hossein piri sahragard

Altitude is considered an important factor in creating environmental gradients by affecting many components of the ecosystem. In the present study, the effect of altitude on some environmental processes, including habitat suitability, the trend of changes and seasonal fluctuations in land surface temperature (LST), organic carbon density (OCD), and vegetation cover index (NDVI), in areas of Moringa peregrina presence in the south and southeast of Iran, including the provinces of Sistan and Baluchistan, Kerman, and Hormozgan, were evaluated. In this regard, 61 species presence points were collected in the summer season from 2015 to 2020. LST was prepared using the product (MYD11A1) of MODIS from 2003 to 2023, separated by different seasons of the year, and the trend of its changes was also analyzed by the Mann-Kendall (MK) test. LST variability or heterogeneity was calculated using principal component analysis (PCA). Vegetation Index (NDVI) values were extracted from Google Earth Engine (GEE). Then, the 61 presence points were arranged in increasing height, and the gradients in the mentioned criteria for these points were checked using the non-parametric MK test. Using the results of significant gradients of trend analysis, Pettitt's breaking point analysis was performed between the presence points in order to group them. Also, to group the points using all the variables, K-Mean clustering was used. The results showed that habitat suitability in the presence areas does not increase with the increase in altitude; this condition also exists for OCD and NDVI. But the trend of LST in spring and autumn showed a decreasing trend with increasing altitude. Accordingly, with the increase in altitude, the fluctuation in LST increases significantly in each of the four seasons of spring, summer, autumn, and winter (p<0.0001). Also, the results showed that the height above 400 meters above sea level is considered the threshold of environmental changes in environmental gradients. Based on the analysis of the breakpoint, the available attendance points were divided into two northern and southern groups according to the fluctuations of the environmental gradients. The spatial distribution of groups based on K-means clustering was also confirmed. Overall, it can be said that elevation, due to its influence on climatic parameters (temperature and precipitation) and edaphic characteristics, has affected the presence and distribution of the M. peregrina in the study area's habitats. Therefore, this factor can be identified as the primary influence in the formation of microclimates and the distribution of the M. peregrina habitats.

The conservation and management of endangered medicinal and industrial plants in rangelands are essential for the economic and social benefits they provide. Dorema ammoniacum D. DON. is an important fodder, medicinal, and industrial plant that is threatened due to over-exploitation. This study aimed to predict the potential habitat of D. ammoniacum using logistic regression analysis and to identify the environmental factors that influence its distribution in the Nadoushan rangelands of Yazd province.

We collected data on environmental factors, including topography, climate, soil, and vegetation, from the rangelands of Nadushan. Vegetation sampling was conducted in six homogeneous units using the random-systematic method. Soil samples were collected from the beginning and end of each transect at depths of 0-30 and 30-60 cm. We used variogram analysis to investigate the spatial structure between soil factors, and kriging interpolation and inverse distance weighting methods were used to prepare soil characteristic maps. The logistic regression model was used to predict the potential distribution of D. ammoniacum, using presence/absence data and environmental data.

The results showed that the variables of saturated moisture, sodium and calcium percentage of the first soil depth, organic carbon percentage of the second soil depth, and nitrate of the first soil depth were the most important environmental factors affecting the distribution of D. ammoniacum. The presence of D. ammoniacum had a positive relationship with the percentage of saturated moisture and sodium of the first soil depth, organic carbon of the second depth, and calcium of the first soil depth, and a negative relationship with nitrate of the first soil depth. The logistic regression model predicted that 20% of the studied area had excellent suitability and 24% had suitable habitat for D. ammoniacum.

Logistic regression analysis was useful for predicting the potential habitat of D. ammoniacum in the Nadoushan rangelands, and the results can guide conservation efforts and management decisions. The study identified the significant environmental factors that influence the distribution of D. ammoniacum and provided a useful tool for identifying potential areas for sustainable exploitation and restoration of the species.

