جستجوی مقالات مرتبط با کلیدواژه « طبقه بندی » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture has been taken into consideration. Discriminant analysis models are more complex, more accurate and more efficient in comparison to modern methods. In current study, the areas with infiltration potential located in some parts of Khomein, Shazand, Azna, Aligudarz and Durood areas (Marboreh watershed) were went under investigation using the mixture discriminant analysis (MDA) model. For this purpose, the infiltration samples gathered by double ring test, with the environment-effecting layers on infiltration, were prepared and then introduced to R_studio, employed to run MDA. In order to assess the results, validation indices (ROC curve, CCI, TSS, Recall and Precision indices) were used. According to the results, 6.2, 6.1, 12.7, 13.3 and 15.9% of areas of Shazand, Khomein, Durood, Azna and Aligodarz respectively lie in highly potential infiltration, whereas 1.1 16.5, 14.3, 19.6 and 10.8% of those areas were found to have extremely potential infiltration. Most of these areas have sandy soil texture and Quaternary formations with agricultural and range land uses. The accuracy indices that obtained as 0.89%, 76.66, 0.53, 0.91% and 0.73%, witnessing the acceptance and excellence of model performance. The results of this study can be useful in the decision-making for managers and planners regarding to the groundwater recharge in accordance with urban and agricultural needs, because groundwater resources and ensuring their stability are the main factors for sustainable agriculture.

The purpose of this study is to investigate landuse changes in Gorganrood basin in 2001, 2010 and 2019. Using Landsat and Product-Modes satellite images, used maps were prepared using the classification method of random forest algorithm in Google Earth Engine. Satellite imagery was classified into eight classes including forest, cropland, shrubland, grassland, wetland, urban, barren, and water. The results showed that the highest landuse area in 2001, 2010 and 2019 is related to cropland, respectively, equal to 3150.58, 3403.8 and 3031.82 square kilometers, and lowest amount in 2001 is related to water with an area of 84.67 and in 2010 and 2010 is related to wetlands with an area of 167.98 and 118.7 square kilometers. Comparison of the results showed that the urban landuse study period in 2019 compared to 2001, it has grown by about 34% (341 square kilometers). Also, forest lands at a rate of 5.5 percent (148.92 kilometers), shrubs at a rate of 8.7 percent (135.79 kilometers) and arable land at a rate of 3.8 percent (118.76 kilometers) had a negative growth.

The amount of Total Dissolved Solids (TDS) is an important factor in determining the water quality of rivers. The purposes of this paper was to investigate the efficiency of four intelligent models including, SVM-IWO, SVM-PSO, SVM-ABC, LS-SVM and Bayesian Neural Network, in predicting water quality of Babolrood and Sefidrood rivers, north  of Iran. To achieve this aim, unpublished measured monthly data including; Ca, Mg, HCO3, Na, SO4, EC, pH, and TDS from Quran-Talar station of Babolrood river and Prorijabad station of Sefidrood river were analyzed in the periods of 1966-2015 and 2002-2015, respectively. Evaluation of the four above-mentioned models, based on the correlation coefficient (R2), Root Mean Square Error (RMSE) and the Nash-Sutcliff coefficient (NSE) showed that the SVM-ABC model in both Babolrood and Sefidrood rivers with the highest R2s equal to 0.985 and 0.989 and the least amounts of RMSE equal to 10.493mg/l and 5.289 mg/l and the highest Nash-Sutcliffe equal to 0.983 and 0.992, respectively, has better and faster performance compared to others models in assessing water quality.

Vegetation is one of the most important key components in arid regions for reducing of the effects of erosion and determining the severity of desertification. Decrease in vegetation leads to increase in surface albedo. Accessing and preparing desertification intensity map at the fastest possible time and at the lowest cost is one of the concerns of governments. In the present study, in order to identify the best vegetation index for preparing the desertification intensity map, MSIL-1C data of Sentinel 2 satellite in the arid region of Sistan has been used. For this purpose, the relationship between surface albedo and each of the different vegetation indices of the NDVI, RVI, DVI, PVI, SAVI and TSAVI were conducted. After determining the linear regression equation between the albedo and each of the mentioned indices, the relevant desertification intensity equation was calculated and the desertification intensity map of the studied area at 5 classes was prepared based on albedo and each of the mentioned indices. The results showed the strongest relationship in the study area was between albedo and NDVI, with a correlation coefficient of 0.63, and the lowest correlation of 0.37 was between the albedo and PVI indices. Based on the present study among the indices studied, the NDVI is the best for the preparation of maps of desertification intensity in the arid region of Sistan. Based on this index, 20.3% of the region was classified as severe and 32.9% of the region grouped into the moderate desertification class.

