Physical Geography Research
Year:2010 | Volume:- | Issue:71|Papers Count:10

Remote sensing provides fast and economical information to study morphotectonic and structural geology. Morphological and geomorphological analyses of topographic features have useful in structural and tectonic investigations. Some features and models of topography, for example; peaks, strike - ridge geomorphology, passes, plans, channels , pits ,three-dimensional views with imagery drape, cross-sections profile geometry, slope and aspect calculations, shaded relief, curvature maps, rose diagrams of lineations (faults, ridges,…) and structural trend can be extracted from SRTM through systematic digital tectonic geomorphology and digital terrain analysis. These procedures can be fundamental tools in tectonic analyses base on using remotely sensing data. Altogether, bases of this research are structural geology, geomorphology and digital terrain analysis that all extracted from SRTM. There are close communication between fault type and landforms, therefore, we investigated topographic fractures along Dehshir fault and calculated models of topography around it for morphotectonic study.Materials and methods: We used SRTM to extracted topography features around Dehshir fault. We utilized satellite data include; Landsat, Aster data and geology maps with conjunction fieldwork observations. Digital topography investigation methods for systematic study of tectonic geomorphology features by DEMs along Dehshir fault are constructed respectively; (1) Geometric investigation of features linked to lineations. (2) Feature detection and parameter extraction from DEMs for tectonic geomorphology. In this section, we analyzed; elevation data, numerical differential geometry, digital drainage network and digital geomorphometry. Then, digital image processing of terrain data and spatial analysis of lineaments are done.This paper describes the morphotectonic of Dehshir area using SRTM. Therefore, to access to the goal of study, analysis of  digital drainage network analysis; digital geomorphometry; digital image processing; lineament extraction and analysis; spatial and statistical analysis and digital elevation model-specific digital methods, such as shaded relief models, digital cross-sections, 3D surface modeling and topographic features are extracted from SRTM. Structural information from other sources, such as geological and topography maps, remotely sensed images and field observations were analyzed with geographic information system techniques.Results and discussion: SRTM analysis of the study area showed a distribution of elevation. Cross - sections across perpendicular of Dehshir fault in NE – SW direction also illustrated fault scarps. Shaded relief images have obviously shown lineaments (e.g.; drainages, channels, fault trace, ridges …). Contour map extracted from SRTM elongated along Dehshir fault and showed fault scarps. Rose diagram of lineaments illustrated morphotectonic pattern NW-SE lanforms of the study area. In classified aspect maps are predominantly NE and SW hillside. Lineaments in the shaded relief, aspect, slope and curvature models showed much information for explanation of morphotectonic.The results provide an acceptable regional morphotectonic view of the study area. The geomorphology of the study area is dominated by a pattern of sub - parallel mountain ranges and intermountain plains (lowlands, mainly grabens). The study area landforms are strongly influenced by active faults movements (particularly strike-slip faults). The piedmont areas are covered with low slope surfaces (predominantly Holocene alluvium), originating from the mountain channels and spread for kilometers towards the lowlands. The bottom of the medial plains, known as "Kavir" (Persian term for playa), is covered with playa-type mud flats, evaporate (salt) lakes (e.g. Abarkooh, Marvast) and, only locally, by mobile sand dunes (e.g. along Dehshir fault at east of Abarkooh playa).Conclusion: The results show that digital terrain analysis methods applied on SRTM in the proposed way in this study could extract morphotectonic features from SRTM along Dehshir fault and they contributed to the tectonic interpretation of the study area. According to the evidences extracted from SRTM along Dehshir fault, for example; fault traces, deflected and beheaded drainages, pattern of network drainages, erosion surfaces of uplifted and back erosion of drainages because of the location (situated in quaternary landforms), they are neotectonic evidences for activity of Dehshir fault during quaternary.