The object of this study was to compare predictive accuracy of some individual modeling methods versus ensemble modeling approach in estimating the spatial potential distribution and identifying ecological requirements of Artemisia sieberi in desert rangelands of Yazd province, Central Iran. For this purpose, the species presence data were collected using the random systematic sampling method in 2019. Individual modeling of the species distribution was performed using Random Forest, Classification and Regression Tree and Generalized Additive Model after preparing environmental variable maps using GIS and geostatistics. Predictive performance of individual models was evaluated using Area Under Curve and Root Mean Square Error statistics. Furthermore, the Ensemble model was used based on the weighted average AUC. The appropriate threshold limit value was calculated based on True Skill Statistic for conversion of continuous maps to binary ones of habitat suitability. Comparison of the performance of individual models showed that the RF model had a more accurate prediction compared to the other models (AUC= 0.971 and RMSE= 0.256). Evaluation of the models implemented using threshold-dependent metrics such as Sensitivity, Specificity, and Kappa index also confirmed this finding. The overall comparison of the results from the three models versus the Ensemble model also indicates the high performance of this model compared to the individual models. Based on Ensemble model results, 45.38% of the study area had a high suitability for the establishment of A. sieberi. Based on the analysis of the importance of variables in the RF model, elevation (42%), Clay (40.02%) and pH (38.97%) in 0-30 cm soil depth had the highest effect on the presence of species. In general, Ensemble modeling can reduce the uncertainty and provide more reliable results by combining the results of the different algorithms of individual modeling.

The present study aimed at preparing the potential distribution map and identifiying the environmental requirement of Prosopis farcta L. using tree-based and regression methods in the marginal rangeland of Niatak river in Sistan region. For this purpose, species presence data was recorded randomly. Environmental variables were prepared using field sampling and digital elevation model. In order to achieve the pseudo-absence points, first habitat modeling was performed using the domain model, then pseudo-absence points were prepared using the prediction map obtained from this method. Species distribution modelling was conducted using random forest (RF), classification and regression trees (CART) and generalized additive model (GAM). The accuracy of the models used was evaluated using the area under curve criterion. Result showed that the RF with area under curve 0.98 has the highest accuracy. Generalized additive models and classification and regression trees were ranked after RF. The highest and lowest values of kappa index were assigned to the RF with 0.75, and GAM with 0.43 Kappa value. Accordingly, the RF model is the most accurate model in predicting the potential habitat distribution. Analysis of the variable’s importance showed that in the studied scale, edafic factors and distance from the river have greater effect on species distribution than other factors. So that, in all models used, acidity and electrical conductivity were identified as the most important variables. In general, it is suggested that habitat development plans for Prosopis farcta should be planned in the central and marginal parts of the Niatek river due to better suitability of these regions for species distribution.

The present study aimed to model potential habitat distribution of A. sieberi, and its ecological requirements using generalized additive model (GAM) and classification and regression tree (CART) in in the Poshtkouh rangelands of Yazd province. For this purpose, pure habitats of the species was delineated and the species presence data was recorded by the systematic-randomize sampling method. Using DEM and geostatistical method, digital layers of environmental variables (soil and physiographic variables) were prepared with the same spatial resolution (pixel size 30×30 meter). Plant distribution modeling was conducted using CART and GAM models in the R.3.3.1 software environment. The prediction performance of the models was evaluated by the AUC (Area Under the Curve) In addition, The TSS (True Skill Statistic) was used to determine the optimal threshold limitof species presence. The classification accuracy of the presence/ absence map was investigated using the Kappa index. Based on the results, The CART model had a better predictive performance than the GAM models (AUC=0.97 and 0.89, respectively). Furthermore, the Kappa coefficient of the CART model was higher than GAM, based on the obtained Kappa coefficient values (0.97 and 0.89, respectively). This study concludes that the CART model were more accurate in estimating the distribution range of A. sieberi in comparison with the GAM model. The analysis of the importance of variables showed that the electrical conductivity (EC) and acidity (pH) of the first soil depth had the highest effect on the distribution of A. sieberi. In general, it can be concluded that application of data-driven methods, such as CART model, can be useful for accurate estimation of the potential habitat distribution of plant species on a local scale. Therefore, the application of these models to introduce the suitable species in vegetation reclamation plans of Iran's desert rangelands is recommended.

