Yunfei Shi
email: shiyunfei_001@163.com

Biao He

Creating Topologically Consistent 3D City Models of LOD+ with Extrusion

Keywords: 3D city model, extrusion, topological consistent, heterogeneous structure building

There are many methods to create 3D city model, in which the simplest one is to extrude building footprints to obtain "block-shaped" polyhedra representing buildings. While the resulting 3D city models are not topologically consistent, if the 2D topological relationships between the footprints are not taken into account. As a result, the created models only meet the purpose of visualization. In this paper, we focus on creating topologically consistent 3D city models from the footprints of heterogeneous structure buildings. The generated models are more detailed than LOD1 of CityGML and are called LOD1+, which not only construct topology between buildings but also generate internal structure topology of them. We have implemented the procedure, tested it with real-world datasets, and validated it.

Introduction

3D city models have been increasingly applied as data source or visualization method in a growing number of fields such as urban planning, architecture, archaeological reconstruction, tourism, civil engineering, mobile telecommunication, energy supply, navigation, etc [START_REF] Tack | 3D building reconstruction based on given ground plan information and surface models extracted from spaceborne imagery[END_REF].Different applications require various detail and accuracy of 3D model. For this purpose, 3D city models were divided into five levels of details (LODs) by City Geography Markup Language (CityGML), from LOD0 where only the terrain is stored to LOD4 where buildings have detailed roofs structures, windows, rooms and even pieces of furniture [START_REF] Ledoux | Extruding building footprints to create topologically consistent 3D city models[END_REF].

There are many ways to create 3D city model,in which the simplest one is arguably with extrusion. That is, given a set of footprints representing the buildings or rooms (polygons in the plane), assign them a height and then push them upwards to create polyhedra [START_REF] Ledoux | Topologically consistent 3D city models obtained by extrusion[END_REF]. Extrusion is indeed easy if the only thing you want to do with your 3D city model is to look at it! [START_REF] Ledoux | Extruding building footprints to create topologically consistent 3D city models[END_REF]. Many commercial software can extrude polygons to obtain buildings, but the resulting models cannot be stored in a topological data structure [START_REF] Ledoux | Topologically consistent 3D city models obtained by extrusion[END_REF]. The reason is that the created models are not topologically consistent. In a nutshell, that means that extruding results contains duplicate points, overlapping faces, faces intersecting where there are no points, etc [START_REF] Ledoux | Extruding building footprints to create topologically consistent 3D city models[END_REF]. Fig. 1 illustrates a process where two building footprints are being extruded. Extrusion without considering other footprints only guarantee that each resulting polyhedron is valid, but not ensure that a group of footprints will generate a topologically consistent 3D city models. In this level, whole building is represented as a "block-shaped" model with flat roof structures and no internal floors and rooms. However, a building often includes many floors and rooms in the real world. Existing studies only consider the topological construction between buildings, but do not take into account that of internal structure (floors and rooms) of buildings. In fact, topology of internal structure of buildings is very important for many applications, such as indoor navigation [START_REF] Lee | A 3D data model and topological analyses for emergency response in urban areas[END_REF], safe escaping and so on. In addition, because a building contains multiple floors and the different floor corresponds to different structure (i.e. heterogeneous structure), that make the extruding process need to consider simultaneously vertical and transverse topology. Therefore, topological construction of internal structure of buildings is more complicated than that between buildings.

In this paper, we focus on creating more detailed topologically consistent 3D city models than LOD1 and are called LOD1+, which not only construct topology between buildings but also generate internal structure topology of them. Our main contribution is to create topologically consistent 3D City models of LOD1+ level from heterogeneous structure buildings. The remainder of this paper is organized as follows. Section 2 provides the basis of research. Section 3 gives our extruding approach for heterogeneous structure buildings. Section 4 gives ours experiments. Finally, section 5 offers conclusions of our work.

2

The Basis for research

Topology is one of the mechanisms to describe relationships between spatial objects. Thus, it is the basis for many spatial operations. Let S be a set of spatial objects in 3 , the three dimensional Euclidean space. Spatial objects in S are formed by four geometric primitives: (ⅰ) ρ 0 is a point(0 dimensionality); (ⅱ) ρ 1 is a straight line segment (1-dimensionality), which is referred to simply as a line in the following; (ⅲ) ρ 2 is a polygon (2-dimensionality); (ⅳ) ρ 3 is a polyhedron (3-dimensionality). Topological consistency: Let S be a set of spatial objects in 3 , we say that S is topologically consistent if the following rules are valid [START_REF] Ledoux | Topologically consistent 3D city models obtained by extrusion[END_REF]:

(1) every line segment ρ 1 in S is formed by two points ρ 0 also in S;

(2) the intersection of two lines ρ 1

1 and ρ 1 2 , denoted ρ 1 1 ∩ ρ 1 2
, is either empty or is a point in S;

(3) the intersection of two polygons ρ

2 1 and ρ 2 2 , denoted ρ 2 1 ∩ ρ 2 2
, is either empty or a finite set of primitives in S (points or lines).

(4) the intersection of two polyhedrons ρ 3 1 and ρ 3 2 , denoted ρ 3 1 ∩ ρ 3 2 , is either empty or a finite set of primitives in S (points or lines or polygons).

The related storage structures of topological data include half-edge, winged-edge [START_REF] Baumgart | A polyhedron representation for computer vision[END_REF], Quad-edge [START_REF] Guibas | Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams[END_REF] and so on. From the view of manifold, they belong to manifold topology. In addition, many researchers also propose non-manifold data structures, such as Face-edge [START_REF] Dobkin | Primitives for the manipulation of three-dimensional subdivisions[END_REF], Radial edge, X-maps and so on. The paper uses half-edge as data structure to storage the data with extrusion.

Our extruding approach

This section presents our algorithm to create topologically consistent 3D city model. The algorithm is based on the idea that the floor plans taken as the input are a set of topologically consistent polygons (in 2D). We use existing GIS tools (e.g. arcgis) to create topologically consistent 2D dataset as input and output topologically consistent 3D models of buildings and a set of faces embedded in 3D. In created models, a building (polyhedra) is enclosed by its bounding faces and simply has a reference to these faces, and faces (or edges or nodes) shared by adjacent polyhedra (or faces or edges) are not duplicated.

Generation of ROOF-PLAN and FLOOR-PLAN

According to the above analysis, the generation of floor faces or roof faces is influenced by the structure of adjacent floors. Therefore, extruding process needs to consider synthetically the relationship between the extruded floor and its adjacent floors. For this reason, we divide floor plans into three types: STRUCTURE-PLAN, ROOF-PLAN and FLOOR-PLAN. STRUCTURE-PLAN is used to create vertical walls (polygons) and determines the unit structure of the whole floor. If the extruded floor is the nth, STRUCTURE-PLAN will be the original floor plan.

ROOF-PLAN is used to create roof face of the extruded floor. If the extruded floor is n, ROOF-PLAN will be obtained by overlapping the nth and the (n+1)th STRUCTURE-PLAN (As shown in Fig. 2).If building only has n floors, ROOF-PLAN is equivalent to STRUCTURE-PLAN. FLOOR-PLAN is used to create floor face of the extruded floor. If the extruded floor is n, ROOF-PLAN will be obtained by overlapping the nth and the (n-1)th STRUCTURE-PLAN (As shown in Fig. 2). If n is equal to 1, FLOOR-PLAN is equivalent to STRUCTURE-PLAN.

From these three definitions, we can find that ROOF-PLAN of the nth floor is equivalent to FLOOR-PLAN of the (n+1)th floor and FLOOR-PLAN of the nth floor is equivalent to of the (n-1)th ROOF-PLAN floor. For a building with n floors, it needs n STRUCTURE-PLAN and n+1 ROOF-PLAN or FLOOR-PLAN.

Fig.1. Generation of ROOF-PLAN and FLOOR-PLAN

The overlap operation can use existing tools (e.g. arcgis) to implement. In order to ensure overlapped plans are topologically consistent, clean operation is used to reconstruct topology. In this way, we create topologically consistent ROOF-PLAN and FLOOR-PLAN for every floor of a building.

Topological construction of vertical direction

After the generation of ROOF-PLAN and FLOOR-PLAN, the next step is to create vertical walls and body data. ROOF-PLAN and FLOOR-PLAN only determine the structure of roof face and floor face of extruded floor, but the whole floor structure is determined by STRUCTURE-PLAN. It's noteworthy that STRUCTURE-PLAN is only used as reference to create vertical walls but itself is not the topological primitive of created model, which is different from the usage of ROOF-PLAN and FLOOR-PLAN.

Let's use a simple sample to illustrate. As shown in Fig. 3, vertical shaded face (wall) is created by extruding the line segment ab. But ab is not a part of the created face. The edge set of the created face is composed by the following line segments:

{V a" V", V"V b" , V b" V b' , V b' V',, V'V a' , V a' V a" }.
It can be seen that each vertex in STRUCTURE-PLAN creates a vertical edge, and each edge and each polygon in STRUCTURE-PLAN creates a vertical face and a polyhedron separately.

Fig. 2. Generating process of vertical wall

Generation of vertical elements. Creating vertical edges. Taking out a vertex from the vertices set of STRUCTURE-PLAN, it seeks the vertex corresponding to counterpart in ROOF-PLAN and FLOOR-PLAN respectively and forms a vertical edge. For instance, vertex V a corresponds to vertex V a' in FLOOR-PLAN and V a'' in FLOOR-PLAN respectively (Fig. 3).Connecting V a' and V a'' , a vertical edge is created. In order to facilitate following work, the direction of edge is from the vertex in FLOOR-PLAN to the vertex in ROOF-PLAN. Iterating the process, we can obtain the vertical edge set.

Creating vertical faces (walls).

