فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 84 (تابستان 1392)

IntroductionSustainable management requires knowledge and a good understanding about processes of horeline changes. Shoreline change detection can ensure health of beach areas. The study area s located in the coastline of the Caspian Sea in an approximate length about 15 km in Sefidrud elta. Delta Sefidrud have been formed in different periods that experienced symmetrical or emi-symmetric and asymmetric morphologies on the shoreline. General currents of the Caspian ea have a west to east direction. Sea dynamics in the direction and sediment movement have he greatest impact on the shoreline. Hence, the shoreline should always be oriented towards the stuary of East River Delta, while aerial photography and satellite imagery shows something ontrary. Thus, in addition of waves and the coastal currents dynamics no changes have ccurred along this direction during the Holocene, so, some other factors may contribute to orming of the delta with periodical differences in geometry. Therefore, contribution of these actors should be characterized in the present curvature of the shoreline. The purpose of this tudy is to evaluate variables influencing rapid changes and dynamics of coastline over the last ixty years.MethodologyTo achieve the purpose of this research, some data including sediment discharge from rivers, ind and sea level change statistics, aerial photos, topographic maps and satellite images in everal times have been used as the material of the research. Methodology of the work is based n transects in three separate portions. To do this, after atmospheric and geometric corrections f Landsat images and aerial photos in ENVI software, the images were processed for better isualization and interpretations. The images, then, have been imported into ArcGIS 9.3 oftware. The shoreline positions have been separately extracted from each of the images as istinct layer files. Given longitudinal and transverse movement of the shoreline, Kiashahr main oad position, without any changes during this period, has been taken as constant milestone in a eparate layer to measure rate of progress and retreat in the shoreline. The changes have been alculated relative to the main road. The entire shoreline is divided into three regions in the elta basal. With overlay of the images on the coastline, erosion analysis and sedimentation in he Sefidrud Delta base during the period 2011-1955, the contributing factors have been easured for each of the three regions of shoreline.Results and DiscussionThe results show that in the first period there has been a decrease in sea level changes. With the isplacement of river along the course from the east to the west of Sefidrud Delta basal, drastic hanges have occurred in the curve of the coastline. The changes have occurred in the two eriods before and after the construction of SefidRood Dam. Shoreline changes in the second eriod with an increasing trend of sea level are dominated by the Shas operations. With creation f transects in this term, it is specified that coastline compared to the previous period has ncreased in the first and second zones and is reduced in the third. With reduction in sea level during the third period, Shas operations have been stopped and the top part of the delta has been roded under the influence of waves. The results indicate that changes in the rate of sediment output from Sefidrud Dam with Shas operations have the greatest impact on the delta basal to account for the rapid changes in shoreline. Aggradation during Shas operations has been recorded with average speed of 26 m per year (1998-1981). This shows a significant difference in acceleration of shoreline change in 19 m per year before the Shas operations and 9 m per year after the operations. Hurricanes can also play an important role in the changing shoreline occasionally. In the time periods, river mouth has more changes within the delta base. More changes have been related to the changes and irregularities in the shape of the coastline. With a fixed location in the mouth for a long time, sand bandwidth will be increased in the delta base. In aerial photos of 1955 and 1966 in addition of satellite images of the 1978, 1987 and 1998, when the changes have occurred in direction and movement from East to West of river estuary delta base, the changes is measured to be very high in the shoreline. It should be noted that in some parts of the shoreline there appears to be some critical points. The addition of new and old river estuary of Sefidrud, tributaries in the river estuary are also clearly visible. These areas as critical points can be viewed with a special look.

