This PDF file contains the front matter associated with SPIE Proceedings Volume 7492, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

As a key element for land use cover change research, change detection technique is of urgent demands and has great potential in scientific applications. Conflation is the process of combining the information from two (or more) geodata sets to make a master data set that is superior to either source data set in either spatial or attribute aspect. The objectives of conflation include increasing spatial accuracy and consistency, and updating or adding new spatial features into data sets. Based on the analysis and summarizations of researched home and aboard, the paper focused on Land Use/Cover Change detection using feature database of basic types based on vector-image data conflation, that is : Combining of Land use map and RS image, features(grey feature, texture feature and shape feature) are extracted. This methodology belongs to "Feature class" of LUCC. It should be pointed out that the researches must be focused on the land use span other then traditional methods of the pixels. Each spans of T2 will be classified according to the minimum Euclidean distance to the T2 sample span accepted, and the corresponding land use type will be assigned to the current patch, Change information are extraction automatically based on Boolean operations. The method is tested on the Quick Bird images of a district in Wuhan and the precision of the results is high as 92.6% (in urbanization).The experimental results demonstrate that the proposed method can cut down the computational costs and improve the accuracy.

The soil organic matter is one of the important criterions of soil fertility. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this paper we compare two measures of multivariate statistical analysis (MSA) and fuzzy recognition algorithm (FRA) for quantitative mapping soil organic matter content using Hyperspectral remote sensing. This study was tested in Henshan County, northern ShanXi Province of China. On the one hand, the ratio of the reflectivity reciprocal-logarithm's first derivative of 623.6nm against the reflectivity reciprocal-logarithm's first derivative of 564.4nm was chosen as the sensitive retrieval parameter and build up the retrieval models. Then, the best quadratic retrieval model was utilized to map the SOM content by calculating each pixel of Hyperion image, the adjusted R square coefficient is 0.8684. On the other hand, by analyzing the correlation between spectrally reflective data and SOM concentrate, the first derivative of logarithmic reflectance at sensitive bands of 393nm, 444nm, 502nm, 1455nm and 1937nm were confirmed as the retrieval indicators due to the notable correlation coefficients. Finally, the most optimized-retrieval model, utilized to the Hyperspectral data for SOM quantitative mapping, was build up by using the fuzzy recognition theory. The correlation coefficient of the retrieval model is 0.981. It is found that result of fuzzy recognition algorithm is better than that of traditionally statistical analysis, with the mean predicted error of 8.43% as compared to 10.42% for quadratic retrieval model. It is concluded that this fuzzy recognition algorithm for Hyperspectrally quantitative mapping SOM is available and the result map is reliable and significantly correlative with known stabilization processes throughout the study area. Moreover, the fuzzy recognition algorithm developed in this paper could be applied to other domain of quantitative remote sensing.

This paper suggests a method of calculating the elevation and the volume change of the terrain based on the multitemporal digital elevation model (DEM) data sets for the same area. Two methods for calculating the surface change are introduced: One is based on the regular square grids (RSG), another uses the triangulated irregular network (TIN) generalized from the original source data by the 3D Douglas-Peucker algorithm so that not only the accuracy of generalization using 3D Douglas-Peucker is verified, but also the kinds of data formats of DEM for this purpose have been expanded. Finally, the formulae used by these two methods are introduced, and the experimental results calculated from the same original DEM data acquired in 1971 and 2000 respectively form the area of Bayanbulak in Xinjiang are compared. The experiments have shown that the results of the two methods are relatively identical even if under the great generalization degree of DEM for the second method. Therefore, it shows that the second method can greatly heighten the efficiency of the calculation while insuring its accuracy.

Recently land use/cover change detection (LUCC) has become an important aspect in nature resource and environment monitoring and protection. A data fusion method for LUCC is presented concerning the situation that there are only the remote sensing (RS) images of updated period and the land use/cover maps of original period. Firstly, multi-spectral and panchromatic images of SPOT-5 are fused by using principle component analysis (PCA) algorithm on Erdas Imagine platform. Then, after the co-registration of the land use/cover map and RS image, the RS image of the updated period is classified by K-means algorithm and the precise classification chart of the land use/cover is obtained. The land use/cover map is transformed from vector to raster format and the land class code is used as each pixel's value of the transformed raster image. Finally, land use/cover changes are found by comparing the corresponding land class codes. The study area is located in Huangpi District of Wuhan City, and experiments demonstrate the proposed data fusion method for land use/cover change detection is a feasible resolution.

Keeping informed of the collieries exploitation is of great importance for the benefit of our country. Currently, with the high spatial resolution remotely sensed imagery, we are able to monitor the open air collieries directly without in situ inspections. This paper presents the application of active contour models(snakes) for semi-automatic extraction of the contour from the typical collieries in remote sensing images. After carefully examined the characteristics of the collieries, we improved the active contour model. The boundary of the collieries is not very clearly on the images, and the influence of the random image noise is extremely large. Therefore, improving the image power of Snake model is necessary. As the Snake model can only work on panchromatic images, we first fused the multi-spectral image to make full use of the spectral information contained with in the image. Then we used Canny edge detector which is anti-noise to extract the features. At the same time, a gauss filter is performed to the edge image to enlarge the envelope of the edge. We found that the image power calculated from the processed image is more efficient than it from the original image. A software package build in ArcGIS has been made based on this method.

Urban information is a kind of multi-sources data, and the variety of these data demands that we should set up an information system. One of the major tasks is to store massive spatial data and non-spatial data and manage these data effectively. One of the other major tasks of urbanization integration is how to search for spatial data and non-spatial data what we need into massive information, so we need to establish indexes for spatial data and non-spatial data and construct the relation between the two kinds of data in order to convenient query. The paper is focused on data indexes construction, classified indexes for non-spatial data, R-trees index for spatial data and puts forward an area hiberarchy index tree to build up direct relationship between spatial data and non-spatial data for seamless queries, and the experiment shows that the hiberarchy index tree is much validated and something useful is obtained.

Image segmentation refers to the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analysis [1]. These advantages of this process make it be widely used to find out the interesting objects in high resolution remote sensing images. Watershed algorithm is based on the topology of the image, although it can easily split images into homogenous partitions, it also leads to over-segment in practical implementation. Some solutions were proposed to solve this problem in the past few years [2][3]. Using pre-defined seeds and extracting the pixels clusters which are grown from these seeds is a reasonable method to overwhelm the obscure of over-segmentation. In this paper; we present a novel framework to improve the results of watersheds segmentation by using edge detection to find out the position of seed points. Then the immersion simulations suggested by Vicent has been used to segment the image. The algorithm consists of four steps: a) Edge detection with embedded confidence, b) Thinning processing on the previous results, c) Label the seed points on each side of the thinned edges, d) Detection of watersheds on gradient magnitude image using immersion simulations. At last, we use high resolution remote sensing image to qualify the framework and the experimental results are presented. It shows that reasonable results which preserve the edge could be gotten by applying this framework.

Similarity is a psychological cognition; this paper defines the Difference Distance and puts forward the Similarity Measuring Model for linear spatial data (SMM-L) based on the integration of the Distance View and the Feature Set View which are the views for similarity cognition. Based on the study of the relationship between the spatial data change and the similarity, a change detection algorithm for linear spatial data is developed, and a test on road data change detection is realized.

In the geospatial data processing and GIS analysis applications, extracting the axis of the double-line river is an important part of graphic generalization. At first, this paper introduces the principle of Delaunay triangulation and the extraction principle of the triangulation skeleton line. On the bases of these principles, it puts forward the method which is about extracting the double-line river axis based on the principle of Delaunay triangulation. The method is different from other methods of extracting the axis of the double-line river which deal with polygon objects, but adopts the bank lines as the treatment object. This method uses the simple line objects instead of the polygon objects whose data structure is more complex. Therefore, in data processing, the procedure complexity is reduced greatly. And it enhances the operability of the treatment process. The experiments show that the method can extract the double-line river axis perfectly and quickly. The method is highly effective and easy to maintain the topological relationships.

The classification by extracting of remote sensing (RS) data is the primary information source for GIS in land resource application. Automatic and accurate mapping of region LUCC from high spatial resolution satellite image is still a challenge. The paper discussed remote sensing image data classification techniques based on C4.5 algorithm and rough sets and the combination of C4.5 algorithm and rough sets. On the basis of the theories and methods of spatial data mining, we improve the classification accuracy. Finally validates its effectiveness taking a test area as example. We took the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The classification rules are discovered from the samples through decision tree C4.5 algorithm, Rough Sets and both with together, which integrates spectral, textural and the topography characters. And the classification test is performed based on these rules. The traditional maximum likelihood classification is also compared to check the classification accuracy. The results have shown that the accuracy of classification based on knowledge is markedly higher than the traditional maximum likelihood classification. Especially the method based on combine Rough Sets and decision tree C4.5 algorithm is the best.

Terrestrial laser scanning is a new technology uprising in 1990's. Owing to its ability to obtain a large number of 3D coordinates of points, called point clouds, in real time, it has been attracted attentions of surveying field. To build threedimensional models, multiple scans from different viewpoints are required due to occluded surfaces and limited field of view of the scanner. These multi-view point clouds then must be transformed into a common reference frame in order to describe complete object being researched. This process is called registration of point clouds. Pairwise registration often is a common method for registration of multi-views point clouds. Owing to characteristics of error accumulation and propagation, using pairwise registration method will result in severely distortion of researched object. In order to overcome this shortcoming, we present a new registration method. Firstly, we use pairwise registration method to calculate transformation parameters of adjacent two scans, and by selecting coordinate system of first scan as a uniform coordinate system, we connect all point clouds into a loosely network. Secondly, according to condition that common points between first scan and last scan must have the same coordinates in uniform system, we use nonlinear correction model to compute the distortion parameters of loosely network and lastly we correct distortion of each single point clouds and determine the best position of each point. Experiment is carried out and the results show that the registration error has reduced from 1.7cm to 5mm after correction, which demonstrates correctness of the method.

The grassland ecosystems in west Northeast China plain are sensitive indicators of environmental impacts from both climate change and direct human activities. NDVI imagery over each ten-day period in the past ten years was calculated based on the VGT sensor. The Maximum Value Composites method, annual average value, monthly average value and difference value of two indices were used to analyze the inter-annual changes of vegetation cover. Linear trends could be detected in the NDVI time series that differ by season. We pointed that grassland cover in this area decreased with an obvious undulating trend during 1998-2007. The yearly maximum NDVI mean over the study area decreased from 0.644 in 1998 to 0.615 in 2007 indicated the decreasing trend of grassland activity. There was a significant decrease of monthly maximum NDVI in spring and early summer months, while positive NDVI trends were described for autumn. The degraded vegetation area occupied about 20.29% of the whole study area, while vegetation in some area was increase taking up 16.03%. Precipitation and temperature had different effect on yearly maximum NDVI of grassland in this semi-arid region. Grassland degradation may be explained by the duration and severity of drought.

Land use cover change (LUCC) provide important information for environmental management and planning. It is one of the most prominent characteristics in globe environment change, and not only limited by natural factor, but also affected by the factor of social, economics, technique and histories. Traditionally, field surveys of land cover and land use are time consuming and costly and provide tabular statistics with out geographic location information. Remote sensing and GIS are the most modern technologies which have been widely used in the field of natural resource management and monitoring. Change detection in land use and updating information on the distribution and dynamics of land use have long term significance in policy making and scientific research. In this paper, we use multistpectral images of Spot period two different of time 2002 and 2007 for detection on LUCC base on Scale Invariant Feature Transform (SIFT) method. An automatic image matching technique based on SIFT was proposed by using the rotation and scale invariant property of SIFT. Keypoints are first extracted by searching over all scales and image locations, then the descriptors defined on the keypoint neighborhood are computed. The proposed algorithm is robust to translation, rotation, noise and scaling. Experimental results, urban is the most part of Huangpi area which have been changed.

Airborne LIDAR is one of the most effective and reliable means of coastal survey. Using LIDAR data for coastal survey is becoming a standard practice in spatial related areas. However, the effective processing of the raw LIDAR data, the generation of an efficient and high-quality DEM and feature extraction remain big challenges. This paper reviews some critical issues on the LIDAR data pre-processing, removing and separating the LIDAR data, mainly including the following: How to extract and classify the buildings, vegetation, roads and the other objections and afterward having some discussions on the image registration and multi-source data fusion.

Three Differential Synthetic Aperture Radar Interferometry (D-InSAR) methods are compared on the capability of obtaining land subsidence temporal data. These three methods including, Interferograms time-series stacking, Permanent Scatters D-InSAR(PS-InSAR) and Multi-Baseline D-InSAR are all base on interferometry phase spatial-temporal analysis to increase reliability. They are used to extract land subsidence mean velocity from time serial SAR images in nearly two years. Experimental results are compared by data source quality requirement, extracted data density and precision and then evaluated their applicability. The results show that though these D-InSAR methods limited by sensor restriction, baseline decorrelation and atmosphere affect, they could get reliable mean velocity distribution of land subsidence from difficult condition by careful selection of data source and methods.

Popularity of high resolution image makes it possible to interpret the internal land use condition within settlement. The categorization of settlement is firstly made as rural (mountain, plain) and urban (city village, living block) areas. Then object-based and generic network image processing methods are primarily used in the extraction from the merged Quickbird and SPOT high resolution image. With mathematical as well as geometrical model, indices such as building density, floor area ratio, green ratio and space ratio are extracted accordingly. Meanwhile, land use map, field survey and cadastral map are employed for the estimation and accuracy assessment. In the end, analysis and comparison are made for land use condition within settlement.

In this paper, we propose an analysis on the combinative effect of high-resolution airborne image and light detection and ranging (LIDAR) data for the classification of complex urban areas. In greater detail, the proposed system is composed of three models briefly. Model one includes an advanced kernelized fuzzy c-means classification method for high-resolution airborne image. The characteristics of LIDAR point cloud are introduced in model two, membership degree function of buildings, vegetations and naked land have been built. In model three, high-resolution image and elevation data form LIDAR point cloud are jointed. Experiment carried out on a complex urban area provide interesting conclusions on the effectiveness and protentialities of the joint use of high-resolution image and LIDAR data. In particular, the elevation data was very effective for the separation of species with similar spectral signatures but different elevation information. Experimental results approve that elevation data can improve classification accuracy in building occupied area obviously.

According to the actual situation of aided design system of land consolidation, the shortcomings of the traditional aided design software of land consolidation are concluded in the paper. And with the development of the technology of the computer aided design and software, the aided design system of land consolidation on the platform extended graph-element, which adopts the mature and advanced technologies of extended graph-element as well as parametric design, and displays geospatial phenomenon to the users by the interactive design technology with CAD, is constructed to improve the speed and quality of the design. Considering about the main problems of the current land consolidation aided design, following the practical and systematic design idea, functional modules of the land consolidation aided design are constructed, and the composition and the working process of each function module is described in details.

