The Problems of Building Geoinfomatical Database for Urban Ecological Research

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1 The Problems of Building Geoinfomatical Database for Urban Ecological Research Andrea Pődör a Ferenc VÉGSŐ a a Department of Geoinformatics, University of West Hungary Faculty of Geoinformatics, Székesfehérvár, Hungary Abstract The presentation deals with the problem, how to build an appropriate database for urban ecological research. Three cities will be examined in this research (Sopron, Szombathely and Szekesfehervar). The building of the GIS Database for this purpose is based on the data produced by different applied research fields like hydrology, geology, pedology, climate research, remote sensing etc. containing attributes and other data is needed to integrate into their analyse. The all data of the applied sciences will be fit in one GIS system so the researchers will be able to carry out integrated analyses of the environment and the change of the environment. The researchers who are responsible to build the Geoinformatical System faced to different methodological problems in integrating the different types of data on many scales. First the researchers defined what type of reference data (topographic maps, aerial photos etc.) is necessary and what kind of data sources are available. Next step was to define the necessary attribute information. Another problem is the implementation of the measured data into the system. The last problem is that the researchers should investigate what kind of thematic maps can be created from this data. Further development will be a model which is built for the monitoring of the urban ecological analyses for the three cities; this must be solved based on this GI System. Keywords: GIS/ Geodatabase / Visualisation 1. INTRODUCTION/ 1.1. Background and objectives The aim of the research project is to analyse the interaction between organisms themselves and the community in an urban or urbanized community and to develop an integrated monitoring system by which decision makers can establish better landscape planning and decision making. All available and collected data will be stored in one geoinformatical database which will give a strong scientific background to enhance the analysis of the interference of the natural and artificial impact of urban environment with each other. The planned research will help to understand the natural and artificial sub-systems of the environment, the different processes which takes place and the interaction between them. The data acquisition and the validation by remote sensing and GIS techniques give the ability to show the spatial and thematic coherence in wider region of the three cities. The processing of the data of the different research field gives the opportunity for the governance of the cities to establish their regional planning on scientific base. There is a continuous exchange of substances, energy and information in urban settings and its surroundings, but still there is lack of information about the quality of environmental impacts. One of the objectives of this research is to discover more facts about the interaction

2 between the natural and artificial sub-systems of the environment. The three analysed cities have different natural and artificial environment, they can be characterized by different landscape elements, so the interactions also will be different. Understanding the interaction between the urban settlements and their surroundings will make it possible to examine the different landscape elements which contribute to different environmental state. The first step of the research is the survey and evaluation of the anthropogenic impact of the cities followed by the analysis and mapping of the effects of the changed ecological system. The measured data will be introduced to a complex geoinformatical system. The data will be measured according to the characteristic landscape elements and pedological, climatical, hydrological and biotical data will be collected within each landscape element. A geographic information system is a system designed to capture, store, manipulate, manage, analyze, present, monitor and model all types of geographically referenced data. (MÁRKUS, 2010) By the establishment of geoinformatical system the researcher will be able to build models for examining the change of the different parameters, later with the help of the model it will be possible to give trend analysis to help in the urban planning process. Remote sensing will be the method which help researcher to define land cover and spatial structures of the different cities. The population of the world has doubled between 1800 and Today 80% of EU population is living in cities and in their agglomeration (EEA 2006). 67% of the Hungarian population is living in cities (KSH 2009). Interactions between non-living factors such as sunlight, water and biological factors, such as plants, microbes take place in all environments including cities. Concentrating humans and the resources they consume in metropolitan areas alters such things as soil drainage, water flow, and light availability. For example, sidewalks and rooftops may change the hydrology of the area by increasing rainwater runoff and may contribute to higher urban temperatures by storing heat and acting as an artificial heat sink. However there are measures that can help to reduce these problems in urban communities. Tree planting helps to limit the total surface area of concrete in communities allowing groundwater to recharge, reducing overall temperature, and helping to purify air. Activities such as community gardens or home gardening in urban communities are encouraged by urban ecologists. It saves community members money, and limits demand inputs from outside into the city. ( ttp://en.wikipedia.org/wiki/urban_ecology#cite_note- 0 ) 1.2. Research fields An urban ecological research project necessarily involves different field of sciences. Geoinformation technologies have a role in the processing of the data, remote sensing is used for characterizing landscape elements and with the help of it researchers are able to perform the landscape evaluation. Also characterizing the geology and the soil state of the analysed region is essential in measuring the status of the environment and defining the landscape characters. Characterizing the hydrology of the given area and the examination of the built up area are also inevitable in estimating the environmental impact of the cities. Describing the state of the air (climate and noise) and the natural environment (flora and fauna) are also very important elements of the surroundings. 2. APPROACH AND METHODS Database design makes GIS implementation effective (ZEILER. 1999). The important thing in the design process is to ask appropriate questions. Therefore at the beginning of the project all 2