Nowadays, desertification and land degradation are among the most important environmental chalenges caused by various factors, including climate variation and human activities, especially in arid and semi-arid regions. Therefore, understanding of the desertification status is essential for future management and planning. In this regard, using remote sensing indicators to prepare basic desertification maps and its monitoring can be efficient. In this study, Topsoil grain size and albedo indices were used as indicators to identify desertification in the center of Khuzestan province. After constructing the above-mentioned indices using Landsat ETM+ sensor image, the values of 411 randomly selected samples on the images were used to construct the Albedo-TGSI feature space model. The correlation between the variables was 0.83. The DDI desertification degree equation is then obtained based on the slope of the fitted line. In the next step, by applying natural break classification on the DDI index, different levels of desertification and the break values ​​were obtained for random samples. These break values ​​are then applied to the whole study area, and finally, the 2018 desertification status map was obtained. Results indicated that about 70% of the study area fell under the severe and high desertification intensities, whereas 18.3%, 8.3% and 4.1% fell under the medium, low and none desertification grades respectively. The accuracy of the produced map with a kappa of 92.1% and an overall accuracy of 94.3% showed that the feature space model is a useful and robust tool for extracting desertification degrees in barren lands or areas with extremely low vegetation coverage.

Land cover change is one of the most effective factors of environmental change. So, understanding and predicting the causes, processes and consequences of land cover has become a major challenge. The remote sensing and satellite data technologies can be used effectively to detect and quantify land cover changes and its effects on the environment. This research used the MODIS MCD12Q1 yearly data at 500 m resolution to evaluate the spatio-temporal changes in the national land cover of Iran from 2001 to 2015 in five years periods. Land cover maps of Iran were generated based on 12 classes for years of 2001, 2005, 2010 and 2015 and then evaluated using “Landcover Change Matrix”. Areal distribution analysis showed that the most parts of Iran covered by barren areas (more than 60%). The area of forests, croplands and grasslands were reduced from 0.8, 4.8 and 21.4 percent respectively in the year of 2001 to 0.6, 4.5 and 20.9 percent of Iran’s area in 2015. Furthermore, to evaluate land cover changes in five years’ period, the land cover map of the first and last years of each period were compared using “Landcover Change Matrix”. Results showed that in the five years periods of 2001-2005, 2005-2010 and 2010-2015 land cover of Iran changed increasingly as 2.13, 3.25 and 3.30 percent respectively of Iran’s area. And the overall changes of land cover in Iran from 2001 to 2015 is 6.08 percent of Iran’s area.

As a valuable asset that play a key role in the environment, natural resources, and the production of agricultural products, soil provided an appropriate ground for plant growth and vegetation development. Therefore, any disregard to the preservation of such a valuable capital may result in food shortages, soil erosion, and degradation of natural resources. From among different indices offered for land degradation, soil saturation and salinity are regarded as the main factors involved in desertification. According to the estimates presented by the US Environmental Protection Agency, about 20 percent of the worldchr('39')s agricultural land is under salt stress, and soil salinity is a major constraint on the use of arable land. Meanwhile, soil salinity has rendered many parts of different regions unusable and inappropriate for agricultural activities and natural resources and it is considered as a major problem in arid and semiarid regions. Thus, as more than one-third of the worldchr('39')s soils and much of Iranchr('39')s soils are located in arid and semi-arid regions, it is necessary to take soil salinity into account. In this regard, assessing the environmental potentials could be used a solution for optimal use of soil and water facilities with the least environmental consequences.  Using laboratory methods for estimating salinity is generally time-consuming and costly. Also, due to high spatial variability of soil salinity, it is better to use remote sensing data to determine and monitor saline soils. Considering the importance of the subject and the capability of remote sensing, this technique has been extensively used for studying salinity inside and outside Iran. However, few studies have ever been conducted with eight Landsat images and all salinity indices.  