Presently, there are three methods of retrieving land surface temperature based on TM thermal infrared band (band 6). These methods are: the Radiative transfer equation (RTE), the Mono-window algorithm and the General single channel algorithm. Due to the general unavailability of situatmospheric profile data, the RTE method is seldom applied. Though the other two methods are relatively widely researched, the fields are almost limited on the urban hot island study by retrieving hot seasonal temperature. With the increasing emphasis of city energy saving in China and the increasing heating energy consumption, more researchers are beginning to study the construction of city buildings and hot diffusion in winter with remote sensing methods. In order to judge which one of the other two LST retrieval methods is optimal to retrieve cold seasonal temperature, this paper focuses on the retrieval of cold seasonal LST from the Landsat TM6 data. This data was captured on 4th Jan 2007 over the Xi Cheng District in Beijing by using the Mono-window algorithm and the General single channel algorithm. The results indicate that, both methods can produce a similar LST spatial distribution. However, with the higher relative precision, the Mono-window algorithm proved to be more suitable than the General single channel algorithm to this research area. Furthermore, the author presents some suggestions on the choice of winter temperature LST methods.

Remote sensing techniques have been widely applied to capture spatial/temporal information for water resource studies and they provide great useful information for keeping the managements and sustainable developments of aquatic ecosystems. Poyang Lake, the largest freshwater lake in China, is located at the southern bank of the middle Yangtze River, and the high water quality makes it an important international wetland, allowing its ecosystem to provide significant benefits to the society. This paper aims to review recent applications of remote sensing techniques on capturing spatial/temporal information for water resource studies in Poyang Lake. The Poyang Lake and remote sensing techniques are briefly introduced first. Then the applications of remote sensing techniques on the studies of water level, water area, flooding disaster, water quality (e.g. water clarity and suspended sediment concentration) and eutrophication of Poyang Lake are reviewed. Finally some potential applications of remote sensing techniques on Poyang Lake and conclusion are summarized. It is hoped that this paper might provide necessary and integrated information for the researchers to understand the applications of remote sensing techniques in the water resource studies and to establish foundation for their further studies in Poyang Lake.

Mining and smelting often produce acidic wastes that can cause severe biogeochemical changes downstream from these mines. Dexing copper mine, as the largest open cast mine in China, is connected to Poyang Lake by Le An river. Water and spectra samples were taken from Le An River and two of its branches, and afterward the concentrations of Cd, Cu, Pb and Zn were measured in the lab. Different spectral pre-processing methods were applied to the spectra, including Savitzky-Golay spectral smoothing, SNV, first derivative, second derivative spectral transforming. On the purpose of estimating metal concentrations from differently pre-processed spectra, partial least squares regression was then used in model calibrations. For deciding the optimal number of PLS factors included in the PLS model, the model with the lowest root mean square error of validation is chosen. The coefficient of determination (R²_v) between the predicted and the reference values from the test set are used as an evaluation mean. For estimating Pb concentration, R²_v = 0.915, which is acceptable. For Cd concentration, R²_v = 0.697 and 0.683 for Zn. PLS model seems to failed in estimating Cu concentration, for the best R²_v for PLS model of Cu is lower than 0.5. From the aspects of spectral pre-processing methods, first derivative after Savitzky-Golay smoothing performs superior to others. In conclusion, PLS models based on carefully pre-processed hyperspectral data turn out to be a promising solution for detecting certain heavy metals concentrations in river.

The traditional land use classification technology of remote sensing(RS) image is in a low level of automation and intelligence,land use classification of RS image is an uncertain problem which contains random and fuzziness,the cloud model integrates fuzziness and random together,constituting mapping between qualitative and quota. According to the above,this article introduces the cloud theory into land use classification of RS image,establishing a cloud mapping space based on gradation,aiming to solve the problem of land use classification of RS image. At the same time,this paper has constituted the result evaluating indicator system in aspect of the homogeneity indexes of the region and the boundary location,and has carried on the empirical analysis of the SPOT image of Nanhu area of Wuhan,elaborated the model construction process in depth,explored the serviceability of this method in this domain by the evaluation and the contrast of the classification results.

Context-Sensitive, also known as context-aware, is a new computing model which taking people as a dominant factor, it requires the computing devices can detect the user's context information to adjust the system's behavior. Web Map Service (WMS) is a GIS (Geography information system) specification which is established by the OGC in accordance with the development of Web Service technology. In order to make the map services more user-friendly and get better user experience, our works are people-oriented and dynamically provide corresponding map services on the basis of the user's context information. In this paper we address an architecture of web map service which concentrates on context-sensitive. It can convert varies user's context information to different kinds of parameters, about the visualization of geographic data, to get variable map images in response to the users' context., and finally realizes providing context-sensitive Web Map Service.

This paper reviews the characteristics of spatio-temporal data and existing techniques for visualizing them, and then proposes a temporal tree structure which used to record and manipulates spatio-temporal object's predecessors and successors for the data source of dynamic visualization by algorithms of multi-temporal vector data correlation. Then, dynamic visualization methods of geometry shape and thematic were proposed with the support of temporal tree structure and case study were used to verify the methods proposed. The results and conclusion show that our method of dynamic visualization is an effective and generic way to implement dynamic modeling and visualization processes of spatio-temporal objects with various states along time.

At present, the application of three-dimensional GIS becomes more and more widespread gradually, but due to the defect of representing time, four-dimensional GIS based on spatial-temporal expression is facilitated to emerge and progress. Combined with developing the 3D dynamic demonstration of Tianjin center fishing port, this paper researches the mass data and animated simulation of building process and provides an approach that the data is dealt with in the way just as 2D map does such as classification and partition to get clarified data. At the same time, a scene integration method is proposed by dividing a large-scale 3D scene to several sub-scenes with a number of levels and various covering areas. And through editing and synthesizing the commentary, time axis and flight routes, the dynamic simulation and automatic demonstration are achieved. Based on the study above, a system of simulating and illustrating the port building process is designed and implemented.

3D spatial access method for globe data is very crucial technique for virtual earth. This paper presents a brand-new maintenance method to index 3d objects distributed on the whole surface of the earth, which integrates the 1:1,000,000- scale topographic map tiles, Quad-tree and R-tree. Furthermore, when traditional methods are extended into 3d space, the performance of spatial index deteriorates badly, for example 3D R-tree. In order to effectively solve this difficult problem, a new algorithm of dynamic R-tree is put forward, which includes two sub-procedures, namely node-choosing and node-split. In the node-choosing algorithm, a new strategy is adopted, not like the traditional mode which is from top to bottom, but firstly from bottom to top then from top to bottom. This strategy can effectively solve the negative influence of node overlap. In the node-split algorithm, 2-to-3 split mode substitutes the traditional 1-to-2 mode, which can better concern the shape and size of nodes. Because of the rational tree shape, this R-tree method can easily integrate the concept of LOD. Therefore, it will be later implemented in commercial DBMS and adopted in time-crucial 3d GIS system.

The travel craze in China has become one of the largest sources of expenditure increases. However, the limited patterns of information demonstration hindered the availability of necessary information to the public as well as the decision makers. Google Earth provides a useful platform for integrating environmental information with geospatial data from different sources. Using KML files, various environmental data may be viewed in Google Earth. In this paper, Google Earth is used as basic GIS platform. Placemarks of main tourist areas of Hubei Province are made and loaded by KML. The most up-to-date information is released using text, picture and video in HTML files. This approach transformed the traditional two-dimension travel information system into multi-dimensional information platform, facilitating data updating and information sharing over the Internet. It would also help decision making of the government and promote information availability to the public.

The subject intersection becomes a hot research topic recently. This paper tried to couple the Bibliometrics and Geographical Information System (GIS) technologies for studying on the spatial information mining and visualization from the Qinghai-Tibet Plateau's literature. All the literatures about Qinghai-Tibet Plateau research were indexed in the ISI Web of Knowledge. The statistical tables about the authors were extracted from the papers by using the method of bibliometrics. The spatial information of the author's countries was linked with the GIS database. The spatial distribution was presented by the format of maps based on the GIS technologies. Comparing with the regular presentation forms of the bibliometrical analysis, the spatial distribution maps can afford more abundant and intuitive senses for the users.

There are many large-scale historical relics in China's stone-desert district, all this sorts relics are threatening by wind deflation. For documenting the cultural heritage in detail and avoiding a long working time in formidable desert conditions, we need an accurate and fast way to record the relics' 3D information. Laser scanners offer various applications on conservation of cultural heritage in recent years, such as static surveying, precise modeling and visualization for data acquiring purpose. Point clouds generated by terrestrial laser scanner and aerial image both are valuable data sources for the reconstruction of objects'3D models. This study exploits an approach for recording relic's datum by long-range terrestrial laser scanner (Optech ILRIS-3D) and fusing the scan data with gray information for visualization tasks. As a result of obtaining full scene needs several scans, we transform every scan individually into one global reference frame for decreasing the accumulative errors of point-clouds registration of common ICP approaches, and build 3D point-clouds model of historical relics by this way. For the purpose to add texture information on the point-clouds model, we consider using the approach by fussing the corresponding aerial orthophoto with the model because the objects have very similar texture information in desert. Results show its efficiency and feasible.

The main purpose of this paper is to realize the expression of the three-dimensional dynamic visualization of spatialtemporal data based on three-dimensional virtual scene, using three-dimensional visualization technology, and combining with GIS so that the people's abilities of cognizing time and space are enhanced and improved by designing dynamic symbol and interactive expression. Using particle systems, three-dimensional simulation, virtual reality and other visual means, we can simulate the situations produced by changing the spatial location and property information of geographical entities over time, then explore and analyze its movement and transformation rules by changing the interactive manner, and also replay history and forecast of future. In this paper, the main research object is the vehicle track and the typhoon path and spatial-temporal data, through three-dimensional dynamic simulation of its track, and realize its timely monitoring its trends and historical track replaying; according to visualization techniques of spatialtemporal data in Three-dimensional virtual scene, providing us with excellent spatial-temporal information cognitive instrument not only can add clarity to show spatial-temporal information of the changes and developments in the situation, but also be used for future development and changes in the prediction and deduction.

Virtual-real registration in Outdoor Augmented Reality is committed to enhance user's spatial cognition by overlaying virtual geographical objects on real scene. According to analyze fiducial detection registration method in indoor AR, for the purpose of avoiding complex and tedious process of position tracking and camera calibration in traditional registration methods, it puts forward and practices a virtual-real spatial information visualization registration method using affine representations. Based on the observation from Koenderink and van Doorn, Ullman and Basri in 1991 which is given a set of four or more non-coplanar 3D points, the projection of all points in the set can be computed as a linear combination of the projection of just four of the points, it sets up global affine coordinate system in light of world coordinates, camera coordinates and virtual coordinates and extracts four feature points from scene image and calculates the global affine coordinates of key points of virtual objects. Then according to a linear homogeneous coordinates of the four feature point's projection, it calculates projection pixel coordinates of key points of virtual objects. In addition, it proposes an approach to obtain pixel relative depth for hidden surface removal. Finally, by a case study, it verifies the feasibility and efficiency of the registration methods. The method would not only explore a new research direction for Geographical Information Science, but also would provide location-based information and services for outdoor AR.

Recently, the visualization of spatial-temporal data in historic events is emphasized by more and more people. To provide an efficient and effective approach to meet this requirement is the duty of Geo-data modeling researchers. The aim of the paper is to ground on a new perspective to visualize the multivariate spatial-temporal data of the Red Army Long March, which is one of the most important events of the Chinese modem history. This research focuses on the extraction of relevant information from a 3-dimensional trajectory, which captures object locations in geographic space at specified temporal intervals. However, existing visualization methods cannot deal with the multivariate spatial-temporal data effectively. Thus there is a potential chance to represent and analyze this kind of data in the case study. The thesis combines two visualization methods, the Space-Time-Cube for spatial temporal data and Parallel Coordinates Plots (PCPs) for multivariable data, to develop conceptual GIS database model that facilitates the exploration and analysis of multivariate spatial-temporal data sets in the combination with 3D Space-Time-Path and 2D graphics. The designed model is supported by the geo-visualization environment and integrates diverse sets of multivariate spatial-temporal data and built-up the dynamic process and relationships. It is concluded that this way of geo-visualization can effectively manipulate a large amount of distributed data, realize the high efficient transmission of quantitative and qualitative information and also provide a new research mode in the field of the History of CPC and military affairs.

Geovisualization is an important means to understand the geographic features and phenomena. Urban space, especially buildings, keeps changing with social development. However, traditional 2D visualization can only represent the plane geometric description, which is unable to support 3D dynamic visualization. Only with 3D dynamic visualization can the buildings' spatial morphology be exhibited temporally, including buildings' creation, expansion, removing, etc. But these buildings' changes are impossible to be studied in traditional 2D and 3D static visualization systems. As a result, it becomes urgent to find an effective solution to implement 3D spatial-temporal visualization of buildings. Inspired by 2D spatial-temporal visualization methods, like snapshot and event-based spatio-temporal data model(ESTDM), we propose a new data model called Spatio-Temporal Page Model(STPM) and implement 3D spatial-temporal visualization in Google SketchUp based on STPM. This paper studies 3D visualization of real estate focusing on its spatio-temporal characteristics. First of all, 3D models are built for every temporal scenario by the Google SketchUp. And every Geo-object is identified by a unique and permanent ObjectID, the linkage of Geo-objects between different time spots. Then, each temporal scenario is represented as page. After having the page series, finally, it is possible to display its spatial-temporal changes and create an animation. Underlying this solution, we have built a prototype system on part of real estate data. It is proven that users are able to understand clearly the real estate's changes from our prototype system. Consequently, we believe our method for 3D spatial-temporal visualization definitely has many merits.

The visualization of 3D city information is the precondition and foundation to construct Digital City and the uniformity of 2D and 3D GIS platforms to make the Digital City more widely applicable. This article firstly analyzes some kinds of 3D visualization software. Secondly, using the Google SketchUp and ArcGIS as examples and discuss the 3D visualization of city information and modeling, and lastly, carrying out research on the 3D model construction process.

Currently, Real-time visualization of large-scale digital elevation model mainly employs the regular structure of GRID based on quadtree and triangle simplification methods based on irregular triangulated network (TIN). TIN is a refined means to express the terrain surface in the computer science, compared with GRID. However, the data structure of TIN model is complex, and is difficult to realize view-dependence representation of level of detail (LOD) quickly. GRID is a simple method to realize the LOD of terrain, but contains more triangle count. A new algorithm, which takes full advantage of the two methods' merit, is presented in this paper. This algorithm combines TIN with quadtree structure to realize the view-dependence LOD controlling over the irregular sampling point sets, and holds the details through the distance of viewpoint and the geometric error of terrain. Experiments indicate that this approach can generate an efficient quadtree triangulation hierarchy over any irregular sampling point sets and achieve dynamic and visual multi-resolution performance of large-scale terrain at real-time.