Species distribution models (SDMs) are the basis of informed decisions in vegetation management by quantifying the relationship between species distribution and influential environmental variables. The present study aimed to evaluate the GAM and CART models' performance in estimating the potential habitat distribution as well as recognizing the ecological needs of plant species in the Khezri rangelands of Bayaz plain of southern Khorasan. According to the regional condition and field observation, in an area of about 14500 hectares, vegetation sampling was done using the randomized-systematic method. Eighteen environmental variables including land characteristics, Normalized Difference Vegetation Index (NDVI), and salinity index were used as an estimator to generate maps of predictor variables. After modeling the habitat distribution prediction using CART and GAM methods in R 3.5.2 software, the accuracy of the models was assessed using the subsurface area (AUC) statistics. After determining the threshold by the TSS method, the continuous utility map was converted to the presence/absence map and the degree of conformity of the maps with the kappa index was calculated. Based on the results of the used models, the variables of the base level of the channels network, the vertical distance to the channels network, the depth of the valley, the wetness index, and the relative position of the slope are effective in habitat suitability for species establishment. In general, the GAM method has high accuracy in estimating the habitat distribution range of all species studied (Kappa≥ 0.9). According to the maps obtained from the GAM model, the highest and lowest potential habitats belong to S. rigida and T. serotina species. Therefore, the GAM method can be useful in accurately identifying the ecological needs of plant species and therefore their distribution useful at the local scale. As a result, it is suggested that this method be used as part of a management support system in the protection and restoration of vegetation in the rangelands of the Bayaz plain.

This study aimed to identify the most important physical variables affecting the distribution of four range plants species (Tamarix aphylla, Calligonum comosum, Prosopis spicigera and Salsola rigida) habitats and to prepare potential habitat map of the species using Maximum Entropy (MaxEnt) method in rangelands of Jiroft city, Kerman province, located in SE Iran. To this end, sampling of vegetation including species type and percent cover was conducted with randomized-systematic method in 2015. Sample size was determined as 60 plots with a quadrat size of 25-100 m2. For soil sampling, eight profiles were dug in each habitat and samples were taken at two depths, i.e., 0–30 and 30–60 cm. Results indicated that the classification accuracy of the model was acceptable and soil variables including EC, percentage of lime, organic matter, moisture content and texture had the greatest effect on the distribution of the studied plant species habitats. Correlations between the actual and predicted maps for Tamarix aphylla and Calligonum comosum habitats were at a very good level, Kappa = 0.81 and 0.79, respectively, for Prosopis spicigera habitat was at a good level, Kappa = 0.75, and finally for Salsola rigida was at a moderate level, Kappa = 0.53. These results indicate that the MaxEnt method can provide valuable information about the physical conditions of plant habitats in arid rangeland. Knowledge on physical characteristics of plant habitats can be useful in determination of potential habitats and rangeland improvement projects.

