فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 79 (بهار 1391)

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.

Drought is a natural disastrous phenomenon that occurs due to the continuous reduction of rainfall over a short or a long period of time. A deficit in precipitation (meteorological drought) can result in a recharge deficit, which in turn causes lowered groundwater heads and a deficit in groundwater discharge (Peters et al., 2003: 3023). Given the importance of water in human life, regulating the access to reliable and sustainable water resources and planning for proper consumption are considered essential in any region. There are two types of limitations resulting from natural phenomena or improper managements by human. An increase in population in the plain of Dehgolan, the agricultural nature of the region and urban / rural development has led to the reduction of the groundwater. This phenomenon is evident when the above mentioned factors emerge. The purpose of this study is to survey the ground water responses to drought in different periods of time according to characteristics of severity and duration.

The applied dataset is divided into two groups: Precipitation, evaporation and runoff were recorded in stations located at the Dehgolan basin or adjacent points. This dataset was obtained from the Meteorological Organization and the regional water supply company of Kurdistan province during the water year period from 1986-87 to 2002-2003. Ground water level (from mean sea level) from 51 wells located within the Dehgolan plain over 1987-88 to 2003-2004 were obtained from regional water supply company of Kurdistan province. In order to evaluate the effect of droughts on ground water resources in the Dehgolan basin, drought occurrence was calculated using SPI index. It's easy application for different time scales, different purposes, and different climates are due to its normal properties (Hayes, 2003: 2). It can be calculated using the following equation: SPI=(Pi-p̅)/S where SPI is the Standardized Precipitation Index, pi is the monthly rainfall value, p̅ is the average of rainfall for the selected 16 year, and s is the standard deviation of precipitation. The correlation coefficient between each climatic variable (including the SPI index, temperature and evaporation), runoff (as independent variables) and groundwater level (as a dependent variable) were calculated at 95% confidence interval. After identifying the drought periods and calculating the severity and duration (drought magnitude), the rate of groundwater level reduction was classified in Arc map using spatial analysis tools and Kriging interpolation method.

The correlation coefficient results indicate that there is no significant coefficient in SPI index, temperature and evaporation related to the ground water level while changes in groundwater levels depend on surface runoff at the basin outlet. An increase in correlation between runoff and ground water level is due to the snowmelt runoff that takes place in late winter and early spring. The groundwater level is affected significantly in this time of the year. A review on the drought severity and its duration show a direct relationship between drought and the above mentioned features. The results of ground water changes on the severity and the duration of droughts in Dehgolan basin indicate that in comparison with the less severe short-term drought, the severe long-term drought has got a higher effect upon lowering the level of ground waters. The zoning classification of the drought and groundwater level using spatial analysis in GIS environment shows that two major factors are involved in changes in the level of groundwater plain: First, as mentioned, in the case of severe long-term droughts, the reduction amount of ground water levels are more and second the fact that the surface drainage density is an important factor in the recharging process of ground waters.

Changes in surface runoff rate throughout the year and especially during droughts has important role in discharge of groundwater. During the short-term droughts, those zones with high elevation in Southern parts of the Dehgolan plain faced a decrease in groundwater more than other parts of the plain due to higher transmission ratio. While in continuous and prolonged droughts, middle parts of the plain faced more level drop. Drainage density as the most important determinant factor in each groundwater basins has a dominant role in determining the decreasing rate of groundwater level in parts of no drainage density. Given the potential of drought occurrence as a regular feature of this region and an increase in water demands for agriculture, drinking and industry versus the continues reduction of groundwater level in Dehgolan plain, critical condition in the status of water resources will be expected in this region.

