Document Type : Original Article
Abstract
Landuse planning is a systematic attempt to model the interaction between human activities and environment, and arranges landuses for sustainable development. One of the main stages in landuse planning is calculation of land ecological capability. This stage includes definition of landuse requirements, assessment of land characteristics and corresponding matching, between these two.
Study of definitions and methods related to the calculation of ecological capability shows that it is not possible to calculate an exact value for the suitability of land unit for a specific landuse. In this context, many inaccurate and uncertain processes can be modeled mathematically, using fuzzy logic. Such models can be used for decision making in uncertain conditions.
In Iran, Makhdoum’s model is widely applied for ecological capability assessment, in different scales and areas. This is mainly because of its comprehensiveness regarding socio-economic and environmental characteristics of Iran. In this model, phenomena like soil type, climate and slope, which are continuous changes in the nature, are presented in crisp maps. On the other hand, the weight and relative importance of various factors are not covered in Makhdoum model. Methods like fuzzy Weighted Linear Combination (fuzzy WLC) and fuzzy Ordered Weighted Average (fuzzy OWA) are needed to define the weight of ecological characteristics maps. Therefore, fuzzy inference rules are used for the assessment of ecological capability.
In this research, Makhdoum model is implemented using crisp and fuzzy approaches. Considering the simplicity of implementing fuzzy logic in raster GIS environments, pixel is selected as a spatial unit, for spatial representation of ecological models. In crisp approach, the ecological models are generated and land suitability index for spatial units is calculated. It should be mentioned that, in Makhdoum’s original model, the suitability of each pixel is defined as either zero or one.
Land ecological capability calculation using fuzzy logic includes three stages; normalization and fuzzification of environmental maps, integration of maps using fuzzy inference, and defuzzification of the results. In this research, first, all input maps (slope, elevation, aspect, soil texture, soil depth, erosion, climate and geology) and output maps (ecological capability for different classes of agriculture, pasture, urban and rural residential and industrial) are normalized and fuzzified, and the fuzzy membership functions are defined for the inputs and outputs of the model. In the second step, fuzzy data are integrated using fuzzy inference rules. In this process, the fuzzy if-then rules are defined and the related rule-base is created using expert knowledge and the fuzzy membership functions of the input and output of the model. Finally, in the defuzzification stage, the output of the fuzzy inference, which includes several fuzzy numbers, are converted into one crisp number, using gravity center method and Mamdani decision model.
In this research, the fuzzy ecological capability maps are generated for different landuses in Borkhar and Meymeh township. Usually, either the administration units or the ecological ones are considered as spatial units for decision making. According to Makhdoum model, the suitability of each ecological unit and each rural district, for different landuses are calculated by aggregating the suitability of pixels inside these units. Spatial analyses and development capabilities of ArcGIS Ver. 9.3 are used for preparation and analysis of ecological maps. In addition, Matlab Ver. 7.6 is used for defining fuzzy membership functions, creating fuzzy rule-base, and doing other fuzzy calculations.
Using fuzzy logic, the ecological characteristics maps can be produced and integrated much closer to the reality, although the amount of calculations grows significantly. Implementation and comparison of Makhdoum model in crisp and fuzzy approaches show significant improvement in the environmental characteristics maps, especially around the boundaries of features and where thematic classes are changing. The method developed and applied here is independent of the number of landuse types and criteria, and can be used for other conditions with minor modifications.
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