Identifying and reviewing land use change can help managers and planners to identify the factors affecting the change of user and the adoption of appropriate management decisions at different levels. The present study was carried out with the aim of studying land use change and land use classes using remote sensing method in Noukabad watershed in Khash city, Sistan and Baluchestan province. For this purpose, after determining the boundary of the study area, using maps 1:50000, associated temporal data were obtained from the Landsat Satellite of the American Geological Survey. After geometric, radiometric and atmospheric correction, land use classification based on satellite images for study periods (1994-2000-2005-2010-2016 years) were determined. The accuracy of the production maps was determined using the general accuracy and kappa coefficient. After the application of the land use map in the ArcGIS software environment, the comparison of land use changes between the studied periods (each statistical period with the previous statistical period) was performed. Based on the results, overall classification accuracy of user-generated maps for the years 1994 (97.45%), 2000 (97.21%), 2005 (98.04%), 2010 (97.09%) and 2016 (97.06%) were rated as relatively good. The results of land use change trend showed that the most changes were in mountainous rangelands and the least changes were related to rivers, and in the meanwhile, agricultural lands and residential areas had moderate changes, which the main reason of these changes can be considered climate change and human intervention. These results indicate that the correct recognition of land use changes is caused, Managers identify the strengths and weaknesses of their executive plans and take steps to resolve them.

Identification of suitable sites for solar power plant establishment in arid regions is very important for using high potential solar energy. This study aimed to identify more important factors influencing site selection for solar power plant establishment and to determine highly suitable sites to establish solar power plants in Sistan region. Influencing factors on site selection were selected based on experts’ opinion using Analytic Hierarchy Process (AHP) with regard to climatic, geographic, socio-economic and environmental variables. Paired comparison of criteria and sub-criteria was performed using fuzzy analytic hierarchy process to determine the weight of each criteria. After preparing of the layers of criteria using GIS teqhniques, weights of criteria were multiplied in their corresponding layer. Finally, site suitability map for solar power plant establishment in Sistan region was prepared at five classes through overlaying of layers. Based on results, 4.8 percent of Sistan region, which is located in the southern part of Zahak and Hamoun cities and eastern parts of Nimrouz city, has an excellent suitability for solar power plants establishment. In addition, 36.6% of the study area has a good suitability, 0.5% has a moderate suitability and 18%, which is located in Bandan and Sefidabeh areas, has a poor suitability for solar power plants. Furthermore, sunny hours, temperature and elevation parameters because of having more weight, were identified as the most important criteria. Results indicated that about 45% of Sistan region has high suitability for solar power plants establishment. Therefore, the investment risk of the solar power plants in Sistan region is low. Maximum utilization of solar energy can provide a favorable condition for economic development and increasing the survival of people in Sistan region.

Extended abstract
1- Threshold wind velocity is a major influencing variable in transportation of soil particles and dust production. It is considered as an important component in many theoretical equations and numerical models due to its importance in wind erosion studies (Stout & Zobeck, 1996). The wind tunnel method (fixed and portable), empirical relationships and sediment traps are the common methods to estimate the threshold wind velocity (Refahi, 2005). Estimation of this variable can assist researchers to recognize critical points, in addition to providing suitable activities to prevent depletion of limited resources in desert region. This study aimed at comparing the accuracy of geo-statistical techniques (Kriging and Co-kriging) and artificial neural network model, estimating the threshold of wind velocity, selecting more accurate methods to be used in wind erosion combating projects, as well as identifying more important variables in threshold wind velocity in the Sistan region.
2- The study site with an area of 60 km2 lies within 30º 49′ 43′′ to 30º 54′ 49′′ N latitude and 61º 30′ 22′′ to 61 º 37′ 05′′ E between Sistan river and Fourth Chah-Nimeh reservoir which supplies drinking water of Zabol and Zahedan cities. Threshold wind velocity was measured using wind tunnel in 60 points of the topsoil. Soil sampling was done at the same points. Soil dataset was checked for normal distribution, then threshold wind velocity estimation and validation of obtained results, were respectively done by using the Kriging and Co-kriging and cross validation methods. The multi-layered perceptron model was implemented and obtained results were evaluated using root mean squared error statistics and coefficient of determination, after data standardization and determining the model architecture using trial and error method.
3- Based on the results, among the geo-statistic methods, co- kriging interpolation method with spherical variogram model in comparison with kriging method was determined as an optimal model for the estimation of the threshold wind velocity (R2= 0.60 and RMSE= 0.45). Study of the importance of effective variables also indicated that the variables related to soil texture (clay, silt, sand percents), acidity, salinity, organic matter percent, average weight of particles diameter and the percentage of surface soil gravel were the main contributors to the accuracy of predictive models and hence determining the threshold wind velocity in the study area. The overall assessment of the models used showed that the lower error rate and therefore, the more precise estimation of thresholds wind velocity erosion in the present study, were carried out by multi-layer perceptron model with three layers and three neurons per layer which utilizes Gaussian' transfer function and Levenberg- Marquardt training rule.
4- Discussion & According to the results, the Co-kriging method enabled to perform interpolation with high precision because it utilizes an auxiliary variable and uses covariance between two variables for interpolation (Lark et al., 2014). Furthermore, some capabilities such as increasing data processing power, problems related to measuring some variables, as well as, the correlation between measured variables and the availability of various software tools has increased the usage of this estimation method in various environmental studies (Amini et al., 2002). Performance superiority of the Co-kriging method compared to the Kriging method, has also been reported by Behnia et al. (2016). Likewise, a set of soil characteristics has the greatest effect on the threshold wind velocity in the Sistan region. In line with this findings, it has been reported that the soil crust properties affect the threshold wind velocity; as a result, the threshold value varies in soils with different characteristics (Webb et al., 2016). According to the findings of this research, it is stated that due to utilization of the pair of input and output patterns, artificial neural networks are as powerful tool in various studies related to natural resources. In general, it can be said that through the development of vegetation using native species in the southern and western parts of the study area can increase the threshold of wind erosion in these areas in addition to reducing soil erodibility and its consequences.