Long-term high-resolution climate proxies are essential for understanding climate variability in the region, where few long-term climate records are available. Due to cyclic variations of climate, long term climatic data for understanding of its fluctuations is necessary. One of the major problems of climatic studies in Iran is unavailability of longer useful instrumental data. Trees added annually a ring to their past annual rings in moderate climate. Annual tree rings can provide important information on long-term climate fluctuations. Trees are widespread and silent witnesses of climatic changes during their long life span which record them in their growth ring width and structure. Since annual tree rings are usually distinguishable in woody species grown in cold and temperate ecosystems, they can be used to reconstruct the past climate of different regions with annual or seasonal resolution. This paper presents a reconstruction of October-May precipitation variations in Kermanshah province using Quercuse Infectoria tree rings over the last 305 (1705-2010) years. In this study 20 cores from 10 Quercuse Infectoria Olive were extracted at breast height with an increment borer in Faryadras site. The cores were mounted on sample holders and after air drying, the surface of a core was prepared with razor blades and the surface contrast was enhanced with chalk. Ring widths were measured with a LINTAB5 measuring system with a resolution of 0.01 mm and all cores were cross-dated by statistical tests (sign-test and t-test) using the software package TSAP-Win. The raw ring-width series were standardized to remove biological growth trends as well as other low-frequency variations due to stand dynamics with the ARSTAN program. The residual chronology (RC) was selected to calibrate the ring-width- climate relationships. Mean monthly precipitation from prior October to current May was provided by Kermanshah meteorological station (1951-2010) near to our sampling site. Based on a linear regression model, we reconstructed October–May precipitation for the region over the last 300 (1705-2010) years. All Quercuse Infectoria trees have a similar growth trend in Faryadras site. Similar growth patterns among the trees indicate that they are influenced by common environmental forcing factors. The length of the residual chronology is 305 (1705 – 2010) years. In fact this is the longest chronology for the west of Iran so far. Tree-ring widths positively respond to each month's precipitation from previous October to current May. Positive correlation between the residual chronology and previous October- March precipitation shows that monthly precipitation in this period has an important role for growing of tree rings in the next year. Correlation analysis between chronology and previous October to current May precipitation shows that the highest correlation is given to prior March and current May precipitation (p<0.001). In addition, ring widths are significantly related to the total precipitation of October-May (p<0.001). El Nino events have positive effect on tree-ring widths and La Nina events have negative effect on tree-ring widths. The results show that severe wet years coincided with historical El Niño events. Our reconstruction showed that last decade of 18th century and second decade of 19th century are the wettest and driest decades respectively. Wet periods occurred in 1710s, 1740s- 1890s, 1800s, 1820s. 1850s, 1890s, 1930s, 1950s, 1970s and 1980s.Dry decades occurred in 1720s, 1730s, 1810s, 1830s, 1840s, 1860s- 1880s, 1900s- 1920s, 1940s, 1960s, 1990s and 2000s. We extracted 20 cores from 10 Quercuse Infectoria trees in Faryadras site, Kermanshah. Precipitation is the major factor affecting the radial growth of Quercuse Infectoria trees in the site. After investigation of correlation coefficients between the residual chronology and climatic data from Kermanshah meteorological station during the period 1951-2010, a linear model was used to reconstruct October-May precipitation. The results show that precipitation from prior October to current May has positive effect on tree ring widths. Severe wet years coincided with historical El Niño events and many of sever dry years controls by La Nina events. The results indicate that there is a great potential to extend the tree-ring series in length in the study area, which could provide high resolution paleeoclimatic records that span the last several centuries.

