saeedeh Nateghi; Rostam Khalifehzadeh; Mahshid Souri; Morteza Khodagholi
Abstract
Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of ...
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Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of Asuran, Semnan province. Soil sampling was performed using stratified random sampling method. After creating a map of homogeneous units in the area, in each homogeneous unit according to its area, several sampling points were selected completely randomly. A total of 145 sampling points were collected. At each sampling point, a composite soil sample (a mixture of 9 observations) was taken. Soil organic carbon was measured using Valkyli-Block titration method. Data of 114 samples were used to calibrate the model and data of 31 samples were used to validate it. The results showed that the correlation of spectral variables obtained from Landsat OLI sensor with surface soil organic carbon is higher than non-spectral variables obtained from 1: 25000 topographic maps. Also, the results of factor analysis by principal component analysis with eigenvalues greater than one showed that the total cumulative variance explained by 14 variables was equal to 90.2%, which was explained by three factors. The regression equation generated by the three extracted factors had suitable potential for predicting surface soil organic carbon (R2 = 0.59). The root mean square error (RMSE) of the proposed model was calculated to be 0.3.
hosien شظهیه; omonabin bazrafshan; Abdolreza Bahremend; Arash Malekian
Abstract
The purpose of this study is the effects of the morphometric factors on peak discharge in 108 hydrometric stations in the southern watersheds, Iran. After homogeneous tests and random data, a time period (from 1983-1984 to 2013-2014 was chosen and used to choose the best probability distribution function. ...
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The purpose of this study is the effects of the morphometric factors on peak discharge in 108 hydrometric stations in the southern watersheds, Iran. After homogeneous tests and random data, a time period (from 1983-1984 to 2013-2014 was chosen and used to choose the best probability distribution function. Overall, the 84 morphometric and geometric parameters were calculated in ARC GIS software. In this research, the structural equation modeling with the least approach in smart – PLS software was used to check the most effective factors on the annual maximum discharge. 18 variables were identified as effective factors on the maximum discharge. between more than 84 structures, the effect of the focus time structures, positive height ratio, miller slenderness ratio structures ,the main river- slope characteristics , elevation number and the main river-slope height properties are negative than can predict overall the %46 of the annual maximum discharge changes in the watershed areas of Iran s southern parts. These factors affect directly on the flood in the total focus time about %38 thus, the most effective factor on the flood discharge is the focus time factor that should be considered in the flood management in Iran s southern areas.