Introduction In climate, Iran is a part of the Afro-Asian belt of deserts. This climate is almost rainless and has very arid climatic condition. The desert soils are mainly covered with sand and pebbles. These materials are largely carried by the wind. Dasht-e Kavir is a large desert lying in the middle of the Iranian plateau (at longitudes from 58 to 53 and Latitudes from 36). As the desert is surrounded by Alborz and Zagros Mountains, the moisture cannot penetrate into the desert area. In summer, Establishment of the Azores high pressure account for the dry conditions in the Dasht-eKavir. These conditions reduce the total amount of rainfall and the lack of sufficient vegetation in the Desert. Therefore, Wind has a high potential for Erosion, Transport and accumulation of Sediment. As a result, the conditions for the formation of Ergs and Sand sheets are provided in the desert. Furthermore, Dasht-e Kavir was surrounded by various high lands so that they are the most effective factor in the deposition of sand and switching in location of Ergs. The temperature difference in Desert mountain ranges is always a generator for different daily local wind. The local winds can play important role in morphological changes of the Desert Surface.MethodologyFormation of dune areas is determined by the production of sediment by a range of suitable particle sizes, the availability of the sediment for transport by wind, and the transport capacity of the wind. In this Study, satellite images were first obtained to reconnaissance the location of the study area. Thus, the location of highlands and the scattering of Khartouran, Chah Jam, Sargardan And Jen Ergs around the dasht-e Kavir was examined by observation DEM, ETM+, MSS and Google Earth Images through ArcGIS Software. By this, the role of highlands of the concentration and accumulation of Sand is observed. Second, Ergs morphology is detected on the satellite images. Prevailing wind direction was determined based on Ergs Landforms. Prevailing winds around the desert, Wind Rose, was identified via Wind data from meteorological stations by WRPLOT Software. In addition, Uwind and V-wind in Dasht-e Kavir was examined by wind dynamics data from 925 HP leveland PANOPLY Software. Third, the vorticity of summer in Dasht-e Kavir has been Examined in relationship to Ergs morphology by dynamics data 925 HP level via PANOPLY Software. Results and Discussion The results of morphological effects on satellite images and their relationship with local wind regime have illustrated that Wind regime in the Dasht-e Kavir was coincident with Ergs Morphology so that Wind direction and Ergs Morphological, follows from a process of convergence. Sand roses for summer around Dasht-e Kavir have been shown to be in east to west in the half Northing, while in the southern part of the Desert they have been west to eastern winds. Desert U- winds are more oriented to the East-west while the most of V- winds in the desert have a North - South. Vorticity of the desert in summer also represents a trend in the direction of rotation. The Vorticity of the desert in summer also represents a trend in the direction of rotation in Anticlockwise.ConclusionErgs Morphology is corroborated in common systematic morphogenesis in the Dasht-e Kavir. This common Systematic morphogenesis around the desert represents a spatial route, Sothat the morphology of the north- east of Dasht-e Kavir, sand Khartouran, is North East - South West. This trend is changing with the movement to the west so that vary to North – South direction in Chah Jam Erg. These winds continue to Northern Rig-e Jen and in the southern parts of the Rige Jen divert towards North West- South East and West – East direction in Choupanan Town. Concurrent review Vorticity and Erg morphology around the Dasht-e Kavir, represents the interaction between Ergs morphology and weather patterns so that Ergs Morphology and Patterns of weather have a convergent path. Thermal low pressure system is generated in Dashte Kavir in Summer so that direction of rotation in thermal low coincides with the Erg Morphology around Dasht-e Kavir. Therefore, the topography has influenced the situation of Ergs, Vorticity and Wind direction has been effective in Ergs morphology.

IntroductionEcotourism as a shortened term of Ecological Tourism is the possible leisure activities of people in the nature. It is based on purposeful trips for visiting nature and cultural and spiritual perceptions of natural attractions and also for enjoying a variety of natural phenomena (Rezvani, 1380). Generally, visiting nature makes ecotourism different from other kinds of tourisms (Zahedi, 1382). Dynamic nature and a variety of leisure activities available in coastal areas, has made the areas highly favorable for tourists. This has transformed coastal areas into one of the most influential regions for local and national economies in the world. Climate can also affect attractiveness of places for tourists and may have a major role in selection of tourist sites. Based on past evidence, climate can be a key factor in vacation planning and satisfaction of vacation experience so it is known as a central stimulus for vacation planning. One piece of information that tourists need to know for vacation is the climate of destination cities. Most of the tourists take this factor into account for choosing their destination. However, climate has a low presence in tourism literature, while it is highly important in vacation planning of tourists. Considering above-mentioned issues, no comprehensive and detailed work is carried out on standard conditions of swimming in coast areas of Caspian Sea. This paper has studied the subject as the first. MethodologyIn this research we have tried to reveal one of these tourism potentials, that is, swimming activity in the southern coast of the Caspian Sea in Gilan Province. For this purpose the hourly data of temperature, wind speed, relative humidity, and sun shine duration were obtained from the Meteorological Organization of Iran for the period 2005-2008, the period when the sea surface data such as wave height and temperature were available. The data have been analyzed for the stations of Astara, Anzali, and Lahijan. It is worth