For each edge of STRUCTURE-PLAN, corresponding edge set is sought in ROOF-PLAN and FLOOR-PLAN. An edge in STRUCTURE-PLAN may correspond to multiple edges in ROOF-PLAN and FLOOR-PLAN. For example, the corresponding edges of edge V a V b are V a' V' and V'V b' in ROOF-PLAN (fig. 3).The found corresponding edges (crosswise edges) and the created vertical edges generate vertical faces in common. As shown in fig. 3, the vertical face created by edge

V a V b is V a" V"V b" V b'' V' V a' V a" , which is formed by edge set {V a" V", V"V b" , V b" V b' , V b' V',, V'V a' , V a' V a" }.
Here, we need to sort the edge set and make them arrangement in clockwise or counterclockwise order.

Creating polyhedra. For each face (polygon) in STRUCTURE-PLAN, corresponding face set is sought in ROOF-PLAN and FLOOR-PLAN. Similarly, a face in STRUCTURE-PLAN may correspond to multiple faces in ROOF-PLAN and FLOOR-PLAN. The corresponding faces of face f 2 are f 1 2 , f 2 2 , f 3 2 and f 4 2 . The found corresponding face (horizontal face) set and the created walls compose polyhedra in common. Algorithm of seeking corresponding relationships. The above three steps all have a core issue: seeking corresponding relationship, namely, finding vertices, edges and faces in STRUCTURE-PLAN corresponding counterparts in ROOF-PLAN and FLOOR-PLAN separately. The seeking process of three corresponding relationship is following.

Seeking corresponding vertices. Due to the model obtained by extrusion, the extruded vertex should have same x and y coordinates in vertical direction. When we seek the corresponding vertices of the vertex A (x, y, z) in STRUCTURE-PLAN, z coordinate can be ignored and seek the nearest vertex to A (x, y). Theoretically, the coordinates obtained nearest vertex should equal to A. However, owing to storage error of computer, the nearest vertex is not equal to A and has a little offset. Therefore, the nearest vertex is the seeking corresponding object exactly.

Seeking corresponding edges. Based on the method of seeking corresponding vertices, we can obtain the corresponding vertices in ROOF-PLAN and FLOOR-PLAN of vertices of each edge in STRUCTURE-PLAN. As shown in fig. 3, corresponding vertices in ROOF-PLAN of vertices of edge V a V b are V a' and V b' . After confirming corresponding vertices, it needs to check whether V a' and V b' can make up an edge. If they can, the edge V a' V b' is corresponding edge. Otherwise, we need to ignore z coordinate and find the vertex which distancing V a' V b' less than given threshold in ROOF-PLAN. If the vertex is found, we think that the vertex is on the line. For example, Vertex V' is on the V a' V b' in fig. 3. By the way, corresponding edge set can be found.

Seeking corresponding faces. With the method of seeking corresponding edges, we can acquire the corresponding edges (in ROOF-PLAN or FLOOR-PLAN) of all edges of each face in STRUCTURE-PLAN. The obtained corresponding edges draw up a range in ROOF-PLAN and FLOOR-PLAN. If the corresponding edges just surround a face, the surrounded face is corresponding face. Otherwise, we need to judge all faces of ROOF-PLAN (or FLOOR-PLAN) and pick out those faces which drop in the tagged range of the corresponding edges. These faces compose the corresponding face set.

With above algorithm, topological consistent 3D models of every floor are created. The next step is to build the topology between floors. Because the extrusion of every floor is independent, the common elements (nodes, edges and faces in ROOF-PLAN or FLOOR-PLAN) of adjacent floors are reconstructed twice. Moreover, the duplicated common elements are same in structure. Thus, the topological construction of adjacent floors can be implemented by deleting the duplicated nodes, edges and faces and adding references to the reserved common topological primitives for adjacent polyhedra in different floors. By this way, the created 3D buildings models include a set of complete topological relationships, in which common faces are shared by adjacent polyhedrons, and common edges are shared by adjacent faces and the common nodes are shared by adjacent edges.

Experiments

With the proposed algorithm, we establish an experiment system which takes Visual C++ as development tools, and use Open Scence Graph (OSG) as graphic interface and choose a small residential area as test data. The data include floor plans and elevation tables of every building. Floor plans provide the structure information of every floor and elevation tables provide the height information of every floor.

Conclusion

The paper analyzes the application demands of 3D city model and proposes a method for constructing topological consistent 3D city model. With the method, we can quickly generate 3D data of large area buildings, avoid the re-survey of buildings and save the cost of data collection. The shortage of the method is that it can't handle the building with slope walls, which is our further goal.

Fig. 1 .

 1 Fig.1. (a)Two polygons in the plane.(b)Two polyhedra obtained by extrusion of the two polygons.(c) To be topologically consistent the front polygon of the polyhedron obtained by the extrusion of A should be modeled with two polygons: efgh and aehgfbcd. Many studies [2] [3] have been developed on topologically consistent 3D city model by extrusion. Models created by existing studies belong to LOD1 in CityGML.In this level, whole building is represented as a "block-shaped" model with flat roof structures and no internal floors and rooms. However, a building often includes many floors and rooms in the real world. Existing studies only consider the topological construction between buildings, but do not take into account that of internal structure (floors and rooms) of buildings. In fact, topology of internal structure of buildings is very important for many applications, such as indoor navigation[START_REF] Lee | A 3D data model and topological analyses for emergency response in urban areas[END_REF], safe escaping and so on. In addition, because a building contains multiple floors and the different floor corresponds to different structure (i.e. heterogeneous structure), that make the extruding process need to consider simultaneously vertical and transverse topology. Therefore, topological construction of internal structure of buildings is more complicated than that between buildings.In this paper, we focus on creating more detailed topologically consistent 3D city models than LOD1 and are called LOD1+, which not only construct topology between buildings but also generate internal structure topology of them. Our main contribution is to create topologically consistent 3D City models of LOD1+ level from heterogeneous structure buildings. The remainder of this paper is organized as follows. Section 2 provides the basis of research. Section 3 gives our extruding approach for heterogeneous structure buildings. Section 4 gives ours experiments. Finally, section 5 offers conclusions of our work.

Fig. 3 .Fig. 4 .Fig. 5 .

 345 Fig. 3. (a) Structure plan of first floor; ( b) Structure plan of second floor;(c)Model by extrusion Fig.5 is the modeling process of complicated building with two floors. (a) and (b) are the structure plans of the first floor and the second floor separately. (c) is the extruding result.


Xueyuan Chen
email: chenxueyuan666@sohu.com.

Yongchang Wu

Bingwen Zhao

Study on Agricultural Park Planning Methods Based on Omni-Directional Information Processing Technology

Keywords:

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The region, a highly abstract and broad concept, is a regional space divided for the human's production and life [START_REF] Hao | Theory and method of regional planning[END_REF] . There are different choices for an area type as the regional unit of regional planning for different research purposes and decision-making backgrounds [START_REF] Zhongmin | Thoughts on the regional planning of the cross-administrative areas[END_REF] [START_REF] Xiaoying | The Analysis of the Trinity Planning of Eco-agricultural Tourism Park[END_REF] . Aiming at different planning areal units, some disciplines emerge, such as urban planology, which is involved in the comprehensive arrangement for future urban development, the rational urban layout and overall urban arrangement of engineering construction [4] . In other words, the Urban Planology is a discipline dealing with urban and rural built environment by their land utilization and urban material spatial planning [5] . The idea of the balance urban and rural development is put forth in the Report of the Sixteenth CPC National Congress. So, many areas or parks are built successively nationwide such as agricultural sci-tech park, agricultural high and new tech area, agricultural sci-tech demonstration area, combined agricultural test area, high-tech agricultural park, agricultural demonstration area with high production and efficiency, and modern agricultural experiment park [START_REF] Lijuan | Design and research of modern agricultural park planning-An example of Yangling modern agricultural demonstration area[END_REF][7] [START_REF] Fachao | Methodology of agricultural landscape classification with rural scale and its application in planning of new rural construction[END_REF] . To sum up, all belong to modern agricultural area construction substantially, and are the important ways for development of modern agriculture [START_REF] Huafu | Analysis of agricultural landscape and the zoning layout of agricultural function in Beijing[J[END_REF][10] [START_REF] Zhenxing | Empirical research on theory[END_REF] . Currently, there is no uniform evaluation standard and construction principle for planning and construction of numerous agricultural areas. Through a long-term practical study on planning and design of agricultural areas, we put forward the agricultural regional planology different from the urban planology and urban & rural planning in research object and target. The agricultural regional planology deals with the agricultural park planning, construction and development with adjustment of agricultural structure and increase of farmers' income. This regional planology offers not only a method to realize modern agricultural regional planning by multilevel and full information, but also sustainable utility of agricultural resources and means of multi-dimensional technology. This will be a better approach for modern agricultural park planning in thinking and technology.

Design Method of Park planning System

The traditional agricultural park planning is expressed in the form of the planning text and plane plan. With great increase of the planning and information contents, the single expression cannot well present the planning idea and contents, and introduction to new technical means becomes a new orientation of the park planning research. The routine park planning is available from time, space and information, but with integration of social economy and combination of space and time, we set forth the concept of the omni-directional processing technology to fit for new planning form, expression and contents. For the omni-directional processing technology, the coupling relation of the multi-dimensional space with time and information should be considered fully besides dependence of objects, scale effect, anisotropy of space and information layering linear in the multi-dimensionalal space. By calculating 3D VR technology as a basic application means, the data information and time & spatial elements can be integrated and utilized in the park planning to the maximum so as to form a park planning system with plane, space, time and information resources.

The core of the omni-directional processing technology is the integration and application of information, time and spatial dimensions and resources, and the key to realize this processing is dependent on a tool system-the whole-park planning & omni-directional VR processing system. By the special VR processing engine developed specially aiming at the park planning, the multi-dimensional space is built in the planning target area, and the information and time can be overlaid and integrated with the multi-dimensional space. With application of functional components, such omni-directional planning results as a scale result presented in the form of 3D VR, planning scheme expressed by time and space, and automatically-generated maps and income from investment can be available. The omni-directional planning results include three major parts as shown in Fig. 1.