Vegetation has a major role in dynamic and morphology of sand dunes in desert area. Vegetation affects the movement of sands and traps sands that are transported by wind. Nebka is one of the most important accumulations landforms in desert area. The type and species of vegetation can affect on the characteristic of Nebka. This research attempted to identify echogeomorphology characteristic of Tamarix and Prosopis cineraria nebkas in the west of Lut Plain in southeastern of Iran. Lut is a desert area with many especial landforms such as Yardang, Barkhan, Star dunes and other desert landforms covering 175000 sq. kilometers area. Descriptive and analytical methods were used for studying of Nebkas. In addition morphometric characteristic of selected nebkas were measured during the fieldwork. Moreover, the sediment samples were taken and components of landforms were measured. After identification of study area using aerial photos and satellite images, sediment samples were taken. In fact, we identified four transects and selected some Nebkas in transects, morphometric characteristic were measured. Also statistical parameters of samples using SPSS software were calculated. Finally, using shaker and granulometry method, grain size distributions were analyzed. For analyzing of grain size distribution, we used Gradistat 4.5 to calculate all statistical parameters of grain size taken from Nebkas sediments. Among 30 selected Nebkas’, four types of plant with different morphometric characteristic can be recognized. Correlation analysis between nebkas morphometric characteristics, such as dunes height, plant height, dunes area, width and perimeter, showed that the correlation between above mentioned characteristics and Tamarixaphylla and Prosopis Cinerarias, in 99 percent of significance level are high. In addition, correlation between above mentioned characteristics with Tamarix Kotschyi and Tamarix Floridas in level of 95 and 99 percent of significance level are also high. Mean heights of Nebkas in Tamarixaphylla are 7.8 meters and mean height of plant crowns are 11.09 meters. Results for other kinds of Nebkas with different plants are lower. Moreover in point of view of area, perimeter and width’s mean, maximum results belongs to Tamarixaphylla with 322.39, 68.3 and 18.24 meter. Also results from granulometry show that coarser grain belongs to Tamarixaphylla and other kind located at next ranks. The results from statistical analysis of morphometric characteristic of Nebkas provide reliable information. Increase in height of the plant crowns is accompanied with an increase of Nebkas height and the consequently increase of Nebkas area. In addition, increase of height causes perimeter and width of Nebkas to be increased. These conditions are identical for all Nebkas. Tamarix Aphylla nebkas with higher height in comparison with other kinds of Nebkas plant have bigger areas, perimeters and widths. In inverse situation, Tamarix Kotschyi has lower morphometric characteristics values such as height, area, width and perimeter relative to other kind of plant in study area. Moreover, results from mean of area, perimeter and width of Nebkas show that the higher values, belongs to Tamarix Aphylla with 322.39, 68.3 and 18.24 meters. Second rank belongs to Tamarix Florida and next ranks belong to Tamarixaphylla, Prosopis Cineraria and Tamarix Kotschyi. Maximum standard deviation, variance, kurtosis and skewness belong to Tamarixaphylla, Prosopis and Tamarix Kotschyi. Moreover, between all Nebkas, maximum area, perimeter and width belongs to Tamarixaphylla with 565.7, 93 and 30.5 meter and the minimum area and perimeter belong to Tamarix Florida with 11.84 and 11 meters. Finally, the minimum width belongs to Tamarix Kotschyi with 2.9 meter. Results from granulometric analysis of grain size distribution demonstrate that the coarser sands are belonging to sediments of Tamarix Aphylla, other kinds of plants located at lower rank. Wind data analysis shows that the slope of windward sides of nebkas are more than other parts. In fact, directions of dominant winds are from north and north west, and north and north west side of nebkas are stuper.

Entropy not only quantifies the quantity of energy but also quantifies the quality of energy and this quality is the measurement of disorder in a system (Mansly and Colleages, 2008: 145). In summary entropy in the meaning of quantification, discuss the disorder between causes and results and decisions in different topics (Van, 2009: 238).Geomorphologic hazards as a threat cause a lot of damages to human societies. In this concept natural disaster is a harmful element that exists in the physical environment for human (Ayla, 2002: 408). Landslide as one of the geomorphologic hazard cause lots of destructions as direct and indirect costs and plays an important role in destructing human facilities and causes human death, forest destruction and sedimentation in watershed basins. Identifying and classifying the areas that are vulnerable in sliding and its danger has an important role in identifying environmental hazard.According to destructive landslides that happen near Nesar anticline, the location of villages and City of Gilan-Gharb and development facilities in amplitudes anticline, it is necessary to provide the landslide map of the area for better management.The purpose of this study is to quantify the occurrence of Nesar anticline landslides and also to know the amount of each five factors in landslide occurrence. It is also determined to provide the map of landslide risk by using entropy model. Finally the last aim of the article is to propose scientific management ideas of the area against landslide hazard. At first, by using visual interpretation of IRS satellite images, landslides of the studied area were identified. Then, by studying occurred landslides in the area with five factors: litho logy, distance from faults, slope topography, the slope and elevation were defined as effective factors in the landslide occurrence and five informational layers came in as raster data and the amount came in identity. According to the landslide features of the area (shown in table 1), weights were given to the layers and then the entropy matrix were completed (Table 2). Decision matrix contains information in which entropy can be used as a criterion for evaluating it. After calculating the entropy matrix and the whole weight of five factors〖 w〗_j, the amount of Hi which is the landslide risk occurrence was achieved and decisions were made to area based on landslide occurrence. Finally, based on the landslide occurrence zoning map and area features analysis, some management ideas as basically and scientifically were given. The data container existed in matrix was generated as Pij and for every five elements the amount of Ej was calculated and after area model the amount of landslide risk in the area was generated as below: H=0 G+0.24987 S+,0.238713 DF+0.403101 E+108334 A that H: the risk of landslides in the area. G; lithology, S; slope, Df; distance from the fault, E: elevation and A is the slope. Based on the above relationship, map of landslide hazard zoning was prepared in the studied area (figure 4). As it has been mentioned in Table 3, areas with medium and high risk consist 98% of the area was suggested to have the high risk area in terms of landslide risk.According to entropy model calculations based on distance, elevation, slope, fault distance, aspect and lithology had the greatest effect in the landslide occurrence in the area. High incident risk areas are located in high elevated places of anticline. These areas have the highest slope and the height and are located in territory of fault area and in territory of Asmari formation. Moderate-risk zone as longed tape has taken middle parts of anticline and has allocated most of the area. This area has a large slope and the classes of less than 1500 height and lithology Asmari formation has got the most places in this area. Low risk zone in the northern and southern anticline are in classes less than 1100 meter, and is based on the consistent of the soft match’s formations of the area including quaternary, low slope and a high distance of the faults in the area. Final precision zone map with occurred landslide occurred in the study area shows that the 33.33% pitch slips occurred in the moderate risk zone, and 66.66% of landslides were located in high risk zones. This suggests optimal performance of the entropy zoning model in landslide hazard zonation. About 1.93 percent of the region across the low risk and 58.26 percent of the region across the moderate risk zone, and 39.8 percent are located at high risk. Finally, we can conclude that the studied area is a high-risk area. The factor of altitude with amount of 40% had maximum role and the lithology has no effect on landslides risks of occurrence. To manage the hazards of the area these two ideas are suggested:Preparing landslide hazard zoning map of the region and preventing development activities at the margins of less than 2 km from anticline.Prevention of excavation operations and watershed operations in the anticline as accelerating factors of the landslides.

