5747

5362

كارشناسي ارشد

خاكشناسي

اصفهان: دانشگاه صنعتي اصفهان، دانشكده كشاورزي

1389

سيزده،102ص.: مصور﴿بخش رنگي﴾،جدول،نمودار

ص.ع.به فارسي و انگليسي

شمس ا.. ايوبي

حسين خادمي، نورايرتومانيان

27/1/90

محمدرضا مصدقي، جهانگير عابدي

1396/05/29

كتابنامه

مهندسي كشاورزي

ID5362

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Abstract Prediction of soil organic carbon SOC is a crucial proxy to manage and conserve natural resources monitoring CO2 and preventing soil erosion strategies at the landscape regional and global scales The objectives of this study were i to evaluate capability of regression models to predict SOC using terrain attributes and remote sensing data ii to evaluate capability of artificial neural network models to predict SOC using terrain attributes and remote sensing data and determining the most important factors which could explain the variability of SOC in the hilly regions and iii to evaluate spatial estimation of SOC by Ok IDW RBF and Cok using auxiliary data including the remotely sensed data and terrain attributes A study area of 24 km2 in hilly regions of Zargham Abad in south of Semirom central Iran under natural range land use located at 51 392 E longitude and 31 182 N latitude was selected The average elevation of the study area is 2500 m a s l The mean annual temperature and average annual precipitation are 10 68 C and 350 mm respectively 125 soil samples from 0 10 cm depth were collected Soil organic carbon was measured for the collected soil samples The elevation data were used to create a 3m digital elevation model DEM using ILWIS Then primary and secondary topographical indices were generated from the DEM using ILWIS DIGEM and TAS softwares Remote sensing data used to develop the models included Landsat ETM Image geocoding was performed using ground control points obtained through 1 25000 topographic maps with UTM coordinates with 0 21 pixel accuracy Finally regression and ANN models were developed for SOC estimation in the study area and then the developed models were validated by additional samples 25 of total data set In the four developes models different groups of inputes were included In model 1 and 2 terrain attributes and remote sensing data were considered as predictors respectively In model 3 inclusion of terrain attributes and remote sensing data were evaluated and soil texture in addition to model 3 were examined in model 4 The results showed that the regression models explained 60 54 71 and 83 and ANN models explained 89 84 94 and 95 of the total variability of SOC in the study area using models 1 2 3 and 4 respectively Sensitivity analysis based upon the fourth ANN model revealed that the profile curvature NDVI band1 slope band2 wetness index sediment transport index band5 stream power index aspect plan and band7 were identified as the important topographic attributes and remote sensing data could explain the variability SOC distribution within the selected hill slope Prediction of the four regression models in the study area resulted in root mean square error RMSE values of 0 27 0 26 0 11 and 0 09 and for four ANN models 0 11 0 13 0 04 and 0 04 respectively Different spatial prediction approaches including determitic and geostatistic methods were comperd to predict SOC in unsampled points Results showed that Ok method had more accurate interpolation results than IDW and RBF for SOC and there was significant difference between Ok and Cok methods Results also showed that cokriging with wetness index were superior to cokriging with remotely sensed data Key words Artificial neural network Normalized difference vegetation index Soil organic carbon Terrain attributes Cokriging

شمس ا.. ايوبي

حسين خادمي، نورايرتومانيان

محمدرضا مصدقي، جهانگير عابدي