noting that, a survey from a sample of fifty people of Tehran citizens had a major role in form of the tourism. Tourist activities have been examined in this study and this make it possible to find out the demands of tourists according to the survey. Among the four options of: 1) the beach and the sea, 2) forest and mountains, 3) ancient monuments and culture of the area –customs, and 4) souvenirs, about 46% of the tourists introduced the beach and sea as a main tourist attraction in Gilan. From the four activities of 1) surfing on the beach, 2) swimming, 3) boating, and 4) fishing, the swimming was the activity 50% of the respondents in both cases had the highest demand among all other options. Selection of beach, sea and swimming activity by the tourists helped choose the type of tourism and the specific tourist activity in accordance with the following model. Marine tourism» Beach tourism» Beach sport» Aqua» Swimming According to the data a suitable indicator of environmental conditions was first created for swimming in the lake. This index includes the suitable temperatures for environment, the suitable temperatures for water, maximum wave height that is permitted to swim, maximum wind cooling does not cause discomfort in people, no phenomenon of lightning, and no rain. This index is called the standards of swimming. Then the factors have been taken from abovementioned stations filtering the days when the facts were consisted of suitable days for each station according to the annual, monthly, weekly, and weekend intervals. Referring to this stage it was clear that, which month, which week and which weekend were the best times for swimming activities in each station in the Caspian Sea. Results and DiscussionThe best days have been viewed and compared in two hours of 9:30 am and 15:30 pm at three stations studied during timescales annually, monthly, weekly and for weekends. It was found that the rate of good days for swimming activities in the coast of Gilan at 15:30 pm is more than those in 9:30 am. The results of this study indicated that, in addition to other factors involved in standard conditions for swimming, solar radiation is the most important factor in choosing the appropriate time for swimming ashore. Because it has a direct effect on factors determining the appropriate time of swimming such as air temperature, water temperature, wind speed and cool and etc., the sun is, indeed, neutralizing their negative effects. ConclusionThe results have indicated that the best months for swimming, in order, are August, July, June and September. Astara is the most suitable sea side for swimming during week days and weekends. Anzali and Lahijan are the second and third best places in order.

Introduction One of the main principles in the process of spreading floodwater is use of the water in arid and semi arid areas for an efficient utilization of both the water and the soil. Executing more than one decade research plans on floodwater spreading in the realm of Iran aquifers have proved that the plains and deserts have got a great potentiality in order to supply water and to prevent the irreparable damages of flood and desertification. The first and the most important step inexecuting a floodwater spreading project is a suitable zonation for water spreading and to penetrate it into underground water tables. It is impossible to use Geographical Information Systems (GIS) in order to site select potential zones for floodwater spreading without using Multi-criteria Decision Making system (MCDM). Floodwater spreading plan except to gather water and transfer waste water to nourish the aquifers by the purpose of reducing soil erosion and improving the vegetation is studied with a multi-purpose attitude. One of the most appropriate tools in site selection for certain zones is the application of computerized conceptual models in the Geographic Information System (GIS) environment. Because there are a variety of models in this field, identifying and introducing the best model is one of the most essential actions in executing these operations or plans. We have tried in this research to observe the important factors and criteria such as: geocentric factors (geology, geomorphology and soil), hydrology, geohydrology, slope and physiographic characteristics of basin and also discussing certainty or uncertainty of effective locative data in site selection of the potential zones to spread floodwater. On the other hand we have attempted to identify and introduce the most suitable model in site selection of the potential zones to spread floodwater in the Garabaygan aquifer basin in Fars, Iran. FAHP model and GCA with some of their operators are the selective models in this research.MethodologyStudy area: Garabaygan region in the Fasa is located in 190 Km away from southeast Shiraz in lat. from 28° 41' to 21° 41' N and long. from 53° 53' to 45° 57' E. Also it's located at 1120 to 1160 above sea level.Methodology Firstly in this research we calculated nine effective factors including geomorphology, geology, slope, height, land use, alluvium thickness, drainage density and electrical conductivity in floodwater site selection by using FAHP and GCA models and then we provided and classified the information layers of these nine factors by using Arc GIS 9.3. Considering the weights of every factor and the scores that they have been assigned, we made the final map of zonation based on these models by classifying them into five classes: very unsuitable, unsuitable, average, suitable, very suitable.GCA

