جستجوی مقالات مرتبط با کلیدواژه « Climatic Factors » در نشریات گروه « جغرافیا »

Although nowadays developing small and village based husbandry in Iran seems irrefutable, there are no enough considerations around this topic. This lack of consideration has been caused by focusing on only industrial husbandries. The aim of this research is to study the situation of animal husbandries and its buildings in rural area of MAZANDARAN especially in DAZMIRKANDEH village one of the sari's Urban Area. In this research data has been gathered by questioners and three hypothesis have been assumed for this research. The data has been analyzed with both two descriptive and inferential statistical methods with the using of SPSS software and independence characteristic exam. After accepting the hypothesis it can be conclude that designing suitable buildings based on environments climate, space standards, water usage analysis with using indigenous material and considering sanitary factors can help developing and expansion of rural husbandry and its functionality.

This study has been compiled with the aim of evaluating and measuring climatic conditions and factors in tourism processes with emphasis on tourist satisfaction, along with climate data and tourism climate indicators, to evaluate the results of these indicators, due to natural and human differences between destinations. Tourism and special characteristics of tourists, changes in the process of assessing the climate for tourists, apply and provide new results. For this purpose, Bandar Chabahar city in Iran was selected as a case study for this evaluation. Chabahar port is considered by domestic and international tourists due to its important political, economic and tourism situation and special climatic conditions. To achieve this goal, the Comfort Climate or Tourism Climate Indicators TCI, PMV and CIT were selected for combined evaluation over a period of 30 years (1985-2014). The results showed that according to the CIT index, all months and periods of time in a day and night in the port of Chabahar, is desirable or acceptable for tourists, which was a little far from reality. In assessing the climatic conditions, this study By applying minor changes in the CIT index and combining the results of the PMV index with the level of tourists' satisfaction with different thermal sensation conditions, different results were achieved, which showed the greater impact of field data on expert opinions. Due to the different attitudes of tourists towards the residents of destinations and their desire to gain specific tourism experiences, the desirability of the climate for tourists may

The consideration of citizens' color preferences in urban spaces is one of the factors that can lead to satisfaction with urban space. Various factors such as age, culture, and gender have been investigated in studies on the color references of citizens, but the impact of climate change on the color preferences of individuals has less been investigated. The aim of this study is to determine the effect of thermal comfort parameters (temperature, humidity, wind speed, and thermal comfort) on the color preferences of individuals by choosing cards and urban couches based on warm and cold colors. For this purpose, after studying the literature and extracting the relevant variables and based on the field method, the climate parameters were measured using thermometer, humidity meter and wind tunnel, and the thermal comfort and color preferences were assessed using questionnaire. To measure the color preferences of people, two colored couches were made and colored cards were used in warm and cold colors. Using Pearson correlation method, it was found that in conditions of p<0.005 there is a significant relationship between temperature, relative humidity, wind speed, heat sensation and warm or cold colors preferences in the colored cards and couches. The thermal sensitivity variable with correlation coefficient r = 0.139 had the highest correlation with the warm and cold colors in urban couches and relative humidity with a correlation coefficient r =0.302. The highest correlation was obtained with warm and cold colors in the colored cards. The results of this study, along with other studies, can be a good guide to choosing colors in different climates.

We witness cities high risk taking in recent years as a result of climate changes. Climate changes result in higher temperatures, rising sea levels, severe storms and increased droughts. However, reduced risk and vulnerability were ignored.  The research method is survey and its logic is inductive and its most important advantage is interoperability of the obtained results. The SPSS and Lisrel Software are used for data analysis and statistical tests and Arc GIS software were used in order to display spatial maps. In this study, spatial management institutions variable including institutions and stakeholders of Bonab city and surrounding villages management was considered as dependent and endogenous variable was measured and analyzed in thirteen dimensions based on Hyogo vision and Framework. The reduced risks of climate fluctuations were measured and analyzed in six dimensions in order to increase the region's resiliency as exogenous variable. Output calculations based on single-sample t-test (T) indicated that urban- regional management principles were unfavorable in the study area and generally in spatial management dimension. The casual network that is determined by structural equations is 2.32 based on X2 / DF value and since the obtained value is smaller than 3, it shows that the model fitness is appropriate. The root mean square error of approximation (RMSEA) must be less than 0.08 that is equal to 0.063 in the proposed model. GFI (goodness of fit index) and (adjusted goodness of fit index) AGFI indices are equal to 0.91 and 0.90 respectively that must be greater than 0.80. The results of five indices are presented including increased comparative fit, relative fitness, Bentle Bonett normative fit, and Tucker-Lewis fit and comparative fit index based on data goodness of fit and structural model.

