hamid goshtasb

Precipitation is including of every rainfall. Level of rainfall is 240 ml in Iran and average of rainfall is 860 ml in the world. For evaluation of precipitation by remote sensing data, TRMM (Tropical Rainfall Measuring Mission) was used. The goals of these picture are measurement of precipitation and energy in tropical and subtropical regions of the world. The aim of this research is evaluation of precipitation trends in Central Plateau of Iran and habitats of Gazella bennettii. This aim was done by remote sensing data and time series. Using the data for evaluation of precipitation is related to daily pictures of TRMM 3B42 during 2002 to 2018. For this purpose, significance of Mann Kendall and Linear correlation were used for SUM monthly and annual parameters. Analyses show the similarity between two significant methods. The result indicates that in Central Plateau of Iran, there are increasing of significant in Qom, some parts of Semnan and Yazd provinces. Decreasing significant for SUM annual parameter was shown in Khorasan-e Razavi and Semnan. For SUM monthly parameter, there was not any significant reduction. In habitats of Gazella bennettii, there was not any significance including increasing or decreasing for two parameters. Research indicate the low and irregular rainfall in arid areas. Current research approved this result too.

The temperature of the earth has been rising by about 0.74 degrees Celsius over the past century. A gradual increase in the average annual temperature has been reported by many researchers worldwide, while other reports suggest a decrease in this parameter. The assumption is that there will be more areas of the world experiencing higher temperatures. The climate changes are effectively represented by temperature changes, which is considered one of the main indicators in climate studies. The chemical composition of the atmosphere has changed because of the increase in human industrial activities, so it is responsible for unprecedented changes in the global climate in the past century. The increase in greenhouse gas concentration is the cause of this change. The evidence indicates that the increase in atmospheric gas concentration has caused a significant increase in global temperature. The use of thermal data from sensors is widely used in the study of terrestrial phenomena, as indicated by many studies. The temperature of the earth's surface is directly and indirectly linked to all human activities. It is still not possible to calculate the temperature of the earth's surface with perfect and accurate methods, but some sensors with suitable temporal, spectral, and spatial performance are able to take photos of the entire surface of the Earth. The study is more important due to the fact that various species of animals, such as Jebeer (belonging to the Bovidae), are exposed to climate changes in arid and desert areas. Due to its impact on humans, other creatures, and the entire environment, it is imperative to pay attention to climate change nowadays. In this regard, the main aim of the current study is to evaluate the LST (Land Surface Temperature) trends, changes, and temperature threats of the land surface in the Central Plateau of Iran. Time series remote sensing data of the MODIS (MOD11A2) sensor and Terra satellite, in 8 days with spatial resolution of 1km from 2002 to 2018 have been used.

 The current study has been focused on the central plateau of Iran. The central plateau of Iran lies within the arid lands belt of the northern hemisphere. The current study has been attempting to extract exact information from the images by employing specific techniques. To achieve this goal, the MOD11A2 product of Terra satellite MODIS sensor, the trend of temperature changes and time series construction of the significance of Man Kendall methods and linear correlation parameters such as maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature, minimum annually temperature for daily and nightly temperature were used in TerrSet software and Earth Trends Modeler section to extract significant increasing and decreasing areas. After identifying some parts of provinces with significant temperatures based on analysis and results, we can identify the vital numerical value of the temperature in each pixel of those significant parts in the next stage. This can be achieved by utilizing the difference between the final temperature and the initial temperature. Trend analysis was used to simulate daily and nightly temperature changes for parameters of maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature and minimum annual temperature.