There is no direct solar irradiance in shadow area. The terrain information of shadow area is often neglected in preceding researches for that hillshade values are calculated only by direct solar irradiance. In this paper, a model simulating hillshade values in natural environment based on the total irradiance which consists of direct solar irradiance and diffuse sky irradiance are developed. According to isotropic model of diffuse sky radiation, the author provides a formula to calculate the illumination of diffuse sky irradiance. The relative hillshade values can be calculated according to solar position and the ratio of diffuse irradiance to total irradiance on horizontal surface. This research shows the new method is more suitable to express topographic information in complex terrain areas.

Evapotranspiration, which is an important component of water cycle, is a critical variable in determination of local water resources available. This paper selected the upper Huaihe River as case study site. The daily reference evapotranspiration was computed by the Penman-Monteith method, and then the mean annual reference evapotranspiration (ET_ref), the decadal average values of annual ET_ref and the decadal average values of annual seasonal ET_ref were calculated and mapped. The results revealed that the spatial distribution of mean annual ET_ref was uneven with a clearly decreasing gradient in the mean annual ET_ref from the east to the west; The spatial and temporal distribution of decadal average values of annual ET_ref varied with decades and from 1960s to 1980s , there existed a decreasing trend with an increasing trend from 1980s to 1990s; The spatial and temporal distribution of decadal averages of annual seasonal ET_ref varied with seasons and decades. The decadal average in spring decreased with decades, while the value in summer had a decreasing trend from 1960s to 1980s with an increasing trend in 1990s compared with 1980s, and the decadal averages both in autumn and winter increased from 1960s to 1970s then decreased in 1980s with an increasing trend in 1990s compared with 1980s. The output of this paper provides a valuable reference for the forecast of future water resource available and the sustainable development and utilization of water resources in the Huaihe river basin.

3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. And now, plenty of representative 3D data models are proposed. However, existing models just take the theoretic 3D formal representation and the topology between objects into account, and neglect the display result and the consumption of storage space. Based on the review of existing three-dimensional (3D) GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. Secondly, data structure and formal representation of 3D surface model is developed to construct and store data of 3D model. In order to compare this 3D surface model with other 3D data model, the building models in an experimental area are reconstructed and stored by the 3D surface model. The result demonstrates that the newly proposed 3D surface model is superior to the existing data model in terms of data volume, moreover, it can acquire fast visualization speed.

Map symbol is a basic means that represents spatial data. As a significant tool of information representation and transfer, it has an important role for map display and output. ArcGIS is a GIS platform with strong functions, but its embedding symbol libraries, ESRI.Style files ,are not conform to Chinese map standard, thus can't meet the demand of map display and output for Chinese urban fundamental GIS. On the basis of ArcEngine map output modules this article elaborates classification of topographic feature, design and construction of map symbol library and map symbolization in large scaled GIS, so as to provide technical experiences for related applications and improve the efficiency of system developing.

In many application domains, data is collected and referenced by its geo-spatial location. Spatial data mining, or the discovery of interesting patterns in such databases, is an important capability in the development of database systems. Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. For spatial data mining of large data sets to be effective, it is also important to include humans in the data exploration process and combine their flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of today's computers. Visual spatial data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the information and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper a visual interactive spatial data mining prototype system (visual geo-classify) based on VC++6.0 and MapObject2.0 are designed and developed, the basic algorithms of the spatial data mining is used decision tree and Bayesian networks, and data classify are used training and learning and the integration of the two to realize. The result indicates it's a practical and extensible visual interactive spatial data mining tool.

With the rapid development of science and technology and the quick increase of information, a great deal of data is accumulated in the management department of science and technology. Usually, many knowledge and rules are contained and concealed in the data. Therefore, how to excavate and use the knowledge fully is very important in the management of science and technology. It will help to examine and approve the project of science and technology more scientifically and make the achievement transformed as the realistic productive forces easier. Therefore, the data mine technology will be researched and applied to the science and technology management information system to find and excavate the knowledge in the paper. According to analyzing the disadvantages of traditional science and technology management information system, the database technology, data mining and web geographic information systems (WebGIS) technology will be introduced to develop and construct the science and technology management information system based on WebGIS. The key problems are researched in detail such as data mining and statistical analysis. What's more, the prototype system is developed and validated based on the project data of National Natural Science Foundation Committee. The spatial data mining is done from the axis of time, space and other factors. Then the variety of knowledge and rules will be excavated by using data mining technology, which helps to provide an effective support for decisionmaking.

Constraints in the real world must be seriously considered in the process of spatial clustering. In this paper we study the spatial clustering issue in the presence of obstacles. The cluster algorithm is based on the K-medoid algorithm, and an improved algorithm Guo Tao is introduced to obtain the distance of spatial objects in the presence of obstacles. It is more efficient for small and medium-sized data through theoretical analysis. The experiments results prove that the algorithm is feasible.

Hyperspectral remote sensing technique provides fine and detailed spectral information by contiguous and narrow spectral channels. For the traditional classification algorithms, most of them are based on spectral information; spatial information which is useful for the hyperspectral data analysis is paid a little attention to. So, hyperspectral image classification based on effective combination of spectral and spatial information needs further investigation. In this paper a new classification method for hyperspectral remote sensing is proposed in order to mine spatial information which hidden in the image. Firstly, some spectral features which are statistics indexes (MinBand, MaxBand, AvgBand, StdBand, etc.) are extracted from OMIS image data. Secondly, spatial structure information and spatial information such as Area, Length, Compact, Convexity, Solidity, Roundness, and so on are extracted. Then the spectral and spatial attributes are computed and used for the following classification. Lastly, several kernel functions which using joint spectral and spatial information such as Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid kernel are adopted for SVM classification model. The experiments proved that the classification algorithm which joint spectral and spatial information can work more effectively compared to the traditional classification methods, and this new approach is useful for hyperspectral classification and imaging analysis, and has the potential ability in hyperspectral remote sensed data processing.

China faces a serious problem is that dramatic expansion of construction land cause largely reduction of cultivated land. To control the scale of construction land is the focus of land use and planning management work, whose core is land use control, and how to forecast the quantity of construction land scientifically, reasonably and correctly is an important content in general land use planning. In this paper, based on the field survey and statistic data of land changes in Changjiang Hainan province during 1996-2005, the gross construction land in this region was simulated and predicted using trend analysis method, exponent smoothing method, remnant GM(1,1) method, Markov model and multifactor optimal combination method, respectively. From the compare of the average relative error, GM(1,1) Method is better to predict, but its parameters c and p indicate this model can't be used to forecast long term data. From the result of Markov model, the average relative error is small, but its maxerror is 4.15%. By comparison of these models, Multifactor optimal combination method is more reliable and effective for policymakers of land management, and which is preferable for predicting construction land demand of county's general land-use planning.

Many methods of data mining model were widely applied for land evaluation, and they show different characteristics of the application for land evaluation. In order to analyze different classification model effect for land evaluation, this paper took land in Longchuan County as a case study, established three models using decision tree, back propagation neural network (BP) and logistic regression on land evaluation. The result of study shows that the accuracy of three models changes remarkably according to 6 groups of training samples. The accuracy of the decision tree and BP model can reach high level in support of 4000 training samples, but decision tree model is superior to BP model at intelligibility of model and consuming-time aspects. The overall performance of Logistic regression model is worse than other models at the massive samples. Moreover, three model have different the characteristic of error distribution by means of confusion matrix. The error of decision tree distributes evenly, and the error distribution of BP has opposite result of Logistic regression. Results indicate that the model of decision tree is the best model for evaluating Longchun County land at comprehensive thought, and it has a good effect on application.

Nuclear energy as one of clear energy sources takes important role in economic development in CHINA, and according to the national long term development strategy, many more nuclear powers will be built in next few years, so it is a great challenge for uranium resources exploration. Research and practice on mineral exploration demonstrates that utilizing the modern Earth Observe System (EOS) technology and developing new multi-source geological data mining methods are effective approaches to uranium deposits prospecting. Based on data mining and knowledge discovery technology, this paper uses multi-source geological data to character electromagnetic spectral, geophysical and spatial information of uranium mineralization factors, and provides the technical support for uranium prospecting integrating with field remote sensing geological survey. Multi-source geological data used in this paper include satellite hyperspectral image (Hyperion), high spatial resolution remote sensing data, uranium geological information, airborne radiometric data, aeromagnetic and gravity data, and related data mining methods have been developed, such as data fusion of optical data and Radarsat image, information integration of remote sensing and geophysical data, and so on. Based on above approaches, the multi-geoscience information of uranium mineralization factors including complex polystage rock mass, mineralization controlling faults and hydrothermal alterations have been identified, the metallogenic potential of uranium has been evaluated, and some predicting areas have been located.

Abundant and up-to-date basic geographic information is urgently required by various users. Reasonably measuring the amount of information can provide technique foundations for describing, evaluating, and choosing products by producers, quality controllers of information and users. Entropy is a kind of information quantity evaluation based on information theory and mostly applied to measure of map information. However, measures for topographic features of information database are absented. In this paper, existing measures for map information are analyzed. An experiment is conducted by selecting several maps from national topographic database and the assessed feature is road layer. The authors completely evaluated statistical information, geometric information, topological information and thematic information entropy. Results show that entirely evaluating the amount of information for features is necessary and feasible, and it can reflect the distribution and thematic characters for features in topographic database.

Support vector machines (SVMs), which are based on statistical learning theory, have recently got considerable potential in data mining by their good ability of generalization. Land grade evaluation, which provides information for land planning and decision-making, is a process of evaluating land quality for a particular use. It can be referred to as multiclass classification problem. As a result of SVMs' binary nature, they can not be directly applied to land grading process. By integrating SVMs with a binary tree, this paper applied a binary tree based SVMs (BTSVMs) approach into land grade evaluation. Arable land in Heping County, Guangdong, was chosen as study area and BTSVMs model was then applied to the data. In addition to BTSVMs, the same data were classified using decision tree (DT) and artificial neural network (ANN). Compared with DT and ANN, results showed that BTSVMs had better classification accuracy. While decreasing the size of training data, the accuracy of each approach dropped down positively with BTSVMs relatively more accurate than others. In general, BTSVMs is potentially feasible in the application of land grade evaluation with its good performance.

In order to obtain high accuracy results, the gross errors in observations must be correctly detected and repaired. In this paper, the theory and methods of singularity detection based on wavelet transform, support vector machine regression model are introduced. The wavelet multi-resolution analysis (MRA) was carried out and the location of the gross errors can be detected by ascertaining the points of modulus maximal value of the wavelet coefficients since the gross error can be regarded as the singular point of the observation time series. Then the time series regression model based on support vector machine (SVM) was established to repair the gross errors. Practical test results indicate that the gross errors can be validly detected by wavelet method as well as be correctly repaired by the method based on support vector machine.

At present, no feasible and effective methods meet the requirements of constructing a comprehensive and representative indicator system for degraded land evaluation, which orients spatial multi-scale and diversity of evaluation objects as well as integrates experts' judgments and objective information. This paper, tying to solve the problem, firstly proposes three propositions on evaluation indicator knowledge base (EIKB), universe evaluation indicator set (UEIS), evaluation indicator subset (EIS) and Mapping Rule of EIS (MREIS), and then constructs an heuristic indicator mining model (HIMM) based on above theories, variable precision rough set and information entropy. Finally, we applied HIMM to practical degraded land evaluation and examined the effectiveness of HIMM. The result shows that HIMM is applicable, especially in the aspect of solving the comprehensive and representative problem in the process of indicator system construction.

Data skew is one of most important reasons to deteriorate the performance of parallel spatial database. This paper studies the issues of handling data skew in shared nothing parallel spatial database system architecture. A novel data skew handling method is proposed, which fulfill spatial data distribution balancing based on the spatial proximity of data fragments. The minimum spatial proximity is used to be the principle of moving data fragments among different network parallel nodes. Our experimental results show that the proposed data skew handling method can achieve dynamic data load balancing and offer significant improvement for reducing response time of parallel spatial queries.

Generalization is needed to describe relevant information on an appropriate level of detail. However, the harmony between different generalized geographic features are always difficult to be ensured even if the data sources come from the same topographic maps or geographic databases, as the generalization is carried out separately in the context of computer assisted cartography. This paper introduces a new approach for the harmonic generalization of terrain and water system based on the integrated geographic feature retrieval using 3D Douglas-Peucker algorithm. The advantage of the research is two folded: firstly, it focuses on the geographic nature of water system and terrain; secondly, the 3D Douglas-Peucker algorithm is developed to make this generalization of the two kinds of features possible. The spatial representation of water system in vector data and that of the terrain in DEMs are unitized into one set of general character points. Then the 3D Douglas-Peucker algorithm is performed for the features retrieval. After that, the result is returned to generate the abstracted terrain and the simplified water system. In this way, the harmonic registration between the generalized terrain and the generalized water system can be ensured. The preliminary experiments show that this harmonic generalization is a promising way both in cartography and GIS.

In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block image will get better classification results.

With the development of "3S" technologies, a large quantity of spatial-temporal data related to land use has been accessed. Being scattered across different departments and lacking of relevant analysis tools made them utilize insufficiently. Although some experts have applied data mining to solve this problem, most of them have only provided one method for single task to build the mining systems. However, it is undesirable to use just one method to mine. In addition, the single function systems can not be used widely and conveniently. Hence, under full investigation on operations of land use, a multi-source data mining prototype system for land use is proposed by integrating of technologies of GIS and spatial data mining. According to the general data mining process, aiming at the multi-demands of land evaluation and land planning and so on, the system is developed by using ArcEngine 9.0 and VB.net. The system integrates basic geospatial data, land use/cover data, and thematic data as data sources, excavates different knowledge of land Quality, land use zoning rules, land use patterns and change rules and so on. Based on the types of knowledge, the system accordingly provides several different mining methods, including decision tree, support vector machine, artificial neural network, time series, spatial association rules, etc. Wide adaptability of the system is demonstrated by using some cases. The results of the system can meet multipurpose needs and be used to support decision-making of the land management department.

With currently China's farmland transformation for non-agricultural advancement is speeding up, such disadvantages as low standard and simplified mode of compensation obviously appears in our land expropriation (requisition) system. And land expropriation (requisition) price has been distorted seriously, which has caused a series of social problems aroused more attention from many fields. It's high time to establish new criteria of land compensation. This paper presents a new method to analyze the compensation standard of cultivated-land Expropriation and requisition respectively through defining and normalize the connotation of tenure system and relevant rights of cultivated land in China, and to explore the value composition of rights over cultivated land. Methods of logic analysis, comparison and empirical analysis were applied. The results show that the tenure system of cultivated land is composed of five parts: natural productive price, social security price, social stabilization price, ecological security price and development right price. The values of all these rights vary under different socio-economic conditions, and they have to be embodied gradually in the process of land Expropriation and requisition. Moreover, the new proposed methodology has been applied to a case study of paddy lands located in Nanyang City, Henan Province in order to demonstrate its goodness. From the results of this work we can conclude that the approach proposed stands out as a good alternative to current compensation standard of cultivated-land Expropriation (requisition).

