the main limiting factor that restricts lander positioning accuracy, and an accuracy of 10 m positioning solution may be obtained if this bias is removed.
Differential Code Bias is also called instrumental bias,and is one of the main error sources affecting PNT services. GPS instrumental bias is usually solved with ionospheric model coefficients. However, BDS method is now a regional satellite navigation system, which is difficult to obtain precise instrumental bias results using only BDS itself. In this paper, we propose a means to estimate BDS satellite and receiver instrumental biases using combined GPS/BDS observations in spherical harmonics modeling. The precision of BDS satellite instrumental bias was about 0.3 ns using this method, and the GEO/IGSO satellite instrumental bias precision was more stable than the MEO satellites. Besides, the precision of the satellite instrumental bias is better than receiver instrumental bias.