Daily temperature data in the Central Plateau of Iran, which includes monthly minimum temperature, annual minimum temperature, monthly maximum temperature, annual maximum temperature based on monthly maximum temperature, monthly median temperature and annual maximum temperature based on monthly median temperature, common in Semnan and Isfahan provinces, showed a significant increase in linear correlation according to the results. In Isfahan province, the linear correlation decreased significantly between the maximum annual temperature based on the maximum monthly temperature and the median monthly temperature. There was no significant trend in other provinces. The linear correlation between temperature data in Isfahan and Semnan provinces, including the minimum monthly, minimum annual, maximum annual, and median monthly temperature, decreased significantly. The linear correlation between average annual temperature, average monthly temperature, maximum annual temperature determined by maximum monthly temperature, average monthly temperature, and maximum annual temperature determined by median monthly temperature increased significantly in Yazd and Isfahan provinces. No significant trends were observed in other provinces. To estimate the amount and approximate number of significant increases and decreases, simulations of temperature changes were conducted. The range and approximate range of numbers for significant increase and decrease in temperature were calculated in degrees Celsius. In all analyses, the parts with higher temperatures had a reddish color. The intensity of the red color increased as the temperature increased, and as the temperature decreased, the red color became fainter and turned blue. The central plateau of Iran recorded a maximum temperature of 44C°and a minimum temperature of -7C°according to this study. The central plateau of Iran has three main provinces, which include Isfahan, Semnan, and Yazd. Considering the temperatures mentioned for these three provinces, the temperatures obtained from this study are very similar, which means that the conducted study is approved to a large extent. Animals are considered to be at risk due to temperature changes. Future research should emphasize the impact of climate change and temperature increase on the living conditions of various animals, particularly those found on the central plateau of Iran.

The study of snow cover changes, as one of the sources of water supply is very important. The purpose of this study is to investigate the trend of snow cover changes in Tehran province using MODIS sensor images of Terra satellite and Aqua satellite (since 2003) until the end of 2018 and compare the performance of MODIS sensor in these two satellites. The trend of snow cover satellite images changes was determined by ETM analyzer of TerrSET software, their significance was determined by non-parametric methods of Mann-Kendall significance test. Then, the satellite images were validated by analyzing the data trend of synoptic stations in Tehran province using regression methods. Also, the trend of snow cover changes obtained from Terra satellite was compared with the trend of snow cover changes obtained from Aqua satellite by Kappa index. And the time series of these two satellites were compared by the correlation coefficient of Linear Modeling analysis. According to the results, changes in total days with monthly, annual and seasonal snow cover (autumn and winter) have a significant decreasing trend in a scattered manner in most parts of Tehran province. Seasonal trend analysis (STA) showed a delay in the start of days with snow cover and its shift from late January to late March. The correlation coefficient of MODIS sensor time series between two satellites in most parts of Tehran province was 99% and showed high similarity. Validation results showed that the trend of snow cover satellite images changes is 85% similar to the trend of synoptic stations snow cover data changes in Tehran province.