CClimate-friendly architecture can be considered the closest structural design with a resilient economy approach in which, while human comfort and convenience by replacing clean and natural energy sources, a healthy environment with a high degree of comfort, saving energy consumption and consequently reducing Created dependency. Also, recognizing climate design methods and updating it can bring a favorable and appropriate environment in the residential space and in accordance with comfort indicators, extract appropriate architectural solutions and present it as the principles of climate design. In this article, the comfort climate of Semnan city has been analyzed using Mahani and Giuoni methods. For this purpose, the statistics of the Meteorological Organization in an 81-year period (from 0222 to 0282) were used. The purpose of this article is to study the climatic conditions of Semnan city using Mahani thermal comfort indices, and consequently Giuoni to study appropriate solutions and proposals in the field of architecture and energy compatible with the climate. In case of implementing the architectural rules obtained from the criteria for determining thermal comfort, the duration of using mechanical heating and cooling devices inside the buildings of Semnan city will be significantly reduced and in the direction of moving towards zero energy in the urban environment of dry and cold climate. will be.

The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental.  The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1  of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level  in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.

Vegetation is one of the essential factors in structure and function of terrestrial ecosystem and it is one of the principal loops in water-soil-plant-atmosphere continuum. Several studies have demonstrated that vegetation covers are sensitive to alteration of climatic, edaphic, topographic and human activities. Thus, alteration in vegetation and its relation with the mentioned factors are important of high importance. In order to investigation of vegetation changes and its effective factors, the current study was conducted in Kharestan region placed in Fars province, Iran. In this regard, the images obtained from ETM Landsat 7 (2000-2017) and meteorological data gained from local and 17 regional meteorological stations were used. Using these images, temporal and spatial changes NDVI and NDVI anomaly were studied. A supervised classification method was used to extract land use map. Finally, the relationship of NDVI with climatic, topographic and anthropogenic factors was investigated. Relationship between NDVI and climatic and topographic factors was estimated using GWR and OLS methods, respectively. Generally, temporal variations showed a slow increasing trend in NDVI value. NDVI anomaly was mostly negative before 2008 but it turned to positive after 2009. NDVI spatial distribution showed an increasing tendency from north toward center and continued to south-west of the study area. The study shows that the vegetation cover change was caused by both natural factors and human activities. NDVI increased in agricultural and pasture lands. Also, natural vegetation has been affected by climatic factors more than irrigated vegetation (agricultural and gardens). Furthermore, vegetation variation influenced by topographic factors likes height, slope and aspect. Also, with an altitude over than 2500 m, NDVI showed a decreasing trend, on slopes lower than 5° it increased. NDVI values in north and east directions were higher than in southern aspects. The overall trend indicates an increase in temperature and a decrease in precipitation during the study period. The maximum positive and negative correlation between mean annual precipitation and NDVI using ordinary least squares method were 0.93 and 0.83, respectively. Also the maximum negative and positive correlation between NDVI and temperature were 0.65 and 0.5, respectively. The highest local R2 values between NDVI with precipitation and temperature were 0.45 and 0.44, respectively, which was observed in the central parts of the region. According to the obtained results through the present study, it can be stated that environmental factors like precipitation, altitude, slope and aspect are the Influential factors controlling vegetation in Kharestan (Fars province, Iran).

Nowadays, human bioclimatic studies are base of regional construction planning, especially in field of settlements and urban issues, architecture and tourism. This project evaluates temperature maximum and minimum by using of tourism climate index (TCI), climatic factors affecting on climatic conditions for tourism activities in Khouzestan. This research is an applied research that is done analytical- descriptive method and in the research intended indicators are taken account for 12 synoptic stations in Khouzestan during statistics period (1993-2012). Then, results are entered to GIS setting and tourism climate is zoned for Khouzestan seasonal and finally, tourism calendar is designed for the province. In this research, obtained results show that Khouzestan climate potentials are more desirable in cold months for tourism and these conditions are undesirable as we approach to the warmer seasons and there is not comfort, in the other words, the best bioclimatic conditions is winter and autumn in this state and warm seasons are undesirable due to temperature and relative humidity high changes. In the spring, microclimatic conditions can be created by to build hotels, motels and camps according to climate for tourists feel peace. But, in the summer there are tourism downturn and unfair climate conditions due to climatic drastic changes.

