Land Suitability Evaluation For Rice Based On GIS And Fuzzy AHP

Land Suitability Evaluation (LSE) for crop is one of the most major contents of land resource research in last decades. Various evaluation methods are introduced in it. Based on mathematics modeling and combining with new technology like GIS and so on, these methods achieve fast quantitative LSE. In foregoing LSE, Analytic Hierarchy Process (AHP) usually is applied to obtain weight, and it seldom standardize factor parameter of different scale. Fuzzy AHP frequently is applied to rank alternative of non-Spatial Multi-criteria Decision-Making, and it seldom model the vagueness and uncertainty in LSE.Having agricultural land (excluding forest land) of the district of Heyuan City, Guangdong province, China as the object, according to A Framework For Land Evaluation, this research confirmed "class" as the level of LSE. According to actual conditions of the studying area and basic condition of factor, this article confirmed 7 evaluation factors. Firstly, AHP is applied to standardize class of single evaluation factors and weight of 7 evaluation factors, and integration index of evaluation unit is confirmed by each factor's weight with standardization. Secondly, based on AHP, by introducing triangular fuzzy numbers, AHP is optimized as Fuzzy AHP, and fuzzy integration index of evaluation unit is confirmed by each factor's fuzzy weight with fuzzy standardization. Finally, rationality of the evaluation result of AHP and Fuzzy AHP is analyzed by comparing special distribution of the each suitable grade of this study and standard polt, agricultural land type, natural quality rank of the agricultural land classification.The result shows: (1)AHP is one of effective approach to standardizing factor parameter of different scale. (2)The result of Fuzzy AHP is provided consistency with the production of agricultural land classification. (3)Fuzzy AHP can be applicable in GIS-based LSE. (4)Fuzzy AHP in part models the vagueness and uncertainty in LSE, and sensitivity analysis of believe interval(α-cut and) and optimism index(λ) can address the vagueness and uncertainty in LSE. (5) The different method of defuzzifition will obtain different result, and the result of equal defuzzifition accord with the fact more.