Introduction: Focusing on the problem of estimating sediment transport over a fluvial system is one of important aspects in environmental management and applied geomorphology. Artificial neural networks (ANNs) have been applied to runoff-sediment modeling and flood forecasting. One of the most important parameters in runoff - sediment process is geomorphic characteristics. Generally, two methods exist for modeling: function - driven and data driven modeling. Function - driven modeling is based on fitting a suitable function such as regression curves but data - driven modeling such as neural networks work by giving weight to each data in a try and test in a frequently algorithm process.Therefore, neural networks are alternative and complementary set techniques to traditional models. The purpose of this paper is to apply and compare both regressions and neural networks for runoff - sediment modeling in a watershed scale by geomorphic parameters and without them. Therefore, this study evaluates performance of two artificial neural networks - a geomorphology based artificial neural network (GANN) and a non- geomorphology based (ANN); and two regression models, power relation (PR) and multivariate adaptive regression spline (MARS), for prediction of suspended sediment.Material and Methods: The study area comprises of the Plasjan River, in Eskandari watershed, north east of Zayandeh roud basin, Esfahan, Iran. The watershed has an area of approximately 1640 km2. The recorded data of runoff and suspended sediment values are available, measured at one station in outlet of the Eskandari station. Data set of flood runoff and sediment at the same time for the years of 1995 to 2006 were provided by the Esfahan Water Agency. Several geomorphic parameters were used to create geomorphic models such as relative relief, form index and drainage density.There are several stages processes to develop artificial neural network for simulation application as follow:1. Data selection: gathering an appropriate data set.2. Selection of an appropriate predictand: to decide what is to be modeled.3. Artificial neural network selection: to select an appropriate type of network and choose a suitable training algorithm.4. Data preprocessing: to process the original data in terms of identifying suitable network inputs (predictors) and perform data cleansing as appropriate, for example, if necessary, remove trends or seasonal components. In addition, one must normalize and split the data into training, validation and testing data sets.5. Training: to train a number of networks using the chosen training algorithm and preprocessed data6. Using appropriate assessment criteria evaluate the model produced and select the best solution for subsequent implementation.It was revealed that the feed-forward ANN model with back propagation algorithm performed well for both the GANN and ANN models. The sediment loads predicted by these models were compared with observed data for the same watershed and compared with regression models including power regression and multivariate adaptive spline with evaluation indexes of root mean square of errors and determination index.Results and discussion: Development of regression and neural network and using geomorphic parameters besides to runoff and sediment showed noticeable results. However, the GANN predicted better with highest coefficient of determination (R2) of 0.98, root mean square error (RMSE) of 4.49 in comparison to ANN (R2=0.96, RMSE = 5.35). The regression model performance was inferior (R2=0.89, RMSE=8.66) for ARS and (R2=0.81, RMSE = 15.05) for PR to the ANN models. Therefore ANN technique especially GANN is a powerful tool for real - time prediction of sediment transport in a complex network of rivers.Conclusion: Neural network (NN) is a suitable tool for simulating the behavior of sediment transport in a river system. One major advantage the NN approach has over traditional input-output modeling is that it makes fewer demands of data. Unlike multiple regressions, where the constraints preparation the number and distributions of data, are often simply used, NN do not make assumptions about the statistical properties of a data set. Data for several variables can be use flexibility on temporal and spatial scales. Therefore NN find a non-linear pattern carefully that it does not with traditional methods. An advantage of the results is the effective rules of geomorphology parameters in modeling procession fact which illustrate importance of them in vision of river’s behavior in a catchment.

Introduction: One of the important requirements of tourism planning is to study the climatic conditions in tourist attraction areas. Such information is very useful for tourists and tourism planners. Different researcherers have used various tourism climatic indexes to evaluate climatic conditions.  Iran is one the important travel destinations of the world which is in the list of ten countries in the world that have tourism attractions. Therefore the study of different aspects of development of tourism industry in Iran is necessary. One of the main aspects is to study the climatic conditions associated with facilities required for tourism activities such as hotels, traveling, attractions and so on. In this study tourism climate of Iran was evaluated using tourism climate index (TCI) developed by Mieczkowski. This index systematically assesses climatic conditions for tourism using different climatic factorsMaterial and methods: In this research TCI is computed for 144 synoptic weather stations of Iran with common period recorded (1990-2004). Data used for computing tourism climatic index are maximum daily temperature, mean daily temperature, minimum daily relative humidity, mean daily relative humidity, total precipitation, total hours of sunshine and average wind speed. After gathering the required data, computations took places based on Mieczkowski methods. In this methods all considered index compute the specific value to display comfortable or uncomfortable situations and then all computed indices combined in a final formula as TCI=2(CID+CIA+2P+2S+W). In this formula CID indicates daily comfortable index, CIA: 24 hours comfortable index, P: precipitation, S: sunshine hours and W: wind characteristics. The collected data were imported to GIS database and zoning climate tourism for all months was performed. The computed indexes for each weather station was considered separately based on statistical software and then the point based calculated index converted to aerial values based GIS function using IDW interpolation