The most important function of the theory (GCA) is proposing a modern method to study and survey systems in the uncertainty situation which is based on the gray sequence, creation of a collection of gray numbers provided that values of gray numbers are not known, but the area in which those values lie is given. Gray systems are named after colors of the concerned topics. With the purpose of clarity, in this theory information and data are displayed as indicators of the degree of darkness of the colors (color sequences from white to black). The word "black" is assigned to the information and data which their inner structure and relations are totally unknown and hardly possible to be encoded. GTS is one of the mathematical which helps much in solving problems in the three following situations: 1. Uncertainty 2. Discontinuous data 3. Insufficient data. Results and Discussion In this research we have used nine effective factors including geomorphology, geology, slope, height, land applying, alluvium thickness, drainage density and electrical conductivity in floodwater site selection. In this study some criteria (i.e., geology, slope, drainage density and alluvium thickness,) have maximal effects whereas some others (i.e., elevation, landuse, and geomorphology) have minimal effects. Final map of both methods are supplied in 5 classes from completely suitable to unsuitable. Completely suitable class in FAHP model has an area of 17.101 hectares and in GCA model has an area of 12.195 hectares of total area (7946 hectares) of the province. The table 1 shows theConclusion In this study, FAHP and GCA were used in combinative approach with GIS in order to determinate appropriate areas for flood spreading in Garbaigan plain. The findings show that susceptible regions for flood spreading are in quaternary units like: Qc2, Mm-1, Qb, Qgsc, Qscg, and PLQb. Also according to geomorphology and land uses, cone carters, plains and low density pastures are the totally appropriate zones for flood water spreading. These zones are in correspondence with the location of the Kosar floodwater spreading station. They have the special characteristics for spreading floodwater. On the other hand, according to this, our obtained results is the best reason for choosing the Fuzzy model and Gray System Theory for evaluating the quality of data in comparison with other applied models. Also comparison of finding obtained from this two models show that GCA model is more accurate than FAHP model to find susceptible regions for flood spreading.

IntroductionPrediction of sediment load is used in a wide range of topics to estimate volume of dams, sediment transport in rivers and etc. In recent years, artificial neural network was used in rainfall-runoff modeling, prediction of discharge intensity and estimation of sediment load. Sediments are sources of pollutions such as chemical compounds. The results of the many researches indicated the effectiveness of modeling in hydrological predictions. Jin (2001) used Artificial Neural Network (ANN) method to assess the relationship between discharge and sediment load and stated that the ANN model can achieve better results than the sediment rating curves. Tayfor (2002) used the neural network model in sediment transport and concluded that this model was more predictive than the physical models. In this paper, Neural Fuzzy Inference System (ANFIS) is used as a non-linear model to estimate the suspended sediment load. The comparisons showed that the ANFIS method has achieved better results in predicting the daily suspended sediment load than MLR models and SRC models. Dogan et al (2005) also used Artificial Neural Network model (ANN) and fuzzy logic (FL) to predict monthly suspended sediment load in the Sakarya River Basin in Turkey.Methodology In this study, to determine the amount of suspended sediment load, average daily discharge, rainfall and Gharnghu river basin sediment data (1387 to 1388) have been used as the material. Thus, the above data first have been entered in fuzzy neural models (ANFIS), multivariable regression (MLR) and the sediment rating curve (SRC). Then a comparison between them has been made to determine the ability of each model. Observed data and predicted data replaced with R2 and RMSE and according to these values the best model has been determined.Results and DiscussionThe purpose of the suspended sediment modeling studies is establishing significant relationships between discharge and sediment data. For this purpose several methods have been used. In this paper, daily discharge, current and the previous day rainfalls and suspended sediment load data have been used as the inputs for the model. The amount of sediment has been predicted by the neural fuzzy inference system, multiple regression equations and sediment rating curves. Then, a comparison was made between the results and the ability of each model. The comparisons have showed that the ANFIS model with R2 value about 0.9668 and RMSE about 190 has achieved the best result. Table 2 shows that the ANFIS model performs better than the MLR and SRC models. The ANFIS and MLR models have given better estimates of the maximum sediment load than the SRC model. The ANFIS, MLR and SRC models have predicted the maximum amount of the sediment load up to 6549, 5982 and 5329, respectively. These values have been estimated 11, 19, and 28% lower than the observed value. ANFIS models in comparison with the MLR and SRC models have high potential in establishing relationship between discharge and suspended sediment load. Sediment rating curve models establish the linear regression relations between the logarithm of the sediment and discharge values. Thus, these models require a normal distribution of the data and this is one of the main weaknesses of the models. The main characteristic of the ANFIS model is its flexibility and ability in making nonlinear relationships.Conclusionsediment load. The inputs of these models are rainfall, discharge and sediment data. In the first part of this research, regression equations have been set between discharge and rainfall data. In the second stage, discharge, rainfall and sediment variables set as the ANFIS model inputs and have been used in estimating suspended sediment load. Then in the third phase, the ANFIS model is compared with SRC and MLR models. The value about 0.9668 has been obtained for ANFIS model by using R2 factor and it shows that the ANFIS model has better performance than the other models. Besides, the MLR model has achieved better results than the SRC model. To estimate suspended sediment load in SRC model, the discharge factor has been applied. Conducted researches indicate that rainfall and sediment data must also be used beside discharge data. The main advantage of the ANFIS model relative to other models is their capabilities in modeling nonlinear relationships. Overall, the ANFIS model achieves better results than other models.

