Showing 4 results for Solaimani
A. Solaimani Pour, A. R. Nikooie, A. Bagheri,
Volume 9, Issue 1 (spring 2005)
Abstract
This study was conducted to determine the problems of marketing channels of damask roses and to seek appropriate solutions to enhance marketing efficiency. The results of the study revealed that traditional and industrial rose production lacked the quality demanded by the market. The efficiency index was % 92.9 in traditional and %55 in industrial production. In addition, with regard to the marketing parameters for each type of production, the share of the factors was calculated. So we can conclude that the reducing units have the most important roles in this process. According to the study, traditional units with %47.2 had a greater share compared with the industrial units (%44.5).
The results have also shown that production retailer wholesalers and middlemen shares were in the lower ranks respectively. Marketing cost coefficient results showed that %70 of the retailer selling price for 1 kg of the product was related to the marketing costs. The costs for industrial units with high and low capacity were %67.7 and %65.4, respectively.
K. Solaimani, R. Tamartash, F. Alavi, S. Lotfi,
Volume 11, Issue 40 (summer 2007)
Abstract
In order to manage the rangeland resources, remote sensing data is able to provide a sensible role of different cases in flora community such as biomass. The study area in SefidAb subbasin of the Lar Dam basin is located in central Alborz, where the climatic condition is semihumid and near to moderate. For the assessment of the sattelite data and their capability in estimation of the range production, Landsat-TM data with different bands was used. In this research, the field data was collected using random-systematic method in 20 sampling units of 200 plots. For geographic coordinates of the sampling units and related pixels in digital data, GPS and also existing benchmark data of the nearest points were used. Then correlation between ground data and vegetation index from different band combination was investigated and the reasonble vegetation indices were obtained. Finally, the best models were extracted for this purpose, which showed sensible relation between the field data and vegetation index. Therefor, it is possible to estimate range production using Landsat TM data related to ground control.
S. H. Roshun, Gh. Vahabzadeh, K. Solaimani, A. Khaledi Darvishan,
Volume 21, Issue 3 (Fall 2017)
Abstract
Sand and gravel mining from the most of our country rivers causes morphological, hydrological and geomorphological changes in these rivers. This study investigates the effects of removal of sand and gravel from the river bed on sedimentological features of Zaremrood River in Mazandaran province. For this purpose, by determining four sections before and four sections after the sand removing point, the river bed sediments sampling in combined approach and in a plot within the river were performed and sedimentology features such as the large, medium and small diameters (a, b and c), roundness (Rc), form factor (Sf), normal diameter (D), sphericity (S), and width ratio (W), were measured and calculated in the laboratory and analyzed by SPSS software. The results showed that the variations of sediment statistics a, b, c, Sf, D, S and W in the pre- and post- harvest location has a significant difference but the Rc statistic does not show any significant difference. The reduction of the triple diameters after the excavation site is caused by the fracture of the sediments in the mining area, so that the sphericity of grains also decreased in the mining area. Roundness of sediment particles after the excavation site is decreasing up to 600 meters reach and then it tends to increase.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (Winter 2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