Energy as a contributor to human well-being plays an important role in the sustainable development of human societies. The growing demand for energy, higher standards of living, global warming, and decreasing fossil fuel resources have focused the global attention on renewable energies (Kaya and Kahraman, 2010). Owing to the rapid development of wind energy extraction technologies, low cost of this type of energy, and easy installation of wind turbines, this kind of energy is considered to be a viable alternative to current energy systems (Yang et al., 2016; Zaim et al., 2014; Tsoutsos et al., 2016). The present study aimed to identify the factors with more weight and more suitable sites for wind power plants in Sistan by considering climatic criteria (wind speed and direction), geographical criteria (elevation and slope), socioeconomic criteria (distance from residential areas, distance from routes, and proximity to electrical grids) and environmental criteria (distance from protected areas and waterways) through fuzzy hierarchical analysis and GIS.
The present study applied available 20-year weather statistics including wind speed, wind direction, temperature, and pressure in the Sistan region (Zabol, Zahak, Hirmand, and Hamoun) and the regions around the Sistan plain (Zahedan, Nehbandan, Birjand, Qaen, Shahdad, and Bam) from 1996 to 2016. Initially, expert opinion was used to extract climate measures (including wind velocity and direction), geographical factors (elevation and slope), socio-economic criteria (distance from residential areas, distance from communication pathways and vicinity to energy transport networks), and environmental scales (distance from protected areas and waterways) as significant and effective factors, which were later compared in pairs. Criteria and subcriteria weights were then obtained using fuzzy hierarchical analysis with Fuzzy AHP SolVer software. Maps needed for locating areas for the construction of wind power plants in the study area were prepared using GIS according to the sub-criteria. In the next step, each of the maps was classified using GIS. Based on the paired comparisons, each class was then scored according to the expert opinion, and the weight of each class was thus obtained using Fuzzy AHP SolVer. Finally, a map of suitable sites for the construction of wind power plants was developed, and the final map was provided in the form of a raster map with a precision of 500 square meters.
Determining the weight of criteria: According to the study results, the climatic criterion is of greatest importance among the criteria considered for locating wind power plants, and the environmental criteria was identified as the least important. The most important criteria were found to be wind speed with a weight of 0.57 (among the climate criterion), elevation with a weight of 0.66 (among the geographical criteria), distance from route, and proximity to electrical grids with a weight of 0.42 (among the socioeconomic criteria), and distance from protected areas with a weight of 0.66 (among the environmental criteria).
Final map for the location of wind power plants: The resulting map was classified into four classes (excellent, good, moderate, and poor) according to relative average change in each parameter. The results of the present study showed that an area of 5941 km2 of the total area of the study area (16208 km2) (36.6%) includes restricted areas, and the authorized areas for the construction of wind power plants (in the excellent class) are part of the Sistan plain (Nimruz and Hamoon cities) with an area of ​​7130 km2 (44%). In terms of land suitability, 611 km2 (3.7%) of the area was classified in the moderate class and 462.5 km2 (15.1%) in the good class. In addition, the regions of Bandan and Sefidabeh around the Sistan plain with an area of 62.5 km2 (0.38%) are among the areas less suitable for construction according to the criteria.
Discussion & Wind speed is one of the most important climatic criteria considered for the construction of wind power plants. The higher the wind speed, the greater the power generated by the wind turbines. On the other hand, the more a wind blows in one direction, the higher the wind is scored as it more effectively rotates the turbine blades. Consistent with the findings of the present study, wind speed was reported to be the main climatic criterion for the construction of wind power plants (Azizi et al., 2014). among the geographical criteria, elevation was found to be the most important one for the construction of wind power plants in the studied area. The possibility for building facilities and agricultural activities decreases with higher elevation as a limiting factor (Sabokbar et al., 2010). Therefore, areas with higher elevations must be avoided when locating a suitable site as higher elevations increase the investment cost (Bennui, 2007).
Considering general results of this study, we can conclude that 44 percent of the regions highly suitable for establishment of wind power plants, contributing to 7130 kilometers in area. Resulting regions with high potential for wind power plant establishment, mainly areas in the central plaint of Sistan (Nimruz and Hamoon cities) were selected from regions consistent with the inclusion criteria of this study. According to the study results, the combined use of fuzzy hierarchical analysis and GIS as a decision-making support system can be an effective strategy to identify more potential areas to create the conditions for the regional sustainable development, reduce the costs, and speed up the implementation of development projects and plans aimed at new energies. The study results emphasized the necessity of more serious and effective efforts for proper management and effective solutions to fully exploit the potential of the Sistan region. It is also imperative that part of the investments is spent annually on sustainable development plans for new energies, in particular wind energy in Sistan. The production of electricity as a sustainable energy can play a significant role in economic, social, and cultural development in the region.