Fig. 1 Dimensional structure for omni-directional processing technology

Information dimension

Information is a description of state and characteristics of objective things, and information itself is abstract and invisible with independency of carriers. Its transmission and effect of action are not limited to time and space. It is an independent dimension which can be beyond time and spatial dimensions defined in the physical conception. With a long-term practical research on the agricultural park planning, it is believed that the information dimension involved in agricultural park planning refers to parts as follows:

(1) All kinds of agricultural information and data supporting the planning design in the process of agricultural park planning, including requirements and target of the region construction, land utilization, water conservancy and transport, agricultural resources, climate resources, social economy, etc.;

(2) Design idea, design thought, national and local industrial policy, industrial standard, and calculating standard for income from the region investment formed during agricultural park planning design;

Above parts belong to key ones that the information dimension covers in the omni-directional conception, and all are important ideas and data foundations forming the agricultural park planning design results based on the omni-directional processing technology.

Time dimension

Time, of objectivity and one dimension, is a parameter used to describe the occurring process of an event. It is an imaginary line of change and development of a thing. Both time and spatial dimensions consist of the space-time carrier of matter existence. The time dimension in agricultural park planning refers to parts as follows:

(1)The elements related to agricultural industry described in the process of agricultural park planning. They are reflected in development and change by time, including history, current situations and future planning of target regional agricultural industry at the macro-level; and include difference of characters in different time in different seasons, climates even a day for a specific planning project at the micro-level;

(2)The time dimension of agricultural park planning, which is overlapped with information and spatial dimensions based on omni-directional processing technology in the agricultural park planning processing, so that the designed results present the switching process of design scheme by time.

Spatial dimension

Space is a form of matter existence relative to time. The space geographically includes the visual contents from the one-dimensional line, two-dimensional plane to three-dimensional body. Any thing has its distributional pattern, form and layout in space with interaction and mutual effect. It is found that the traditional multi-dimension from one dimension to three dimensions can't express and cover the planning information and idea completely. So, the spatial dimension aiming at agricultural park planning refers to the following parts:

(1)Such information as 3D terrain scenes, water infrastructure, electrical infrastructure, agricultural infrastructure, civil buildings, forests and fruit trees in the planning target region contained in agricultural park planning field;

(2)The visual expression for omni-directional agricultural park planning in the planning design. The visual expression is formed by structuring above multi-dimensional spatial information of the planning target region, with an overall application of CAD tool, GIS information, 3D modeling tool, database building and integrated information system.

Applications of the omni-directional processing technology in the park planning-An example of Zhengding of Hebei province 3.1 Background of Zhengding modern agricultural park planning for Hebei province

The main target for the Zhengding agricultural park planning construction is to recover and develop the geological environment of the Cihe Ancient Road Sandpit, where there are the typical criss-cross sandpits and ravines. In this case, the planning key and difficulty are how to carry out the planning of all kinds of projects in such a complicated terrain condition and how to design spatial layout and 3D levels rationally, so that the limited land and basic resources can be used sufficiently.

Supposing only the traditional planar technique is taken as the planning design method in the case, it is difficult to well express such time factors as seasonal variation and light change which impact the planning contents. For above said reasons, the omni-directional idea and method in this case can well be applied in the planning design. Meanwhile, by sufficiently making use of information, time and spatial elements obtained in study, the planning of the case can be well completed with the VR processing software which is developed independently by us.

Technical path of omni-directional processing technology application

In the park planning survey, it is very important to collect digital elevation terrain information, on-site live-action photos, positioning information, information of historical changes, and seasonal and climatic variation information about the planning area besides data acquisition and field survey and research. In implementation of the planning design, all kinds of information and data collected in the survey are put into the omni-directional VR processing system software which is developed independently by us, and the multi-dimensionalal spatial environment is built taking advantage of automatic processing unit analysis and processing digital elevation terrain data provided by the system. By this way, the 3D park planning design is realized. Major technical path is seen in Fig. 2.

Fig. 2 Applied technology path of omni-directional processing technology for

Zhengding Agricultural park planning in Hebei province

Technical essentials for omni-directional processing technology application

In the realization of the case, major technical points are listed as follows:

(1)Work out the terrain maps of the planning area by the CAD application; realize the exhibition of 2D layout on the basis of the layout of agricultural regional planning;

(2) Collect types, distribution and scales of land use by GIS application and social economy data, to realize combination of spatial data and attribute data;

(3)Analyze digital elevation data and recover the terrains in the planning area through digital terrain processing units of the omni-directional system, so as to show the planning projects in valley economy and water-area ecological agriculture with complex spatial relation and layered distribution; (4)Deal with a dynamic description of geographic advantages of the target area by a dynamic loading process of the supersized area scene of the system. Describe the abstract design conception directly about 3D agriculture, four-in-one ecological agriculture and channel scene transform in the planning target by making use of the system support to the 3D space expression;

(5)Realize the straight comparison of primitive state to the planning state in the target area, as well as direct exhibition of related influence on the planning projects in different seasons by overlapping process of space and time;

(6) Realize to complete the planning design based on omni-directional VR system software, as well as automatic uniform summarization output of the analytical data of planning investments and incomes.

All kinds of survey data, planning ideas and layout are integrated rationally with processing of above major key technical nodes. The major key technical nodes and system structure are shown in Fig. 3.

Discussion and conclusions

The agricultural park planning based on omni-directional information processing technology should be further improved and developed in the following aspects through application and study on the combination of the traditional method and omni-directional processing technology of the agricultural park planning:

(1) The system graphic analyzing unit and module should be further improved. As the spatial and attribute data are put into the omni-directional VR processing system only by external software, for example, CAD and GIS's processing and information extraction, so this not only increases working load of data processing but also confines matching of file layout. Thus, improving the analyzing units and modules will reduce the working load of the data pre-processing and promote work efficiency;

(2) The social economy data processing and analytical system should be integrated. The social economy data in the planning area are the premise of the whole planning. The distribution and data analysis of the social economy in the planning area are the effective basis of the planning. Thus, the integration of the processing and analytical system of social economy data is an important supplement and perfection to the planning system;

(3) The coupling of units and modules in the system should be strengthened further. The omni-directional VR planning system is a new one which is a result of accumulation and research on the basis of the long-term agricultural park planning. The proposed analytical units and modules should be further adjusted and improved, and specially in coupling relation, it needs further studying to improve efficiency of data processing;

The agricultural park planning is a result on the basis of study on the layout of agricultural area and management of agricultural resources. It covers research results in theory as well as design results of agricultural engineering, and is a concentrated reflection of the small area and large-scale agriculture. Thus, it is very important to develop and improve technical means of the modern agricultural planning, and of importance for construction of the technical and standard system of the agricultural park planning.

Fig. 3

 3 Fig. 3 Technical nodes and system structure of the omni-directional processing technology in Hebei Zhengding agricultural park planning

Foundation Project: National Key Technology R&D Program in the 12 th Five year Plan of China " study and demonstration on urban carbon sinks protection and improvement of key technical integration"(2011BAJ07B05) Author Introduction: CHEN Xue-yuan(1983-),male, Institue of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Doctor, Maily engaged in Land-Use study.


Dan Wang
email: wd@mail.caas.net.cn

Design and realization of Agricultural Information Intelligent Processing and Application Platform

Keywords: agricultural information, automatic collection, automatic indexing, information search, information management

The work of this paper aims to provide a practical software product, the intelligently gathering, processing and utilizing for the leaders of agricultural departments, research institutions, library and information systems and information consultancy departments, etc. The software product is an information processing platform integrated intelligent gathering, automatic indexing, and search and utilization of agriculture information. This software is tested to be accurate and can substitute people to process information to some extent.

Introduction

After 40 years in the development of the Internet and after 20 years in the development of the World Wide Web, we have entered into the era of digital information. There is a mass of real-time and valuable information and electronic documents scattered in various departments on the Internet. Currently the effective use of the digital information is affected by the three restricting factors. Firstly, how to crawl the desired information from the mass information on Internet in real time and collect electronic documents scattered in various departments to the local database. Secondly, how to classify and deal with the digital information from the local database, make it the standard information resources so that the information retrieval and utilization. Thirdly, how to retrieve the information they need from the local database easily, accurately and quickly,to achieve the purpose of efficient information exchanging, using and spreading.

To solve the above technical problems, we have developed the platform of the Agricultural Information Intelligence Collection, Processing and Utilization, also called information processing platform(IPP). The IPP is a general platform which provides information intelligence collection, information ordering classification, subject indexing and information retrieval for the leading departments, agricultural information and advisory organizations, the construction unit of agricultural information resources.

Agricultural information institute of the Chinese Academy of Agricultural Sciences (hereinafter referred to as AII)have made a lot of basic research work in the information organization and management of agricultural domain from 1990s, which have prepared "Agricultural Science Thesauri", "Chinese Library Classification -Agricultural Professional Classification" and "Multifunctional Agricultural Vocabulary", and developed "the Chinese information automatic indexing system". The AII has officially studied and developed IPP in order to enhance the original scientific and technological achievements with the development of the information age. R & D goal of the IPP is to develop agricultural information processing platform based on B/S structure for agricultural information intelligence collection, automatic classification, subject indexing and the use of agricultural information retrieval. The functions of the IPP can be classified the following parts: 1) the subsystem of integrated classification vocabulary of agricultural information management; 2) the subsystem of automatically grabbing information on the Internet and smart obtaining electronic document; 3) the subsystem of the automatic classification and subject indexing of agriculture information; 4) the subsystem of database definition and data storage; 5) the subsystem of retrieval information; 6) the subsystem of data import and export.

Agricultural Information Intelligent Processing and Application Platform

3 Architecture and Functional Design of the IPP

System Architecture

Information processing of the IPP is firstly a collection of agricultural information that is stored in the temporary database. Secondly, the information is automatic processed and stored in the formal database. Thirdly, information is retrieved and used. Based on the flow of information, the agricultural information Integrated classification vocabulary management subsystem, the database management subsystem, and system operating parameters participate in together to achieve the goal of the IPP.

System Functions

The main functions of the IPP can be concluded in the six following aspects:

Intelligent real-time acquisition of the agriculture network information

The IPP may intelligently collect personalized information real-time from the agricultural website. The IPP can collect requirement information on the Internet and store in the local database by way of user-set information acquisition parameters of generic and professional templates.