During the process of economic growth, the industry structure transforms at different developing sections and that industrial composition as well as each department interior demand for land resources would reflect on land-use structure reform. This paper takes Hubei as the research zone, through a consecutive time sequence of 10 years period (1996-2005) just before and after the 1 plus 8 Eastern Hubei Metropolitan Area project, a quantitative study of the correlation between the industry structure and land-use structure is made based on the entropy theory. According to the classification of industrial composition, the land-use structure here is also redefined into four types as Land Use for Primary Industry, Land Use for Secondary Industry, Land Use for Tertiary Industry, and Land Use for Potential Reserve, in the aim that it should model new methods for researching the relationship of industry structure and land-use structure, and the instinct driving force would be presented more evidently at the same time. The outcomes indicate that the change of land-use structure has close relationship with the structure of industry composition; the trend of information entropy in Hubei mostly keeps increasing during the past 10 years which predicating the symmetrical degree of land-use structure is gradually built; and Eastern Hubei Metropolitan Area is of favorable power far superiority other units within province in promoting regional development, yet land-use structure adjustments are still not stable and a optimal mode of land use needs further approach.

The knowledge of automated map generalization mainly derives from map specifications, experience of experts and spatial data. The representation, acquisition and reasoning of knowledge for cartographic generalization have been widely recognized as difficulty. In an example with results of cartographic generalization, it is certain that some special knowledge consist in spatial data, which represent spatial relationship of geographical objects. The meaning of acquiring the knowledge hided in data lies in that other unknown data can be inferred by the knowledge. In this paper, a model of knowledge representation and acquisition for automated map generalization based on granular computing is first proposed. Then, the conceptions concerning knowledge granule and its structure are defined, and intrinsic mechanism and method of knowledge acquisition are further discussed. Lastly, the model and method mentioned above are illustrated through a case study. The conclusion is that knowledge acquisition lies on the dependence degree of decision-making attributes for condition attributes. Each attribute has a different effect on the result of cartographic generalization. The decision-making rules knowledge acquired by difference of dependence degree is just the representation condition of cartographic objects, by which other unknown data with similar distribution characters can be inferred.

During the land-use planning, the demand forecast of construction land is complex. If the number of forecasting is excessive, the land resources would be wasted. On the contrary, the local economic develop would be restricted for lack of enough construction land. So it is important to determine an appropriate quantity of land for construction. The demand of construction land is affected by many factors, so it is difficult to use a simple mathematical model to simulate it. Therefore, this paper analyzes the important influencing factors on the quantity of land for construction and builds a model to simulate the demand of construction land using a neural network. After comparison, the forecast result by using the neural network is better than by the linear regression .The neural network is useful in the land-use planning.

Land registration is public act which is to record the state-owned land use right, collective land ownership, collective land use right and land mortgage, servitude, as well as other land rights required the registration according to laws and regulations onto land registering books. Land registration is one of the important government affairs , so it is very important to standardize, optimize and humanize the process of land registration. The management works of organization are realized through a variety of workflows. Process knowledge is in essence a kind of methodology knowledge and a system which including the core and the relational knowledge. In this paper, the ontology is introduced into the field of land registration and management, trying to optimize the flow of land registration, to promote the automation-building and intelligent Service of land registration affairs, to provide humanized and intelligent service for multi-types of users . This paper tries to build land registration procedure ontology by defining the land registration procedure ontology's key concepts which represent the kinds of processes of land registration and mapping the kinds of processes to OWL-S. The land registration procedure ontology shall be the start and the basis of the Web service.

How can the efficiency of spatial information inquiries be enhanced in today's fast-growing information age? We are rich in geospatial data but poor in up-to-date geospatial information and knowledge that are ready to be accessed by public users. This paper adopts an approach for querying spatial semantic by building an Web Ontology language(OWL) format ontology and introducing SPARQL Protocol and RDF Query Language(SPARQL) to search spatial semantic relations. It is important to establish spatial semantics that support for effective spatial reasoning for performing semantic query. Compared to earlier keyword-based and information retrieval techniques that rely on syntax, we use semantic approaches in our spatial queries system. Semantic approaches need to be developed by ontology, so we use OWL to describe spatial information extracted by the large-scale map of Wuhan. Spatial information expressed by ontology with formal semantics is available to machines for processing and to people for understanding. The approach is illustrated by introducing a case study for using SPARQL to query geo-spatial ontology instances of Wuhan. The paper shows that making use of SPARQL to search OWL ontology instances can ensure the result's accuracy and applicability. The result also indicates constructing a geo-spatial semantic query system has positive efforts on forming spatial query and retrieval.

A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some non-spatial predicates. Most spatial data mining methods, including previous study on spatial association rules, use spatial objects with exactly known location. However, in real situations the extensions of spatial objects can be known only with a finite accuracy because of limits of measurement technology and uncertainties of objects et al. The inappropriate data mining methods based on these uncertain data will result in poor, even unaccepted quality of mining models. How to employ spatial association rule to extract hidden knowledge in uncertain and imprecise data is worth researching. Hence, in this paper we extend the technique for the discovery of spatial association rules from uncertainty data, based on geo-rough space theory. According to the concepts of the lower approximation set, the boundary set, rough topology relationships and rough distance relationships in geo-rough space, rough spatial predicates of Rough-Near, Rough-Touch, Rough-Overlay and Rough-In are proposed. Then we put forward more reasonable rough confidence and rough support for excavating spatial association rules from uncertainty data by using the Apriori algorithm. The result of case indicates the method works well.

Sewerage network is an important part of municipal infrastructure for a city. Obstruction of sewer causes street flooding and affects people's daily life directly. To investigate reasons why some sewage pipes are blocked frequently in Kunming, China, we employ spatial analysis and data mining technology to analyze the data on the basis of a municipal sewerage geographic information system of the city. In the GIS, all of map layers and attribute tables are organized and saved in a relational database with Geodatabase model. First, we combined SQL attribute query with spatial location query to find out the sewage pipes that are blocked frequently. Then, we carried out buffer analysis and intersect analysis on the layers of the frequently-blocked pipes and buildings along the streets to extract buildings that are close to these frequently-blocked pipes. Joining the buildings in the buffer scope and the frequently-blocked pipes forms a big table prepared for spatial data mining. We used Apriori algorithm to mine spatial association rules from the data in the big table in order to search implicit reasons of obstruction of the pipes. The results from data mining indicate that strong spatial and non-spatial associate rules exist between the obstruction and restaurants in the buildings, as well as attribute slopes and diameters of these sewage pipes.

As is well known, the contour line has always been the key unit in such projects of automatic recognition of geographic maps, but how to handle the contour lines' breakpoints automatically is still not settled thoroughly. This paper first synthetically analyzes four classical algorithms of the contour lines' breakpoints connecting methods, then presents a layering-based breakpoints connecting policy. Taking brown plate map as data source, separately recognizing and extracting the influencing factors such as elevation annotation, dry riverbeds, cliffs symbols which lead to the contour lines' long-distance break from the map as the reference layers, then judging on the reference layer when connecting the breakpoints, and according to the different influencing factors, designing the corresponding algorithms to achieve the true connecting of the contour lines.

Traditional analytical methods for traffic information can't meet to need of intelligent traffic system. Mining value-add information can deal with more traffic problems. The paper exploits a new clustering optimization algorithm to extract useful spatial clustered pattern for predicting long-term traffic flow from macroscopic view. Considering the sensitivity of initial parameters and easy falling into local extreme in FCM algorithm, the new algorithm applies Particle Swarm Optimization method, which can discovery the globe optimal result, to the FCM algorithm. And the algorithm exploits the union of the clustering validity index and objective function of the FCM algorithm as the fitness function of the PSO algorithm. The experimental result indicates that it is effective and efficient. For fuzzy clustering of road traffic data, it can produce useful spatial clustered pattern. And the clustered centers represent the locations which have heavy traffic flow. Moreover, the parameters of the patterns can provide intelligent traffic system with assistant decision support.

The multi-factor integrated classification is usually used in urban land grading evaluation. The weights of urban land grading evaluation factors determine the evaluation results. Therefore, the method used to determine the weights of evaluation factors is of significant importance for the evaluation result. In this paper, a new method for determining the weights of urban land grading evaluation factors, i.e., K-means clustering and spearman rank correlation analysis, is proposed. This method is capable of decreasing human error and enhancing the effectiveness of the automatization process.

Aiming at the complexity and uncertainty in the interpretation of rocky desertification from multi-source data, this paper promotes fuzzy Bayesian network embedded Gaussian mixture model(GMM) for extracting rocky desertification information.This model make a fuzzy quantization for continuous variable through GMM, use the convex function of multiple Gaussion density functions to fit the "true" distribution of the data better, and avoid variable's discretization in traditional Bayesian network. All nodes's parameter are then integrated utilizing naïve Bayesian network. Experiments indicate that this model have high accuracy than hybrid Bayesian network and with research value in data mining of multi-source data.

Agricultural regionalization, which is largly achieved according to the similarity within a certain area and the difference between this area and other ones, is the foundation of agricultural production. Due to the fact that clustering is much like regionalization, methods for clustering are also commonly used in regionalization. However, the clustering algorithms applied are usually for only numerical attributes and large amounts of categorical data with great values cannot be handled with traditional clustering methods, which largely limits the utilization of clustering in agricultural regionalization. In this paper, we propose a new spatial clustering algorithm which combines the ROCK algorithm with fuzzy mathematics, can handle both numerical and categorical data at the same time, to satisfy the actual needs of agriculture regionalization. The effectiveness of the new algorithm is tested on the agricultural regionalization of ZengCheng, GuangZhou, China. During the test, we test both the effectiveness of the new spatial clustering algorithm and some old algorithms. The final result shows that the new algorithm performs better in agricultural regionalization, and its result is also closer to the artificial agricultural regionalization.

A model of synergistic coupling for land-use data mining based on extension theory is put forward in this paper, which can mine the relation of synergistic coupling for various land use activity in land use database to guide all kinds of activity. In order to mine the knowledge of synergistic coupling, some knowledge must be obtained from database of land use. Changjiang is a county in west of Hainan province, China. The data of land use is studied as a case through the changes of construction to influence other land use types. The results show that the model can be used for the mining in the land-use synergistic coupling relations. The knowledge that obtain in this case based on the model of synergistic coupling for land-use data mining is essential for decision-makers to deal with the paradoxical problem of land-use.

The research of the regional ecological environment becomes more important to regional Sustainable Development in order to achieve the harmonious relationship between the person and the nature. The advent of spatial information technologies, such as GIS, GPS and RS, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Particle Swarm Optimization has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Particle Swarm Optimization model for spatial data mining. Firstly, the theory of spatial data mining is introduced and the characteristics of Particle Swarm Optimization are discussed. A framework and process of spatial data mining is proposed. Then we construct a Particle Swarm Optimization model for spatial data mining with the given dataset. The research area is focused on the distribution of pollution sources in Wuhan City. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, draw a conclusion and show further avenues for research. Through the empirical study, it has been proved that Particle Swarm Optimization algorithm is feasible and the conclusion can provide instruction for local environmental planning.

Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential rules and outliers among the spatial data. Most existing spatial clustering methods cannot deal with the uneven density of the data and usually require predefined parameters which are hard to justify. In order to overcome such limitations, we firstly propose the concept of edge variation factor based upon the definition of distance variation among the entities in the spatial neighborhood. Then, an approach is presented to construct the minimum spanning tree-like (MST-L). Further, an adaptive MST-L based spatial clustering algorithm (AMSTLSC) is developed in this paper. The spatial clustering algorithm only involves the setting of the threshold of edge variation factor as an input parameter, which is easily made with the support of little priori information. Through this parameter, a series of MST-L can be automatically generated from the high-density region to the low-density one, where each MST-L represents a cluster. As a result, the algorithm proposed in this paper can adapt to the change of local density among spatial points. This property is also called the adaptiveness. Finally, two tests are implemented to demonstrate that the AMSTLSC algorithm is very robust and suitable to find the clusters with different shapes. Especially the algorithm has good adaptiveness. A comparative test is made to further prove the AMSTLSC algorithm better than classic DBSCAN algorithm.

At present,the request for the rapid response to the various types of emergencies from countries and governments at all levels are urgently,and most information of the natural disaster is relevant to the spatial location. So we should make full use of the basis of mapping information,and make a rapid positioning about the location information,then mark the disasters-event information,and at last achieve the integration of geographical information and multi-layer disaster information.Based on the above-mentioned,it can offer the comprehensive analysis,statistics and mining to the historical disaster database which are with the location information.It offers information to support decision-making and resources platform for the disaster prevention and mitigation of countries and regions.

This paper briefly is based on the discussion of three fundamental characteristics of the ground (elevation accuracy, validity of elevation order, and the preservation of elevation features) and the concept of the model, then analyses major theoretical and practical shortcomings of the DEM generated in the mechanical model in depth, finally we proposes and discusses objects for feature modeling and the way of digital generalization, and discusses the principle of DEM digital generalization. We also give the 1:5 0000 and 1:1 0000 two series of DEM generalization maps, which achieve the desired results. Experimental results clearly show that: Only with digital generalization based on reliable DEMs which have expressed all terrain features, we can express DEM of required terrain feature on designated resolutions. That is no generalizing, there will be no DEM. The excellent consistency of Theoretical analysis and experimental results, makes this paper believe that objects for feature modeling and digital generalization will bring qualitative change to DEM, and will be a promising new way to solve hundred-year problem -combination and generalization of contours and water portfolio.

Inter-operability is a key notion in geographic information science (GIS) for the sharing of geographic information (GI). That requires a seamless translation among different information sources. Ontology is enrolled in GI discovery to settle the semantic conflicts for its natural language appearance and logical hierarchy structure, which are considered to be able to provide better context for both human understanding and machine cognition in describing the location and relationships in the geographic world. However, for the current, most studies on field ontology are deduced from philosophical theme and not applicable for the raster expression in GIS-which is a kind of field-like phenomenon but does not physically coincide to the general concept of philosophical field (mostly comes from the physics concepts). That's why we specifically discuss the cell-based GI ontology in this paper. The discussion starts at the investigation of the physical characteristics of cell-based raster GI. Then, a unified cell-based GI ontology framework for the recognition of the raster objects is introduced, from which a conceptual interface for the connection of the human epistemology and the computer world so called "endurant-occurrant window" is developed for the better raster GI discovery and sharing.