IntroductionDrought is a temporary and recurring meteorological event which results from the lack of precipitation over an unusual extended period of time. Early indication of possible droughts can help set out drought mitigation strategies and measures, in advance. Therefore, the drought forecasting plays an important role in the planning and management of water resource systems. Stochastic models have been extensively used for forecasting hydrologic variables such as annual and monthly stream flow, precipitation, and etc. in the past. But they are basically linear models assuming that data are stationary, and have a limited ability to capture non-stationarities and nonlinearities in the hydrologic data. However, it is necessary to consider alternative models when nonlinearity and non-stationarity play a significant role in the forecasting. In the recent decades, artificial neural networks have shown great ability in modeling and forecasting nonlinear and non-stationary time series due to their innate nonlinear property and flexibility for modeling. The aim of this study is to compare the stochastic and artificial neural network models in forecasting the standardized precipitation index (SPI) in some stations of Iran. This is because of the multiplicity of drought occurrences in Iran and the necessity to determine the best forecasting model.MethodologyThe monthly total precipitation data (1973-2007) related to four synoptic stations of Iran including Bandar Anzali (with very wet climate), Hamedan Nojeh (with semi arid climate), and Bushehr (with arid climate) and Zahedan (with hyper arid climate) have been used after the homogeneity and adequacy of data have been confirmed by statistical tests. In the present study standardized precipitation index (SPI) time series (at 3-, 6- and 12- month timescales) have been calculated for the period of 1973-2007. The most suitable distribution function for precipitation at 3-, 6- and 12- month timescales has been determined by Easyfit software on the basis of kolmogorov-Smirnov statistic. This is performed separately for each month. Then, each cumulative probability density function is transformed into a cumulative standardized normal distribution. The SPI values for the period of 1973-2000 are used to calibrate the models and the rest of the data to be tested. Development of stochastic model consists of three stages of identification, estimation, and diagnostic checking (Box and Jenkins, 1976, 19). During the identification stage the candidate forms of the models are determined using the autoregressive function (ACF) and partial autoregressive function (PACF) and general forms of the models are determined on the basis of Schwarz Bayesian information criterion (Schwartz, 1978, 461–464) and Akaike information criterion (Akaike, 1974, 716–723). In the estimation stage the model parameters were calculated using Minitab14 software. Finally, diagnostic checks of the model are performed using kolmogorov-Smirnov (K-S) and Portmanteau test (Makridakis et al., 2003, 185) to reveal possible model inadequacies and to assist in selecting the best model. In the present paper two different approaches of neural networks including recursive multistep neural network approach (RMSNN) and direct multi-step neural network approach (DMSNN) are used for forecasting several time steps ahead. The RMSNN approach based on one output node forecasts a single step ahead, and the network is applied recursively, using the previous predictions as inputs for the subsequent forecasts. DMSNN is based on the multiple outputs, when several nodes are included in the output layer, and each output node represents one time step to be forecasted. The models are evaluated with statistical tests, correlation coefficient, and error index for 1- to 12-lead time ahead forecasting over the period of 2001- 2007. Also, the capability of the models in forecasting the SPI classes is investigated using Cohen’s Kappa statistic (Cohen, 1960, 37–46).Results and DiscussionThe results of stochastic modeling of SPI time series showed that the null hypothesis related to the normality of residuals is accepted for 3- and 6- month time scales but rejected for 12-month time scales at 1% significant level in all stations. The results of Portmanteau test signify that the chosen stochastic models are adequate on the available data at 1% significant level. The results of artificial neural networks (RMSNN and DMSNN) modeling of each SPI time series are presented as optimal architectures of the best number of input and hidden neurons. The significance lead times of drought forecasting are determined based on correlation coefficient and Kappa statistic between the observed and forecasted values of the SPI time series in the stations of interest. Accordingly, the most appropriate models for SPI values and classes have been determined by a comparison of three models for each time series.ConclusionThe results have revealed that generally, for 3-, 6- and 12-month time scales, stochastic models (with average error of 0.678, 0569 and 0.344 and average correlation coefficient of 0.682, 0.777 and 0.919, respectively) are more accurate than artificial neural network models to forecast SPI values. The comparison of models in forecasting SPI classes also showed that the most accurate model for forecasting SPI classes for 3-, 6- and 12-month time scales is DMSNN, RMSNN and stochastic model (with average Kappa of 0.397, 0530 and 0.750) in sequence.

