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Integrated utilization of Remote Sensing and GIS in Shashi's urban general planning

Chen Jun, Qiu Zhaoyue
Wuhan Technical University of Surveying and Mapping
Wuhan, China


Abstract
Since 1986, remote sensing and geographic information system (GIS) techniques have been used by the authors to assist urban planning and management in Hubei Province, China. This paper described the methodology and practices of integrated utilized of multidate airphotos, satellite imagery and GIS in Shashi's urban general planning.

Introduction
Urban general planning is based various types of data (including physical environment, resource distribution, urban landuse and utility, socio-economic development etc) and comprehensive analysis. As far as the basic urban data is concerned , not only quantitative composition and geographic distribution of the relevant urban phenomena in different scales are required, but also the changing characteristics or dynamic process of some phenomena are needed. On the other hand, a large number of facts and /or extensive calculations need to be processed for the simulation of physical situations by means of mathematic methods, for the systemato-logical and synthetic analysis, comparison and selection of alternative plans.

In order to meet with these requirements, remote sensing and GIS techniques have been used by the authors to support urban general planning in the middle sized cities in Hubei Province, China. In the urban general planning of Shashi, sequential airphotos and satellite imagery are used to collect more complete, accurate, reliable basic urban data, and GIS is used to assist urban environmental quality accessment, traffic volume forecast and analysis, socio-economic development forecast.

Comprehensive survey of basic urban data using Remote Sensing methods
As we may see from Table 1, the large-scale air photos taken in March 1989 were used for collecting the basic urban data in urban construction area, planning area, as well as in some specific zones. TM and MSS imageries were used mainly to collect basic data in regional scale and to study some specific phenomena in the planning area, Sequential airphotos and satellite imageries were used for urban change (or expansion) analysis, urban landuse plan implementation checking, river and lake change analysis.

Table 1. The airphotos and satelite imagesries used in Shashi's urban planning
No Year Type Photo scale or IFOV Remarks
1 1957 black and white 18x18 1: 60 000  
2 1977 pancramatic 18x18 1 : 20 000  
3 1989 infra-red 23x23 1: 12 000  
4 1989 panchromatic 23x23 1 : 2 800  
5 1976 Landsat MSS 79mx56m  
6 1986 Landsat TM 30m x30m  

Table 2. Basic urban data collection by integrated utilization of remote sensing and auxiliary data.
Levell Contents
Regional level
( 11. 000 km2)		 1.1 Regional geological linear features and fault structures:
1.2 Geographic distribution and change of rivers and lakes:
1.3 Regional land form land covers:
1.4 Geographic characteristics of cities and towns in the region:.
1.5 Dynamic hydrological phenomena (breached reach of Yangtz river etc).
Planning area
( 300km2)		 2.1 land occupation and relevant quantities:
2.2 unfavorable geographic phenomena for urban contraction:
2.3 touristic landscape, ruins of ancient cities
Urban built-area
(30km2)		 3.1 urban landuse (10 classes 39 sub classes and 31 smallest classes):
3.2 urban densities of each geographic unit (including coverage density, floor area ratio, green coverage, population density,...)
3.3 geographic location of tall chemenies, escaped wasted water, solid waste, etc:
3.4 urban change and / or expansions (morphology, fringe area, landuse,....):
3.5 implementation rate of last urban plan and the relevant causes.
old - city
(2km2)		 4.1 existing landuse landuse in more detailed classification
4.2 building coverage and floor area ratio for different building classes etc.

As far as the technical scheme is concerned, the 1:10,000 air orthophotos and 1: 100,000 satellite and 1:100,000 satellite mosaic were firstly made. The whole planning area was then divided into 135 geographic units according to the spatial homogeneity, natural borders and the existing administrative boundaries. Further study of the geometric and spectral characteristics of urban objects or phenomena were carried out with the help of all available auxiliary data. On the basis of the work, the basic urban data listed in Table 2 were collected. And the intermediate and final results were provdied to urban general planning in the forms of series of thematic maps, alpha-numeric data tables, analytic reports, or data bases..