The progressive transmission of vector map data requires efficient multi-scale data model to process the data into hierarchical structure. This paper presents such a data structure of road network without redundancy of geometry for progressive transmission. For a given scale, the road network display has to settle two questions. One is which road objects to be represented and the other is what geometric details to be visualized for the selected roads. This paper combines the Töpfer law and the BLG-tree structure into a multi-scale representation matrix to answer simultaneously the above two questions. In the matrix, rows from top to bottom represent the roads in the sequence of descending classification of traffic and length, which can support the Töpfer law to retrieve the more important roads. In a row, columns record one road by a linear BLG-tree to provide good line graphics.

Change Detection is one of the most popular topics in the field of Multi-temporal Remote Sensing (RS) applications. In this paper, a novel approach was introduced for the change detection of the urban area. This approach adopts the Dempster-Shafer(D-S) algorithm for feature fusion of the multi-temporal RS images. It, in the first place,,constructs difference images of pixel and context respectively. These two difference images present the features of changes in different scales. The pixel difference image is obtained by fusing the results of the subtraction operation and the division operation, while the context difference image is obtained by the image context. Secondly, by using the difference images, two evidences could be constructed. These evidences are not certain, but they can give more reliable combination result if considering the average support of the evidence to different subsets in the assignment framework. And based on these evidences, the criterion function could be established by the D-S theory. At last, an improved D-S algorithm is applied to fuse the two different features for detecting the change information of the RS images. An experiment, using the SPOT and TM images of Wuhan urban area, has compared the accuracy of edge detection by using the new fusion algorithm and the existent ones. The result shows that the method of improved D-S is solid and efficacious, which has preferable value in remote sensing applications.

Research on geographical spatial reasoning aims at expression of spatial relationships, geo-spatial reasoning rules and reasoning mechanism that could be used for geo-spatial knowledge discovery and spatial analysis. Spatial reasoning is intelligent spatial data processing technology in support of geo-spatial decision-making. Geographic ontology is clear formal definition of geographical concepts, which defines the basic terms and relations of these concepts, and the rules combining these terms and relationship. Therefore, it can well meet the formal knowledge representation requirement for geo-spatial reasoning that carry out reasoning by using geographic ontology. In this paper, methods of creating geographic ontology are discussed, and the rules based on spatial reasoning are summarized. Furthermore, a path query method based on geographic ontology is proposed, by creating a road ontology system and the corresponding administrative region ontology system, it can be used to solve large-scale spatial path query problem.

To further explore the intrinsic mechanism of urban expansion, this paper was mainly related to natural geographical factors on urban expansion based on MSS and TM multi-spectral remote sensing image of the 34 provincial capital level cities in China from 1975 to 2005. With the help of MATLAB, SPSS and ARCGIS, the methods that the principal component factor analysis, correlation analysis, and meanwhile overlay analysis had been used. And the results showed that the natural geographical conditions of Chinese cities had a deep effect on their urban expansion. Firstly, mountainous cities were limited by the terrain that their expansion rate was lower than that of the cities in the plain. At the same time, with average altitude increasing, urban expansion rate had a tendency of decreasing. The spatial and temporal changes of urban expansion were bound up with the average slope of the province. And the speed of urban expansion varied greatly on different levels of slope. Secondly, urban expansion showed distinct characteristics respectively in different weather conditions. Correspondingly, the temperature, precipitation and sunshine hours were selected as different grade reference factors to analyze urban expansion characteristics. Thirdly, analysis on the relationship between the hydrological and urban expansion had been done. In conclusion, this paper had made a try on the study of natural geographical factors on urban expansion. And it had an important practical significance to classify different cities with natural environmental indicators.

The paddy rice is our important crops. In study of the paddy rice yield estimation, compared with the scholars who usually only take the remote sensing data or meteorology as the influence factors, we combine the remote sensing and the meteorological data to make the monitoring result closer reality. Although the gray system theory has used in many aspects, it is applied very little in paddy rice yield estimation. This study introduces it to the paddy rice yield estimation, and makes the yield estimation model. This can resolve small data sets problem that can not be solved by deterministic model. It selects some regions in Jianghan plain for the study area. The data includes multi-temporal remote sensing image, meteorological and statistic data. The remote sensing data is the 16-day composite images (250-m spatial resolution) of MODIS. The meteorological data includes monthly average temperature, sunshine duration and rain fall amount. The statistical data is the long-term paddy rice yield of the study area. Firstly, it extracts the paddy rice planting area from the multi-temporal MODIS images with the help of GIS and RS. Then taking the paddy rice yield as the reference sequence, MODIS data and meteorological data as the comparative sequence, computing the gray correlative coefficient, it selects the yield estimation factor based on the grey system theory. Finally, using the factors, it establishes the yield estimation model and does the result test. The result indicated that the method is feasible and the conclusion is credible. It can provide the scientific method and reference value to carry on the region paddy rice remote sensing estimation.

Map coloring is a hard task even to the experienced map experts. In the GIS project, usually need to color map according to the customer, which make the work more complex. With the development of GIS, more and more programmers join the project team, which lack the training of cartology, their coloring map are harder to meet the requirements of customer. From the experience, customers with similar background usually have similar tastes for coloring map. So, we developed a GIS color scheme decision-making system which can select color schemes of similar customers from case base for customers to select and adjust. The system is a BS/CS mixed system, the client side use JSP and make it possible for the system developers to go on remote calling of the colors scheme cases in the database server and communicate with customers. Different with general case-based reasoning, even the customers are very similar, their selection may have difference, it is hard to provide a "best" option. So, we select the Simulated Annealing Algorithm (SAA) to arrange the emergence order of different color schemes. Customers can also dynamically adjust certain features colors based on existing case. The result shows that the system can facilitate the communication between the designers and the customers and improve the quality and efficiency of coloring map.

With the rapid development of urbanization, demands of urban land increase in succession, hence, to analyze temporal and spatial variations of urban land use becomes more and more important. In this paper, the principle of trend surface analysis and formula of urban land sprawl index ( ULSI) are expatiated at first, and then based on land change data of Jiayu county, the author fits quadratic trend surface by choosing urban land area as dependent variable and urbanization and GDP as independent variables from 1996 to 2006, draws isoline of trend surface and residual values; and then urban land sprawl indexes of towns are calculated on the basis of urban land area of 1996 and 2006 and distribution map of ULSI is plotted. After analyzing those results, we can conclude that there is consanguineous relationship between urban land area and urbanization, economic level etc.

Multi-scale handling and representation of terrain is one of the key hot topics in GIS. It can meet the demand of different users on the different scale geo-spatial data. Through multi-scale representation of terrain the non-scale GIS can be achieved and the users can acquire the info they care about easily by such GIS. This paper analyzes the concept of multi-scale reprensentation of terrain and the relative researches on multi-scale terrain representation. Also the construction of multi-scale terrain is discussed, and its application in terrain analysis is described.

Aiming at the orbit floating or so called constellation rotation in navigation satellite autonomous orbit determination, an approach based on multi sources data fusion was introduced, that is to introduce angle observation from on board star sensor toward fixed star to fix the ascending node Ω, one of the six orbit elements so as to avoid constellation rotation. During data processing, a generalized measurement model based on matrix Cholesky decomposition and the method of measurement noises de-correlation was adopted. On the foundation of roundly analysis of present theories and methods of multi sources data fusion, and characteristics of multi sources measurement data fusion taken into account, two sorts of multi sources data fusion measurement models based on independent same distribution and correlation of measurement noises were conducted. Theoretical analysis and simulation experiment show that navigation satellite autonomous orbit determination based on multi sources data fusion can well solve the problem of orbit floating contrasting against that of only inter-satellite observations used.

In recent years, data fusion has become a very popular method in remote sensing image enhancement. In this paper, a comparative study was conducted on data fusion methods based upon SPOT5 image. First land types of forest, paddy field, dry land, water and building was selected through field survey. Then supervised classification and non-supervised classification were used upon original image and four fusion images (HIS, PCA, high-pass filtering (HPF) and Brovery) respectively. Land change area, change rate and classification accuracy were calculated. Finally suitable SPOT5 fusion method for every land types was presented. The result showed: (1) In all four fusing methods PCA held the highest land change rate, being average 49.0% for non-supervised classification and average 18.9% for supervised classification. So visual interpretation was a better way for PCA fusion image. (2) HIS produced some distortion to the original spectrum and made flaky features into pieces. This method was suitable to extract small features in complicated urban areas because of its high spatial resolution. In the research, building change rate in HIS fusing image under supervised classification was lowest, only 3.32%. (3) For HPF land change rate was low for no matter non-supervised classification or supervised classification, being average 16.3% and 11.2% respectively. This fusion method held low distortion and more high-frequency spectrum. It was suitable to be used as the basic image data for both supervised classification and non-supervised classification. In our research image classification accuracy of urban areas in HPF fusion image was 93.1%.

Multi-spectral images are of narrow-banded and high spectral resolution, however the transmission energy is low, which result in a large scanning IFOV and a loss of spatial resolution; while SAR (Synthetic Aperture Radar) images, as the angle reflection of buildings and low backscattering of waters, have excellent performance on texture structure and water bodies[1]. Therefore, adopting an appropriate fusion algorithm could obtain more accurate and abundant information than any a single data. Wavelet and IHS transform are complementary. An improved algorithm based on them applied to multi-source image fusion is presented, which could overcome the disadvantages that classical algorithms have such as inconspicuous improvement of space resolution, low level of information integration and serious spectral distortion. Intensity component is first extracted from the multi-spectral image by IHS transform, and then I component and SAR image are decomposed respectively by selected wavelet filter and decomposition layer. The modulation factor is gained through regional energy measurement in sub-windows for the new I-component. Finally the fused image could be acquired through inverse IHS transform. With different wavelet filters and decomposition layers, parameters are eventually fitted on Coiflets for four layers with subjective and objective indicator criteria. Through regional energy fusion, we could divide the smooth areas and marginal areas of the image in frequency domain, which could make a significative feature measurement in smaller ranges. Experimental results indicate that this model would achieve an excellent effect on the maintenance of spectral information in multi-spectral images as well as texture and edge in SAR images.

Automatic generalization of geographical information is the core content of multi-scale representation of spatial data, but the scale dependent generalization methods are far from abundance because of its extreme complicacy. Most existing algorithms about automatic generalization do not relate to scale directly or accurately, not forecast and control the generalized effects, and cannot assess the global consistency of the generalized results. The rational and quantitative methods and criterions of measuring the extent of generalization have not still been sought out. Lifting Scheme is a new branch of Wavelet analysis burgeoning in last decades. It has several noteworthy aspects comparing with the Binary wavelet Transformation. The fundamentals of Lifting Scheme and the three constructing steps, which include Split step, Predict step and Merge step, are presented detailed in this paper. DEM can be represented in multi-scale model by the methods of The Lifting Scheme and The Binary Wavelet Transform. Compare with two methods, the Lifting Scheme has several superiorities by analyzing the experimental results: Firstly, the trend of relief could be preserved in course of transforming; secondly, the Lifting Scheme can process the points of boundary of DEM efficiently and the spatial data precision can also be maintained, and at last the calculation process of Lifting Scheme is more speedy.

Nowadays, large amount of paper-based topographic maps are still existed in many government department. The scanned maps of them are very useful for research on city history migration, city planning and so on. However, the brightness of these maps is not uniform, and creases are existed, so uniform color process is always needed. If the classical Retinex algorithm is used, the map would have a low brightness and contrast ratio. Therefore, a normal intercepting SSR algorithm of linear extending is presented in this paper. This algorithm first uses the classical SSR algorithm to process the data, and then the average value of image and variance are introduced to do normal intercepting linear extending on the map. Experiment results show that, the improved SSR can not only efficiently eliminate creases and uniform the map brightness, but also increase the global brightness and contrast ratio. Moreover, this algorithm can also be used in the pretreatment of grid image registration, thus to enhance the precision, velocity and accuracy of registration.

LIDAR is applied in a variety fields as a reverse engineering technology, such as factory, cultural heritage protection and Digital City Modeling etc. Point cloud pre-processing has been a research hotspot in recent years, one of the factors restricts its application is merging point-cloud which affects the quality of data very much. In this paper, an effect method is proposed to merge point cloud with different point views to one single point cloud model to represent the real objects. In this method, a base point-cloud model is selected and other models are subtracted according to the missing data of base model, precise alignment is made to smooth the patched point cloud. The method will merge the different point cloud views effectively and reduce the redundant data, thus it will be effect for the following processing. It's proved to be a good method for merging huge point-cloud.

With the development of Terrestrial 3D Laser-scanning technology, as the one of main methods of earth observing architecture, Terrestrial 3D Laser-scanning technology is increasingly widely applied in the field of ancient architecture protection. Triangulating a set of scattered 3D points is a common approach but a difficult problem to surface reconstruction from unorganized point clouds. Because the amount of point cloud data of ancient architecture is very large and the surface of ancient architecture is very complex comparing to reverse engineering field, Universal triangulation algorithms are inefficient and ineffective for complex surface. On the basis of full analysis of the characteristics of the ancient buildings, a modified surface projection-based triangulation algorithm was proposed in this article, which integrated with the subdivision of the grid by K-D tree and tangent plane projection triangulation. The algorithm was used in a real project. Experiments show that the method is more efficient and effective and support for the 3D model reconstruction of ancient buildings is provided.

In view of the problem that some relief symbols are difficult to build models in 3D topographic map, we take cliff symbol as an example to set up an illumination model of it according to the visual sensation rules under the different illumination conditions, propose a set of automatic modeling principles, educe automatic modeling formulas and introduce the automatic modeling method of cliff symbol in detail. The experiment proves that it is feasible. The study will be helpful to build the models of other similar relief symbols automatically.

Being short of evolution mechanisms, theories of spatio-temporal representation and modeling have great challenges to describe and represent the continuity and gradual geographical entities, still less deeper information retrievals and knowledge mining. The spatio-temporal dynamic semantics, which contains geographical entities, changes of entities and evolution mechanisms, is presented. According to the intrinsic process characteristics of dynamic entities, a hierarchical semantics structure and process object sequences included by level are given. This paper describes the process relationships and behaviors, and formalizes them into evolution operators, which subdivide into the process recurrence operators and recurrence ones. Based on the object-oriented technologies, the evolution operators are integrated into process object sequences, and a process-oriented dual representation framework on process objects' recurrence and recursion (DRF-PORR) is presented as well. And finally, as a case study, the typical oceanographic phenomena are taken to carry out the case modeling.

Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes. The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is described and implemented. We take points and polylines as objects in this model in the management of panoramic map data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient in the application of urban panoramic maps.