Analysis of Land Surface Temperature (LST) time series data significantly improves our understanding of long-term climate change. Since about two-thirds of Iran is covered by arid and semi-arid climates; Therefore, it is important to study temperature fluctuations in the country in order to determine and predict the resulting crises. The purpose of this study was to investigate the trend of temperature changes in Tehran province because in recent years we are facing with overpopulation, which is one of the issues affecting climate change in Tehran province because overpopulation increases consumption of fossil fuels, greenhouse gases emissions and As a result, the temperature rises. In this essay, the trend of temperature changes was investigated using the time series of MODIS sensor images of Terra satellite (since 2002) and the time series of MODIS sensor images of Aqua satellite (since 2003) until the end of 2018. The trend of temperature changes of the received images was determined using TerrSET software ETM analyzer and their significance was determined by parametric methods of correlation coefficient and non-parametric Mann-Kendall significance test (at 1% level). These methods were used to identify the trend of changes in maximum, medium and minimum temperature variables on a monthly and annual basis. The trend estimate of these changes was converted to degrees Celsius by ordinary least squares analysis (OLS). In addition, the trend of minimum temperature data changes of synoptic stations in Tehran province was obtained using regression methods and was compared and validated with trend maps of minimum temperature changes obtained from satellite images. In order to compare the performance of MODIS sensor in Terra and Aqua satellites, the time series of MODIS sensor images of Terra satellite were compared with the time series of MODIS sensor images of Aqua satellite by Linear Modeling analysis and R correlation coefficient. Also, the classified areas of significant increase, decrease and no significant trend obtained from the MODIS satellite sensor were compared with the classified areas obtained from the Aqua satellite sensor by the Kappa index. In addition, in order to identify the ecological effects of the trend of surface temperature changes, the shape file of protected areas and national parks of Tehran province was added to the maps of the trend of temperature changes in Arc Gis software. According to the results, the maximum, average and minimum monthly temperatures during the years under study were almost without a significant trend However, a significant increase in surface temperature was observed in the study of annual variables in most parts of Tehran province, especially in Tehran city, desert and plain areas. Also, the irrational or sudden increase in temperature in an area has reasons such as uncontrolled construction, proximity to industrial towns and drying of rivers, lakes, wetlands, etc. Comparison of the minimum temperature trend of satellite images with the minimum temperature trend of synoptic stations datas in Tehran province showed 98.3% similarity. Based on this, it can be said that there is an acceptable agreement between the trend of changes in satellite images and the trend of changes in terrestrial data, and because the degree of concordance of the results obtained from the Aqua satellite is more similar to the data of the synoptic stations than the results obtained from the Terra satellite, it is therefore recommended to study the temperature changes of the Aqua satellite. Comparison of MODIS sensor images time series of Terra satellite with Aqua MODIS sensor time series in most parts of Tehran province showed a high correlation coefficient (99% similarity). Except Tehran city and the east of Tehran province (Firoozkooh city) which can be the difference due to housing, human interference, pollution due to higher population density and agricultural use of land and harvest. Also, kappa index is below 0.7, which indicates the lack of similarity in the classification of significant areas in the whole province by the MODIS sensor between the two satellites. Based on this, it can be said that although MODIS sensor is recommended for long-term changes and better understanding of the change process but there seems to be uncertainty in the interpretation of the data obtained from the classification of MODIS sensor images. Therefore, care must be taken in choosing the type of satellite for the MODIS sensor. Based on the results obtained from identifying the ecological effects of temperature changes, Jajroud Protected Area has a trend of more temperature changes than other protected areas in Tehran province. Considering the trend of rising land surface temperature and its role in increasing evapotranspiration, we should look for solutions to better manage water resources and improve its exploitation methods, especially in agriculture and industry in Tehran province.

In Iran, no hunting areas are managed for maintaining the biodiversity for 3 to 5 years to join the four conservation areas managed by the Department of environment in the event of improved habitat and wildlife. Habitat suitability models can provide a rapid assessment of the status of index species as a tool. So with strong support, protection level of no hunting areas is enhanced. One of these areas with high-diversity is no hunting area of Eshkevarat. The purpose of this study was to evaluate the status of four species in the region including wild goat (Capra aegagrus), Roe deer (Capreolus Capreolus), brown bear (Ursus arctos) and leopard (Panthera pardus). For this purpose, random forest (RF) model was used and the validity of the model was evaluated by using area under curve (AUC), root mean square (RMSE) and crossed mean entropy (MXE) statistics. Model evaluation results showed that RF method has been successful in implementation. Elevation was evaluated as a factor in the suitability of bear, goat, and roe deer habitats while proximity to prey had the greatest impact on leopard distribution. The optimal habitat area for bears, leopards, roe deer and goats was 34.79%, 2.12%, 3.48% and 11.34% of the total area, respectively. Spatial distribution of habitat patches of the mentioned species indicates high habitat diversity in the area, which reveals the strong role of this area in biodiversity conservation of the mentioned species and upgrading its level of protection can eliminate many of the challenges facing the area.