method. Finally climatic indexes maps for each month were prepared. In order to evaluate classification of climatic indexes of the country, central weather station of 30 provinces was selected based on cluster analysis and all regimes of tourism climate indices classified into 6 different categories.Results and discussion: Analyses show TCI for Iran has considerable variation. In winter months, excellent tourism climate conditions are in southern parts of the country that toward northern parts, these conditions decreased. In spring months, the northern part of country has good tourism climatic conditions except northwestern and northeastern parts in initials of season. In summer months nearly bad tourism climatic conditions are in the country except the northwestern and northeastern parts of country. In autumn months, the good tourism climate conditions move toward southern parts of Iran and in this date northern coasts of Iran have good conditions in initials of season. Also concerning performed clustering TCI showed six regions climate tourism for Iran that each of them have homogenous tourism climatic condition features. In first group the weather stations located in northwest of Iran such as Uromieh, Tabriz, Zanjan and Ardebil weather stations have comfortable conditions peak in summer months. The second group has comfortable tourism conditions in initials of spring and autumn and cover east of Caspian Sea areas such as Gorgan weather station. The third groups including central provinces of the country which have two comfortable peaks in end of winter and initial of spring and second observed in initial of autumn. The weather stations of this group are Semnan, Shiraz, Kerman, Birjand, Esfahan, Zahedan and Yazd, The forth group cover western provinces and like first group with two similar peaks in summer months including Shahrkord, Arak, Sanadaj, Ghazvin, Kermanshah, Yasoj, Mashahd, Khoramabad and Tehran. The fifth group including Ahvaz, Banadar Abas, Boushehr weather stations cover southern coasts weather station that have comfortable conditions in winter moths in spite of other geographic areas. The sixth group covers southern coast of Caspian Sea mainly in west that comfortable conditions observed in end of spring and initial of autumn.Conclusion: The result of this study shows variation in climatic condition of the country. The tourism climatic conditions varied in different regions of the country and all of the year in country the regions with good conditions observed in only one region of Iran. So, there is a potential of tourism travel in all of country. Other results indicated that the used indexes for evaluation of tourism climatic conditions in this research have good efficiency.

Introduction: Longitudinal bars are stream geomorphic features formed in the central part of river channels. Usually they are classified and range from simple to compound the obstacles based on the shape and position of the river which showed absence of multiple phases and deposition of sediment transport during the various flows are included. The form of these sedimentary bars is long and drop form. Sediment particle size varies from pebble to fine-grained particles such as gravel and sand. Sediment particle size in the upstream part of these bars is larger and downstream side is smaller. These deposits are usually formed the imbricate that shows the flow of water is during the flood period. Longitudinal bars play a significant role in the process of braiding. Hence, in this research, we analyzed longitudinal bars formation and its role in river pattern change. The study area is located in Lavij roud catchments in Mazandaran province, south of Noor city and northern central Alborz. This basin located from 36o 16' to 36o 27' 30" degree of north latitude and from 51o 58' to 52o 05' degree of east longitude. The area of basin is about 116 square kilometersMaterials and methods: Methodologically, three reaches of stream channel were selected which contain longitudinal bars.Reach 1: In this reach, between 50 and 90 percent of cases, the channel is located in the valley margins. The river channel from one side of the valley is located on the sidelines and on the other hand is a floodplain.Reach 2: This reach of river is entirely alluvial and valleys to a depth of approximately 15 meters are filled from alluvial deposits. This reach was limited to the southern part of the fault North Alborz with a rapid and then it is subsidence. Hence is filled with alluvial sediments.Reach 3: In this reach, between 10 and 50 percent of cases, the channel is located in the valley margins. Gradient is less than 3 percent. Bankfull channel discharge is estimated 13.5 cubic meters per second.Survey of channel performed with the use of GPS and positions of longitudinal bar are determined in channel stream. Then, some cross sections are mapped and size of sediment determined with Pebble Count Method in the reaches. Bankfull discharge identified using field evidence. Then entrainment analyzed with boundary shear stress, critical shear stress and bed relative stability functions.Results and discussion: In the studied reach, the total shear stress at all levels is more than the critical stress (the bondary) when the current bankfull is flown. Hence, the river has the ability to carry sediments flown existing longitudinal bars and channel bed is the current bankfull. If the number bed relative stability is larger of one, shows bed stability and everything is much more to show that sedimentary particles in the bed on the floor will remain stable. In the studied reach, this ratio is less than one. Hence this relationship shows that in the bottom sediments of streams in the current bankfull or less are removable and the bed in case of sediment movement is unstable.There is no doubt that existing longitudinal bars in river channel change could lead to direct and meander pattern to be braided. Therefore in sections of reach 2 because of existed midpoint bars channel is divided into several parts.Conclusion: Low values of bed form shear stress show that a large part of the river's energy is used to overcome the resistance of bed forms. The bed relative stability ratio less than 1 in this reaches represent low stability of sediments in the river's bed. Hence, longitudinal bars form in wide channels and the flow lower than bankfull lead to much sediment supply and decrease of competence limit. Number of longitudinal bars in some reaches indicated that river pattern shifts from straight to meandering which leads to more instability of the channel.

