پديد آورنده :
جعفري، اعظم
عنوان :
مدل سازي خاك - زمين نما و نقشه برداري رقومي خاك در منطقه زرند كرمان
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده كشاورزي
صفحه شمار :
چهارده،129ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
شمس ا... ايوبي، حسين خادمي
استاد مشاور :
احمد جلاليان، نورامير تومانيان، محمد هادي فرپور
توصيفگر ها :
سطوح ژئومرفيك , پارمترهاي سرزمين , شاخص هاي سنجش از دور و تنوع لندفرم ها
تاريخ نمايه سازي :
6/3/91
استاد داور :
مصطفي كريميان اقبال، محمد حسن صالحي، محمد علي حاج عباسي
كد ايرانداك :
ID427 دكتري
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
131Soil Landscape Modelling and Digital Soil Mapping in Zarand Regionof Kerman Azam Jafari E mail address a jafari@ag iut ac ir Date of Submission February 14 2012 Department of Soil Science Isfahan University of Technology Isfahan 84156 83111 IranShamsollah Ayoubi ayoubi@cc iut ac irHossein Khademi hkhademi@cc iut ac irAhmad Jalalian Norair Toomanian Mohammad Hadi Farpour Department Graduate Program Coordinator Ahmad Riasi Associate Professor and Professor Department of Soil Science College of Agriculture Isfahan University of Technology Respectively Professor Department of Soil Science College of Agriculture Khorasgan Azan University Assistant Professor Department of Soil and Water Isfahan Agricultural Research Center Associate Professor Department of Soil Science College of Agriculture Bahonar University of KermanAbstractThe main objectives of this study were to compare the regression models to produce soil class maps in the Zarand region of southeast Iran Among the predictors geomorphology map was identified an important tool for digital soil mapping approaches as it helped increase the prediction accuracy The results of prediction showed higher mean probability values for each soil great group in the areas actually covered by the soil great groups compared to other areas indicating the reliability of the prediction However results showed that the predictions were poor for some soil great groups due to low sample size high variability of ancillary predictors and the inability of geomorphological map to differentiate the strata in detail In most predictions the global purity was slightly better as compared to the actual purity for the models however both models provided poor predictions for Haplocambids and Calcigypsids The results showed that soils with better purity were those highly influenced by topographic and geomorphic characteristics e g Haplosalids and soils with very low purity and accuracy of prediction were hardly influenced by topographic and geomorphic characteristics e g Haplocambids Keywords regression models soil great groups geomorphology mapIntroduction Conventional soil mapping methods are efficient in medium to low intensitysurveys because they use relationships among soil properties and more readilyobservable environmental features as a basis for mapping However the implicitpredictive models are qualitative complex and rarely described in a clear manner Therefore developing an explicit analogue of conventional survey practice suited tomedium to low intensity surveys is of great importance 5
استاد راهنما :
شمس ا... ايوبي، حسين خادمي
استاد مشاور :
احمد جلاليان، نورامير تومانيان، محمد هادي فرپور
استاد داور :
مصطفي كريميان اقبال، محمد حسن صالحي، محمد علي حاج عباسي