Quantitative and qualitative information of vegetation and its changes in duration of time as a basic foundation of determination of  habitat quality, priority of protected area and also determination of price of ecosystem services in order to optimum management of natural resources and sustainable development is a very important technical point. In other hand, researchers are interested in remote sensing as an efficient tool to access timely and accurate information about land coverage especially vegetation coverage. In current study post classification comparison method among different methods of change detection was used because of possibility of achieving optimum accuracy by using an efficient and accurate classification method. In order to determine vegetation classes by field-based resources and Landsat images and also, slope-based and distance-based vegetation indices derived from these images, two artificial neural networks; percentage of vegetation cover (overall accuracy 94.3% and mean square error 5.7% for test data) and dry weight of standing biomass (overall accuracy 86.6% and mean square error 11.4% for test data) were built and vegetation maps according to these qualified models were prepared. Results of this research show high capacity of the artificial intelligence technique in vegetation classification if, the average number of field-based samples and variety of images in terms of time were possible. By the vegetation classification maps made, change detection maps with pixel by pixel comparison that show change classes from class … to class … in three duration of time; 2000-2015, 1986-2000 and 1986-2015 were prepared. Accuracy of these maps is totally depended on classification accuracy and demonstrated qualitative enhancement in a wide area of case study after conservation.

Today, most of protected areas in the country require serious monitoring and protection measures due to reduction of quality. Degradation model with quantifying the effects of human activities can be used in monitoring and improving the situation in these areas. To implement this model in Bashgol protected area, the sub watersheds were selected in the area as the impact units. Using maps, field observations and experts’ opinions, 25 destruction factors in the whole area were identified and their intensities were determined, also ecological vulnerability of the area was calculated with GIS tools using geological, physiographic, climatic, vegetation, habitat and other maps (16 factors) and was classified in terms of intensity. After calculating the physiological density, destruction coefficients were calculated in each unit and were classified based on fuzzy theory into 3 categories and 6 classes. The results showed that 8465/51 hectares (34/7%), 12013/44 hectares (49.23%) and 3920 hectares of area (16/07%) are located on the 2nd class prone to development , the 3nd class (need to rehabilitation) and the 5nd class (need to conservation), respectively in terms of the quality of the land. Generally, according to local conditions, without the further development, the objectives of area management must go toward the restoration and protection measures.

Most of Iran's vertebrates are birds that are distributed in vast parts of the country like deserts, forests, coastal areas, islands, lakes and other habitats. Eshkevarat No-hunting area in Guilan Province is one of the suitable habitats for Alectoris chukar which harbors a good population of the species. The mountainous and hilly habitats of the area dotted with rocks provide a good condition for a large number of Alectoris chukar. In order to model the habitat suitability of the species, we used ecological niche factor analysis (ENFA) in Biomapper software. The presence of species was used as the dependent variable and the environmental variables (slope, aspect classes, altitude classes, water sources, residential area and roads) as the independent variables. Habitat suitability maps prepared in this study for Alectoris chukar show that the favorable habitat for this species are located in the Eshkevarat No-hunting area in an altitude between 1700 and 2800 meters and on slopes greater than 40 percent. The mountains, Sorkheh Tale Lima, Ghazi Chak and Delijan are habitats with high frequently of Alectoris chukar presence. The most important variables were found to be slope and aspect.

Parks and green spaces are one of the most important elements of cities. The design and function of urban parks should be in line with the requirements of urban life and in response to the needs of citizens, as this can be used to create a healthy urban environment. The purpose of this research is to model the satisfaction of urban parks visitors using the artificial neural network. In this study, a multi-layer perceptron network was used to process the data with the intelligent neural network tool. First, 103 urban parks were selected in Karaj and Tehran, and information about regional, service and aesthetic variables was collected in all parks. Then, the collected data was considered as network input and the results of satisfaction level assessment as network output. The value of determination coefficient (R2) in this study was 0.72 which indicates the suitability of artificial neural network for satisfaction modeling in urban parks. The results of sensitivity analysis showed that variables of landscape quality, number of sports fields, food centers, and barbeque have had the most impact on satisfaction of urban parks. Therefore, in planning and managing public places such as urban green spaces, consideration of users' perceptions of the environment should be highlighted.