IntroductionRegarding the importance of the air, it needs to be mentioned that a human being is able to survive some days without food and water yet some seconds without air; thus, the quality of the air we breathe plays an important role in human health which affects all of his activities. In order to inform people of air quality daily and continuously, the Air Quality Index (AQI) is used; this is used to predict daily air quality and helps the society to comprehend the meaning of the effect of local air quality on health. The index classified into six groups indicates whether the air is clean or polluted as well as its relevance to human health.
Theoretical FrameworkNatural and human factors are the most effective factors in Tehran's air pollution; natural-climatic factors, such as wind and rain, may improve air quality and make it clean. The most important sources of pollution in Tehran are movable resources, or automobiles, whose reduction of activity on holidays could be a reason for air clarity. Regarding the importance of enjoying clean air and its role in regional planning of sensitive tasks prone to contamination, this study surveys the event daily, monthly, and yearly to identify the causes of the occurrence of the days with clean air quality and pave the way to predict the event. Owing to the abundance of the occurrence of polluted days and its harmful damage on all aspects of human lives as well as the environment, most studies related to Tehran air pollution come in different titles. Nevertheless, the reasons for the occurrence of the days with clean air have not been studied so as to predict the occurrence.
MethodologyDaily data related to air quality index were obtained from Tehran Air Quality Control Company as well as the climatic data related to downpour, wind direction, and the speed of Meteorological Organization in a 16-year period whose indicator value was between 0 to 50; these days were separated and selected as days with clean air. Thus, monthly and yearly distribution of days with clean air was extracted. The factors were selected and coded based on three factors of holidays, wind direction and speed, and downpour.
Code 1: if wind speed is more than 10 meter/second (m/s)
Code 2: if wind direction is the West and Northwest
Code 3: if downpour happened the same day (a day with clean air)
Code 4: if there is no downpour the same day yet it happened a day before
Code 5: if there is downpour the same day yet no downpour happened the previous day
Code 6: no downpour happened the same day or the previous one
Code 7: if downpour happened both the same day and the previous one
Code 8: if wind speed was more than 15 m/s the previous day and its direction is from the West or Northwest
Code 9: if the same day is a holiday
Code 10: if the previous day was a holiday
Code 11: if both the same day and the previous one were holidays
Code 12: if neither the same day nor the previous one were holidays
Code 13: if the day with clean air or the previous day was a holiday
After extracting 13 features according to wind, downpour, and holidays, the data were ready to enter the analysis stage; they were divided into two groups of individual data, i.e., wind, downpour, and holidays each presented separately, and combined data, i.e., wind and rain, wind and holidays, rain and wind and holidays, and rain and holidays, so that the effective factors on the event of clean air could be recognized in the order of priority and the possibility to predict a day with clean air in Tehran was determined.
Results and DiscussionsNatural factors, downpour and wind, as well as human factors are taken as the reasons for the event of clean air. Automobiles are the most important sources of pollution, which constantly transfer pollutants inside the atmosphere every day. Hence, it could be claimed that dominant specific synoptic modes determine air clarity. Studying the days with clean air reveals the fact that wind and rain are effective in improving air quality, however, they are unable to change the conditions into the clean air and there is a possibility for other factors with more influence. Due to severe pollution of the city, one factor cannot merely eradicate massive amount of pollution and lead the air to reach the standard of clean air index. Studying the air quality of the days with clean air shows that a mixture of factors was able to aid cleaning the air.
Conclusions and SuggestionsThe annual distribution of abundance of the days with clean air shows that the maximum number belongs to 2006 with 36 days while the minimum number goes back to 2000 with 1 day. Monthly distribution shows the maximum abundance in April with the most official and non-official holidays, after which are the months with maximum atmospheric instability. All features of the days with clean air are presented in an algorithm based on three factors of downpour, wind direction, and speed as well as holidays, which are divided into 13 codes; the results showed that in 52% of the cases with clean air and 22% of the cases with a one-day delay, downpour could be effective in air clarity; wind speed in 41.5% of the cases was more than 10 m/s and its direction in 72% of the cases were the West and Northwest; the days with clean air in 53% of the cases happened in holidays while in another 51% it happened when the previous day was a holiday and clarity happened with one day delay. Compared to two other factors, in 72% of the cases the days with clean air happened when the day, itself, or the day before was a holiday. The event of the day with clean air in a number of cases depended on one factor or multiple factors, simultaneously. The results showed that the factor of holiday, among other individual ones, was the primary one with the highest percentage. The factor of a holiday with downpour, among other combined factors, wined the biggest percentage. Eventually, based on individual and combined factors, it could be concluded that the most important factor in the event of the days with clean air is the factor of holiday in spite of the common belief naming wind and downpour as the most highlighted ones.

