جستجوی مقالات مرتبط با کلیدواژه « Soil Map » در نشریات گروه « جغرافیا »

Soil maps have considerable significance as basic maps in many environmental and natural resources studies. Digital soil maps are based on the relationship between environmental variables and soil properties. The main purpose of this research was to analyze error and uncertainty of digital soil classes predicted at different taxonomic levels of Soil Taxonomy system using an artificial neural network. One hundred and twenty soil profiles were described and sampled based on a regular grid scheme in Shahrekord plain. Two groups of soil properties (qualitative and quantitative) and auxiliary parameters (including geologic map, landform map, landform-phase map, traditional soil map, normalized difference vegetation index, and some derivatives of digital elevation model) were used to estimate soil classes. After preparing the soil properties maps and checking their accuracy, these maps were used along with auxiliary parameters for estimating soil classes using an artificial neural network model in the R software. Finally, the accuracy and uncertainty of the model were evaluated by overall accuracy and confusion index, respectively. Results showed that the entry of more details in the soils classification at the lower levels of the Soil Taxonomy system, while increasing the number of classes, leads to decreasing the overall accuracy and increasing uncertainty. It is noticeable that the artificial neural network model has a good accuracy up to the great group level through the acceptable level of overall accuracy (i.e., 75 %), hence it has a high degree of uncertainty. Therefore, the accuracy of the model could not be effective in its selection trough the modeling process; however, paying attention to its uncertainty is also very important along with the model error. Accordingly, we suggest using the other methods of soft computing for modeling in plain areas or in low relief regions.

Introduction and Background: Recent droughts have more than ever raised the use of modern irrigation methods and cultivating land having restrictions. This is especially important in rural areas. Aims: This research was conducted for studying the effect of new irrigation methods to increase the area under cultivation with saline and alkaline soils limitation for pistachio land utilization type in part of the south of Tehran province. Field studies were conducted such as drilling of 10 soil profiles, 10 augers and 86 soil sampling. various thematic maps such as soil map, land classification and suitability were prepared for two irrigation method as surface (traditional) and localized (modern) using auxiliaries’ data such as satellite imagery, geological and topographic maps and weather information and laboratory analysis result from soil sampling. The soils of region were classified at typic torrifluvents sub-group and differentiated at three series and six phases. Land classification study was conducted for bubbler and furrow irrigation method. Land suitability evaluation was handled according to selected irrigation method using parametric corrected land index method for pistachio land utilization type. The results showed the use of bubbler irrigation rather than one may lead to the promotion land classification classes (II S to I) or reduce limitations on a class (II STW to II W). The use of mentioned irrigation method enhances land suitability classes due to insensitivity to soil and land limitations such as topography and salinity (increased from N to S2n) or will reduce the number and severity of limitations. However, the change of irrigation method from traditional to modern needs high costs and demand greater levels of initial management, but in the long term, while increasing irrigation efficiency compensate the preliminary costs and reduce land modifying operations.