This study aimed to identifying and zoning biodiversity risk criteria in the Central Alborz  protected area by using multi-criteria multivariate decision-making methods (DANP) (ANP_DEMATE method combination). firstly, using a review of various sources, some of the criteria for evaluating biodiversity hazards in two socio-economic and ecological sectors were determined in the 33 sub-criteria and related indicators. Then, the existence of these sub-criteria in the study area was investigated and Sub-criteria in the region were identified. The indicators were screened by Shannon Entropy method and were prioritized using the DANP combined technique and the sub-criteria that were more important were zoned. As a result, the risk of the region in the five critical classes is about 48.1%, at high risk, about 34.9%, at medium risk, about 96.9%, at low risk, about 34.5% and at invulnerable class, about 69 / 1%, classified. The results of this research indicate that the effectiveness of this research method in determining the appropriate indicators and also determining the areas requiring conservation programs in protected areas.

Integrity and connectivity of habitats in protected areas is important for the plants survival and animals' populations. Investigating on the integrity of areas could help to interconnect islanded patches and preventing the negative impacts of fragmentations. This study aimed to quantify the fragmentations of the landscape to study the habitat of the important wildlife species in Jajroud protected area. To achieve this goal, after preparing area land use map using remote sensing and geographic information system techniques, the fragmentation elements were extracted to create fragmentation geometries. The acquired data were employed to study structure and the wild goat habitat integrity, wild sheep, the leopard and gazelle considering the landscape metrics. The results indicate that, the greatest increase in the fragmentation in landscape and between animal habitats has been happened due to the obstacles of the first geometry, which is included man-made lands and asphalt roads. Also, the greatest increase in fragmentation for wild sheep and gazelle occurred in first geometry, for wild goat in second geometry (garden) and for leopard in the fourth geometry (dam). Many conflicts pass through the sensitive species habitats of the wildlife, and the irreparable effects of conflicts will be more identified in the long term. The results of this research indicate that by analyzing landscape metrics in protected areas, it is possible to identify and prioritize the most important obstacles of maintaining the integrity of the habitat of different wildlife species; and also planning for the conservation of the habitat of the wildlife species will be prepared.

Integrity and connectivity of habitats are among the important factors for the conservation of plant and animal populations within protected areas, particularly when the habitats are connected to each other. Investigating on the integrity of these areas could help interconnecting islanded patches and preventing the negative impacts of fragmentations. This study aimed to quantify the fragmentations of different regions within the protected area of Jajroud. Land-use map was extracted using remote sensing (RS) and geographic information system (GIS) techniques. The acquired data were employed to study the fragmentation of the regions considering the landscape metrics. The results indicate that the northern parts of the region including the national parks of Khojir and Sorkheh Hesar, in general, possess larger patches and higher level of connectivity, while the southern part is tending towards small-grain structure. In addition, it supports the applicability of landscape metrics in assessing the management of the protected areas and identifying the protection patches in order to prioritize the protection of landscape patches.

