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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS

文献类型： 外文期刊

作者： Wang Da-cheng 1 ; Li Cun-jun 2 ; Song Xiao-yu 2 ; Wang Ji-hua 1 ; Yang Xiao-dong 2 ; Huang Wen-jiang 2 ; Wang Jun-ying 3 ;

作者机构： 1.Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol, Hangzhou 310029, Zhejiang, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Agr Technol Extens Stn Beijing, Beijing 100029, Peoples R China

关键词： LSP;ANN;suitable areas;wheat;RS and GIS

期刊名称：AGRICULTURAL SCIENCES IN CHINA （ 影响因子：0.82； 五年影响因子：0.997 ）

ISSN： 1671-2927

年卷期： 2011 年 10 卷 9 期

页码：

收录情况： SCI

摘要： It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.

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