جستجوی مقالات مرتبط با کلیدواژه « west of iran » در نشریات گروه « آب و خاک »

Mapping and spatial analysis of wheat fields are very important in studying macroeconomic and social issues, including agricultural management. Highly variable crop pattern maps and its preparation using terrestrial data are associated with many problems. The purpose of this study is to implement a practical method for extracting wheat fields by using changes in vegetation index and spatial analysis of wheat fields in western of Iran. Investigation of the changes curve of vegetation index of wheat typic farms showed that the highest amount of reflection index of wheat farms is in June and early July and after harvest the reflectance index decreases extremely. In this regard, Sentile sensor data was processed in the Earth Engine system and the 12-month vegetation index of 1398 was extracted as a data set. By introducing training data to the data set created by the support vector machine classification method, the land use of the study area was obtained in five classes. By applying altitude filter and removing the extracted fields above 3,000 meters, the distribution map of wheat fields was verified with the remaining 48 ground data. The total accuracy and the kappa coefficient were obtained 0.86 and 0.79, respectively. Since in the proposed method, more training data are given to the algorithm, the overall accuracy of the classification is increased. The spatial pattern of wheat fields with the mean function of the nearest neighbor and P_value <0.05 indicating the cluster dispersion of the fields and the Caripley function indicating the non-random scattering of wheat fields up to distances of 21,000 meters. The results of this research and its output maps can be used to obtain information for agricultural planning as well as the allocation and spatial distribution of resources and facilities.

Plant transpiration is a process through which part of the water in the plant is transfered out of the pores. The amount of plant transpiration data is useful in developing strategies for water sustainability. Field measurement of plant transpiration is pointwise and discontinious and has some difficulties. The purpose of this study is to provide a plant transpiration map using satellite images and spatial modeling to identify the impact of environmental variables on transpiration in western Iran. First, by using the algorithm written in Google Earth cloud system, the plant vegetation translocation map was extracted as a dependent variable, and then the layers of solar radiation, water vapor pressure, wind speed and maximum temperature, vegetation index were selected as independent variables for modeling. The results showed that the prevalence of plant transpiration in the studied area range from 0 to 2.6 mm per day. Field data collected from 16 typical farms of Agricultural Research Center of Kermanshah and Kurdistan Provinces were used for validation and by comparing the map pixels and the ground data, the root mean square error and the Nash Sutcliffe coefficient were obtained to be 0.71 and 0.63, respectively. After implementing general regression and spatial regression models based on evaluation criteria, the spatial regression showed better explanatory and estimation power than the general regression. Based on this model, the coefficients of each variable were estimated spatially, making it possible to determine the spatial variation of the relationships between the variables. Also, the results of both models showed that the vegetation indices and water vapor pressure deficiency in western of Iran have the most positive effect on vegetation transpiration. Using the results of this study, areas prone to severe plant transpiration can be identified for improving the management of irrigation systems and providing intelligent agricultural services.

Occurrence of extreme rainfalls specifically in short-time scales causes heavy damages to human communities, municipal crowd regions and natural ecosystems. Studying and precise identifying of extreme rainfalls is essential and crucial in different agriculture and natural resources, meteorology and hydrology, engineering and natural environment aspects. The damage intensity of extreme rainfalls does not equally act in different regions and it is essential to assess the risk extent of such hazardeous rainfalls in regions with different climatic conditions. Therefore, the aim of this study is to identify and zoning of extreme rainfall occurrence risk in different 6, 12 and 24-hour time scales for West parts of Iran, which has a noticeable diversity in terms of the climatic and topographic conditions. to perform this research, a number of 27 synoptic stations located in five provinces including Kurdistan, Kermanshah, Hamedan, Ilam and Lorestan were selected and the maximum annual values of extreme rainfalls in three time scales of 6, 12 and 24-hours in a 25 year time period (1992-2016) were extracted and by fitting different statistically distributions to each of these time series and by adopting the Chi-square test, the statistical distributions with best fit were regoized and were used to performing propabilitistic analyses. The 30, 40 and 50 mm thresholds were used to defining the torrential extreme rainfalls in 6, 12 and 24-hours time scales, respectively and after calculating the risk extents of the mentioned torrential extreme rainfalls for all of the studied stations, the risk zoning of torrential extreme rainfalls occurrence was performed by applying the multiple linear regression models between the risk extents and geographical properties (longitude, latitude and elevation) for all of the studied region. To enhancing the models accuracy, the long-term average of the number of days per year with precipitation greater than 1 mm was employed in the structure of the regression models as auxiliary variable in some cases and to achieve higher accuracy of regression models, the studied region was divided into three distinct regions. The results showed that among different fitted statistical distributions to the time series of extreme rainfalls of 6, 12 and 24-hours in the studied region, three distributions including Log-Logistic, Pearson and Gama were recognized as the best fit distributions. In terms of the accuracy of the multiple linear regression models, the results showed the high accuracy of these models for all of the three distinct regions and whole of three time scales of 6, 12 and 24-hours. The overall results of this research showed that the risk occurrence of the torrential extreme rainfalls in West of Iran has a notable diversity so that this risk is very low in some centarl parts and very high in some west and south parts of the studied region. The overall results of this research revealed that the general applied algorithm of this research to estimating spatial distribution of torrential extreme rainfalls ocuurence risk was led to obtaining the appropriate and acceptable accuracy in regional estimating and generalizing the stational point results to the regional scale. Therefore, it is essential to adopt appropriate tasks and more attention in contrast to the negative consequences of the extreme rainfalls in the parts with higher degree of risk occurence.