Models of observed daily weather sequences are frequently used in water engineering design, and agricultural, ecosystem or climate change simulations because observed ground-based meteorological data are often inadequate in terms of their length, completeness or spatial coverage. These statistical models are also known as ‘weather generators’ since they can fill missing data or produce indefinitely long synthetic weather series by simulating key properties of observed meteorological records (i.e., daily means, variances and co-variances, frequencies, extremes, etc.). To date, the majority of weather generators have focused on the precipitation process in recognition of the dominant control exerted by rainfall on many environmental processes, and due to the complexity of building internally consistent, multivariable models (Hutchinson, 1995). However, companion algorithms that simulate other meteorological variables are also in routine use. Rather than simulating rainfall occurrences day by day, spell-length models operate by fitting probability distributions to observed relative frequencies of wet and dry-spell lengths. This kind of model is sometimes called an ‘alternating renewal process’ (Buishand, 1977; 1978; Roldan and Woolhiser, 1982), in that random numbers are generated alternately from the wet and dry spelllength distributions. That is, a new spell length (L) is generated only when a run of consecutive wet or dry days has come to an end, at which point a new spell of the opposite type is simulated. In this research, the performance of different probability models were analyzed for simulating the distribution of dry and wet spells in Qazvin synoptic station (period 1959-2008), using four 1) Fitting the best models to the data of each month; 2) Fitting geometric distribution to the data of each month; 3) Fitting the best models to the data of each 3-month periods; 4) Fitting the best models to the data of each season. The models were: 1) Geometric Distribution (GD); 2) Log Series Distribution (LSD); 3) Mixed Two Geometric Distribution (MGD); 4) Mixed Geometric Poisson Distribution (MGPD); 5) Mixed Geometric Truncated Poisson Distribution (MGTPD); 6) Mixed Two Log Series Distribution (MLSD); 7) Mixed Log Series Geometric Distribution (MLGD); 8) Mixed Log Series Poisson Distribution (MLPD); 9) Mixed Log Series Truncated Poisson Distribution (MLTPD); 10) Negative Binomial Distribution (NBINO); 11) Poisson Distribution (PD). The results showed that in simulating dry spells, 3-parameter models (specially the mixture of two geometric distributions and the mixture of a geometric and a Poisson distribution) were selected as the best models. These revealed better performance of these models in simulating longer periods because in simulating wet spells series (which includes shorter periods), 1-parameter models were selected as the best models. For wet spells, the bias (RMSE and MAE) of all methods increased in the dry periods of the year. This statement holds also for dry spells because the biases increase with the start of the wet periods of the year. Again, in simulating dry spells, the performance of the first and the second methods were better in keeping the statistics of observed series. But in simulating wet spells, the third and the fourth methods performed better. The first method performed better in simulating the transitional probabilities from a dry day and the third method outperformed the other methods in simulating the transitional probabilities from a wet day. The results revealed that the 3-parameter models outperformed the 1- and 2-parameter models in simulating long spells. So, it could be recommended to use such models in order to simulate (long) dry spells. Also, it was concluded that choosing the best models (according to AIC criteria) for each month and using the geometric distribution for all months could results in a better simulation of the statistics of the observed series. But, aggregating the monthly data into 3-month and seasonal periods could increase the accuracy in the simulation of the wet spells. It is recommended to analyze the performance of these probability models in other climatic stations in order to choose the best model for each station.