GIS-Based Urban Quantitive analysis
  1. Urban Environmental Quality Accessment

    Urban Environmental Quality Accessmentand environmental pollution accessment as well as the relevant synthetic accessment were carried out. Firstly, the geographic units were taken as the evaluation units, and nine factors were taken into account as showed in Fig. 1. Data of building vocerage density, floor area ratio, green coverage and population density came directly from the remote sensing survey. The digitized air pollution source points and monitoring points were used as the reference points or starting points of mathematical interpolation or simulation in order to derive SO2, Nox, Dust, C12 and T.S.P. for each evaluation unit.


    Fig.1 urban environmental quality accessment based on CIS

    Fuzzy weighting method was used for multifactor synthetic accessment. Suppose k factors be selected. with their weights w1, w2,.... wk, p ranks are classified for each factor. Let x1, x2,.... xk be the values of a given evaluation unit, the subordiance degree of xi to the jth rank of the ith factor is defined as:


    xijl,x iju are respectively the low limit and upper limit of the jth rank of the ith factor. In this way, fuzzy subordinace degree matrix R was created for k factors and p ranks,


    and the fuzzy set of class St was determined as follows:


    The evaluation unit in consideration was assigned to the class corresponding to the maximum of s1, S2,..., Sp.

  2. Urban Traffic volume Forecast and Planning



    On the basic of traffic survey and geographic analysis, the planning area was divided into 42 traffic zones.

    The existing traffic volume was firstly analyzed to obtain O-D distribution, time variation of traffic volume for certain road segments, traffic generation between basic traffic zones in different mode (by bicycle, by vehicle or on foot). Then the so-called "four-steps" pattern was used to forecast the trip and goods volume in the planned years.

    As far as the trip volume forecast is concerned, the total trip volume and the volumes for different trip purposes were calculated according to the average trip volume per person and the future population. The generated and attracted trip volume were simulated by EVANS model,

    Tij=KiLjOiDjf(dij) ------------------(3)

    where Tij is the volume between the ith and jth traffic zones
    dij is the distance between the ith and jth traffic zones.
    Oi is the traffic volume generated by the ith traffic zone.
    Dj is the traffice volume attracted by the jth traffic zone:
    f(dij)=dij


    Then the traffic volume was classified into different mode (by bicycle, by vehicle or on foot), and the relevant traffic volume was assigned to the temporary road network using shortest path algorithm.

    Goods volume forecast was related to the non-agricultural, existing and planned urban landuse, category of enterprises etc. Similarly, the EVANS model was used for forecasting the generated and attracted goods volume of each traffic zone. The resulted traffic volume was assigned to the temporary road network. The diagnosis, evaluation and adjustment were then followed to optimize the road network and its relevant plan.

  3. Urban Socio-economic Development Forecast

    An optimal equilibrium model was used for forecasting the industrial structure and industrial output values of Shashi in 1995, 2000 and 2010. The objective selected was to maximize the final social product and minimize the quantity of wasted water, gas and solid waste. Energy resources and raw materials. Labor resources etc were selected, after a detailed study, as the other constraints in the model. And the urban population production was carried out according to the discrete differential model.
Conclusion
The integrated utilization of remote sensing and GIS technology in Shashi urban general planning made it possible to provide with urban authority and urban planners, in a rather short time more complete, accurate and reliable basic data and to perform more comprehensive and efficient quantitative analysis, simulation and optimization.

Reference
  1. Chen Jun et al. the Build up and application of spatial urban information System city planning review no 1988.

  2. Chen Jun et al urban general planning information engineering based on remote sensing and GIS paper presented at the 2nd Beijing international workshop of GIS 1990.

  3. Sun Yuguo et al computer Aided Urban traffic planning of Xian fan city journal of Wuhan technical university of surveying and mapping No 1 1990.

  4. sun Yugao grey Fuzzy linear programming Model and its application paper presented the shangai international symposium models of geography