Introduction: Forecasting precipitation in arid and semi-arid regions, in Iran for example, has particular importance since precipitation is the unique source of water in such regions. Precipitation is a complex phenomenon that varies both in time and space and affects other components of the hydrological cycle, including surface runoff, infiltration, groundwater, seepage, percolation, evaporation and transpiration. Temporal variation comes from the seasonality and inter-annual variability of the atmosphere, whereas spatial variation is due to the topographical heterogeneity of the earth surface at the local scales as well as the teleconnections at the global scale. Forecasting precipitation ahead of time has been an important problem in hydrological studies. The Artificial Neural Networks (ANNs) modeling has been used increasingly in various aspects of science and engineering because of its ability to model both linear and nonlinear systems without the need to make any assumptions as are implicit in most traditional statistical approaches. In most hydrological and water resources studies, precipitation is an important parameter to estimate. Since the numbers of precipitation gages are usually insufficient and there are high uncertainties in measurement, the estimated precipitation is not accurate. Forecasting precipitation is most important in estimating of runoff, drought, catchments management, agriculture and etc.Materials and methods: In this paper, the usefulness of artificial neural networks as a suitable tool for the study of the medium and long - term climatic parameter variations is examined. The objective of this investigation was forecasting monthly precipitation with artificial neural networks. The monthly precipitation data of Tehran synoptic station for period of 1951 to 2003 obtained from Tehran Meteorology Center. Then artificial neural networks were used as a nonlinear method for forecasting precipitation. The samples were divided into two sets. The first set is the learning set for the ANN training, while the other set represents the holdout set for precipitation prediction to verify the efficiency and correctness of the model. With these collected data, the ANN system is ready to launch its training scheme. Then the year and month set as input layer and precipitation set as output layer. TanhAxon function which is the most important function in Back propagation method is used as irritates function. Neurosolutions for Matlab software is used for training artificial neural network. To reduce forecasting error, train and error on the network parameters carried out. The Multi-layer perceptron (MLP) model has been used. The ANN is composed of an input layer of neurons, one or more hidden layers and an output layer. Each layer composites multiple unites connected completely with the next layers.Results and discussion: Precipitation is one of the main sources of water without which humankind cannot survive, so understanding, modeling, predicting or forecasting of precipitation has always been important. Among a host of modern nonlinear data-based techniques, artificial neural networks have been extensively applied in hydrology. The several ANN models with varying numbers of nodes have been trained. The results of this study after network testing with different hidden layers and training coefficient indicated that using of artificial neural network with 2 hidden layer perceptron, 0.1 training coefficient and 0.7 momentums has presentation comparatively a better model.Conclusion: Although concerns and criticisms regarding ANN applications in hydrology remains, but there is no doubt anymore that ANNs are useful tools in hydrological, meteorological and climatological practices. The classic or linear models are used for trends that with increase of a parameter, another parameter increases or decreases. Thus, using of these models for nonlinear trends increases the error rate. The neural networks can be a useful tool to model the relation between variables because we can consider the ANN as being very general forms of non-linear regression models. In fact they can model time series very effectively and are employed when the classic methods do not work effectively. There is a nonlinear trend for precipitation. When network trained without genetic algorithm, the correlation and adjust coefficient are 0.87 and 0.77, respectively.  So after testing network and training with different hidden layer and training coefficient in combination with genetic algorithm indicated that combination of network with mentioned characters with genetic algorithm decrease the error and increase speed of calculation and finally present a better model. When network trained with genetic algorithm the correlation and adjust coefficient are 0.91 and 0.83, respectively. In summery artificial neural networks forecast the nonlinear trend of monthly precipitation. The combination of genetic algorithm with artificial neural networks increases the speed of analyzing and processing accuracy which leads to decrease in error rate.