Assessment of land ecological capability as the core of environmental studies, is necessary before development for different uses. To achieve the planned objectives according to the IUCN, it is necessary for protected areas to be zoned in the process of evaluation of environmental capabilities. This study was done to determine the ecological capability and zoning of Bashgol protected area by systemic analysis method for the conservational reclassification of the Protected area. To achieve it, 1012 environment units in the area was achieved with preparing the map of stable and unstable ecological resources, and socioeconomic resources of area and its analysis and concluding by GIS (scale 1:25,000). The maps of ecological potential were prepared by comparing the homogeneous units with special ecological model of the area, and then with integration of units with the same usage, first base map of zoning and eventually the final zoning map with prioritizing and organizing the primary zones as the units of planning was prepared. The results showed that the percentages of the restricted nature zones, the protected zone, the extensive recreation zone, the other uses zone, the recovery and restoration zone, the buffer zone, the historic and cultural zone and the special use zone, are 25.14%(6133.14 hectares),58.41% (14250.99 hectares),0.32 %(77.4 hectares),13.63%(3324.65 hectares), 1.87%(456.87 hectares),0.6%(145.26 hectares),0.04%(9.12 hectares) and 0.005%(1.32 hectares), respectively. Due to the large extent of the protection zone, Bashgol area has high conservation value, but does not have one of the necessary zones (Intensive recreation zone) to be upgraded to national park level.

Considering the fast-growing destruction of natural resources and the existing threats ‎to bio-diversification the necessity of investigating and elevating the conservation level of such areas‎ has become a challenge. The forenamed research with purpose of Alvand No-Hunting Area of zoning, for the sake of access to conservation optimum pattern and development was performed. Using system analysis led to the creation 1434 was environmental unit which the Ecological Capability Evaluation of each unit respect to the appropriateness zoning were identified in the area. Finally, with Prioritization and zones Organizing, zoning map serve as sample planning was prepared. Findings: The results suggest that in the study area, zone 2(%62/33) was the first priority, respectively, zone 11 (%22/20), zone 6 (%8/22), zone 3-4 (%7/01) and zone 5 (%0/20) were the next priorities. Discussion and Conclusion: due to the high percentage of protected zones of Alvand region (%62/33), and also due to the corridor between the two protected areas and gorge of worthful Ovis orientalis species benefits the high conservation value respectively.

A species distribution model evaluates the relationship between distribution of species and environmental factors and therefore gives a better understanding of future management plans. Artificial neural network is one of these modeling tools which in these days have become a very important gadget for decision making. According to the International Union for Conservation of Nature (IUCN), Capra aegagrus is one of the venerable species in Iran and it categorizes in VI Section. Therefore, the purpose of this study is to model Capra aegagrus Habitat suitability in Ghomishloo national park using artificial neural network. Different variables which used in this study are altitude,slope, vegetation, distance from the main roads and water resources. In order to analyze the data, multilayer perceptron network have been used and MATLAB R2015b was used for the calculations. The results show that the most important variables which effects on Capra aegagrus distribution are altitude, species distance from water resources, slope, the distance from the main roads and vegetation, respectively. And the results also indicate that the most suitable habitat for Capra aegagrus in Ghamishlo national park is in the areas with an altitude of more than 2200 meters and an area of 3982 hectares which includes 12.36 percent of this national park.

Brown bear has the highest distribution within all bear species and lives in Asia, North America and Europe. It is the biggest carnivore with wide distribution and aboundance in Iran, especially in mountainous area. The largest population of the species are inhabiting along the Alborz Mountains with the highest abundance in the Central Alborz Protected Area and the Golestan National Park in Mazandaran and Golestan provinces, respectively. To increase our knowledge instead of improve the conservation level of the species remained populations in protected areas and in order to be able to predict the effective factors on habitat suitability, we had a survey on this issue in Golestan, Guilan and Mazandaran Hyrcanian forests with the Maximum Entropy (MaxEnt) software. The results show that 59% of the study area is suitable for Brown bears. Moreover, Brown bears prefer the areas with high vegetation and high slope in northern aspect. According to the results, increase of annual temperature will reduce the suitability of habitats. Also, along with determining new protected areas, it should have an appropriate distance from human settlements and villages.