this study sought to summarize and study all salinity indices used in remote sensing for soil salinity zonation. To this end, using modern remote sensing and GIS software is inevitable. Therefore, as the first step in applying the remote sensing method, spectral indices were used to extract the soil salinity map. For this purpose, after studying salinity indices such as BI, DVI , NDSI, IPVI, SI, SI1, SI2, SI3, SI5, SI6, SIA, SIT, the brightness value of the sampling points was extracted. Based on satellite images, the study region was classified into urban areas (class 1), low salinity areas (class 2), high salinity areas (class 3), mountainous lands (class 4), agricultural lands (class 5). Considering the ability of each of these indices in presenting soil salinity maps, the data were analyzed by SPSS software. Except the EC parameter which was normalized via logarithmic transformation, all other parameters were found to be normal. Having assured of the normality of the data, the correlation between the maps derived from these indices and actual data collected from the area were compared and contrasted.  

Having examined the correlation between the obtained data, it was found that that NDSI and SI3 had the highest correlation with field study data, and that they were the best method for preparing salinity maps without any need to sampling. Moreover, from among the indices investigated in terms of effectiveness, DVI, IPVI, and SI2 were found to have the lowest accuracy in revealing salinity.

The findings of this study indicated that remote sensing techniques were much more useful in preparing soil’s salinity maps than other methods in terms of accuracy and costs. Generally, it could be said that remote sensing is a very powerful tool in soil salinity mapping. Considering the sensor’s low resolution and radiometric accuracy, the fact that salinity changes are not very sensitive and detectable, and that salinity, unlike vegetation, indirectly reveals the waves’ reflections, it should be noted that achieving the best index requires higher resolution images in a wider region so that more significant results could be obtained for defining a regional index. According to the study’s findings, the electrical conductivity of the intended region ranged from 6/11 to 5/5. Ds/m. Moreover, based on soil salinity maps, the southeast lands were found to have more salinity, and that all twelve indices used for identifying the salinity of the region could also be used to distinguish the saline lands. Researchers such as Darwish Sadif et al. (2000), Chitaz (1999) and Abdi Nam (2004) have produced salinity maps using correlation coefficients of spectral values ​​of images and electrical conductivity. Akhzari and Asadi (1395) introduced the NDSI index with 88% correlation as the most appropriate index and identified SI1 and SI2 as inappropriate indicators for soil salinity examination in their study area. The findings of the current study indicated that NDSI and SIT were the best indices in the study region. Moreover, DVI, IPVI, SI2 were found to be the least efficient indices in salinity detection, and that the largest saline area belonged to NDSI and SIT.

In recent years, a considerable part of the western oak forests of the country has been faced with drought and decline. Initial studies have identified one of the factors affecting oak forest dryness as increasing frequency and severity of dust phenomenon. In this study, the MODIS sensors with a resolution of 1 km and 250 m and meteorological data of Kermanshah Province synoptic stations over a 15-years period (2014-2000) was used to investigate the dust phenomenon and the process of forest vegetation changes in the region. Dust intensity was investigated using visibility data and unsupervised image classification method. Also, in this study, the relationship between the occurrence of dust and vegetation changes in oak forests was investigated. Survey of dust events showed that Kermanshah Province had the highest number of dust events in 2008 and 2009, respectively. The highest incidence of severe dust was in 2012. In addition, satellite imagery and spectral indices also showed that the highest reduction of vegetation in dense forests (>50% canopy cover) by 67% and with an overall accuracy of 62% in the south and southeast of Kermanshah Province and at the border of Lorestan and Ilam provinces has happened. Results also showed that the frequency of dust phenomenon is highly correlated (70%) with the phenomenon of oak forest degradation; and dust has the effect of reducing the greenness of oak forests in Kermanshah province.