The paper introduces much about the DEM digital progressive generalization based on new DEM theory for the diversification of demands in applications, at the same tine, gives the algorithm to realize this process. A number of data experiment results show that we should do the digital generalization to keep the DEM quality. And only the high fidelity DEM can reserve features well. From the progressive digital DEM generalization, we can do the multi-scale visualization of contour lines well. These series of research work and data results have open our minds in DEM thinking.

Vegetation Index can reflect the growth situation of vegetation. Meteorological factors can reflect climate change which can act on the growth situation of vegetation. The paper analyses the relationship between vegetation index and meteorological factors during each January and May from 2003 to 2005 in north Hubei Province where the majority of plant is wheat. Using the correlation coefficient of normalized difference vegetation index (NDVI) and precipitation, insolation duration, accumulative temperature to find out that the accumulative temperature is the most important factor in the relationship between vegetation index and meteorological factors in this area. The experiment compares correlation coefficients in 9 regions, and find out that the correlation coefficient is greater where accumulative temperature changes fast.

The representations of spatial data over scale space have some properties, such as limitation, polymorphism and hierarchy and so on. Firstly, the spatial data can only be represented on a limted scale range||S0, SE||, which can be considered as the representation lifespan. Secondly, druing the representaion lifespan, the spatial data may be describled by different geometry types and semantic structures. Thirdly, the changes between two consecutive representaions may be abrupt or gradual. Usually the abrupt changes are the cumulations of many gradual changes. We can consider the representations related to abrupt changes as the key frames, and the others as the general frames. Based on these properties, this paper presents a multiple representaion data model based on state and behavior, which includes two basic elements, the key frames and the scale transformation operations, the former sketching the blueprint and the latter constructing the intermedial states. If we denote the key frames as a state set G, and the scale transformations as a behavior set F, then the representation at any scale point can be calculated by this formula: Rs=f(g,s), f∈F , g∈G and s∈[S₀, S_E].

Based on spatial data modeling, bus transit features can be represented at different levels of detail, which provides possibility of more precise estimation of travel impedance. The simple route-stop bus data model is efficient at the macro level and has been applied in many transport-related applications. However, a more comprehensive data model is needed to cope with different levels of needs as well as complicated situations existing in heavily bus-oriented cities. For this purpose, a bus transit data model is developed based on representation of transit features at the detailed spatial level. With this data model, the impedance between any pair of routes at each transfer junction can be computed. For testing the data model, an integrated approach is developed to identify optimal bus trips with consideration of both on-board travel time and transfer time.

Modeling and representing spatio-temporal process is the key foundation for analyzing geographic phenomenon and acquiring spatio-temporal high-level knowledge. Spatio-temporal representation methods with bottom-up approach based on object modeling view lack of explicit definition of geographic phenomenon and finer-grained representation of spatio-temporal causal relationships. Based on significant advances in data modeling of spatio-temporal object and event, aimed to represent discrete regional dynamic phenomenon composed with group of spatio-temporal objects, a regional spatio-temporal process description method using Top-Down Hierarchical approach (STP-TDH) is proposed and a data organization structure based on relational database is designed and implemented which builds up the data structure foundation for carrying out advanced data utilization and decision-making. The land use application case indicated that process modeling with top-down approach was proved to be good with the spatio-temporal cognition characteristic of our human, and its hierarchical representation framework can depict dynamic evolution characteristic of regional phenomenon with finer-grained level and can reduce complexity of process description.

Entities in the real world have non-spatial attributes, as well as spatial and temporal features. A spatial-temporal data model aims at describing appropriately these intrinsic characteristics within the entities and model them on a conceptual level so that the model can present both static information and dynamic information that occurs over time. In this paper, we devise a novel spatial-temporal data model which is based on Geodatabase. The model employs object-oriented analysis method, combining object concept with event. The entity is defined as a feature class encapsulating attributes and operations. The operations detect change and store the changes automatically in a historic database in Geodatabase. Furthermore, the model takes advantage of the existing strengths of the relational database at the bottom level of Geodatabase, such as trigger and constraint, to monitor events on the attributes or locations and respond to the events correctly. A case of geographic database for Kunming municipal sewerage geographic information system is implemented by the model. The database reveals excellent performance on managing data and tracking the details of change. It provides a perfect data platform for querying, recurring history and predicting the trend of future. The instance demonstrates the spatial-temporal data model is efficient and practicable.

In this paper, spatio-temporal changes of vegetation in Jilin are explored with MODIS NDVI images time series from 2000 to 2006. MODIS NDVI images time series are organized into a spatio-temporal data cube. We build a linear regression model of NDVI time series at each pixel of the MODIS image. The slope of the linear regression model indicates the change trend of vegetation. Using these change trends data, we further develop three advanced spatio-temporal statistics of regional statistics, the center of gravity, and spatial autocorrelation for exploring spatiotemporal changes of Jilin vegetation. Three method results have commonly showed that Jilin vegetation change is generally stationary and slightly increasing in space. Meanwhile, Jilin vegetation changes are spatially heterogeneous in local regions. In particular, vegetation change is stationary in eastern Jilin, and is increasing in western Jilin. Vegetation change is decreasing in southwestern Jilin, northwestern Jilin, riverside and lakeside regions, and Changchun downtown. Positive spatial autocorrelation of Jilin vegetation changes is significant. Besides, the regions with a similar vegetation change trend are spatially clustered. Three given methods of exploratory NDVI image analysis have emphasized separable spatio-temporal interaction modeling, that is, a twophrase combination of modeling temporal changes firstly and modeling spatial changes secondly.

With the equation of plane and sphere, we fit them with Linear Least Squares. To cylinder datum fitting, firstly parameterize GQS equation of cylinder from seven parameters to five parameters, then using Local Paraboloid Construct method based on coordinate translation to get fitting initial values, finally evaluate results by Levenberg-Marquardt--a Nonlinear Linear Least Squares. Algorithm. However, initial values with Local Paraboloid Construct method are unstable. So to improve the precision of cylinder fitting ,a robust cylinder fitting method is put forward, which at first gets initial cylinder parameter values by Gauss Image, then fits cylinder by Nonlinear Least Squares for parameterized distance function. After getting reference datums, this paper proposes the methods of creating depth images from scattered point cloud and the specific steps with reference to different datums. Finally we choose some point cloud data of ancient building components from laser scanning data of Forbidden City in China as experiment data. Experiment results demonstrate the stability and high precision of the method of plane, cylinder and sphere fitting as well as the validity of depth images to represent point cloud of object.

Many environmental phenomena are regarded as realizations of random functions which possess both spatial and temporal characteristics. In particular, Geostatistics with an extension of the existing spatial techniques into the space-time domain offers some kinds of methods to model such processes. Although these methods for the analysis of spatial-temporal data are becoming more important for many areas of application, they are less developed than those for the analysis of purely spatial or purely temporal data. In this paper, two kinds of spatial-temporal stationary covariance models are introduced. And the differences between spatial domain and time domain are examined. A product-sum covariance model originally given by De Cesare is extended for spatial-temporal analysis on daily rainfall measurements in the three provinces of Northeast China. Remarkably, this generalized non-separable model does not correspond to the use of a metric one in space-time. The rainfall measurements used for this experiment are taken at 104 monitoring stations from January 2000 to December 2005. In the experiment, the product-sum variogram model is employed for developing ordinary kriging and its application to interpolation of the monthly rainfall data from January 2000 to December 2004 has been used to predict the monthly rainfall of 2005. The true values and the predicted ones are compared. The experimental results have shown that this product-sum covariance model is very effective for rainfall analysis.

It is clear that the development of an integrated watershed data model (IWDM) that encapsulates the data layers describing watershed eco-systems will benefit coupling GIS to watershed models. It is desired that integrated watershed data model will not only store separate layers of information but also provide geographic and temporal, natural and social-economic connectivity to better represent watershed system and all of the features and information within it. The objective of this study is to establish an integrated watershed data model to describe the watershed system and its primary elements in order to support Poyang Lake watershed comprehensive management modeling (PLWCMM), in which many models are coupled with ArcGIS Engine using Visual Studio 2005. In this paper, the integrating framework of PLWCMM was firstly introduced and the requirement analysis of the integrated watershed data model was conducted. In addition, the frame structure and detailed features of each feature datasets in IWDM were described. In the IWDM, the six components of Hydro, LandScape, Weather, Social-Economy, Simulation and TimeSeries were contained, and there are different feature classes in each model component. This data model can connect natural spatial unit in watershed to administration unit by the relationship between their spatial features and also connect spatial data to temporal data.

Various sensors play a very important role in our everyday life. Sensor model language is a vital part of the OGC for the description of the common sensor. SensorML not only describe properties of the sensor itself, but also the process about the sensor. Based on the SensorML which is coding with XML schema, we need an efficient way for modeling a sensor or a system. In this paper, firstly, the SensorML model is introduced, in which the SensorML schema is analyzed, and a common procedure for sensor modeling is defined. Secondly, the establishment of a common sensor model platform comes true using the dynamic generation, reflex, XML DOM in the .NET Framework 3.5. At last, the BJ-1 satellite was used as an example for the SensorML modeling. For the non-physical process chain, the red band and near infrared band of the BJ-1 satellite were as inputs for the NDVI calculation. After all, we validate the BJ-1 satellite SensorML instance.

Sphere target is an important toolbox in terrestrial laser scanning. With the sphere target, multiple point clouds data obtained from different views can be transformed into the same coordinate frame. Determination of the sphere target center is key to complete the task mentioned above. A usual approach to determine the coordinates of the center of the sphere is the Less Squares (LS) method. In this paper, a new method using Total Least Squares (TLS) to determine the center of the sphere target is investigated. For the new method, a special model is assumed that the design matrix contains the linearization, and errors in both the residuals vector and the design matrix can be minimized simultaneously. Various experiments are conducted using the real point clouds data, and it is shown that the proposed TLS approach works effectively and achieves better results than the usual LS method.

The investigation of chaos in time series is the basis of prediction with chaos theory. Though the science of chaos is a burgeoning field and the available methods to investigate the existence of chaos in a time series are in the state of infancy, a wide variety of methods are available. Among these methods, the correlation dimension method is the most popular one. According to this method, a finite correlation dimension is a sign of deterministic chaos, which is understood as the principal. However, a finite correlation dimension may also be observed from a linear stochastic process. Therefore, it is necessary to confirm the absence of linearity in the data to verify the results with application of the correlation dimension method. In this paper, after the reconstruction of phase space, the correlation dimension method was employed to analyze the chaotic characteristics for groundwater depth time series in Hetao Irrigation District of Yellow River in China. Considering that a finite correlation dimension is only the necessary condition of chaotic behavior, the surrogate data method which can distinguish nonlinear characteristics of time series was employed to analyze the chaotic characteristics for the groundwater depth series. As a comparison, classic Lorenz chaos time series and stochastic white noises were analyzed using the surrogate data method at the same time. The results show that there is somewhat chaos in the groundwater depth time series in Hetao District in Yellow River Basin. Meanwhile, the surrogate data method is the necessary complementarity of the correlation method to investigate the chaotic behavior for time series.

With the rapid development of the gazetteers, more and more geographical names databases has been established. Since the geographical names exit in form of records which provide little qualitative description other than quantitative information, geographical names are hardly shared and interoperable. In order to solve this problem, we urgently need to set up knowledge base for geographical names that shall provide qualitative knowledge to describe the essence of the elements. So, we use ontology and production rules to build geographical name knowledge base, where the geographical names ontology is regarded as the foundation for reuse and sharing of the geographical names information, and production rules are used to enhance the expressivity of the ontology. First of all, we analyzed the geographical names concepts and their semantics, the concepts of space and time and their relationships in geographical names to describe the knowledge structure for this field, used Web Ontology Language (OWL) to provide formal descriptions to give them explicit semantics, and proposed a unified semantic framework for description. Secondly, we established the common-sense rules and spatial relations inference rules coded with Semantic Web Rule Language (SWRL) which laid the foundation for geographical names knowledge discovery and automatic reasoning. Finally, we established a geographical name knowledge base combining both the geographical names ontology and rules established above. Through the analysis of examples we showed that based on the geographical names knowledge base the geographical names information can be well shared and reused.

Spatiality, temporality, legality, accuracy and continuality are characteristic of cadastral information, and the cadastral management demands that the cadastral data should be accurate, integrated and updated timely. It's a good idea to build an effective GIS management system to manage the cadastral data which are characterized by spatiality and temporality. Because no sound spatio-temporal data models have been adopted, however, the spatio-temporal characteristics of cadastral data are not well expressed in the existing cadastral management systems. An event-version-based spatio-temporal modeling approach is first proposed from the angle of event and version. Then with the help of it, an event-version-based spatio-temporal cadastral data model is built to represent spatio-temporal cadastral data. At last, the previous model is used in the design and implementation of a spatio-temporal cadastral management system. The result of the application of the system shows that the event-version-based spatio-temporal data model is very suitable for the representation and organization of cadastral data.

The ideal spatio-temporal database not only has the normal functions essential to every spatial database, but also has the ability for keeping track of dynamic data changing with time. Some analyses based on spatio-temporal database, such as spatial network analyses based on different timestamps, should firstly do efficient querying work. With the convenience and improvement in tracking of moving objects, trajectroy can be easily recorded and many novel services have been proposed. So we should provide an efficient technique to finish trajectroy query on moving objects. In this paper, we focus on improving the existing trajectory query. Firstly, this paper states the current research approaches of dynamic query in spatio-temporal database. Secondly, it gives a classification of current query methods and a description of the trajectory representation. Based on the foregoing groundworks, we propose our improved methods and give an example to set forth the advantage of the method in details. Finally, we look ahead and propose a new vista of research in this area.

The land resource in the mining area has been destroyed badly, therefore to establish a land reclamation information system of mining area based on GIS is of great significance. However, mining land reclamation is a complex systemic project, there are a lot of spatial data and spatial information during the whole reclamation procedure. The spatial information database of land reclamation is a temporal one due to the change of the land resource within the coal mining area. The damaged land is always above the corresponding underground working face of the mine, there are many pieces of destroyed land in the mining area. A piece of reclaiming land area can be acted as one object, On the basis of the analysis of the changing course of the land object in the subsidence region and the advantage of event-based spatio-temporal data model, a spatio-temporal data model based on objects and events was proposed in this paper, this model can be applied to administer land information in the mining area. Meanwhile, the spatial information query and the analytic method were also studied in this paper. The advantage of this model is to connect surface event and underground mining event which cause the land information change in the mining area.

The topological relationship between spatial objects is one of the most important aspects in GIS modeling, and it is the basis of spatial query and analysis. The description of spatial relationships has been paid more attention in the field of GIS. At present, the research on topological relation model for spatial objects was usually described simple objects, the research of a regions with a hole are comparatively fewer, it's research has important theory meaning. First, this paper analysised existing method for description of regions with holes in the eighth reference literature, based on the point-set topology theory, redefined the regions with holes. By analyzing the evolution plans of simple area objects' topological relations, put forward a new method which can describe the special topological relations of regions with a hole, and used the new method to discussed the topological relations of two regions with a hole. Through the experiment, in thoery, it has been proved that this method is feasible, can provide a theoretical method for improving the modeling and analysis capabilities of GIS.