TRS format data

TRS format data is the data format of import and export supported by full-text retrieval system which is developed by well-known domestic TRS company. Therefore, the IPP provides TRS format data import feature.

ISO2709 format data

ISO2709 format is the international standard of data format which is widely used in library and information sector for bibliographic information exchange. Firstly the IPP can read the data of the ISO format. Secondly the data can be converted to XML data. Thirdly the data is stored in the temporary database.

Electronic text format data

The IPP can invoke directly WORD processing software that read DOC, PDF and other document, and edit it. Then the data is imported into a temporary database.

Information based on Form

The IPP can automatically generate information form of the database, the user directly login and input the data which is stored in a temporary database.

The data collected from the website and import data from other electronic documents are stored in a temporary database. After information is automatic classified and subject indexed by the IPP, they are stored in a formal database of the IPP.

Information processing function

The IPP provides the function of information processing. The first step is that the data of the IPP is stored in the temporary database. Then, information is batch indexed on temporary database by the IPP selectively automatically, or which is entire database indexed or unattended indexed or artificial auxiliary indexed. At last, index result of the data is edited by the IPP. Specific indexing features:

①The IPP automatically produces classification codes (CLC, category code), the category names, geographical names, geographical codes and information keywords to pretreatment information. ②The IPP supports a variety of indexing methods, including the indexing of single articles, batch indexing, automatic indexing of the entire database as well as unattended indexing. ③The IPP supports the functions of modify and editing for automatic indexing results. ④Artificial auxiliary indexing functions are supported. When users index a message online, they may browse the information classified directory tree at any time and click required indexing category name. The category names and corresponding classification coding are automatic inputted the database by the IPP. ⑤Support the function of the word frequency statistics analysis and the function of updating vocabulary of data. When users may modify correction of the results of indexing keywords, if vocabulary words are not yet logged, the IPP will automatically added , and then the IPP will compute vocabulary frequency. When vocabulary frequency of the word exceeds the prescribed threshold, the word is updated automatically on the vocabulary of data. ⑥Flexible configuration in word field range for indexing, such as the title field, summary field, the body field. ⑦Flexible configuration for the position weight value of the keyword,such as keyword from the title field, from summary field, from the body field, from the first or the tail paragraph of the text, and from the first few words in the paragraph. ⑧The flexible configuration of indexing depth, that is, a literature can be marked with the number of keywords. According to the results of statistical analysis of artificial indexing, indexing depth is generally appropriate in 7-8 words.

Fig. 4. A screen shot of information processing function

Information retrieval functions

The IPP can retrieval information which is stored in the formal database, the specific features can be summed up: ① The user can choose a database or multiple databases at the same time for retrieval. the IPP supports for multi-database searching. ② Support keyword search, the second search and text search in the limited field. ③ Support boolean logic expression to search, according to user's searching requests, composition of search expression with "and", "or", "non", as well as the search expression with operator priority retrieval. ④ Support the search function of the SDI. The IPP save the user's search expression, in order to retrieve the information repeatedly to form a thematic database.

Data processing

Fig. 5. A screen shot of information retrieval function

System management function

System management function contains the configuration and management operating parameters of Intelligence collection, processing and utilization from the agriculture network information, including management of users, databases of custom parameter configuration management, information collection indexing parameters of configuration management, information taxonomies, thesauri, regional tables and information areas of the table maintenance, and run log management. ① User management: It Supports the user's function of increasing, deleting and modifying; It sets up and manages the user name, password, roles and permissions. ② Custom database: Users can flexibly define databases structure in different type databases, describe the properties of the database to determine the taxonomic relationship between the different databases and create a user database. ③ Vocabulary management: It contains maintenance and management of thesaurus of information, the scope of vocabulary, regional tables. Users can add, delete, change keywords according to the above vocabulary. ④ The word frequency statistics and vocabulary Automatic Updates: The IPP computes word frequency for indexing word. If indexing words does not appear in any vocabulary database or word frequency in excess of the threshold, the system will automatically input into vocabulary database.

Data searching

Fig. 1 .

 1 Fig. 1. Agricultural Information Intelligent Processing and Application Platform

Fig. 2 .Fig. 3 .

 23 Fig. 2. The IPP may intelligently collect personalized information real-time

System management functions

The IPP provides the exchange with the outside world database interface that can output the information in XML format document, TRS format documents, ISO2709 format document output.

Conclusion

The user uses login name and password to enter the information processing platform, and click on the system menu to finish related operations. It automatically crawled and annotated 10000 pieces of documents from the websites and the documents provided by Chinese Academy of Agricultural Information Institute, China Agricultural University and other units, agricultural engineering, plant protection, soil nutrients, food economy, vegetable gardening, animal husbandry and veterinary literature information. We compare the annotated result with the results of manual annotation, which proved that the method is very effective.


Bei Cui

Wude Yang

Meichen Feng

Wenjiang Huang

Xiaoyu Song
email: songxy@nercita.org.cn

Study on the Spatial -Temporal Variability of Soil Nutrients During Winter Wheat Growth Season

Keywords: spatial -temporal variability, soil nutrients, geostatistics

The object of the study is to analyze the spatial-temporal variability of soil nutrients in the winter wheat growing season, and try to reveal the dynamic change of soil nutrients in winter wheat field. Measurements of soil nitrate nitrogen (NN), total nitrogen (TN) and available potassium (AK) in wheat field were taken at 132 sample points in April and June, 2006. Classical statistical method and geostatistics method were used in this study. The classical statistical analysis results indicate the average value of NN content decreased obviously from April to June, and the value of AK decreased slightly. But there were no change for the mean of TN content from April to June. All soil nutrient both in April and June were medium-degree variations.

From the spatial distribution maps of soil variables, soil NN changed obviously on distribution between April and June. However, soil TN and AK had the similar spatial distribution that high in the northeast part and low in the southwest of the study field. In summary, winter wheat growth had great effect on soil NN content, whereas little effect on TN and AK. The differences of NN, TN and AK between June and April were negative in most areas and the absorptive capacity of winter wheat was different in different places.

Introduction

Many researchers pointed that the variability of soil nutrients were affected by many factors, such as, land use types [START_REF] Qin | Characteristics of Spatio-Temporal Changes of Soil Organic Matter in Typical Fringe in Beijing for 25 Years[END_REF], soil types [START_REF] Zhao | Spatial variation of soil nutrients and its affecting factors at small scale -A case study of peri-urban areas in Wuxi Jiangsu Province[END_REF], planting types [START_REF] Zhang | Research on Spatial Variability of Soil Nutrients in Tobacco Plantation Field[END_REF][START_REF] Qi | Spatial-temporal Variability of Paddy Soil Nutrient in Hainan Province[END_REF][START_REF] Peng | Study on Spatio-Temporal Variation of Soil Organic Matter in Oasis Cotton Field[END_REF][START_REF] Wang | Study on Spatio-Temporal Variability of Soil Available Nutrients in Typical Vegetable Field of South China: A Case Study at Guangxingzhou, Junshan, Yueyang[END_REF][START_REF] Liu | The Spatio-Temporal Variation of Nitric Nitrogen in Protected Vegetable Soils in Different Years of Cultivation[END_REF][START_REF] Huang | Characteristics of the Spatio-Temporal Changes of Soil Organic Matter of Sugarcane Field in Red Soil Hill Areas[END_REF],

topography [START_REF] Umali | The Effect of Terrain and Management on the Spatial Variability of Soil Properties in an Apple Orchard[END_REF][START_REF] Hattar | Variation in Soil Chemical Properties along Toposequences in an Arid Region of the Levant[END_REF], crop rotation [START_REF] Huang | Temporal and Spatial Variability of Soil Organic Matter and Total Nitrogen in an Agricultural Ecosystem as Affected by Farming Practices[END_REF][START_REF] Song | Spatial and Temporal Changes of Soil Inorganic Nitrogen in Rice-wheat Rotation System at County Scale-A Case Study in Yizheng County, Jiangsu Province, China[END_REF] and so on. There were also some studies on the variability of soil nutrients variables in winter wheat field. Zhang et al [START_REF] Zhang | Spatial Variability of Soil Properties in the Field Based on GPS and GIS[END_REF] researched the spatial variability of soil properties including TN, available N, organic matter, available P, AK and so on in winter wheat field in March 2002. Xie et al [START_REF] Xie | Temporal-Spatial Variations of Ammonia and Nitrate under Application of Inorganic and Organic Fertilizers in Purple Soil of Winter Wheat[END_REF] analyzed the spatial-temporal variations of purple soil ammonia and NN under application of inorganic and organic fertilizers in winter wheat field. Chen et al [START_REF] Chen | Spatial and Temporal Changes for Soil Nutrients and Their Mechanism in Typical Area of Taihu Lake Valley During the Past Two Decades[END_REF] studied the law of spatial-temporal change of soil organic matter, total N, available N, available P, and available K in farmland for last 20 years(1981~2000). However, little study studied the spatial and temporal changes of soil nutrients during winter wheat growth season. This paper studied the spatial-temporal variability characteristics of soil NN, TN and AK during wheat growth period. The study tried to reveal the dynamic change rule of soil nutrients in winter wheat field, in order to prove theoretical basis for winter wheat precise management.

Materials and Methods

Study Area

The study were conducted at National Experiment Station for Precision Agriculture, Changping District, Beijing(116°26'10"E-116°27'05"E, 40°10'31"N-40°11'18"N). The experimental plot had an area of 5.4 ha (Figure 1), and soil type was alluvial soil. Winter wheat (Triticum aestivum L.) cultivar ('Jingdong-8')

was selected in this study.