Dust originates from arid and dry regions where high velocity winds are able to remove mostly silt-sized materials, deflating susceptible surfaces. Dust storms as the dominant phenomena in arid and semi arid areas have the greatest impacts on air quality. One-third of the Earth’s land area is covered by dust-producing surfaces, made up of hyper-arid regions like the Sahara that covers 0.9 billion hectares, and dry lands which occupy 5.2 billion hectares (Jickells, et al., 2005). Dust consists of particles in the atmosphere that come from various sources such as soil dust lifted by wind (an Aeolian process), volcanic eruptions, and pollution. This airborne dust is considered as an aerosol and once in the atmosphere, it can produce strong local radiative forcing. Saharan dust in particular can be transported and deposited as far as the Caribbean and Amazonia, and may affect air temperatures, cause ocean cooling, and alter rainfall amounts (Middleton, Goudie, 2001). Dust storms are important parts of air pollution resources, Phenomenon that occurs in large areas of Iran in recent years. Conditions in neighboring countries. Iran is located on the arid belt. More than half of the country's land area is arid and semiarid climate. 18 provinces and 82 cities have been identified as a critical focus of wind erosion. The west and southwest regions of Iran periodically were affected by dust storms, which in recent years its frequency and spatial scope have been expanded. One of the most severe dust storms occurred in July 2009 that overed 17 provinces in the western half of Iran. Its impacts can be investigated beyond the closure of schools and agencies, flight cancellation, loss of vision, increased respiratory diseases, etc. Recognition of resources and centers originating particles, patterns and the formation and dispersion of dust are the main objectives of this study. The area covers approximately half –west of Iran spanning longitude 46 to 50oE and latitude 28 to 36oN. The area is covered by the Zagros Mountain Ranges and western foothills, including agricultural lands, sparse oak forest. MODIS images were used to identify the main sources of dust. MODIS (or Moderate Resolution Imaging Spectro-radiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM)satellites. Terra's orbit around the Earth passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment. Brightness temperature difference between suspended particles matters in the 8.5, 11 and 12 micrometers wavelengths; providing the possibility of identifying dust phenomenon. Thus, with increasing amounts of dust, the difference (BT8.5-BT11) and (BT11-BT12) is increased and amounts (BT11-BT12) decreased. Also for studying and analyzing of atmospheric synoptic conditions during the dust phenomena, U(Zonal) and V (Meridinal) components of wind, average surface pressure and geo-potential height in 500hpa were used. After the detection of dust and creation of false color images and synoptic situation of desert regions that located in the north and south of Al-raqqeh city, north side of the Euphrates River in Syria and deserts of Iraq were identified as main sources of dust formation. Research results show that Trough axis location, region of upper divergence in 500hpa and formation of thermal low pressure center at ground surface have major role in formation, dispersion and transmission of dust. Interesting point is that the spatial spreads of dust occur in clash with Zagros Mountains Range, so Zagros elevations actas a cause of spatial distribution which spreads dust storms in the north and south side. Trough axis placement in Syria and Mesopotamia in 500hpa level in related with surface low pressure expanded over Persian Gulf and Arabian Peninsula (surface unstable condition) in the absence of moisture result dust storm. Using two sources of remote sensing and weather maps data and information more accurate results from the transfer and distribution pattern of the suspended particles were provided. So that the surface pressure pattern, geo- potential height and wind vectors drawn on maps of surface and 500hpalevel are transmission and dispersion pattern obtained from satellite imagery detection. The most important factor in spatial distribution pattern of dust storms is ZagrosMountains as a barrier that in addition to the accumulation of suspended particles in the western slopes, leads transmission and dispersion to the north and south directions.