This paper discusses the research and design of spatial-temporal relational model in WalkGIS platform, which complies to the OpenGIS standards. The paper falls under the context of National High-tech Supporting project². The spatial-temporal relational model aims at spatial-temporal objects, and one spatial-temporal relation object corresponds to several tense instances, comparing to the traditional object relation model for one instance with several tenses. Object relation model is the special example of spatial-temporal model, which can be defined as single temporal model. Spatial-temporal relational model establishes and maintains constraints and relationship in spatial objects and temporal-serial instance relationship, performs the transformation of object-based discreet relationship (object-relation) to object-based spatial-temporal relationship (object-spatial temporal relation), creates and constraints temporal relation of instances in different times for one object and instance spatial relationship at different times. This article focuses on temporal model management and design for the objectified spatial-temporal model. Spatial-temporal model supports multi-user and multi-scene. Firstly, multi-user online editing. Each version can be edited independently. It checks interactively and coordinates version conflicts while editing and uploading. Secondly, stream model, multi-version creates the stream model. Thirdly, offline capabilities. Fourthly, version compression. Frequently altered versions can be combined into one version at the end of the year. Fifthly, version transfer. Any version can be transferred and stored into another spatial database. This paper covers spatial-temporal model definitions, model application scenario, model design constraints, version conflicts and reconciliation, data structure design, version management design, etc...

Wetland degradation and its negative effect have long been considered as a hot yet complicated topic. Therefore, it has significance to research landscape patterns and their dynamic changes. Based on Landsat-5 TM data in 1988, 2001, 2007 respectively, the dynamic change of landscape patterns in last two decades is quantitatively analyzed by conversion matrix, landscape index model and centroid model for Momoge National Natural Reserve. The results are as followed: (1) the area of farmland increased from 29594.934 hm2 to 34002.354hm2 during 1988-2007, but the wetland area decreased from 68635.274 hm2 to 59356.598hm2 simultaneously. (2) Human and climate interference caused the change of landscape patterns. In all three periods, landscape patterns of wetland became more fragmentized and the patch counts were 160, 88, 91 respectively. The SHDI was 1.932, 1.770, 1.772 and SHEI was 0.879, 0806, 0.807 respectively. The entironment of Momoge wetland worsened gradually, but the rate of degradation mitigated in some sort since 2001. (3) Decrease of wetland area resulted in a shift of wetland landscape patterns centroid southeastward by 4454.95 meters.

Now the geographic information system obtains the widespread application in various fields. These small geographic application systems, covering small regions, may manage large or medium scale data, but on the other hand in regard to the large geographic information systems, managing entire national territory geography information, basically take the small scale data as a foundation. Those system final users not only need to establish the corresponding application in the entire country or the global spatial data, but they often even more pay more attention to some local data or the details. Therefore it needs to establish an efficient spatial index for these multi-dimension vector data in the large geographic information systems. This article introduced a method to improve quad-tree encoding. It is a kind of multi-encoding system for multi-dimension vector data. It can provide variable resolution ability and make neighboring inquiry directly. The method proved to have high level efficiency in organization of different origin, different projection and different scale vector data. It accomplished an integrated information combination between large scale, high resolution maps (mostly focus on cities) and small scale, wide scope maps including national or international data.

Ashtech Solutions and TEQC are the software for GNSS data processing. One common function for both software is translating binary data received by Ashtech receiver to standard RINEX format. Difference in centimeter between the RINEX data translated by them is found in this paper. Moreover, the multi-path of the RINEX data translated by TEQC is much more serious than that by Ashtech Solutions. The reasons caused large differences are analyzed in the paper, and in our opinion, Ashtech Solutions smoothes the data during translating. Unfortunately, general users not only do not know the default option of smooth, but also can not modify it. As it is well known, any correction should be avoided when checking the quality. Otherwise, pseudo-results are obtained. Thus, it is not suitable to check the quality of a site with the data translated by Ashtech Solutions. Nowadays, as there are so many different GNSS random processing software, we have to be careful when using them.

This paper studies the method and technique of soil erosion load estimation with RS and GIS, after analyzing for effects of landform and vegetation factors on soil erosion, based on studies in the Xingzigou basin, Shaanxi Province. In order to get property data of landform, DEM was used, which generates gradient, slope aspect, relative elevation, etc. In terms of collecting data on vegetation coverage, we have analyzed indicating function of vegetation type, which affects change in land cover with techniques of RS and others. With RUSLE, spatial variation of soil erosion modulus has been simulated, and calculated total load of soil erosion in small basin, which is significance both in theory and in practice.

This paper analyses marine delimitation theories of Midline (Mid-axis) and Scale line according to the definition of midline provided by the United Nations Convention on the Law of the Sea (UNCLS). The authors discuss some faults of midline method and point out that the tendency of marine delimitation technology will be all-shaped scale line method on the Earth ellipsoidal surface. In the end of the paper, the authors introduce their foundational researches on midline and scale line methods based on Map Algebra.

Although the project of the Three Gorges Dam across the Yangtze River in China can utilize this huge potential source of hydroelectric power, and eliminate the loss of life and damage by flood, it also causes environmental problems due to the big rise and fluctuation of the water, such as geo-hazards. In order to prevent and predict geo-hazards, the establishment of prediction system of geo-hazards is very necessary. In order to implement functions of hazard prediction of regional and urban geo-hazard, single geo-hazard prediction, prediction of landslide surge and risk evaluation, logical layers of the system consist of data capturing layer, data manipulation and processing layer, analysis and application layer, and information publication layer. Due to the existence of multi-source spatial data, the research on the multi-source transformation and fusion data should be carried on in the paper. Its applicability of the system was testified on the spatial prediction of landslide hazard through spatial analysis of GIS in which information value method have been applied aims to identify susceptible areas that are possible to future landslide, on the basis of historical record of past landslide, terrain parameter, geology, rainfall and anthropogenic activity. Detailed discussion was carried out on spatial distribution characteristics of landslide hazard in the new town of Badong. These results can be used for risk evaluation. The system can be implemented as an early-warning and emergency management tool by the relevant authorities of the Three Gorges Reservoir in the future.

The increase of agricultural pest disasters in recent years has become one of major problems in agriculture harvest; how to predict and control the disasters of agricultural pest has thus attracted great research interest. Although a series of works have been done and some achievements have been attained, the knowledge in this area remains limited. The migration of agricultural pest is not only related to the time variation, but also the space; consequently, the population of agricultural pest has complex spatio-temporal characteristics. The space factor and the temporal factor must be considered at the same time in the research of dynamics changes of the pest population. Using plant hoppers as an object of study, this study employed the biological analogy deviation model to study the distribution of pest population in different periods of time in Guangdong Province. It is demonstrated that the population distribution of plant hoppers is not only related to the space location, but also has a certain direction. The result reported here offers help to the monitor, prevention and control of plant hoppers in Guangdong Provinces.

Nine forest characteristic indicators are developed for differentiating the features and laws of the forest geographical distribution of the 22 cities (or counties) in Changbai mountain areas by factor analysis. It can be found from the factor eigenvalue and cumulative contribution rate that the basic characteristics of forest composition can be conveyed by three main factors, that is, the natural forest, plantations, and near mature forest, which reflect 86.7% of the overall forest features information. According to the analysis of common degrees, the former three factors have reached more than 90% and the forest coverage and open forest volume features have achieved almost 80% while the over mature forest area and young growth have got to 70%-75%, which determine the general characteristics of forest composition. According to orthogonal factor scores, the contour map is plotted to disclose the features and laws of the forest geographical distribution. In conclusion, there is obvious uniqueness and regionality for the characteristics and geographical distribution of forest composition in Changbai mountain areas. The main factors restricting the quantity and quality of forest are natural forest, plantations, and near mature forest. Quantitative factor analysis on the forest resources in Changbai mountain areas can better reflect the characteristics of forest composition. In addition, the geographical distribution laws of forest composition characteristics can also be revealed, providing a reliable scientific basis for the rational exploitation of forest resources, the revitalization of Jilin economy, as well as the ecological environment protection.

The estimation of a landing area plays an important role in landing decision. Due to the drawbacks and limitations of the traditional static methods, a method for estimating the landing area based on marine dynamic geographic information system is developed. Main natural geographic features affecting the landing feasibility are analyzed, visual models of the features for dynamically estimating the landing area are constructed, and a quantitative and visual estimation for the landing feasibility is performed. Experimental results indicated that the proposed method could estimate the landing feasibility of areas digitally and dynamically based on the marine dynamic GIS.

During the past 10 years, the real estate industry in Beijing has been manifesting a strongly growing trend. Researching on the distribution of house prices and their tendencies is helpful to grasp and predict the development of the real estate industry and could be used as reference to city planning. 120 records of housing price data in 2005 to 2006 and open prices in 38 developing projects from the first quarter of 2002 to the second quarter of 2008 were used in this study to analyze the spatial and temporal variations of house price with geostatistical methods and nonlinear regression. Results show that there was a very strong autocorrelation among the house prices in Beijing within the range of about 11 km in 2005 to 2006, which can be well fitted with the spherical model. The isogram of the house prices formed a group of homocentric ellipses, with their long axis extending NW-SE, and the house prices decreased from the center to the periphery. The spatial pattern of house prices in Beijing changed obviously from 2003 to 2006. Although both the spatial patterns for the two periods were homocentric ellipses, the shapes of the ellipses and the directions of the axes changed greatly. And there were more imbalances in 2005 to 2006. The house prices in the Huilongguan-Qinghe residential zone, an example of the typical real estate industry in Beijing, kept growing from 2002 to 2008 and could be fitted with exponential growth model.

In this research, the Digital Land Price Model was proposed to adjust the land grade based on the problem of benchmark price during rectify the land grade. Chose the reasonable interpolation method and then make grid treatment, the three-dimension land price model was obtained using the bargaining land price, which supported by the Arcview GIS. The model provided the structure of land price. The price of each unit was calculated after interpolation, and then the map of the land grade was estimated that compared with the original to make adjust.

In the design of engineering pipeline, spatial collision detection is always a necessary process. If the artificial method is used, a heavy work load is always needed and omissions occasionally happens; if the method of collision detection algorithm for three dimension object in graphics is used, the computation will increasing sharply with the increase of pipeline number. Because the data size of three dimension pipeline is extremely big, the key to solve collision problem is efficiency. In this paper, a new collision detection method for three dimension pipeline is presented. The new method is composed of two parts: the coarse detection based on Axis-Aligned Bounding Box and precise detection based on geometry model. The method abstracts the pipeline to spatial line-segments with semantic information using geometry method. Firstly, an improved Axis-Aligned Bounding Box algorithm is used to classify the pipelines. Finally, the result of coarse detection is used as initial value, the geometry location relation is used to classify the pipelines, and then the shortest connecting lines between pipelines are calculated. The experiment results show that, this method can detect collision three-dimension pipeline efficiently.

To insure the quality of GIS software, and reduce the expense of development and maintenance, the normal and independent software testing must be introduced into the process of GIS software development. Nevertheless, the whole GIS community pays little attention to software testing while emphasizing on the signification of software quality and security on the contrary. In the result, there is a lack of solutions of GIS software testing theoretically and systemically, and also the technological criteria of software testing in GIS industry. This paper focuses on the methods of software testing in GIS, especially TGIS and Spatial-Temporal Database. Some exploratory researches have been carried on in this paper, and devoted the effort to the foundation work of GIS software testing designed based on the testing theory and the empirical testing.

Pattern recognition of landform elements provides fundamental information for landscape research such as landscape evaluation and hazard prediction. Totally different from the existing methods where surface geometrical forms are commonly described by local curvatures, this paper uses zenith angle as a basis for pattern recognition in topography. One property of zenith angle is a regional morphmetrical variable, which has potential to overcoming the lack of a consideration of the center point a part of the regional terrain in curvature-based methods. Moreover, the zenith angles are converted and stored in the form of a feature vector so that a feature vector matching approach can be applied to implement the pattern recognition. The proposed method has been implemented and applied to a 10 m cell size DEM of GISMAP Terrain (issued by Hokkaido-chizu in Japan) as a test case. Through visual observations, the transparent composite map of the classification map and the shaded relief of DEM shows that the recognized patterns match the relief well. The topography profiles also reveals that the results of pattern recognition are compatible with the concave-convex property of the terrain shape. Moreover, the 3D maps of a local terrain data randomly taken from the DEM for each of the feature matching cases show that the recognized pattern of the central point is matched well with the characteristics of the surrounding topography.

A kind of formal system, which can express clearly a certain entity or information, is needed to express geographical concept. Besides, some rules explaining the interrelationship and action between different components are also required. Therefore, the conception of geo-object ontology is introduced. It is a shared formalization and display specification of conceptual knowledge system in the field of concrete application of spatial information science. It can constitute hierarchy structure, which derives from the concept classification system in the geographical area. Its concepts can be described by the property. Property sets can form a vector space with multi-dimensional characteristics. Geographic space is composed of different types of geographic entities. And its concept is formed by a series of geographic entities with the same properties and actions. Moreover, each of the geographic entities can be mapped to an object, and each object has its spatial property, time information and topology, semantic relationships associated with other objects. The biggest difference between ecumenical information ontology and geo-ontology is that the latter has the spatial characteristics. During the construction process of geo-object ontology, some important components, such as geographic type, spatial relation, spatial entity type and coordinates, time, should be included to make further research. Here, taking disaster as an example, by using Protégé and OWL, combined methods used by constructing the geo-object ontology in the form of being manual made by domanial experts and semi-automatic are investigated oriented to disaster to serve ultimately geographic information retrieval service driven by ontology.

Multi-elemental cooperative analysis can not only brings a large number of relevant information but fuzz up key elements, so valid data excavating tools are needed to find element information which influences entities the most. Based on the sample of bus site setting in a city, this paper employs Rough Sets method to analyze multi-elemental cooperation which influence bus site grade and calculate importance of each element. Thus, multi-elemental cooperative analysis can be realized. Compared with conventional multi-parameter analysis, this method based on data self-adapting is intelligent, high-efficiency and have no use for manual intervention.