Soil Sampling and Laboratory Analysis

Soil samples from 0-to 30-cm depth were collected within 51 locations on 30× 30 m grid and 81 locations on 10×10 m grid (Figure 2). The first set of soil samples was taken on April 4, 2006 just before fertilizer application at the wheat elongation stage. The second set of soil samples was taken on June 16, 2006 just before the wheat harvest. The location of each soil sample was recorded by a GPS receiver with a station-based differential signal (DGPS, Trimble 5700 RTK), of which the measuring accuracy was controlled within 20cm. The soil samples were taken manually using a stainless steel probe. For each plot, a composite soil sample comprising five random samples from within a 5 m radius and a depth of 0~30 cm was taken, then placed into its designated plastic bags, mixed, and transport back to the laboratory. Soil samples were air dried, crushed, sieved, and analyzed for soil NN, TN and AK using the agro chemistry conventional analysis method [START_REF]Analysis Method of Agricultural Soil[END_REF].

Analysis Methods

In this study, classical statistical methods and geostatistics methods were used to analyze spatial -temporal variability of soil nutrients. Through the classical statistical methods, the minimum, maximum, mean, skewness, kurtosis, standard deviation and

Boundary of National Experiment Station for Precision Agriculture

Study area coefficient of variation were got. The CV of variables can be used to roughly estimate the variation degree: if the value of CV was<10%, the variable was considered weak variability;if the value was >10%and <100%, the variable was considered the medium-degree variability;if the value was >100%, the variable was considered the strong variability [START_REF] Wang | Characteristics of Spatial Variability of Soil Thickness in China[END_REF].

Fig. 2. The distribution of soil sampling points

Fig. 3. Semivariogram variation curve

Figure 3 was a ideal semivariogram variation curve [START_REF] Chen | Theoretical and Experimental Studies on Variable-Rate Fertilization in Precision Farming[END_REF]. A semivariogram model consists of three important parameters which are C 0, C+C 0 and range.C 0 represents the nugget effect, which is the semivariogram value when the distance is zero. The nugget effect is led to random factors, such as sampling error and measurement error. C+C 0 is the sill, which represents total variance. We adopted the value of C 0 /(C+C 0 )to stand for the spatial class. If the ratio of spatial class was < 25%,the variable was considered strongly spatially dependent;If the ratio of spatial class was > 25% and < 75%, the variable was considered moderate spatially dependent;and if the ratio was > 75%,the variable was considered weakly spatially dependent [START_REF] Iqbal | Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils[END_REF]. Range is also known as the longest correlation distance. If samples separated by distances are smaller than the range, they are related spatially in some extent; if those separated by distances are greater than the range, there were no spatial dependence [START_REF] Cambardella | Field-Scale Variability of Soil Properties in Central Iowa Soils[END_REF].

The classical statistical analysis of soil parameters were operated with SPSS11.5 software. Variowin 2.2 [START_REF] Pannatier | Variowin Software for Spatial Data Analysis in 2D[END_REF] was used to compute the variograms of semivariogram and Arcgis9.0 was used for kriging interpolation. To characterize the spatial variation of the soil properties, experimental variograms were computed using Variowin 2.2.

The parameters of the fitted models were then used with the data for ordinary kriging in Arcgis9.0. The values of the soil properties were predicted on grid at un-sampled locations and the predictions were used to produce interpolation maps.

Results and Discussion

Simple Statistics of Soil Nutrients Data

Average value and coefficients of variation for soil NN, TN and AK were listed in Table1. The average value of NN content (19.20mg/kg) in April was greater than in June (3.51mg/kg). The dramatic decrease was mainly caused by the NN absorption of winter wheat during the growth season of elongation stage to mature stage from April to June. Compared with the average value of AK in April (137.60mg/kg), the mean value in June decreased slightly (110.67mg/kg). But there were no change of TN content on average between in April and in June. In April, the variation coefficient (CV) of NN was 38.59%, which was greater than AK (18.72%) and TN (13.44%). In June, CV of NN increased to 61.05%. TN had the smallest CV of 12.36% in June.

The CV of AK was 18.72% in April and 14.32% in June. All soil nutrients variables both in April and June were medium-degree variations with CV of 12.36%~61.05%.

Compared with CV of soil nutrients variables in April, CV for all soil nutrients in June changed more or less. Except NN with a marked increase of the CV from 38.59% to 61.05%, the CV of other soil nutrients decreased in different degree from April to June.

Skewness and kurtosis values of soil variables indicated that only the soil TN in

April and June was in normally distribution. The other soil properties were in nearly normal distribution after natural logarithm transformation before geostatistics analysis.

Skewness and kurtosis values of all soil properties were also given in Table 1.

Spatial Structure Analysis of Soil Nutrients in Winter Wheat Fields

The semivariogram parameters of soil properties were shown in

The Spatial Distribution Maps of Soil Nutrients in Winter Wheat Field

Temporal variability Analysis of Soil Nutrients in Winter Wheat Field

In order to clearly reflect the change of soil nutrients during winter wheat growth season, the differences spatial distribution of soil NN, TN and AK between April and June were calculated using the band match function of ENVI software. From the spatial distribution maps of soil nutrient, the NN changed obviously on spatial distribution between April and June. However, the spatial distribution of soil TN and AK changed small, which had the similar spatial distribution that high content in the northeast part and low in the southwest part. Winter wheat growth had great effect on soil NN, whereas little effect on TN and AK.

The differences of NN, TN and AK between June and April were negative in most areas, and the changes of nutrients were not same in different places. These indicated that winter wheat growth need large amount of nitrogen and potassium fertilizer and different absorptive capacity of winter wheat on soil nutrients in different places.

Fig. 1 .

 1 Fig. 1. Location of study area

Figure 4

 4 Figure 4 indicated that, soils with high NN content in April distributed in the north and east part of the field with downward trend from SW to EN. NN content in most areas of study area was lower than the average value (19.20mg/kg). Different to the map of NN in April, high NN content in June were found in the north part of the field, whereas, low NN content located in the east part of the field. The NN content average value in June was lower than that of in April (19.20mg/kg). It indicated that

Fig. 4 .

 4 Fig. 4. The spatial distribution maps of nitrate nitrogen (NN), total nitrogen (TN) and available potassium (AK) in April and June

Figure 5

 5 Figure 5 showed the differences spatial distribution of NN, TN and AK between April and June. From those, the differences of NN, TN and AK were negative in most areas, which indicated winter wheat not only absorbed original fertilizer but also absorbed the topdressing at the wheat elongation stage. In different places, the changes of nutrients were not same, which indicated the absorptive capacity of winter wheat on soil nutrients were different in this field.

Fig. 5 .

 5 Fig. 5. The spatial distribution maps of differences of nitrate nitrogen (NN), total nitrogen (TN) and available potassium (AK) between April and June

Table 1 .

 1 Descriptive statistical of soil nutrients properties in April and June

	Variables	Minimum	Maximum Mean	SD	CV%	Skewness	Kurtosis
	NN-April
		6.85	46.78	19.20	7.41	38.59	0.01	-0.25
	(mg/kg)
	TN -April
		0.08	0.13	0.10	0.01	13.44	0.32	-0.62
	(%)
	AK-April
		77.30	237.20	137.60	25.77	18.72	-0.20	0.27
	(mg/kg)
	NN-June
		0.96	15.13	3.51	2.14	61.05	0.50	0.67
	(mg/kg)

Table 2

 2

. The semivariogram of soil TN in June showed Spherical model, whereas other soil nutrients both in April or June showed Gaussian model.

Table 2 .

 2 The semivariogram parameters of soil properties

	Variables	Model	Nugget(C 0 )	Sill(C+C 0 )	Range(m)	Spatial class
	NN-April	Gaussian	0.109887	0.17788	170.00	61.78
	TN -April	Gaussian	0.000090	0.00019	150.00	48.39
	AK-April	Gaussian	0.018496	0.04910	190.00	37.67
	NN-June	Gaussian	0.209590	0.27857	220.00	75.24
	TN -June	Spherical	0.000040	0.00017	170.00	23.26
	AK-June	Gaussian	0.007603	0.01780	61.19	42.71
	△NN	Exponential	34.285300	52.41203	98.83	65.41
	△TN	Gaussian	0.000024	0.00004	19.30	66.67
	△AK	Exponential	270.252000	578.01600	93.59	46.76
	Note: NN= nitrate nitrogen; TN = total nitrogen; AK = available potassium;△NN= nitrate nitrogen content in June
	minus in April;△TN= total nitrogen content in June minus in April;△AK= available potassium content in June
	minus in April.
	The spatial class of TN in June (23.26%) was smaller than 25%, which indicated
	the existence of strong spatial dependence. The spatial class of NN in June was
	75.24%, more than 75%.The other soil nutrients variables had moderate spatial

Table 3 .

 3 The cross validation results of semivariogram model optimal model for each soil variable

	Variables	RMS	ASE	MS	RMSS
	NN-April	6.661	7.334	-0.016	0.937
	TN -April	0.009	0.010	0.004	0.911
	AK-April	20.900	20.320	-0.010	1.043
	NN-June	2.141	1.845	-0.063	1.193
	TN -June	0.007	0.008	0.004	0.870
	AK-June	11.240	11.630	-0.007	0.968
	△NN	6.163	6.730	0.012	0.925
	△TN	0.006	0.006	-0.001	0.968
	△AK	21.240	20.890	0.008	1.020

Note: NN= nitrate nitrogen; TN = total nitrogen; AK = available potassium; RMS = Root-Mean-Square, ASE = Average Standardized Error, MS = Mean Standardized, RMSS = Root-Mean-Square Standardized.

Table 3

 3

presented the cross validation results of the optimal model for soil variables. Generally, the optimal model should be the one that Root-Mean-Square (RMS) is closer to Average Standardized Error (ASE), Mean Standardized (MS) is closer to 0, and Root-Mean-Square Standardized (RMSS) is closer to 1.

ACKNOWLEDGEMENTS

This work was financially supported by program of National Natural Science Foundation of China (31071324), program of National Natural Science Foundation of China(41201326) and Shanxi Province Science and Technology Support Program(20110311038).