Physical development of cities is inevitable and a dynamic process which will change the land cover areas. Urban growth must be led by the most appropriate land use planning. Urban land cover/use maps are used for current and future land use and urban planning. Remote sensing technology and application of satellite data in mapping land cover often will reduce costs, save time, and increase accuracy and speed. There are several methods to classify land cover.If we classify the methods of supervised classification algorithms based on complexity and accuracy, they can be divided into two main methods (the average distance to the minimum, maximum likelihood, etc.) and advanced methods (neural network, fuzzy classification methods and knowledge base methods). In support of image classification, two different methods including, Fuzzy ARTMAP classifier and Multilayer perceptron neural network classifier were used. In this study, in order to produce land cover map of Isfahan city, digital image of LISS-III scanner that was acquired on 8th August 2008 were employed. The study area is 34,500 ha within the Isfahan city. We use image sensor LISS-III of satellite IRS-1D to do land cover classification. First of all, geometric correction was applied. Then, the satellite data was studied using spectral and spatial profiles to ascertain the digital numbers (DNs) of different land cover categories prior to classification. Training samples were selected. In support of image classification, two different methods including, Fuzzy ARTMAP classifier and multilayer perceptron neural network classifier were used. Finely, land cover map of the study region was classified into five classes: water, residential area, barren lands, vegetated areas and road.The Fuzzy ARTMAP Adaptive Resonance Theory (ART) based neural network as developed by Grossberg and Carpenter (1991) has evolved from the biological theory of cognitive information processing. Fuzzy ART is a clustering algorithm that operates on vectors with fuzzy analog input patterns (real numbers between 0.0 and 1.0) and incorporates an incremental learning approach which allows it to learn continuously without forgetting previous learned states. Fuzzy ARTMAP for supervised classification, has four layers, F1 (input layer) and F2 (category layer), the map field layer and output layer. F1 and F2 layers make up the ART (a) model. The F1 layer represents the input feature vector and thus has neurons for each measurement dimension. These two layers make up the ART (b) model. The output and map field layers consist of m neurons each, where m is the output class dimension. There exists a one-to-one connection between these two layers. Multilayer Perceptron Neural Network A multilayer perceptron usually consists of an input layer, one or more hidden layers and an output layer Order to receive process information and represent. MLP undertakes the classification of remotely sensed imagery through a Multi-Layer Perceptron neural network classifier using the back propagation (BP) algorithm. The multi-layer perceptron used in BP back-propagation (BP) learning algorithm is one of the most widely used neural network models. A typical BP contains nodes, indicating unequal connecting weights. The function of the hidden layer nodes is to give an analogy; equivalent to lines that can discriminate points and feature space into several groups. Back propagation involves two major steps, forward and backward propagation, to accomplish its modification of the neural state. During training, each sample is fed into the input layer and the receiving node sums the weighted signals from all nodes to which it is connected in the preceding layer. The purpose of training the network is to get the proper weights for both the connection between the input and hidden layer, and between the hidden and the output layer for the classification of the unknown pixels. The input pattern is classified into the class that is associated with the node with the highest activation level. Land cover maps of the study region were classified into five classes (fig. 1). To assess the classified land cover map precision, it was controlled for ground-truthing with 100 control data GPS in 3500 ha and the error matrices were defined.the high accuracy of geometric correction. After choosing the best educational samples, supervised classification with fuzzy ARTMAP classifier and Multilayer perceptron neural network classifier were applied on image LISS-III bands and the land cover maps were obtained on 5 classes.To assess the classified land cover map precision, it was controlled for ground-truthing with a GPS and the overall accuracies were 88%and 93.29% for fuzzy ARTMAP classification, multilayer perceptron networks, respectively. Fuzzy ARTMAP classifier separated vegetation class from the other cover classes. Residential areas, water and vegetated areas classes were recognized. The influence of surfaces area including roads and residential areas has a large overlap with each other and fuzzy classifier doesn’t provide an appropriate separation compared to Multilayer perceptron neural network classifier. User accuracy of Multilayer perceptron networks in all classes higher than the fuzzy ARTMAP. Fuzzy ARTMAP classifier cannot separate residential areas class in the study area due to mixed pixel class with the barren lands and the road. Multi-layer perceptron neural network classification method has the highest overall accuracy and is able to distinguish five types of coverage from each other. Although our results show that using neural networks has higher accuracy classification compared of fuzzy ARTMAP, but The fuzzy ARTMAP method provided a high accuracy for land cover classification in this study. Fuzzy ARTMAP classification accuracy was equivalent to 88.03 percent but the overall accuracy is recommended higher 85 percent for proper classification.