3 the scientists gathered to find the initial goal what they want from the GI system and GIS experts designed a questionnaire to help developing an appropriate database for the different fields. For each function we should identify the data to fulfil the groups requirements to deliver information. For each data type the necessary data source must be identified (types of data and data sources). At the end we should organize our data into logical groups. The design is an interactive and iterative process, during the implementation phase (ZEILER, 1999). According to Arctur and Zeiler (2004) the design of a geodatabase consists of six essential steps. 1. Obtain or develop geodatabase design 2. Modify the design in GIS software 3. Load data into the geodatabase 4. Build topological relationship 5. Test the model 6. Revise the model and correct it (ARCTUR, ZEILER, 2004) In case we are not using CASE (Computer aided software engineering) tool and we have a simple data model which is already exist we will start the database design with loading the existing data to create the basic schema Data Acquisition The different fields of sciences are responsible to define the appropriate places for their samples. Hydrologists, pedologist and biologist took samples on the spot and they fulfilled further measurements and examination concerning the samples. Also remote sensed data is available for the research project. The building of the GIS Database is based on the different data produced by the different applied research fields (like hydrology, geology, climate research, remote sensing etc.) the kind of attribute data they provide and other kind of data which is needed to integrate into their analysis. Available sources which will be integrated into the GIS: 1. Topographical maps 2. Digital Elevation Models (Resolution 20*20 m) 3. Areal photos a) Coloured, and infrared areal photos(2000,2005,2009) b) Hyperspectral areal photos (2011. August) 4. Satellite images a) High resolution images (SPOT, LANDSAT TM ( ) 5. Attribute data soil, water network, air, flora and fauna and other statistical data 6. Ecological description and old maps 2.2. Survey for demands in GIS (according to the research plan) Since the aim of the research is to build a complex Geoinformation system and database for the monitoring of the cities, it was essential to define the database structure. As many scientists from different field are involved in the project the GIS experts first made the survey to determine the needs of the scientists. The main goal of the survey was to answer what is the specific aim of the research of the different fields concerning GIS. The survey contained many questions according to database. What kind of exploratory data must be stored in the Geo database? 3

4 In what kind of entities should the results of the observations (Point, line polygon, raster) provide? How many elements will be stored per layer? What kind of attribute data should be collected for the different entities? What type of analysis will be fulfilled within the different scientific fields? (what kind of result will be necessary to reach concerning the examined data) What type of analysis will be executed with data of other sub-projects? The evaluation of the results was the base of building the Geodatabase. It is evident that one of the most important sources is the result of the sample point measurements Sample points The nominated sample points are used in the following examination: Soil: built-up area, monitoring of the given status, detection of erosion, deflation, and heavy metals. Water: running-water supply, waste-water management, examination of the quantity and quality of the appearing water in the canal system. (It is essential to use a high resolution DEM) Air: emission sources and values, meteorological data, concentration of pollutant in the air. Flora and fauna: the detection of the composition of the flora and fauna, ecological corridors, measurments of pollutant concentration in the leaf. Green surface and built-up areas: trees, parks, water surface, as habitat.(jancsó, 2010) The dimension of the sample points measured in the field While besides the remote sensed data, the results of the sample point measurements give the basement of the Geodatabase, it is essential to define the characteristics of them. Each sample point possesses the following information: location of the sample, position of the sample, time of sampling, attributes defined and measured by the scientist. The location of the sample is the exact coordinates where the sampling happened. The location in this case is regarded as a point. More samples can be measured at the same location, for example in case of the soil samples where the samples are from different depths, usually from 0-10 cm and cm. They are different in space but their location is the same. Several water measurement and sometimes the air pollution samples can be taken at the same location from different depth and altitude as well. The time of the sampling gives information about when it is measured. The time dimension is also very important, because it can cover several extensions: Time Time interval Time scale (the measurement can be repeated yearly, daily etc) The attributes will give the characteristics of the sample points which is very important for the scientist to model the environment. These attributes will generally define some characteristics: category types (categories must be defined by the scientist) quantities (the dimensions and scales must be known) 4