Flood plains rivers are fertile lands that are considered by humans and people are always demanding the development of these areas. Therefore, protection and management of flood plains is essential. In this research, an interval of about 80 kilometer from the Gamasiab River in Kermanshah province was studied. First, using OLI and sentinel sensor for marginal lands classification object oriented method. The results of the verification showed that the sentinel sensor map is more accurate than the OLI sensor. So, to simulate the flood plain, periods 25, 50, and 100 of the sentinel sensor image were used. The results showed that in all periods, more than 95 percent of the extracted areas are related to two waterland fields and dryland fields, because flood plains of the rivers are fertile lands that are considered by humans and humans are always demanding the development of these areas. In addition, residential properties in the 25 period are 2/29 hectare, all of which are riverside villages, Hence, it is necessary that by establishing the rules and implementation of the civil regulations of flood plains, which is one of the direct control mechanisms of land use in these areas, Incorrect use of the land around the river has become its authorized use and the river boundaries have been identified.

This research was conducted to evaluate the environmental factors affecting functional characteristics (biomass, density, regeneration, cover%, basal area and species richness) of Ferula haussknechtii in Saral rangelands of Kurdistan. After determining three altitude classes (1850, 2250 and 2650 m), a systematic-random sampling was performed to measure aboveground biomass, cover percentage, height, basal area, and density of Ferula haussknechtii with 1.5-m2 plots along transects. In each plot, a soil sample was taken from 0-30 cm soil depth. Soil texture, CaCo3, acidity, electrical conductivity, nitrogen, potassium, phosphorus, and sodium were measured in the laboratory. Analysis of variance of data was done using a randomized complete design with R software, and also CCA was used to compare the relationship between species and environmental factors. The results showed that aboveground biomass, vegetation percentage, height and density of Ferula haussknechtii increased with increasing altitude, while the basal area of this species decreased. The green biomass, cover percentage, basal area and height of this species were positively correlated with nitrogen, carbon, potassium, silt and clay and negatively correlated with acidity, phosphorus and sand. The results also showed that due to the impact of this valuable species on the soil stabilization, forage supply and production of active ingredients, altitude, nitrogen, carbon, clay and silt were the most effective factors in establishing this species.

To better managing of rangeland the vegetation map is one of major factors, because plant communities is planning units of rangeland management and vegetation map shows the current status of plant communities. This research was conducted to produce vegetation type's map using Landsat 8 image classification in Behbahan, Khuzestan province. Rangelands of the study region is warm semi steppe and winter grazing. Geometric correction of satellite image was performed by ground control points with an error of less than one pixel. Atmospheric correction of existing data using the dark object subtraction was done. Field visits for vegetation type's border controlling and sampling training area was conducted. Eight supervised classification algorithms included Parallelepiped (PP), Minimum Distance to mean (MD), Mahalanobis distance (MAH), Maximum Likelihood (ML), Neural Net (NN) and Support Vector Machine (SVM) was performed. The results showed that ML algorithm has the highest overall accuracy (87.5 percent) and kappa (0.867) and PP algorithm has the lowest overall accuracy (67.1 percent) and kappa (0.571). It is suggested that, along with digital methods of classification of satellite images, visual interpretation should be used to clarify the boundary of the obtained vegetation types map.

Understanding the physical and chemical characteristics of sediments of lakes and wetlands is important for sedimentology and erosion studies. Maharloo Lake is one of the most important lakes of Fars Province. Surface sediments of this lake consist of evaporitic and muddy sediments. For recognition of the nature of the sediments, samples were taken from lake bed and carbonate and evaporitic compositions including calcium-carbonate, calcium-solfate and sodium-chloride were measured. After omitting carbonate and evaporitic compositions, granolumetric analysis of terrigenous fraction was performed using hydrometry method and sediment classification was performed using Pettijohn (1975) Method. The result of the percentage of terrigenous and chemical fractions of surface sediments showed that most of the sediments were fine-grained terrigenous sediment, containing chemical salts. Terrigenous sediments are poorly sorted, skewed toward coarser sizes and have slightly gravelly, sandy mud texture. More than 90 % of sediment minerals in the lake is clay and less than10 % is gypsum, quartz, quartz sandstone, and limestone. Depending on the type of sediment, minerals, salts and frequency of deposits, the sediments of Maharloo Lake are prone to wind erosion and dust generation in the region.