Introduction: Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. Many works have been carried out to determine the factors which control air pollution concentrations in order to enable the development of tools to aid in the forecasting of pollutant concentrations. One approach to predict future concentrations is to use a detailed atmospheric diffusion model. Such models aim to resolve the underlying physical and chemical equations controlling pollutant concentrations and therefore require detailed emissions data and meteorological fields. The second approach is to devise statistical models which attempt to determine the underlying relationship between a set of input data (predictors) and targets (predictand). Regression modeling is an example of such a statistical approach and has been applied to air quality modeling. Artificial neural networks (ANNs) can model non-linear systems and have been used with some success to model air pollution concentrations. Tabriz is the most industrialized and populated city in the northwest of Iran and the second polluted city of the country. Location of industrial centers in the west and southwest directions of Tabriz city and blowing winds from those directions, in winter season causes the transfer of pollution to inner Tabriz.Materials and methods: Based on the data from Department of Environment of East Azarbaijan province, 60 percent of air pollution concentration is referred to industrial centers located in west and southwest directions. ANN models are computer programs that are designed to emulate human information processing capabilities such as knowledge processing, speech, prediction, classifications, pattern recognition, and control. The ability of ANN systems to spontaneously learn from examples, “reason” over inexact and fuzzy data, and to provide adequate and rapid responses to new information not previously stored in memory has generated increasing acceptance for this technology in various engineering fields and, when applied, has demonstrated remarkable success. The major building block for any ANN architecture is the processing element or neuron. These neurons are located in one of the three types of layers: the input layer, the hidden layer, and the output layer. First the input neurons receive data from the outside environment. Then the hidden neurons receive signals from all of the neurons in the preceding layer. Finally and the output neurons send information back to the external environment. In this paper, the artificial neural network (ANN) and multiple linear regressions (MLR) have been applied for short-term prediction of ozone in the Tabriz metropolis. MLP is capable of modeling highly non-linear relationship and can be trained to accurately generalize when presented with new, unseen data. MLP learns to model a relationship during a supervised training procedure, when they are repeatedly presented with series of input and associated output data. The MLP has the ability to learn through training. Training requires a set of training data; which consists of a series of input and associated output vectors. During training the MLP repeatedly presented with the training data and the weights in the network are adjusted until the desired input-output mapping is achieved. MLP is a supervised procedure. During training, output from the MLP for a given input vector, may not equal to the desired output. An error signal is defined as the difference between the desired and actual output. Training uses the magnitude of this error signal to determine to what degree the weight in the network should be adjusted so that the overall error of the MLP is reduced.Results and discussion: The objective of this work was developing a model that could make accurate short-term (hourly) predictions, and since the relationship between O3 and meteorology is complex and extremely non-linear, ANNs were used to model and predict hourly O3 concentrations from readily observable local meteorological data. The architecture of such a net is established as follows the numbers of neurons in the input and the output layers are determined by the dimension of the input and the output vector, respectively, while the number of the hidden layers and/or the number of neurons in each hidden layer depends on the kind of the modeled system and should be optimized. Designing of the network architecture is based on the approximation theory of Kolmogorov. The results show that the ANN is more suitable model for the prediction of ozone concentration and that, the R2 in ANN and MLR models are 94% and 51%, respectively.Conclusion: Fluctuations of the Tabriz hourly O3 concentrations for the period of October 2003 were studied. It was found that ANN to be useful tool for the short-term prediction of O3 concentrations. The optimum structure of ANN was determined by obtaining a minimum TRMS for test set. It was found that the structure of ANN with 35 neurons in the hidden layer had the best performance. It has also been demonstrated that MLP neural networks offer several advantages over traditional MLR models. This work has shown that MLP neural networks can accurately model the relationship between local meteorological data and O3 concentrations in an urban environment.