IntroductionChemical concentration of plants is indicator of their biologic status. Among the many foliar chemicals in plants, nitrogen (N) is an important indicator of photosynthetic rate and overall nutritional status. Plants usually take up nitrogen in the nitrate form (NO3 -) and one major source of nitrate leaching is fertilizer applied to the crops. Supplying inadequate N may decrease crop yields and increase the N fertilizer (more than the needs of plants). In addition to economic loss, nitrate ions may move into surface and ground water and contribute to eutrophication of lakes and streams and raise health problem (Liaghat and Balasundram 2010). Thus estimation of nitrogen content is important in many agricultural studies. Traditionally leaf nitrogen content is measured in the lab using different chemical methods. Nitrogen analysis either by the Kjeldahl or Dumas method is expensive and requires specialized equipments. An alternative method for N determination is the digestion of potassium persulfate (K2S2O8). Persulfate digestion requires only a modest initial investment and has few environmental risks. The common problems of all above mentioned approaches are the facts that they are time consuming, expensive and destructive approaches. The advent of remote sensing has proved its usefulness as an alternative measure to these traditional approaches. The aim of this study is to estimate canopy nitrogen content in vast area in northern part of Iran, Gorgan, using remote sensing vegetation indices. Later it was used in calibration of different vegetation indices and for estimation of CNC of a vast area in Gorgan, Iran.MethodologyLANDSAT TM imagery simultaneous to the field campaign was acquired. The field campaign was conducted in the latter half of August 2009 in northern part of Iran, Gorgan (36° 54' N, 54° 53' E). Fifty sample plots of 30 m× 30m were randomly chosen. In each sample plot, 4 to 7 subplots were selected and in each subplot 30 leaves form different parts of Soybean crops were cut and transferred to lab. Then using persulfate digestion, nitrogen content of the leaves was determined. In the field, canopy percentage was measured and multiplied by the leaf nitrogen content to calculate the canopy nitrogen content (CNC). The regression line between different vegetation indices (NDVI, GI, SAVI2, GRI) and CNC was calculated and the results validated using cross validation approach.Results and DiscussionOur study showed that the Persulfate digestion is an accurate method for determination of total N in soybean plant when measured in lab. Persulfate digestion does not produce a large quantities of toxic waste associated with Kjeldahl digestion. Additionally, persulfate digestion requires a minimum of specialized equipment: large screw topped culture tubes, an autoclave or a large pressure cooker and test tube racks. The method facilitates the determination of a large number of samples with the use of simple equipments. The relation of measured nitrogen at leaf and canopy level against indices is shown in figure 2. Clearly the relation at canopy level shows a better behavior than at leaf level. Results showed that GI has close relationship with CNC and can be used to retrieve crop vegetation nitrogen. This index uses green band of the electromagnetic spectrum which is appropriate for chlorophyll estimation and has a direct relationship with nitrogen. The most commonly used vegetation index is NDVI. The NDVI has been used for many years to measure and monitor plant growth, biomass production and vegetation cover from multispectral satellite data. Although in our study NDVI was not chosen as the best index, this index is generally considered a good indicator of the amount of vegetation and, hence, is useful in distinguishing vegetation from soil (Svotwa et al. 2012).ConclusionOur study showed that persulfate digestion does not produce the large quantities of toxic waste associated with Kjeldahl digestion and it requires a minimum of specialized equipments. In comparison to the used indices in this study (NDVI, GI, SAVI2, GRI), the GI index demonstrated a better correlation with canopy nitrogen content. This good relationship is not surprising as GI has been developed for chlorophyll estimation which has a direct relationship with nitrogen. Although, the amount of N is only 26% of leaf dry weight but surprisingly it has strong effect on reflected radiation.