Cover vegetation is much related to climatic conditions. Recognition of seasonal variability the vegetation growth is important to determine the ecosystems response to climate change in seasonal and interannual scales. In this research, to produce the predicted model, we used from climatic factors (precipitation, temperature and relative humidity (maximum, mean, minimum) during 20 years (1987-2006), and these data (141 weather stations) interpolated. So, calculate monthly the maximum value composite of NDVI from NOAA-AVHRR images that same during. Then, have computed Multivariate Least Squares regression from climatic factors (independent variables) and NDVI (dependent variable). The result show, most correlation between climatic factors and NDVI is about of 0.82 and occurring in May, that is highest growth. The lowest correlation is occurring in winters, because is not growing. The annual correlation in calculated model is more than 0.93 with inter the accidental errors. Totally, the calculated NDVI for May and Jun in during 2004-2005 years is close to predicted model, but in winters are distances from together. For the severe drought are lower predicted in 2006 at spring (Jun). In winter the role of temperature is more than precipitation and relative humidity in predicted model, but in earlier of May the role of precipitation and relative humidity is positive and temperature is negative, because is increasing temperature and decrease of precipitation and relative humidity. In autumn, is increasing the role of temperature and decrease the role of precipitation in predicted model.

Although solar energy is one of the important renewable, free and clean energy sources, constant exposure to the sun, especially UV radiation, can lead to some problems. These problems include skin diseases, loss of pregnancy, permanent or temporary infertility, and cataracts (Strom, 2003), weak immunity system, and an increased risk of diseases especially in children (Hoyos-Bachiloglu, 2014). Cofounding variables such as age, gender, history of diabetes and also, the location of city increase the risk of cataract by 33% (Nakashima, 2010). Exposure to different qualities of radiation can result in premature diseases impairing the diagnostic power of eye lens cells (Blakely, 2010). Increased sensitivity to visible light could be determined by the total accumulated dose of radiation during activities (Tremsin, 2001). Many studies have shown that both PSC and cortical opacities are associated with radiation exposure (Blakely et al.,2010). Cortical cataract has a stronger correlation with exposure to UVR than PSC Cataract (Kanamoto, 2010). Chronic exposure to UVR in an interval of three days can lead to promotion of the vulnerability to the eye, but if this time is increased to 30 days, the risk of eye injury is decreased due to the repair of the damaged part (Midelfart, 2010). PSC and cortical cataract have a stronger correlation with a low dose of space radiation in astronauts, in comparison with the nuclear cataract (Chylack, 2010). Visual impairment diseases are prevalent in Chaharmahal and Bakhtiari province. There are 1912 people with various forms of eye diseases. Among these, 900 live in rural areas. This study aimed to study the effect of physiographic features and climatic factors on the prevalence of blindness and visual impairment in some rural regions. Study Area: The villages of Chaharmahal and Bakhtiari province.
Material and In this study, three sets of data were used: 1. Data on the amount of emission radiation and other climatic factors such as sunshine in all stations.
2. The data on visual impairment in 189 rural areas.
3. Digital File DEM map of Chaharmahal and Bakhtiari province in the scale of 1:25000 was used to estimate the basis of measurement and the slope in the village's location and climatic parameters.
As the amount of radiation in village location depends on such morphological factors as slope and its orientation and the location of the village, the radiation of the villages was estimated based on village morphological characteristics, using Duffy and Beckman's (1980) method. Because the slope has a significant impact on the intensity of the radiation received at the earth's surface, the amount of it and the village's slope were calculated based on the topographic map of the province. The villages slope was calculated by the raster map of digital elevation model (DEM) in the scale of 1:25000.