The hyperspectral reflectance of canopy of winter wheat and data of leaf nitrogen accumulation (LNA) were acquired in primary growth stages under different nitrogen levels in order to monitor winter wheat status and diagnose nitrogen using remote sensing method. A new method was developed to estimate the nitrogen nutrition of winter wheat using continuum-removed method, which generally used in spectra analysis on rock and mineral. The continuum-removed method was effectively used to magnify the object spectral absorption features, and it could be convenient to extract the spectral absorption features. Based on the continuum-removed treatment and the correlation between absorption feature parameters and LNA, results show that LNA increased with increasing the nitrogen fertilization. LNA increased from the erecting stage to the booting stage and decreases from the booting to the heading stage under all nitrogen levels. It is the VNIR regions that were sensitive to LNA. By continuum removal operation, it can be found that the method magnify the subtle difference in spectral absorption characteristics arise from the nitrogen stress on winter wheat. At all stages, total area of absorption peak, left area of absorption peak, right area of absorption peak increased with increasing the nitrogen fertilization, whereas the normalized maximal absorption depth by area decreased. The correlation analysis indicated that all the absorption characteristics parameters of continuum-removed spectra highly correlated with LNA, and the correlation relationship of the whole growth cycle was stronger than that of any single growth stage. But the booting stage is the best at the several single growth stages and the NMAD is the best absorption parameter to monitoring the nitrogen of winter wheat canopy. The range 550 nm to 760 nm are the feature bands for extracting nitrogen information of canopy. The regression analysis on the whole growth period showed that the all regression models between the absorption characteristics parameters and LNA were all extremely significant (P<0.001).Therefore, continuum-removed method is a feasible method for quantificational evaluation of winter wheat LNA.

This research analyzes the spatial pattern of large-scale stores based on space syntax theory and explores the correlation between the variations in syntax accessibility and the spatial pattern of large-scale stores. This research develops a framework of spatial topology analysis based on the space syntax theory, which includes the following modifications: the trail to break the traditional long axial line network of space syntax and apply this partitioned network in the topological analysis; the trail to analyze the bus route network; By taking both the syntax accessibility of road and bus network into consideration, we produce the scopes of urban syntax centers of city level, local level and sub local level respectively. In the analysis of the retail distribution pattern, the city level, local level and sub local level urban retail centers are suggested respectively according to the spatial distributions of the quantity and scale of the retail stores. The spatial distribution pattern of each retail format is studied as spatial correlations between the retail locations and the urban space syntax centers based on a case study in Wuhan, China. The Space Syntax can be a useful tool to explain the allocation logic of urban retail space in large cities. We suggest to apply the partitioned transportation network instead of the traditional long axial line network.

The data organization of gravitational field is based on a form of Grid, which is similar to the data structure of DEM in the terrain. So this paper proposes a method of gravitational field analysis for extracting features by adopting some spatial analysis means of topography. First of all, the gravity anomaly data is used to calculate the roughness features of gravitational field. after comparing and anglicizing the features, roughness features are selected as the feature factor of gravity. Then, the method of calculating contour lines is applied to calculate region segmentation of roughness features and extract the vector edge of the larger feature regions, and clustering analysis to these contour of the region. At last, the scope line for Convex Hull of the region is calculated by the Convex Hull algorithm, and so as to obtain a more prominent region (matching region) that have significant changes in gravity anomaly, which provide the necessary reference data for the gravity-aided inertial navigation.

The evaluation of urban landscape and the design of urban space are in need of a scientific visual analysis tool that can wholly include the qualitative analysis, the quantitative analysis and the orientation analysis. With the outstanding saving, management, visualization and analytical functions of 3D space information, 3DGIS can exactly provide powerful technique support for this. Based on the humanistic analysis conception, the concept of visual openness index is introduced, and some visual analysis functions are designed and implemented based on VGEGIS platform. Finally a case study for the application of visual analysis is carried out and the results show that the urban design scheme evaluation is improved distinctively.

Land reservation is defined as a land management policy for insuring the government to control primary land market. It requires the government to obtain the land first, according to plan, by purchase, confiscation and exchanging, and then exploit and consolidate the land for reservation. Underlying this policy, it is possible for the government to satisfy and manipulate the needs of land for urban development. The author designs and develops "Land Reservation System for Eastern Lake Development District" (LRSELDD), which deals with the realistic land requirement problems in Wuhan Eastern Lake Development Districts. The LRSELDD utilizes modern technologies and solutions of computer science and GIS to process multiple source data related with land. Based on experiments on the system, this paper will first analyze workflow land reservation system and design the system structure based on its principles, then illustrate the approach of organization and management of spatial data, describe the system functions according to the characteristics of land reservation and consolidation finally. The system is running to serve for current work in Eastern Lake Development Districts. It is able to scientifically manage both current and planning land information, as well as the information about land supplying. We use the LRSELDD in our routine work, and with such information, decisions on land confiscation and allocation will be made wisely and scientifically.

With the development of aerial aircraft, it has been possible that using the small unmanned aerial vehicles (UAV) for power line corridors surveillance. In Queensland, Australia, the local electric power company used the small UAV loaded the ordinary digital camera for power line surveillance. In order to extract the power lines automatically from the cluttered natural background aerial images, we propose a power line detection method for a vision based on UAV surveillance system. A Gabor filter is developed to remove background noise from the images prior to the Hough transform being employed to detect straight lines. The experiment on real image data demonstrates that the proposed approach is effective.

By discussing the causes behind the high amendments ratio in the implementation of urban regulatory detailed plans in China despite its law-ensured status, the study aims to reconcile conflict between the legal authority of regulatory detailed planning and the insufficient scientific support in its decision-making and compilation by introducing into the process spatial analysis based on GIS technology and 3D modeling thus present a more scientific and flexible approach to regulatory detailed planning in China. The study first points out that the current compilation process of urban regulatory detailed plan in China employs mainly an empirical approach which renders it constantly subjected to amendments; the study then discusses the need and current utilization of GIS in the Chinese system and proposes the framework of a GIS-assisted 3D spatial analysis process from the designer's perspective which can be regarded as an alternating processes between the descriptive codes and physical design in the compilation of regulatory detailed planning. With a case study of the processes and results from the application of the framework, the paper concludes that the proposed framework can be an effective instrument which provides more rationality, flexibility and thus more efficiency to the compilation and decision-making process of urban regulatory detailed plan in China.

As a unique group in China's urbanization, "urban villager" is the concern of various parties of the society. From "farmers" to "urban residents", urban villagers' means of production and life style change dramatically. At present, public facility planning in urban villagers' community always fail to meet their particular demands. Taking PPGIS as an instrument, the paper analyzes the present status of public facilities in urban villagers' community and the new demand on public facilities from the changing production means and life style. The purpose is to put forward suggestions for public facility setting in urban villagers' community and offer theoretic guidance and proposal for Wuhan new urban areas. PPGIS is gradually being applied to social science researches in recent years. Through the integrated platform, it can achieve the objective of communication, coordination, cooperation and collaboration of different interests. In this research, ephemeral mapping, sketch mapping, scale mapping and aerial photographs are used to acquire spatial data of public facilities and attribute data of urban villagers in their community. Through the comparison of data, the research shows that while urban villagers in Wuhan new urban areas gradually accept city life, they inevitably maintain certain rural habits and customs. Therefore, the public facility planning in this particular kind of communities can neither be treated equal as countryside facility planning, nor simply adopt the practice in urban residential areas' planning; rather the planning system should take into account facilities of different categories at all levels, communities of different types and residential groups.

Spatial accessibility denotes the ease with which activities may be reached from a given location using a particular transportation system. There are a number of accessibility measures methods and models, such as time of access to city centre, mean travel costs and opportunity accessibility. But these methods or models ignore the existence of obstacles. In fact, there are many kinds of obstacles in the world, such as rivers, railways, etc. The paper reviews the progress of accessibility measures, and introduces the obstacle to the accessibility measures. Meanwhile, through the analysis of A* algorithm, the advantage of A* algorithm that could avoid obstacles is put forward. Based on the above mentioned, the obstacle oriented accessibility measures based on simple gravity model is discussed in details. Finally, a case study is fulfilled by comparison between the obstacle oriented and non-obstacle accessibility measures.

In order to solve the dependence on the problem of patch technology in the constrained optimization of Particle Swarm Optimization (PSO), conversion will be achieved on the qualitative and quantitative of feasibility rules of the PSO algorithm by the uncertain illation and figure characteristics of cloud .The quantification of binding correction factor will be done in order to realize the common patch methods of different issues. The improved algorithm will be applied to the urban land use planning of Nanning and Yulin city in Guangxi. Quantifying the land conversion factor and combining with spatial analyses technology, analyzing and contrasting the land conversion area of the two cities, this improved algorithm will be proved to settle different problems.

The key problem for implementation of the sustainable development is to design the strategy and policy which incorporates the environmental impacts. This paper puts forward a new model based on GIS of ecological environment protection and sustainable development based on estimating and assessment. The environmental carrying capacity and developing intensity of studied area are analyzed, the ecological security and the level of sustainable development are evaluated, and also the constraints are discussed. According to this analysis, the range of Lichuan is divided into four regions. On the foundation of distinctive characteristics of each area, the designation of the industrial development and environment protection have been ensured; after that, the environmental impact of the given strategies has been identified and predicted; finally, several mitigation measures are suggested.

This paper analyzes the effect of exterior orientation elements on vertical parallax, especially using the orientation parameters of aerial images obtained by a POS (Positioning and Orientation System) after calibration. Firstly, based on the theory of analytical relative orientation of consecutive photo connection, the exterior orientation elements can be easily translated to relative orientation elements. Then, the formula of vertical parallax can be deduced. The results of vertical parallax in left image space coordinate system are compared with the results calculated in the image coordinate system which are parallel to those of the object coordinate system. The validity and feasibility of the mathematical model are tested using two sets of actual data at different images scales. Finally, the differences between the effects of exterior orientation parameters on vertical parallax are compared using exterior orientation parameters obtained by traditional bundle block adjustment and by a POS after calibrated. And how the single element of exterior orientation effected on vertical parallax and how they worked together are analyzed. The empirical results indicate that the effects of different elements of exterior orientation on vertical parallax are different, all exterior orientation parameters can be affected by each other, so the overall effect of vertical parallax accuracy can be restricted by all exterior orientation parameters.

Spatial motion is a part of dynamic geographic phenomena which could be directly observed by human on a certain geographic scale. Most motion processes are generally regulated by external geographical environment, or some social criterions. A geo-referenced pattern states an argument that the similar motion processes perhaps have homological motion characteristics, patterns, trends, and etc. Since this paper concentrates on process model of motion phenomena, firstly we introduce some descriptors for expressing motion process, which include motion trajectory, process area, and process extent. The second, in order to describe a correlation between motion process and geographical feature, we introduce an initial concept of constrained geometry, which is consisted of constrained point, constrained line, and constrained area. Based on definition of constrained geometry, the computing models for three motion patterns named as location-referenced pattern, path-referenced pattern, and region-referenced pattern are constructed. In the end, we use historical typhoon data within the past 3 years from 2004 to 2006 for an experiment, in which 75 typhoon activities are taken as the example for exemplifying the validation of region-referenced motion pattern. The results confirm that the existent region of typhoon activities in a year is similar enough to other years.

Precipitation and topography are the most important influence factors of non-point source pollution, especially for soil erosion. Based on the SWAT model and ArcGIS, the Fuhe watershed was divided into 50 sub-basins. Combined with the standard deviation gradational method, statistical analyses on characteristics of the spatial and temporal variation of rainfall in Fuhe watershed were carried out¹ based on the rainfall observation data from January 1st, 1986 to December 31st, 2005 from 25 stations within the watershed. The results showed that the spatial-temporal distribution of precipitation was uneven. Spatially, the average annual rainfall was divided into five grades. Temporally, there was great variation in inter-year precipitation. The inter-year rainfall distribution was extremely uneven, focused on March to June, which accounted for 56 % of the total. The analysis of topography in Fuhe watershed was carried out by using the standard deviation gradational method. The slope of the basin was divided into five grades. The basin was divided into four grades of the NPS risk by using pollution average slope and rainfall to calculate a weighted value of the risk of NPS pollution. The first and second level district were mainly distributed in the western, the eastern area, and sporadically distributed in certain areas of the southern in the watershed, which accounted for 55.6% of the basin area; the risk values were very large in these districts.

This paper explores data-driven methods for quantifying and incorporating spatio-temporal contextual information in the mapping of land cover change. In remote sensing, area classes of land cover are typically mapped via statistical manipulation of feature-space measurement, e.g., reflectance data, and other ancillary data. Contextual information has been known to have the potential of increasing the accuracy of land cover classification and change detection, on the ground that land cover often exhibits spatial and temporal correlations and, as such, should be properly accommodated. In Bayesian methods, a priori probabilities of class occurrences can be considered as contextual information, which are combined with class-conditional probability densities to arrive at discriminant decisions with minimized misclassification. These prior probabilities may be made to vary locally to honor variability in the strengths of spatial dependence in class occurrences. For deriving local prior joint probabilities in land cover co-occurrences over time, a modified Expectation and Maximization (EM) algorithm was developed, in which a local window size can be adjusted in the light of spatial dependences inferred from class probability densities computed from spectral data. Empirical studies were performed using bi-temporal Landsat TM image subsets in Wuhan, which confirmed the comparative benefits of incorporating localized prior probabilities in land cover change detection.

ESRI's GIS components MapObjects are applied in many cadastral information system because of its miniaturization and flexibility. Some cadastral information was saved in cadastral database directly by MapObjects's Shape file format in this cadastral information system. However, MapObjects didn't provide the function of building attribute field for map layer's attribute data file in cadastral database and user cann't save the result of analysis. This present paper designed and realized the function of building attribute field in MapObjects based on the method of Jackson's system development.

Urban growth has great effect on land uses of its suburbs. The habitat loss and fragmentation in those areas are a main threat to conservation of biodiversity. Enhancing landscape functional connectivity is usually an effective way to maintain high biodiversity level in disturbed area. Taking a small town in Beijing as an example, we designed potential landscape corridors based on identification of landscape element quality and "least-cost" path analysis. We described a general approach to establish the corridor network in such fragmented area at town scale. The results showed that landscape elements position has various effects on landscape suitability. Small forest patches and other green lands such as meadow, shrub, even farmland could be a potential stepping-stone or corridor for animal movements. Also, the analysis reveals that critical areas should be managed to facilitate the movement of dispersers among habitat patches.

One of the World's most famous wetland ecosystems centered on the Euphrates and Tigris river basin and its delta regions in the Middle East was very nearly destroyed by the actions of government. A high quality database is the starting point for any GIS application. For Mesopotamian Marshlands, a large amount of spatial data has been accumulated by numerous ecosystem studies. This study encompasses to introduce a GIS database development in support of these studies. The study includes the following components: the *establishment of a data server conveniently accessible to all users; the selection of appropriate RDBMS/GIS software for both attribute data and geographic data; the development of a general data format and database structure; the formalization of data management procedures, including input, update, conversion and the implementation of data query and retrieval utilities for end users to search, display, print or plot. The increasing focus on integrating socio-economic and biological information with remote sensing and GIS technology can only help to further our understanding and capacity to manage ecosystems in a more sustainable manner.