Introduction Air pressure, also known as the atmospheric pressure, is the magnitude of force exerted by the atmosphere on a certain extent of surface area. The average of atmospheric pressure is about 1013 hpa at the sea level. The air pressure is considered as mean, maximum and minimum sea level pressure. The sea level pressure is often investigated as the first step in the study of meteorological events. Lots of researches have been conducted about sea level pressure, map patterns of the pressure in various regions and their linkage to some of indices or different climatic elements. A number of these investigations will be mentioned as the following. Jones and Simmonds (1993) analyzed the spatial and temporal anomaly of sea level pressure and the center of cyclogenesis in the northern hemisphere. Their findings indicated the significant difference between cyclogenesis centers and the maximum anomaly of the sea level pressure in high latitudes. The highest anomaly of sea level pressure has also been indicated to be in latitudes from 30 N to 40 N and cyclogenesis centers have been seen around 5 to 7 degrees in the north of the region. Knaff(1997) studied the anomaly effects of sea level pressure on tropical cyclones in Atlantic ocean. The results showed that tropical cyclones of Atlantic Ocean are often developed in the condition of extreme negative anomaly of the sea level pressure and the existence of a deep trough in the upper layer of the troposphere. When the anomaly of sea level pressure is high, the mid layers are drier and Adiabatic cooling in the mid layers of atmosphere is enhanced subsequently. The deep trough of upper layer develops severe baro-clinicity leading to the formation of tropical cyclones. Yi Yu and Tae Kim (2011) examined the relationship between oscillation of extra tropical sea level pressure and ENSO position in the center and east of the Pacific Ocean. The results showed that the oscillation of sea level pressure in extra tropical region of Pacific Ocean has a great role in ENSO excitement and its movement. Mohammadi (1388) investigated the sea level pressure of this kind of precipitation in order to synoptically analyze Iran's extreme rainfalls. Three major sea level pressure patterns have been identified to have a role in the occurrence of the rainfalls. In the first pattern Arabian low pressure/Siberian high pressure dominates over Iran and 28 percent of the heavy and widespread rainfall is resulted from this pattern. In the second pattern, Siberian high pressure in northeastern of Africa and Arabian low pressure account for 53 percent of heavy and widespread rainfall. In the third pattern western Siberian pressure- Iraqi low pressure are the dominant patterns accounting for 19 percent of heavy and widespread rainfall in Iran. So it can be concluded that Arabian/Iraqi low pressure are the main factor in providing indispensible condition at the ground for the occurrence of super heavy rainfall in the country.MethodologyIn the present paper synoptic patterns of sea level pressure have been studied in latitudes from 0to 80 E and in the longitudes form 0 to 60 N. The 6 hourly pressure data of sea have been applied for fall and winter seasons for the period from 1948 to 2010. The spatial resolution of the data was on a 1°x1° lat/lon grid. Therefore, two individual databases are developed for both fall and winter seasons. A cluster analysis by the method of Ward is applied on the data of each season. In Cartesian coordinates, if p  (p1, p2,...pn) and q  (q1,q2,...qn) are the vectors, so their distances are calculated as followed. The position of a point in a Euclidean n-space is a Euclidean vector. So, p and q are Euclidean vectors, starting from the original space, and their tips indicate two goals. Ward merging method was used to the linkage of observations.Results and DiscussionAppling a cluster analysis on 6 hourly sea level pressure data in both winter and fall in the period from 1948 to 2010 have indicated that there are seven major pressure regions in the study area. With regard to the location of the grids chosen to be the representative of each region a name was attributed to them. The area and position of major sea level pressure regions are also depicted.ConclusionThe investigations indicated that there are seven major sea level pressure regions for each fall and winter seasons. It can be supposed that the major pressure regions identified in this paper are indeed the major pressure systems (sometimes with different titles) that climatologists often refer to them. To better understanding of theses major pressure regions some of their statistical attributes are presented.