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Evaluation and Generalization of SoLIM for Digital Soil Mapping Using Digital Elevation Model and its Attributes

机译:基于数字高程模型及其属性的数字土壤制图SoLIM评估与推广

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Discrete Models of Spatial Variability (DMSV) have limitations for soil identification in traditional soil maps. New approaches, generally called digital soil mapping (DSM), using continuous methods (CMSV), try to predict soil classes or soil properties based on easily-available environmental variables. The objective of this study was to map the soil classes of the Borujen area, Chaharmahal-va-Bakhtiari province, using digital elevation model (DEM) and its attributes and Soil-Land Inference Model (SoLIM). To do this, eighteen terrain attributes were derived from the DEM of the area. The primary analysis showed seven attributes are the most important derivatives. These derivatives as well as three dominant soil subgroups and seven soil families of the region (41 profiles from 125 profiles) were used to construct the input data matrix of the model. Then, output fuzzy soil maps of SoLIM were converted to polygonal soil map, using ArcGIS. Results showed that different combinations of DEM attributes have different accuracy rates for soil prediction. The accuracy of the interpolation was twice that of the extrapolation. Although SoLIM had an acceptable accuracy for soil nomination, and identification of soil map units’ types, it did not have enough accuracy for the location of soil classes. It seems that using other data like parent material and geomorphic surface maps will increase the accuracy of the model prediction. Keywords: Digital soil mapping, Digital elevation model, Fuzzy logic, SoLIM, Soil map. Full-Text Type of Study: Research | Subject: Ggeneral Received: 2012/12/27 Related Websites Scientific Publications Commission - Health Ministry Scientific Publications Commission - Science Ministry Yektaweb Company Site Keywords ?????, Academic Journal, Scientific Article, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ?? Vote ? 2015 All Rights Reserved | JWSS - Isfahan University of Technology
机译:空间变异性离散模型(DMSV)在传统土壤图的土壤识别中有局限性。使用连续方法(CMSV)的新方法通常称为数字土壤测绘(DSM),它试图根据易于获得的环境变量来预测土壤类别或土壤性质。这项研究的目的是使用数字高程模型(DEM)及其属性和土壤-土地推断模型(SoLIM)来绘制Chaharmahal-va-Bakhtiari省Borujen地区的土壤类别。为此,从该区域的DEM导出了18个地形属性。初步分析表明,七个属性是最重要的导数。这些导数以及该地区的三个主要土壤亚组和七个土壤科(125个剖面中的41个剖面)被用于构建模型的输入数据矩阵。然后,使用ArcGIS将输出的SoLIM模糊土壤图转换为多边形土壤图。结果表明,DEM属性的不同组合在土壤预测中具有不同的准确率。内插的精度是外插的精度的两倍。尽管SoLIM在土壤提名和土壤图单元类型识别方面具有可接受的准确性,但对于土壤类别的定位却没有足够的准确性。看来,使用其他数据(例如母体材料和地貌表面贴图)将提高模型预测的准确性。关键字:数字土壤制图,数字高程模型,模糊逻辑,SoLIM,土壤图。全文研究类型:研究|主题:一般收稿时间:2012/12/27相关网站科学出版物委员会-卫生部科学出版物委员会-科学部Yektaweb公司网站关键字??????,Academic Journal,Scientific Article,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ??投票吗? 2015版权所有| JWSS-伊斯法罕工业大学

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