Yibing Dong

Jianyu Yang
email: ycjyyang@cau.edu.cn;294726701@qq.com

Chao Zhang

Dehai Zhu

Xinyue Tu

Xianzhe Qiao

Research on the Method of Feature-based Multi-scale Vector Data Model

Keywords: scale, feature-based, multi-scale representation, extended E-R model, object-oriented Geographical entity Complex element Polygon Polyline Point

Multi-scale representation of spatial data is a research focus in GIS, while building multi-scale data model is a key to implementing multi-scale representation of vector data. In view of the shortcomings of existing multi-scale data model in geographical cognition and special analysis, this paper puts forward a method of feature-based, and studies on it qualitatively from definition, description and extraction. Compared to conventional single-scale E-R model, in this paper, the key strategies of building multi-scale conceptual model are put forward. Deeply study and analysis are applied on abstraction and expression of multiple geometric characteristics, of multi-attribution, and of semantic relation among different scales,and the design of feature-based multi-scale conceptual model is realized. Finally, the object-oriented multi-scale logic model is researched, which lays a theoretical foundation for building the feature-based multi-scale vector data model.

Introduction

Multi-scale representation of spatial data refers to different expression forms of one geographical entity in geometry, topology and properties, which arise from different scales (resolutions) of geographical entity in the computer storage, analysis and description [START_REF] Li | Multi-scale Spatial Data Representation Model and Its Visualization[END_REF]. With the continuing expansion of application fields in GIS, there is a growing demand, not only to observe, understand and describe the geographic phenomena in different scales, but also to analyze, manage and express multiple geographic representations.

In recent years, as the development of wavelet compression and image pyramid model, the problems of multi-scale raster data representation and visualization have been solved. However, the relationships among spatial elements are complex, the multi-scale representation and management of vector data have been an open problem [START_REF] Cheng | A Bi-hieerarchical Multi-scale Model and Indexing Approach for Large Vector Data Representation[END_REF]. At present, the research of multi-scale vector data representation mainly focus on four aspects, including formal description of the multi-scale data model, design and implementation of data structure, link and update mechanism between different levels and consistency maintenance of different expression versions. Specialists and scholars have long committed to improve previous algorithms and data structures, and made considerable achievements in reducing data redundancy, improving the efficiency of data access and maintaining consistency of spatial data [START_REF] Ai | GAP-tree Extensions Based on Skeletons[END_REF][START_REF] Zhou | Efficiently Generating Multiple Representations for Web Mapping[END_REF][START_REF] Haunert | Constrained set-up of the tGAP structure for progressive vector data transfer[END_REF][START_REF] Yang | An Algorithm for Progressive Transmission Vector Map Data over the WWW[END_REF][START_REF] Ren | The Research of Vector Data Progressive Transmission[END_REF]. In addition, a series of concept were proposed, such as "critical scale", "incremental", providing new ideas and methods for the representation of multi-scale vector data [START_REF] Sheng | Design and Implementation of Multi-scale Databases[END_REF][START_REF] Jean-Michel | An Increment Based Model for Multi-resolution Geodata Management in a Mobile System[END_REF][START_REF] Ai | Key Issues of Multi-Scale Representation of Spatial Data[END_REF]. The existing method of multi-scale representation is broadly divided into four categories: multi-scale explicit storage, initial scale changes accumulation, Key scale function evolution and Senior scale automatic generalization. Their basic ideas, advantages and disadvantages are shown in Table 1. As current GIS lack methods for on-line automated map generalization, the senior scale automatic generalization (the last method in Table 1) remains a challenging area of research, so we cannot get data at any scale [START_REF] Ai | The Lifespan Model of GIS Data Representation over Scale Space[END_REF]. On the other hand, the multi-scale explicit storage (the first method in Table 1) is widely used with simple structure, but the data storage is too large. Therefore, the initial scale changes accumulation (the second method in Table 1) and key scale function evolution (the third method in Table 1) are used as compromise methods for multi-scale vector data representation. Found by previous studies, specialists discuss multiple representation issues from a data-modeling point of view, with emphasis placed hierarchical structure to organize and store data at different scales. But the hierarchical structure has introduced additional problems, including ignore the essential characteristics and intrinsic links of geographical phenomena, educe the efficiency of spatial analysis and complex operation, and bring a certain of error [START_REF] Hun | Simultaneous and incremental feature-based multiresolution modeling with feature operations in part design[END_REF][START_REF] Li | Research on Feature-based Spatial Data Model[END_REF].

Based on the above, this paper introduces a "feature-based" thought into the progress of multi-scale modeling, and studies on it qualitatively from definition, description and extraction. It also includes the design of multi-scale conceptual model, which not only overcome the insufficient of previous model, but also achieve the cognitive and description of the geographical world.

2 Qualitative Research of Multi-Scale Feature

Scale Definition

Typically, the scale is a measure of study object, including two parts of space scales and time scales. The spatial scale refers to the size of study scope (geographical scope) or the level of detail (size and level of geographic resolution). The time scale indicates the duration and frequency of study.

In different study areas, the meaning and application of scale is various. In cartography and GIS, scale is often defined as a ratio of the distance on the map and the actual length of a certain object. Among them, the distance is fixed, the relationship between "space scales" in the actual geographical space and "scale" is same as the relationship between denominator and business [START_REF] Ai | Key Issues of Multi-Scale Representation of Spatial Data[END_REF]. In this paper, large-scale refers to larger spatial areas, corresponding to low resolution. On the contrary, small-scale corresponds to smaller spatial areas and high resolution, which can reflect complex phenomena.

Feature Description

Traditionally, feature was a graphical representation of the real world. By the application of object-oriented methods in GIS, the concept of feature was re-defined as an abstract of real-world phenomena; using these features, all the geographical phenomena can be represented [START_REF] Yin | Featurre-based Data Models in GIS[END_REF]. Geographic feature is an abstract of geographic entity, which includes geographic entity and further descriptions of them.

According to the definition, the feature F in the horizontal is divided into three parts: entity feature EF, attribute feature AF and relation feature RF. Thus, the feature can be shown as:

F=<EF, AF, RF> (1)
Where EF is a description of the spatial position and geometry of geographical entities, AF describes the composition and characteristics of them, such as name, type, while RF is to describe the relationship between entities, including topological and semantic relations.

Through considering the domain of information representation, the spatial positions and distributions of entities can be represented in the geometric domain, as well as the semantic domain can explain the logic pattern of non-spatial information descriptions. Thus, in vertical, feature F is divided into geometric domain feature GDF and semantic domain feature SDF. Combine of two definitions above, features F can be complete expressed as:

GDF=< GEF, GAF, GRF> SDF=<SEF, SAF, SRF> (2)
Where, respectively, GEF, GAF, SRF represents the geometric characteristics of entities, attributes and relationships, and SEF, SAF, SRF is the semantic characteristics of entities, attributes and relationships.

Multi-Scale Feature Extraction

In different scales, a geographic entity will show different features. In this paper, the extraction method of multi-scale features is proposed, which can lay foundations for the establishment of multi-scale vector data model.

According to the definition and description of feature, some features at a variety of scales can be extracted, such as location, geometry, attribute, semantic relationship (shown in Figure 1). The extracted multi-scale features mainly show as the following three aspects:

(1) In different scales, a certain entity shows different geometries or abstract geometric types. For example, with increasing scale, the abstract geometry type of a building will change from surface to point.

(2) The topologic relations of a certain entity in different scales must be consistent, but the semantic relations between different geographical entities will change. For example, as the scale increases, two school buildings and a track field aggregate into a school.

(3) In different scales, the attributes expressed of one certain geometry will be different.

Multi-scale feature

In one scale In GIS, the process of building data model is an abstract to the real world. Like single-scale models,multi-scale models consist of three levels: conceptual model, logical model and physical model. The abstract level of three models is gradually deepened. Now, based on the multiplicity of geometry, attribute and relations in different scales, how to properly abstract, definite and describe the geographic entities and their relations is still a problem, which must face in multi-scale conceptual modeling [START_REF] Wang | Multi-Scale Conceptual Model for GIS Geographical Feature[END_REF].

In different scales

Choice of Conceptual Modeling Tool

The conceptual model is defined as a description of entities in the real world, and there are a lot of methods for conceptual modeling. Entity-Relation (E-R) model is the most famous method, and it can use diagram to represent the relationships between entities and attributes. A basic E-R model, consisted of entities, attributes and relations, can be used to represent geographical features. However, the traditional GIS conceptual model only supported single-level expression that cannot express complex changes between different scales [START_REF] Wang | Multi-Scale Conceptual Model for GIS Geographical Feature[END_REF]. In this study, the traditional E-R model is been expended to meet the demand of multi-scale representation and application. This paper also introduces a series of abstract concept, like generalization, aggregation, classification, to support multi-valued description of attributes, relations and others.

Multi-Scale Extended E-R Modeling

Multi-scale extended E-R model contains three elements: the entities, attributes and relations. According to the composition and law of multi-scale features, this paper is applied to study the multiple geometric features, multi-valued attributes and the change of semantic relations in different scales.

Abstraction and Expression of Multiple Geometrical Features

Borges said, the focus of building a conceptual model was to make a clear expression of geographical entity, and the type of abstract geometry would affect the spatial relationships between different entities [START_REF] Li | Multi-scale Spatial Data Representation Model and Its Visualization[END_REF]. In this paper, geographic entities were abstracted as point, polyline, polygon and complex elements formed by these simple elements. In any scale, entity can be represented by these elements. In addition, the concept of "abstract feature class" is proposed. So the geometric features of a certain entity in different scales can be unified in Figure 2.

Fig. 2. Abstraction and expression of multiple geometrical features

Extraction and Description of Multi-valued Attributes

In order to reflect the abstract information of entities, in the stage of conceptual modeling, it is very important to extract the relationships between attribute domains or attribute value domains. With the abstraction level dropping in different scales, the attributes of a same entity is considered to be redefined, and the attribute values is consider to be refined, Parent said [START_REF] Parent | Modeling Spatial Data in the MADS Conceptual Model[END_REF]. For example, in Beijing's Handian District, the gathered characteristics of geographic elements abstracted at different scales is described clearly (shown in Figure 3). Simultaneously, the attribute domains and attribute value domains of highlighted elements in each layer are extracted (Figure 4). Therefore, the fields of attribute domains (XZQM, JDMC, and JFMC) are different and unrelated, which indicates the geographic elements exhibit different attributes in a variety of scales. However, the refining fields of attribute value domains (QHDM、 JDDM、 JFDM), not only reflect the different attributes of geographic elements represented in different scales, but also link multi-scale data together to establish multi-scale hierarchical structure. Besides, the attribute value domains can be used to extract multiple attributes.