There were 900 blind people in the rural centers, with 547 cases in 57 villages. The percentage in 57 villages was 60.7%, including 30.2% percent of total villages. The highest number was in the south, south east and south west with 256 patients (46.8%) and 45.6% of villages, west and east, with 141 patients (25.8%), and the north, north west and north east, with 150 cases (27.4%), respectively (table 1). The correlation between the slope and the rate of blindness, with R=0.373, was significant. The correlations between slope azimuth to the south and the amount of radiation on slope, and the percentage of increase in the amount of radiation and the slope were R=0.820 and R=0.795, respectively. The village of Sar Aqa Seyyed with high blindness (13.2 in thousands) had a great slope. Sheikh Ali Khan with the highest rate of blindness (49.02 in thousands) also had a southwest slope, which was 14.6 degrees.
Evaluation of the elevation of the villages showed that all these 57 villages were at altitudes above 1170 meters, and the village of Sheikh Ali Khan with the highest rate of blindness was located at an altitude of 2499 meters above sea level. Of the 57 villages, 30 villages with 333 cases of blindness (60%) were at an altitude higher than 2000 meters, 20 villages with 173 cases (31.2 percent) were at an altitude of 1,500 to 2,000 meters, and just 7 villages with 49 cases (8/8 percent) were at an altitude of 1500-1170 meters. In villages with a greater slope, the difference between the red and blue lines was greater. The highest percentage of increase in intensity was in the slopes of the south and south west and south east. Generally, the amount of radiation on the slope level was increased by 3.5 percent in all villages. The correlation coefficient between the amount of radiation on the slope level and the percentage of the increase of radiation in the slope level was 0.892.
Solar radiation increases the epidemic risk of eye diseases such as cataracts. Mountain and high altitudes of Chaharmahal and Bakhtiari province villages made up the sunshine on slopes facing south, south west and the south east. Our study showed that 45.9% of the rural population and 42.1% of villages with a high risk of cataract were in the facing slopes to south and south west and south east. There was a significant relationship between the slope and slope orientation to the south and the rate of blindness. Therefore, by increasing the slope and changing slope to the south, the number of cases of blindness was raised in the villages. There was also a significant association between increased height and the increase in the number of villages with blindness and more cases of blindness (60%) lived at an altitude higher than 2,000 meters. Because of rise in the highland villages, the height of the atmosphere would be shorter with thinner radiation. There can be, therefore, environmental changes to improve the villages’ microclimatic conditions and reduce the risk of eye disease. For example, by increasing the relative humidity, the sun intensity could be reduced. Environmental changes can improve the village microclimatic conditions and reduce the risk of eye disease. Thus, by increasing the relative humidity, the air temperature would be reduced; the brightness and intensity of the sun could be reduced and hence, the risk of eye disease would be decreased. Better decisions can be them made to change the location or reform the physiographic features of villages needs based on further studies.

Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is monitoring and predicting dust storms in south and south-east of Iran. For this purpose 92 Images of MODIS sensor as well as weather data of 18 stations are used. Dusty days (originating in outside and around the station) were extracted. After monthly and annually monitoring of storms, in order to predicting the frequency of dust storms based on spatial regression, climatic factors and NDVI are used. The results show that the number of storm are high in the beginning year and is decreasing in Jun and July. More than 78 percent of dust storms are of near station type. Spatial regression equations could predict amount of storms. Based on the origin of dust storms in this study combating desertification and wind erosion program could reduce frequency of this storms.