This study aimed to estimate of spatial distribution scope of plant species and preparation of predictive distribution maps of plant species using Artificial Neural Network (ANN) in Taftan west rangelands of Khash city. To this end, vegetation sampling was carried out by random-systematic method after identification and separation of plant species habitats. In order to sample the soil at each habitat, eight holes was drilled and samples were taken from 0-30 and 30-60 cm depths. Habitats distribution of plant species was modeled using multilayer perceptron after measurement of soil characteristic in the lab and providing of environmental variable maps including physiographic characteristic (slope, aspect and elevation), geological formation and soil physical and chemical properties using GIS and Geostatistics. Simulation of presence and absence probability was conducted after selection of optimal predictive model for each plant species. Then the optimal threshold was determined using equal sensitivity and specificity method and were examined the compliance between predicted and actual maps by calculating kappa index. Based on Kappa value, the agreement of predicted and actual map was very good for the habitats of Haloxylon persicum .Moreover, predictive maps of Artemisia aucheri, Artemisia sieberi and Amygdalus scoparia habitats have good agreement with actual maps of these species. As well as, correspondence of predictive and actual map of Zygophyllum eurypterum was assessed at moderate level. These results indicate that multilayer perceptron method (MLP) is capable to provide precise prediction models through data mining rules and modeling of nonlinear processes. Besides cost and time saving of research, this can lead to precise prediction of geographic scope of plant habitat distribution, as a result will increase success possibility of rehabilitation plans in the rangelands.