Extraction and Representation Semantic Relations

Although the geometric shapes and details in different scales are different, the topologic relations between geographic entities should be consistent. Therefore, the semantic relations should be extracted to express the scaling relations between different scales. The four basic concepts of semantic relations are described as follows:

(1) Aggregation. Aggregation can combine primary objects with different characteristics into a higher level object-composite object. The composited object can derive some new characteristics from primary objects, but cannot own the characteristics inherited from primary objects. The relationships between composite objects and primary objects mainly represent as "part of". For example, the school is a composite object, and school buildings, roads, playgrounds are the parts of it.

(2) Generalization. Similar to aggregation, generalization is an operation that can extract general attributes or characteristics from common class to establish a high-level class. So the relationship between high-level class and common class can be represented as "is a".

(3) Classification. Classification is a method which can form a hierarchic class structure. In this structure, every class is consisting of objects with the same characteristics, and there is a "is instance of" relationship between object and its class.

(4) Association. In one scale, some objects can be combined into an abstract object. The relationship between objects and abstracted object is represented as "is member of". In object-oriented method, the concept of "class" is a collection of objects with common attributes and methods, where the object is a package of its stages and behaviors. For an object, the stage is a collection of attribute values and the behavior is a collection of methods. In this paper, the concept of feature and class are similar, through class the geographical features can described formally. Shown in Figure 6, the object is an instantiation of class, and the feature is an abstraction of entity. Therefore, feature-based multi-scale logic modeling can be achieved by object-oriented data model.

Geographical feature

Geographical entity Class

Object Abstraction Formalization Instantiation Expression Fig. 6. Formal description of geographical feature

In multi-scale logic modeling, feature is defined as a unity of geographical data and functions, corresponding to the "class" of the object-oriented data model. The "object" described by "feature class" is a geographical entity. Combining the design ideas of feature-based, as well as the method of object-oriented, a unified multi-scale data model can be build from conceptual modeling to logic modeling.

In this study, the feature object FO is divided into geometry object GO and semantic object SO. GO is composed of the spatial locations and relations of geographic entities, which indicates the geometric features. Similar to GO, SO represents the semantic features, which include attributes, non-spatial relations and others (shown in Figure 7). The GO and SO are not independent of each other, and they are encapsulated into FO. Through this method, the features of geographical entities can be represented completely.

Spatial locations Geometric shapes Attributes Spatial relations ……

Geometric objects

Thematic attributes Semantic relations ……

Semantic objects

Feature objects

Fig.7. Composition of feature object

Therefore, the SO are extracted to organize spatial representations in different scales. The semantic relation in SO can be used as the basic description object. Through these semantic relations explained above, we can link a series of spatial representations together, and establish the spatial indexing mechanism efficiently by semantic features. Thus, the multi-scale logic model can be achieved preliminary.

Conclusions and Outlooks

The multi-scale vector data model is an important part of multi-scale spatial data representation. However, in terms of geographical cognition and spatial analysis, the existing multi-scale hierarchical data models had some lacks. For the requirements and applications of multi-scale data representation, this paper puts forward the thought of feature-based, and studies on it qualitatively from definition, description and extraction. The design of feature-based multi-scale conceptual model was realized by extended E-R model. Finally, the feature-based object-oriented multi-scale logic model was researched, which lays a theoretical foundation to building the feature-based multi-scale vector data model. From this study, we can draw the following conclusions:

(1) Qualitatively study and deeply analysis of multi-scale features, not only realize the cognition and representation of the real world, but also obtain a more integral abstraction and description of geographical entity to maintain the consistency of topological relations between different scales.

(2) The thought of feature-based and object-oriented is consistent. Using object-oriented method, the cognitive paradigm of geographical space and the representation of geographical data can be unified together naturally. Therefore, a unified multi-scale data model can be build from conceptual modeling to logic modeling.

Next step, the major works are: (1) Combined with experimental data, the quantitative research will be carried out, which will be extract multi-scale features and change rules of geographical entities in different scales. [START_REF] Cheng | A Bi-hieerarchical Multi-scale Model and Indexing Approach for Large Vector Data Representation[END_REF] Guided by the conceptual model in this paper, we will deeply study the implement of feature-based object-oriented logic model, including the establishment of object-oriented data structure and spatial indexing mechanism, the maintenance of level connectivity relation and consistency. The establishment of feature-based multi-scale data model will lay foundations for the representation of multi-scale vector data.

Fig. 1 .

 1 Fig. 1. Extraction of multi-scale features

Fig. 3 .

 3 Fig. 3. Multi-scale representation of Haidian District in Beijing. The highlighted part in (a) is Haidian district, the highlighted part in (b) is Sujiatuo Town, and the highlighted part in (c) is Beianhe Village.

Fig. 4 .

 4 Fig. 4. Extraction and description of multi-valued attributes. XZQM, JDMC, JFMC is the name field of highlighted element in (a), (b) and (c) respectively; and QHDM, JDDM, JFDM is the code field of highlighted element in them

Fig. 5 .

 5 Fig. 5. Semantic relation of multiple geographical elements of Haidian District in Beijing In this paper, some methods are applied to extract multiple semantic relations of Haidian District in Beijing (shown in Figure 5). For example, the elements in neighborhood layer (Figure a) are classified by street code (JDDM). Then the street layer (Figure b) can be achieved by generalizing all classes. In the same way, through classifying and generalizing the elements by zoning code in street layer (Figure b), the block layer (Figure c) can also be gained. Each element in the layer can be simple element or composite element aggregated from geographical entities with different characteristics. Using the four basic semantic relations and making a certain

Table 1 .

 1 Methods and technical strategies of multi-scale representation

	Technical Strategy	Basic Idea	Advantage and Disadvantage
	Multi-scale explicit storage	Store the pretreated data in different scales explicitly, and when is needed, the data at a certain scale can be extracted immediately	Advantage: data structure is simple and easy to access data Disadvantage: data redundancy large and scale change discrete, cannot get data at any scale
	Initial scale changes accumulation	Store the coarsest data representation and the change data between adjacent scales, therefore, data in any scale can be obtained by accumulated the change on initial data	Advantage: save storage spaces and improve access efficiency Disadvantage: scale change discrete, cannot get any scales of data and present the difficulty of maintaining consistency within the multiple versions of the maps
		Store a series of "key scale" data	Advantage: data redundancy less and can
	Key scale	representation in hierarchical structure,	get data at any scale
	function	and can derive any data representation	Disadvantage: key scale of the different
	evolution	between adjacent scales by interpolation or	objects is inconsistent and difficult to
		Morphing function	select
	Senior scale automatic generalization	Store the most detailed data representation and using on-line automatic generalization, achieve the export of data representation at any scale	Advantage: data redundancy at least and can get data at any scale Disadvantage: subject to the automatic generalization, this method cannot be achieved right now

Acknowledgments. This research was funded by the National Natural Science Foundation Project "Research on Compression and Progressive Transmission of Vector Data in the Network Environment" (NO: 4171309). The authors would like to thank the anonymous reviewers for the constructive comments and suggestions.


Yujia Qin

Zhihong Li

Zhao Li

Glenn Fowler

Yan Fang

The current and future potential geographical distribution of the Italian locust, Calliptamus italicus(Linnaeus) (Orthoptera: Acrididae) in China

Keywords: Callipatamus italicus ( L . ), CLIMEX, ArcGIS, potential geographical distribution, climatic change

. italicus has a wide potential distribution in areas north of the Yangtze River in China. With the climatic changes, the potential range for C. italicus was projected to expand, and the suitability levels of most areas would increase, especially in areas with optimal climatic conditions. We therefore suggest that surveys should be enhanced in the areas suitable for C. italicus to facilitate early detection and reduce potential damage.

Introduction

Callipatamus italicus (Linnaeus) is one of the main insect pests of grasslands in Xin Jiang, and attacks a wide range of host plants including 45 species in 17 families.

The nymphs of C. italicus prefer Artemisia frigida Willd. and Carex onoei Franch. et Sav., while the adults prefer not only A. frigida Willd., but Salvia deserta Schang and Medicago falcate Linn. [START_REF] Xue | Food selection of Calliptamus italicus and loss assessment for the grassland[END_REF]. The Italian locust has become the most common and damaging plant pest in Kazakhstan, regularly invading millions of hectares and causing crop damage of great economic importance [START_REF] Antonow | Forecasting seasonal dynamics of the Asiatic migratory locust using the Locusta migratoria migratoria-Phragmites australis forecasting system[END_REF]. In 1999, Calliptamus italicus was introduced to from Kazakhstan and Russia into Xinjiang province, China. It subsequently damaged approximately 2 million hectares in 2000 and 2007 in Xinjiang province [START_REF] Xue | Food selection of Calliptamus italicus and loss assessment for the grassland[END_REF][START_REF] Zhang | Numerical analyses on morphometrics gregarious and solitarious phase of Calliptamus italicus[END_REF].

The distribution area of C. italicus stretches from Western Europe to the meadow-steppes of Central Asia in Siberia and in Kazakhstan and its neighboring countries. Its range includes countries along the northern and eastern edge of the Mediterranean Sea, Central Europe and Central Asia up to Mongolia and western Siberia [4]. These regions include the former USSR, Russia Federation, Spain, Italy, France, Ukraine, northern Africa; Siberia, Kazakhstan, Uzbekistan, Turkey, Mongolia, Iran, and Afghanistan. In China, it occurs in Xinjiang, Gansu, Qinghai, Shaanxi, and Hebei provinces [START_REF] Zhang | The geographical distribution of agricultural and forest insects in China[END_REF][START_REF][END_REF].