In this study, the parameters for locating favorable climate and land cultivated olive tree that in clues elements of total annual precipitation, The minimum temperature of the coldest month (January) with a duration period of 23years (2008-1986) in the land of the parameters slope, aspect and elevation extraction from digital elevation models for Slope and elevation extraction from digital elevation model was used for the study. To examine the elements of the11stationswithin and adjacent toStation4was used as the support for climate zoning. (Climate-land) olive cultivation, classification of information layers and the weight of each zone was calculated. Finally, on cross-layer information sharing and value-weighted classification method. By sub serve the final map of the potential climate-land suitable for olive cultivation in the province were taken show. Final results from this study indicate the fact that the elements of each of the five elements of olive cultivation varied climate and annual rainfall, degree-days of growth, annual temperature, minimum temperature of the coldest month of the year (January) and relative weighting of scientific expertise and resources in a more effective contribution to the cultivation of olive trees and the ability to show more. Also, the weighting adjustment layers with each layer allowing for the importance of culture in the process of GIS, to identify are as favorable for the cultivation of this valuable tree in the garden has been detected.

Vegetation and environmental factors have close relationships; also they affect each other in the rangeland ecosystems. This study was carried out to investigate creation of plant species spatial prediction map based on environmental factors that affect plant species. Prediction of vegetation spatial distribution across the landscape based on spatial distributions of environmental variables affecting vegetation is defined as modeling of vegetation prediction. Environmental variables maps should be available or create vegetation map to be practical and useful mapping prediction of vegetation. These prediction maps are used for biodiversity conservation, ecological restoration and assessment of impacts of environmental changes on the distribution of vegetation. To increase the accuracy of analysis, researchers have to limit the number of variables in study relationships between the environmental variables and plant species. Relationship between plant species and soil characteristics, climate and topographical factors and their effects on the distribution of plant species using logistic regression were discussed in the study area. This study was carried out in Reineh rangelands on the southern slope of Damavand Mountain. Sampling method was equally randomized classification. 37 study homogenous units were created with the classification of study area based on slope, aspect and elevation. 10 plots were located randomly in each homogenous unit and also 2 soil samples were taken from 0-30 cm depth. Presence or absences of dominant species were noted in each plot. Some of environmental factors including 16 soil characteristics, 3 topographic and 16 climatic factors were selected. IRS satellite imagery were used as auxiliary data. Logistic regression method was used to determine the effective environmental factors on species and model dominant species. Environmental factors maps of affecting plant species were prepared in GIS environment. Logistic regression model of dominant species was applied on the maps of affecting factors on species in GIS environment and prediction map of the presence and absence of species was created. Optimum thresholds for classification of presence and absence of species were determined from sensitivity, specificity and overall accuracy graph. Accuracy of created maps was assessed with calculation of the error matrix for sample point’s map as ground truth map and created map using logistic regression model. Eight species of 107 species and perennial grasses were identified as the dominant species in the study area and prediction map using logistic regression model were created for them. Results indicated that the produced models had high accuracy except model of Astragalus ochrodeucus (with accuracy 80 percent). Only near-infrared band was entered to logistic model of this species. All of the entered variables into logistic models were from all of soil, climate, topography factors and satellite imagery. Soil properties including pH, percent of clay, silt and sand, nitrogen, wilting point, bulk density, true density, phosphorus and soil water storage capability (10 from 16 studied soil properties) were effective on the distribution of species. Slope and climate factors including the number of frost days during the year, the average minimum temperatures in spring, the average rainfall in spring and mean daily temperature in summer season were identified as factors affecting distribution plant species. Since the maps of climate factors were created by interpolation than elevations, then influence of these factors on distribution of species show influence of the height indirectly Only near-infrared band was entered to logistic model of this species. All of the entered variables into logistic models were from all of soil, climate, topography factors and satellite imagery. Soil properties including pH, percent of clay, silt and sand, nitrogen, wilting point, bulk density, true density, phosphorus and soil water storage capability (10 from 16 studied soil properties) were effective on the distribution of species. Slope and climate factors including the number of frost days during the year, the average minimum temperatures in spring, the average rainfall in spring and mean daily temperature in summer season were identified as factors affecting distribution plant species. Since the maps of climate factors were created by interpolation than elevations, then influence of these factors on distribution of species show influence of the height indirectly The relationships among vegetation and some of environmental factors including soil texture and structure, soil fertility, soil moisture, climatic and topographic factors were confirmed in the study area. A hypothesis of this research on determination of spatial pattern of plant species distribution based on environmental variables has been proved. Results of this study may be used for management purposes in rangeland ecosystems, sustainable development, conservation, restoration, monitoring and evaluation in the study area and similar regions.