5 texts multiple characteristics The results of remote sensing will be loaded into the Geodatabase Remote sensed data is very important in defining the status of the environment. Remote sensing has an initial role in detecting the following categories of land cover: vegetation: trees, bushes, grass, horticulture water surface: natural, artificial built-up areas: different types of buildings (tiled roof, flat buildings) roads, treeless surfaces (temporary vegetation) (VERŐNÉ WOJTASZEK, 2011) Remote sensing will help to define and map water networks and pervious areas: impervious surfaces pervious surfaces drainage system (above and under surface) With the help of remote sensing the following categories of spatial structures will be mapped: City centre Blockhouses Detached and semi-detached houses Industrial areas Green surfaces like parks Natural grasslands Barren land Data integration is crucial as it influences how to design the database from the samples in order to: show the most infected areas, how to detect the time scale and how to store it in the database. Scientist rely on this database must be able to carry out analyses and find out the most infected areas in the cities. The decision is that the measurements with different positions at the same locations are organized into different layers. Together with raster data coming from remote sensing these layers can be used for spatial analysis. 3. RESULTS Though the research project is still ongoing, the geodatabase contains the results of the sample point measurements and the result of remote sensing classification methods basically using segmentation. From these data a few derived result is also available Applied classes in the Geodatabase A class can have a name, a set of attributes, a set of operations and constraints. A class is a description of objects. All objects of a class share the same attribute description, associations to other classes, operations and methods. A class represents a concept in a model. Referring the point the present geodatabase contains vector and raster datasets. Vector datasets are: the data of the sample point measurements, (point features) soil, air pollution, and water digital elevation model as point object (point features) 5

6 streets (line features) districts (polygon features) results of remote sensing classification as a *.shp files. Raster datasets are: topographic maps ortophoto imagery results of remote sensing classification Derived datasets are: thematic maps of soil and air contamination interpolation of soil samples measurement digital elevation model generated from the original point dataset. Figure 1. Maps shows the soil types and some heavy metal content of it and the per cent of air pollution The soil samples are densely distributed in the pilot areas it can be interpolated.. In this research we used IDW (Inverse Distance Weighted) as interpolation method, which generates a raster surface. This surface can be examined together with the remote sensed data and correlations can be detected. In case of air quality measurements it is not possible. Concerning the water samples, naturally they are located along a line, so it must be handled differently.( Figure 1) It should be considered if data is examined in vector or raster model so vector-raster or raster-vector conversion is required 4. CONCLUSIONS AND FURTHER PLANS The Geodatabase is available for further research, but the scientist have to plan how to use the recent results for deriving and analysing the impact of the human activities in the future. A monitoring system must be developed which will be a resource for the local authorities for regional planning and the local decision support system. Acknowledgements: We wish to thank for TÁMOP B-09/1/KONV References ARCUR D.-ZEILER M. (2004): Designing Geodatabases, Székesfehérvár. ZEILER M (): Modelling our world CSÁFORDI P.-PŐDÖR A. (2011): Eróziós modell létrehozása az ArcGIS Model Builder segítségével (kézirat) NymE Geoiformatikai Kar, Székesfehérvár. 6

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