Predictive modeling of habitat distribution of range plant species and identification of their potential habitats play important roles in the restoration of disturbed rangelands. This study aimed to predict the geographical distribution of Artemisia sieberi and find the influential variables in the distribution of A. sieberi in the desert rangelands of central Iran. Maps of environmental variables were generated by GIS software (version 9.3). Predictive distribution maps of A. sieberi were produced with Maximum Entropy Method (MaxEnt) and the existing data regarding this species. The agreement of predictive map with the actual map was checked by calculating Kappa coefficient value. Accuracy of predictive models was evaluated using the Area Under the Curve (AUC). Results showed that soil pH and lime content in the surface layer (0-30 cm) and silt percent in both surface and sub-surface soil depths (0-30 and 30-60) had the greatest impacts on the distribution of A. sieberi in the study area. Correspondence of actual map with the predictive one was assessed at a satisfactory level (Kappa coefficient = 0.70). MaxEnt is widely used as compared with the other standard methods since it only requires the presence data of a specific plant species to draw the distribution map of its habitat. Additionally, MaxEnt is a generative method and its output can be easily understood by the field practitioners.

Pot experiment was carried out to investigate the effects of ethylenediaminetetraacetic acid (EDTA) on some morphological characteristics of Thlaspi caerulescens L., and also on the accumulation of lead (Pb) and zinc (Zn) in roots and shoots of T. caerulescens L.. Experiments were then set up in three treated pots with doses of 3, 6 and 9 mmol kg-1 of EDTA and control pots (C: uncontaminated soil and W: contaminated soil). The results indicated the significant effect of EDTA on morphological characteristics and accumulation of heavy metals in the plant (P

The aim of this study was to evaluate the efficiency of artificial neural networks in supplying predictive maps of plant species habitats distribution in Qum province rangelands, Iran. For this purpose, soil and vegetation sampling was done after determination of homogenous unit by combining of slope, aspect and elevation maps and environmental variables maps were prepared using geostatistics and GIS. To prepare the artificial neural network models, the best network structure, was determined following required data preprocessing (normalizing data and partitioning of data into three sets, training, test and validation), improving adjustable parameters (such as transfer function, learning rule, the number of hidden layers, number of hidden layer neurons) and using statistical parameters calculated in the test phase (MSE). After selecting the optimal network, simulations were performed to estimate the probability of the presence or absence of the species and continuous probability maps of the presence or absence was prepared at each species habitat using Arc GIS. Then the optimal threshold was determined using equal sensitivity and specificity method and the compliance between predicted and actual maps were examined by calculating kappa. Based on the results, the most accurate prediction models were obtained for all habitats using sigmoid transfer function and the Levenberg Marquardt algorithm. The results also showed that habitat predictive and actual maps of Artemisia sieberi, Halocnemum strobilaceum, Tamarix passerinoides, Seidlitzia rosmarinus and Artemisia sieberi have excellent, very good, good, fair and poor compliance, respectively. These results indicate that the multilayer perceptron has acceptable accuracy in the modeling and estimation of the geographical distribution of the studied species habitat and if the network input variable be chosen properly, the network can simulate presence or absence of plant species with high accuracy.

Study of the Phenology is the most important ecological issues and it can be a foundation for Resource Management. Phenology Recognition of different rangelands species in relation of grazing management and sustainable utilization of rangelands forage is an important issues. Without knowing Phenology of different rangelands species is not possible to determine the grazing season and grazing management and livestock. Therefore in order to determine of Entering livestock time and on time exit of the rangeland، phenology of Atriplex verrucifera as dominant species In the semi-steppe rangelands of Uromieh Tzkhrab and Soil moisture condition Was studied. The interpretation of Phonological stages was done by climate data. Result showed that the beginning of July to Second half of June and the end September to late October is the best time to start grazing of the Atriplex verrucifera species. Also Soil moisture result showed livestock entrance to rangeland in the late May will not cause a problem.