Introduction: Classification is one of the important methods in extraction of information from digital satellite images. The traditional methods of classification are based on the value of individual pixels in the images which are reflected from territorial features. The ability of pixel based approach in the satellite image classification is limited, when objects have similar spectral information. This circumstance reduces the classification accuracy. Then in this approach the image cannot be classified correctly. The classic pixel-based approach is based on “binary theory”. By this theory, one pixel will be labeled to a class or is not assigned or remains unknown or not classified. In the case of the pixels in the overlapping areas of the feature space, by binary theory, those pixels will be labeled into only one class but they show the affinity with more than one class. With binary theory the classification result will not be accurate.But object oriented image analysis approach is the procedure in image analysis that combines spectral and spatial information. This approach segments the pixels into objects according to the tone of the image and classifies image by treating each object as a whole. Utilizing the texture and contexture information of the object in addition to using spectral information, object orient image analysis has more powerful image analysis ability. The basic theory of object oriented approach is the fuzzy theory, in the case of the overlapping area in the feature space, pixels in the overlapping areas will not be classified only into one information class, which is not correct in the real world, but are given different membership to one (with the value 1) or more than one (with the value between 0 to 1) information classes. This approach of classification is soft classifier (for example fuzzy system), which uses a degree of membership to express an object’s assignment to a class. The membership value usually lies between 1.0 and 0.0, where 1.0 expresses full membership (a complete assignment) to a class and 0.0 expresses absolutely non-membership. The degree of membership depends on the degree to which the objects fulfill the class-describing conditions. The main advantage of this soft classifier lies in their possibility to express uncertainties about the classes’ descriptions. It makes it also possible to express each object’s membership in more than just one class or the probability of belonging to other classes, but with different degrees of membership. This classification can be done by the algorithm of nearest neighbor. The nearest neighbor is applied to selected objected features and is trained by sample image objects. The fuzzy realization of the nearest neighbor approach which is used in eCognition software automatically generates multidimensional membership functions. They are suitable for covering relations in multi-dimensional feature space.  The nearest neighbor classifies image objects in a given feature space and with given samples for the class of concern.Materials and methods: In this study the maximum likelihood classification (MLC) of pixels based and nearest neighborhood of object oriented (O.O) for classifications of satellite images are compared. This comparison is done by extracting the land cover of west Azarbaijan province. To compare these methods we used satellite images of SPOT 5 to extract land use maps of the case study area. To do so, in pre-processing stage of images, geometric correction including georeferencing, orthorectification and atmospheric correction were implemented. In processing stage, images after enhancement were classification in two ways. Frits, pixel-based classification was done based on Maximum likelihood algorithm, then object oriented classification was implemented by using the nearest neighbor algorithm in eCognition software.Results and discussion: After satellite images classified by two methods, to evaluate and compare the results, overall accuracy and Kappa coefficient of the frame were extracted for each algorithm and it was determined that in pixel-based classification algorithm , the maximum Likelihood approach with overall accuracy of 88.37% and Kappa coefficient of 0.87 has lower accuracy in comparison with nearest neighbor algorithm, because Kappa coefficient of classification in nearest neighbor algorithm in object oriented method estimated about 0.94 while overall accuracy was about 95.10%. This means that “O.O” approach has almost 7% improvement in the overall accuracy and the Kappa indices. In another word, the object oriented image analysis can be the best method in classification of satellite images compared to pixel-based algorithms.Conclusion: This research has been done to compare pixel-based algorithms and object oriented image analysis in classification of digital satellite images. The results of this research showed classification based (O.O) method provides more precise results in satellite image processing. Also it will be better to consider geodatabase and calculating geometric characteristics of each land use class in the pos-processing stage. The outcome of the research has been applied in the land cover extraction of East Azarbaijan province and extracting the land use changes of Satarkhan dam basin for the period of 30 years.