In this study we modeled the potential range of C. italicus in China based on climate suitability. Because pest ranges are likely to shift in response to changes in temperature and precipitation, we also assessed the effects of climatic change on its potential distribution.

Materials and Methods

Overview Of CLIMEX Model And ArcGIS

We used CLIMEX 3.0 to model the potential geographical distribution of C. italicus in China under current and future climate scenarios. CLIMEX model is a predictive modeling software that has been used to predict the potential geographical distribution of many pests including: Ceratitis capitata (Wiedemann) [START_REF] Worner | Ecoclimatic assessment of potential establishment of exotic pests[END_REF], Bactrocera zonata [START_REF] Ni | Including climate change in pest risk assessment: the peach fruit fly, Bactrocera zonata (Diptera: Tephritidae)[END_REF], Frankliniella occidentails Pergande [START_REF] Cheng | The potential geographical distribution of Frankliniella occidentails Pergande based on CLIMEX and GIS in China[END_REF], Hyphantria cunea [START_REF] Lin | The potential geographical distribution of Hyphantria cunea in China[END_REF],

Solenopsis invicta [START_REF] Chen | The potential geographical distribution of Solenopsis invicta in China[END_REF], corynespora Leaf Fall Disease [START_REF] Qu | The current and future potential geographic range of Corynespora Leaf Fall Disease in China[END_REF], Nassella neesiana [START_REF] Bourdoˆt | The potential global distribution of the invasive weed Nassella neesiana under current and future climates[END_REF].

CLIMEX is a dynamic model [START_REF] Kriticos | Climate change and biotic invasions: a case history of a tropical woody vine[END_REF] that integrates the weekly responses of a population to climate using a series of annual indices. The model combines the growth index (GI) and the stress indices (SI) into an overall ecoclimatic index (EI). EI describes the climatic suitability of a location for a species as a number between 0 and 100. In this study, we classified the EI into four categories of climatic suitability for C. italicus: unsuitable (EI=0), marginal (EI=0-10), suitable (EI=10-20) and optimal (EI>20) [START_REF] Kriticos | Climate change and biotic invasions: a case history of a tropical woody vine[END_REF][START_REF] Kriticos | Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia[END_REF].

To visualize the results, the CLIMEX output was 'loose-coupled' [START_REF] Kriticos | Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia[END_REF] to a geographical information system (ArcGIS). It can present CLIMEX results in the same map as a species distribution or other information that provides spatial context [START_REF] Sutherst | CLIMEX Version 3: User'sGuide[END_REF]. Both CLIMEX ver. 3.0 and Arc/INFO ver. 9.3 were purchased by the Plant Quarantine Lab of China Agriculture University.

Meteorological Databases And Climatic Change

Two climate databases were used in our model. We first used the CLIMEX standard meteorological dataset, based on 30 year averages from 1961 to 1990, to create an initial model output. This dataset contains information for 2500 weather stations worldwide. Secondly, we used a regular gridded dataset of the normal climate for the same period, TYN SC 2.0 (http://www.cru.uea.ac.uk/~timm/grid/TYN_SC_2_0.html) [START_REF] Kriticos | Predicting the potential geographic distribution of weeds in[END_REF] to fine-tune the model output. The dataset consisted of 67,420 weather stations spaced on a 0.5° latitude×0.5° longitude regular grid for significant land areas worldwide.

For the climate change scenario, we modeled an average annual temperature increase of 2.3-3.3℃and, an annual mean precipitation increase of 5-7% by 2050 in China [START_REF] Qin | Climate and environment evolution in China[END_REF].

Geographic Data Sets

For geographic background datasets, we downloaded the electronic map of China national boundary, province boundary and national grassland distribution from http://nfgis.nsdi.gov.cn/ at a scale of 1:4,000,000.

Comparing Current And Climatic Change Scenarios

In order to visualize the differences in the potential geographical distribution for C .italicus under current and future climate conditions, the respective suitable land and grassland areas for each EI category were calculated in ArcGIS for each scenario.

Fitting CLIMEX Parameters

To fit the CLIMEX model for C .italicus, the parameters were manually and iteratively adjusted until the simulated geographical distribution, as estimated by the EI values, coincided with the species' known native distribution and the reported description of its range. Due to the lack of the data we used the information for the related species Melanoplus sanguinipes (Fabricius) [START_REF] Olfert | Application of general circulation models to assess the potential impact of climate change on potential distribution and relative abundance of Melanoplus sanguinipes (Fabricius) (Orthoptera: Acrididae) in north American[END_REF] to fit the initial values.

Parameters used in the CLIMEX model are presented in table 1.

Temperature Index

The lower developmental threshold of C. italicus was 15.5℃ [START_REF] Wumaerbie | Determination of developmental zero and degree-days to complete one generation of Article black car locust, calliptamus italicus, Gomphocerus sibiricus. Xinjiang Animal Husbandry[END_REF]. In this paper, the minimum temperature for development (DV0) was set at 15.50℃. The lower optimum temperatures for C. italicus's populations were set at 17℃to correspond to its distribution in northern Africa. Because this species is now established in Xinjiang province, so we set the upper threshold temperature to 35℃ [START_REF] Xi | The relationship between locusts lay eggs and climatic factor[END_REF].

Moisture Index

To fit the known C. italicus's distribution in Xinjiang province, the lower soil moisture limit for development (SM0) was set to 0.01. We used this value because locusts normally require a desiccated environment for embryonic development. They survive well when the soil moisture in May is between 0.03-0.3 [START_REF] Guo | The biology of the migratory locusts in China[END_REF]. So in this paper, the lower and upper limits for optimal soil moisture (SM1 and SM2) were set to 0.03 and 0.3 respectively. Based on the C. italicus's distribution in the central Europe, the upper soil moisture limit for development (SM3) was set to 0.6.

Diapauses Index

Diapause is an important biological characteristic that allows locust to survive harsh environmental conditions. Calliptamus italicus overwinters below ground in the egg stage [START_REF] Zhang | biological characteristics of calliptamus italicus[END_REF]. Based on its distribution in Iran, Afghanistan [4], Tibet and Sichuan province, we set the diapause induction day length to 10. We adjusted the diapauses parameters to identify suitable in areas in Xinjiang province based on C. italicus's distribution, north of the Mediterranean Sea, and north of Africa, based on that distribution, we set the diapause termination temperature and the diapause development days to 6 and 90.

Stress Parameter

We adjusted the stress parameters to account for C. italicus' global distribution.

This was done to reduce the likelihood that out prediction maps for climatic suitability would not include areas where C. italicus cannot survive.

Results

Potential Geographical Distribution Under Current Climate

The CLIMEX parameter values were calibrated until the predicted geographical distribution agreed with the observed global distribution for C. italicus. With the current climate scenario our model predicted that North America, southern South America, most of the Europe, the edge of the Mediterranean Sea, isolated parts of north and south Africa, Central Asia, West Asia, East Asia and southern Australia were potentially suitable for C. italicus establishment (Figure 1). Known regions of occurrence, such as Italy, Spain, and Central Europe, with EIs between 0 and 20 were considered to have marginally suitable to suitable climates for establishment.

Locations such as Kazakhstan, western Siberia, and Xinjiang where high population levels occur had EIs>20, and were considered optimally suitable for C. italicus. were at risk for establishment based on climate and host presence (Figure 2b).

Potential Geographical Distribution Under Future Climate

The climate change scenario indicated that China is likely to become hotter and wetter by 2050. As a result, the potential range for C. italicus is projected to expand.

Under the climate change scenario, the percentage of China's land area and grassland area at risk for C. italicus establishment increased from 29% to 31% and 96% to 98%, respectively. , will decrease from 9% to 7%, suitable areas will decrease from 8% to 6%, and optimal areas will increase 11% to 17%. Therefore, the overall suitable area will increase by 2% by 2050. For grassland, the proportion of the marginal areas will decrease from 10% to 4%, suitable areas will decrease from 14% to 7%, and the optimal areas will increase from 71% to 86%, so the overall suitable area will increase by 2% by 2050 (Figure 4).

Discussion

In this study, we combined Climex EI values with grassland host presence to increase the precision of the predictive maps. However, the model predictions discussed above surely have some limitations.

Additional factors like dispersal, host density, competition, and the effects of natural enemies [START_REF] Baker | The role of climate mapping in predicting the potential geographical distribution of non-indigenous pests under current and future climates[END_REF], soil type, geographical barriers and human activities could be incorporated to generate more robust predictions. Also, the use of model evaluation techniques like the ROC curve [START_REF] Wang | Application of ROC curve analysis in evaluating the performance of alien species' potential distribution models[END_REF] could be used to determine which modeling approaches, e.g. Maxent, GARP, DIVA-GIS and CLIMEX, are most appropriate for predicting the establishment potential of pests like C. italicus in a particular area.

Fig. 1

 1 Fig. 1 The global Ecoclimatic Index (EI) values for C. italicus under the current climate scenario (1961-1990 averages) (□, unsuitable (0.00); , marginal (0.01-10); , suitable (10-20); , optimal (20.00+).)

Fig. 2

 2 Fig. 2 The Ecoclimatic Index (EI) values for C. italicus in China under the current climate scenario (1961-1990 averages) with vegetation distribution (□, unsuitable (0.00); , marginal (0.01-10); , suitable (10-20); , optimal (20.00+).) ( a) for land area, b) for grassland area)

  Our model predicted that by 2050, the proportion of the marginal areas

Fig. 3 Fig. 4

 34 Fig. 3 The Ecoclimatic Index (EI) values for C. italicus in China in 2050 with vegetation distribution (□, unsuitable (0.00); , marginal (0.01-10); , suitable (10-20); , optimal (20.00+).) ( a) for land area, b) for grassland area)

Table 1 .

 1 Parameters used in the CLIMEX model for the C. italicus

	Parameter

Acknowledgements. The authors would like to thank Zhimei Li and the other members of the Plant Quarantine Laboratory of China Agricultural University (CAUPQL). This study received financial support from the Special Fund for Agro-scientific Research in the Public Interest (201003079).

* This study received financial supports from the Special Fund for Agro-scientific Research in the Public Interest (201003079).