Classification of land use extraction always been one of the most important applications of remote sensing and why different methods are created. Over time and with greater accuracy were developed more advanced methods that increase the accuracy and the extraction classes that were closer together in terms of quality are better. SVM is one of these methods in the study of this method for the extraction of forest land, farming, pasture, and the city and its various kernel includes a linear (Linear), polynomial (Polynomial), radial (RBF) and ring (Sigmoid) were evaluated to determine the best kernel to extract these applications. The results showed the best overall accuracy and kappa coefficient, respectively polynomial of degree 5, 6 and 4 and the lowest is in the ring or Sigmoid. With increasing degree polynomial (except Grade 2) were added to the overall accuracy and kappa coefficient. Overall, we found that increasing degrees of polynomial boundary between the classes better spectral resolution and in areas that were close to be more successful. It also increases the degree polynomial caused more accurately separate the boundary between the classes. Our goal is classified when the user is using more than two degrees above polynomial (preferably 5 or 6) is recommended.

Plains are one of the most important Geomorphological units, and different parameters for classification are presented in plain areas. One of The classification methods that are used in studies of natural resources in Iran, The plain division are in three types: bare plains, Apandazh and covered plain .The qualitative and quantitative factors are used to distinguish them from each other. In this study the geomorphometrical parameters used for plain classification by using artificial neural networks and sensitivity analysis. These parameters extracted by using mathematical equations and the corresponding relations on digital elevation models. Geomorphometric parameters that using in this study included Percent of slope, Plan Curvature, Profile Curvature, Minimum Curvature, the Maximum Curvature, Cross sectional Curvature, Longitudinal Curvature and Gaussian Curvature. These parameters calculated in area 125000 hectare and sample point are 1500; the result with ground truth map was compared and calibrated. Sampling method in this study is Latin Hyper cube that is kind of stratified random sampling. Result of this study shows the importance Geomorphometric parameters to classify desert plains include: Plan Curvature, Profile Curvature that is more sensitivity in plains types. As the topography is reduced, role and importance of these factors are reduced And highest parameters of the parameters is bare plain and lowest performance are covered plains.

Land use change is one of the most important factors of global environmental change. So, understanding and predicting the causes, processes and consequences of land use and land cover has become a major challenge. The Remote Sensing and Geographic Information System (GIS) technologies can be used effectively to detect and quantify of land use changes and its effects on the environment. The purpose of this study is to evaluate the land use changes in Abolabbas basin in the period of 1990-2009 using remote sensing and satellite images. At the first step, Land sat satellite images have been used for the preparation the land use maps of 1990 and 2009. Then, satellite images were classified using a artificial neural network algorithm with fine accuracy in eight class of land use (rain fed farms, irrigated farms, rainforest, semi-dense forest, thinning forest, fair rangelands, poor rangelands and residential areas). The results showed that area of rainforest and semi-dense forest decreased during this period 8.48 and 12.26%, respectively, and have become to the thinning forest and rangelands that increased 10.39 and 12.35%, respectively. On the other hand, agricultural area (rain fed farms, irrigated farms) decreased 1.79% and residential area increased 0.19%. With regard to the land cover has changed in this period, these changes can have negative effects on the environment and natural resources, also natural disasters such as floods will increase.

The present study was conducted to classify vegetation and determining relationship between plant species distributions and environmental factors in rangelands of Qom Province. Sampling of vegetation was done by Random systematic method. The distance between each transect was 500 meters and the distance between plots per transect also varied depending on the length of transect. With regard to the type of the species the plot size determined by the minimal area method Between 2 and 25 m2 and sample size were determined 60 plots by considering to the changes in vegetation. In addition to recording of vegetation features in plots (Plant species name and canopy cover) related information on habitat such as slope, aspect and elevation were also recorded. At each habitat, soil Sampling was performed by drilling of eight soil profiles and sampling from 0-30 and 30-80 cm depths. After sampling, soil characteristics consisting gravel percent, texture, saturation moisture, available water, lime, gypsum, organic matter, Acidity (pH), Electrical conductivity (EC) and soluble Solute (Na, Ca2, Mg2, K, Cl-, Co32-, Hco3- and So42-) were measured by routine methods. The vegetation classification was done using TWINSPAN method. By performing classification, seven plant groups consisting nine habitats were diagnosed. To analyze the relationship between vegetation and environmental factors, PCA and CCA Analysis were performed. Based on the results, in downstream soil saturation moisture, conductivity, acidity, texture and Lime and in the upstream of the study area gravel, altitude, and slope are factors which have greatest impact on the distribution of plant species habitats in the study area. Understanding of requirment ecological of plant species and environmental characteristics of each region will bring choice of species which are more compatible to the conditions of each region, and as a result the success of vegetation the rehabiltation activities.

Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons including the non-systematic management of these resources. Remote sensing technology and satellite data are useful tools in the studies of rangeland and vegetation sciences. One of the applications of satellite data is mapping range land use. The aim of this study was to compare two methods of maximum probability and fuzzy for rangeland zonation. For this purpose, Landsat ETM+ was used; then, after final geometric and radiometric corrections, the final classification map was prepared. According to the results of accuracy of these two methods using the kappa coefficient, the artificial neural network algorithm of fuzzy Artmap with a coefficient of 0.9614 was more accurate than the maximum probability algorithm with a coefficient of 0.8058. Results of this study also indicated that the traditional algorithms of classification such as statistical methods due to their low flexibility, and parametric types such as maximum probability method because of the dependence on the Gaussian statistics model, could not provide optimal results, when the samples were not normal. In this study, ENVI 4.5, Idrisi Andes 15 and Arc GIS9.3 software were used.

This study aimed to investigate the orographic effects on vegetation status in east part of Alborz Mountain range. Soil and vegetation samples were collected along 19 km north-south of this mountain range from Sorkh Gerive to Badeleh kooh in Hezarjerib area. Two transects with 100 meters lengths in west-east direction (Perpendicular to the dominant slope) were established in each site, located with 100 meter elevation from sea level intervals, and plant species presence and absence were recorded in every 50 cm along the established transects. Classification and multivariate analysis (such as cluster analysis, CCA and PCA) were used to explore the relationships between vegetation, soil and elevation from sea level. According to the results, plant communities reflect the ecological status of the studied sites mainly determined by orographic effects in this area. According to PCA analysis, elevation was the most important variables, explaining vegetation changes in the area. The CCA results revealed that Acantholimon-Acanthophyllum community was mainly affected by elevation. Fagus Carpinus community depends mainly on aspect and had also a positive correlation with potassium content in top soil layer. As Carpinus-Poa sub-community was dominant in slopes received high sun exposure and had the highest mean temperature, therefore it can be concluded that this sub community is well adapted with relatively dry, sunny and warm habitat in this type of ecosystem.

One of the most important problems that empirical model users in Iran are facing with, is that these models are prepared for countries with special conditions such as lithology factor, which scores are given based on geology condition of origin country that are not compatible for a vast country like Iran with a great lithological variation. This research was conducted to reduce such problems and increase the efficiency and localization of these kind of models and provides rock erosion sensitivity factor for their special conditions. In PSIAC and EPM as a common experimental models, lithology factor is an important agent among other effective factors. In this research, a comprehensive classification of erodibility has been produced in 10 orders, based on mineralogy, chemistry, texture, structure and mechanical properties. Regarding to aridity of many parts of the country, and low rate of soil generation, erodibility rates of geological formations would be an important tasks. Results showed that, about 70 percent of the area covered with very low to low strength lithology to erosion and have a high potential of sediment yielding. Geological formations’ erodibility has been classified in 30 Tamab watershed with order of 4 and the results are compared with EPM results and monitored values in river gauging stations. Results also indicated a good agreement with high confidence with natural conditions and with respect to other important factors of the watersheds such as topography and rainfall.

The current research was carried out to find out the most effective environmental factors in plant species occurrence by classification and ordination methods. For this purpose, the study was conducted in Nir rangelands of Yazd province and topography, climate, soil and grazing intensity data of the region were determined. Sampling method was randomized–systematic and within each sampling unit 3-5 parallel transects with 300-500 m length, each containing 30-50 quadrates (according to vegetation variations) were established. Quadrate size was determined for each vegetation type using the minimal area; hence suitable quadrate size for different species was determined 1*2m–10*10m (2-100 m2). Soil samples were taken from 0-30 and 30-80 cm in starting and ending points of each transect. Measured soil properties included gravel, texture, available moisture, saturation moisture, organic matter, lime, gypsum, pH and electrical conductivity. To analyze environmental data, classification (using TWINSPAN) and ordination (using PCA, CCA) were used. The results indicated that soil salinity, texture and available water play the main role in distribution of plant species.