Introduction: Scientific and professional journals are assumed as ducts of scientific production and scientific communication and trying to play these two functions between scholars in different academic fields. Review of historical science shows that efficient factors have had a general role in formation, emergence, growth and development of science with a particular role in knowledge of geography in which we can consider the scientific and professional journals. Scientific and professional journals are specialized publications which are published in regular intervals and play two-function of scientific production and scientific communication. Published articles in professional journals because of the updated and (relatively) short content, transferring the results of writer to the readers speedily, low cost of journal (compared to books), relatively authentic and first-hand information, detailed analysis and effort in using known and logical methods, fast publication and distribution, introducing the past to today's textbooks and resources related to the topic, and removal of the marginal texts (due to observing a finite volume and a journal article) is so important. The place of publications in addition to the role of enormous scientific findings resource in which used by most professional staff members of the community, is another important source assessment, including the level of countries or areas of science degree in special and the ability to assess the extent of scientific research and higher education for young people. Research Journal of Geographical Research (Journal of Geographical Research Institute University of Tehran) is a scientific research journals approved by the Commission reviews of the country - the Ministry of Science, Research and Technology and is the first specialized geography journal in the country in which from past 32 year till now 66 volumes have been published. The journal was named from the year 1988 as “Geographical Report” and after that till now (from 23 years onwards) as "Geographic Research," has made available the latest scientific research and research studies from the geography of Iran for scholars.Materials and methods: A descriptive- content analytic method is used as the research method. This article is about studying the process of published numbers of a geographical quarterly journal during the past 10 years, from no.36 of October 1998 to no.66 of the winter of 2008 in the form of an eight parameter frame of “thematic share of articles”, “science-proficiency group association”, “resources and origins”, “dividing the articles based on the extracting resource”, and “used methods and techniques”. Result and discussion:1- Academic institutions participating: In total, 53 scientific institutions as universities and academic research institutions - participated in 320 evaluated research articles; from 320 published articles in journal, 143 numbers of articles means 44.7 percent of them has allocated to University of Tehran and most of the remaining percent (29.4percent) are from other cities universities. Also, 62 number of articles means 19.4 percent of them were allocated to other universities of Tehran. Share of the rest of country's academic institutions (11 institutions) in total were 3.4 percent and nearly 3.1 percent of the published articles institutions are unknown.2- Author scientific degree: Reviewing the relevant data shows that scientific degree respectively, including : 10.9 percent Master’s degree, 24.1 percent associate professor, 45.3 percent assistant professor, 1.9 percent instructor, 9.7 percent doctoral graduates and doctoral students, 1.6 percent the graduates and master's graduate student, 0.3 percent BA and in 4.7 percent of articles, scientific place of scientific academy members is not mentioned.3- Articles issue share: From 320 reviewed articles was respectively subject climatic geography: 17.5 percent, geomorphology 16.3 percent, hydrology 9.4 percent, rural geography 16.6 percent, urban geography 15.3 percent, geopolitics 8.4 percent, geography of tourism 2.8 percent, geographical area 0.9 percent and remote sensing and cultural geography 0.3 percent is allocated. Thus, the largest issue in these articles is dedicated to climate and geography articles and minimal contribution to remote sensing and geographic medicine.4- Used techniques and methods: Nearly 83.4 percent of published articles not mentioned the method used in writing research, 8.4 percent of the articles descriptive - analytical, 1 percent empirical, 0.9 percent comparative method and 6.6 percent used from other methods.5- Separating articles based on extractive resource: From 320 reviewed articles 21 articles means 6.6 percent of all articles extracted from previous resource and has been presented as article and separately 1.9 percent from published thesis, 0.9 percent from master's thesis and 3.8 three percent are extracted from the research project. Also 287 articles mean 93.4 percent of articles have not been extracted from the previous field of scientific research.6- Participating of scientific– professional groups: In total, 20 scientific – specialist groups have been participating in 320 reviewed articles. However, this diversity is not so deep and 78.4 percent of articles are from the Department of Geography and this group has a general share in writing articles in this journal.7- Time of accepting articles: Reviewing this issue is important to the arbitration process and journal organization for follow-up articles and have effective role in the satisfaction rate of applicants in publishing the magazine. From 320 articles of the time to get acceptance, 164 articles means 51.3 percent of the articles is not mentioned, and mainly are related to the first publishing decade of magazine, but in the other 157 articles the less time for acceptance of articles were 3 months and the maximum duration were 51 months.8- Sources: Generally in reviewed articles 5757 sources have been used in performing research that from this numbers 3594 means 62.2 percent are from Persian sources and 2163 sources means 37.5 percent are from Latin sources. Average resources used for the 320 articles, are equal 18 sources for each article that is satisfactory figure in the use of resources. Average of latin sources used in each paper is equal to 7 sources and for the Persian sources is about 11 sources for each article.Conclusion: The results and findings show the quantitative and qualitative progress of the 8 parameters. The articles with the subjects of “natural geography” have the most share of the topics of the articles, and “continental geography" was the most used subject in these articles with 17.5% percents. The editors with the educational degree of “faculty co-professor” have the biggest number of the studied authors. Different authors of 19 science-proficiency groups participated in giving the articles of this research. Only 6.6% percents of the studied articles had the results of previous science-proficiency works. In most of the articles, the methodology in not mentioned, and the mostly used research method of the articles was descriptive – analytic.