Introduction Vegetation cover is an important indicator in the arid and semi-arid areas and plays an important role in balancing the ecosystems. Monitoring vegetation cover changes is of great importance because of vegetation effects on the environment. This monitoring provides detailed quantitative and qualitative information. It is therefore important to monitor dynamic changes in vegetation and to investigate the factors that are driving these changes in order to guide regional environmental management. Changes in vegetation could be tracked by satellite time-series data and trend analysis. Analysis of vegetation time series improves considerably our understanding of vegetation annual changes. Touran Biosphere Reserve accommodates three areas of Wildlife Refuge, National Park, and Protected Area. This reserve is the second largest biosphere reserve in the world. In terms of international categories, Touran is a special habitat of steppe grasslands in Central Asia and Desert-Saharas peculiar to West Asia. The aim of this research is to evaluate vegetation trend in Touran Protected Area using MODIS vegetation time series data from 2001 to 2015.
Materials and methods The 16-day composite MODIS VI product with 250m spatial resolution from Terra (MOD13Q1) was downloaded from the Land Processes Distributed Active Archive Centre (LP DAAC) for the Touran protected the area and the years from 2001 to 2015. The MOD13Q1 product includes normalized difference vegetation index (NDVI), Enhanced Vegetation Index (EVI), infrared band, red band, and quality assessment (QA) information. Before the time series analysis, different vegetation indices (NDVI, EVI, TSAVI1, and PVI1) were created. Then, the vegetation indices were aggregated to monthly composites by applying the arithmetic max of each month and standardized anomalies (Z score) of the monthly vegetation indices data were calculated to remove the seasonality. Several studies using the more robust Theil-Sen trend analysis and Mann-Kendall tests were conducted to explore the trend analysis of vegetation using NDVI, EVI, TSAVI1, and PVI1. A field survey was conducted to select a suitable vegetation index.
ResultAccuracy assessment results showed the Kappa coefficient for the PVI1, TSAVI1, EVI, and NDVI indices to be 0.78971, 0.5674, 0.5288, and 0.4159, respectively. Field survey results showed that PVI1 is the best index in the study area. According to the results of the Theil-Sen slope analysis and the Mann-Kendall test, the trend was divided into 3 levels. Vegetation with Theil-Sen slope 0, but with Z values ranging from -1.96 to 1.96 has no trend, and vegetation with Theil-Sen slope > 0 and Z > 1.96 has a significantly increasing trend. Results related to inter-annual trends show that vegetation degradation or decreasing trend occur in the northeast, east, and southeast, as well as the west and southwest region. Vegetation cover improves in central and southern of Touran protected area. Areas without a trend or stable regions are dispersed in the entire region. Vegetation cover improves in central and southern of Touran protected area. Areas without trend or stable regions are dispersed in the entire region. Results indicated that vegetation cover declines in 30% of Touran protected area, vegetation cover increases in 26% of study area but in 44% of Touran protected area no trend in vegetation cover was detected.
Discussion and Conclusion Combining methods including the coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test provide an effective way to investigate the characteristics of variations in vegetation. According to experts in this area, the overgrazing on rangelands of this area is one of the dangers that threaten the biosphere reserve. Overall, this study showed that the creation of distant-base vegetation indexes from the MODIS Time Series product could improve the evaluation of the long-term trend on a local scale. The method of this paper provides useful information for identifying vegetation changes in arid and semi-arid areas. For future studies, it is suggested studying the changes in vegetation trends in arid ecosystems in order to evaluate remote-sensing time-series images.

From effects of (human) crowd increase, is increasment of consumption resources and decrease of environmental diversity. The increasing approach of extinction rate, caused human for sake of using natural and useful values of species, enterprises to protect them which its result was forming protected areas. present study zoning Alvand no-hunting area in order to achieve an optimal pattern of conservation and development by using The multi-criteria evaluation method, using a combination of data layers combined method (linear-weighting) accomplished in geographic information system area. According to Delfi method, screen criteria and then using the hierarchical analysis method became weighed. In examining the results of executing the hierarchical analysis method in expert choice Software the priority of indicators weight respectively, In zone 1-2, maximum weight related to indicator of general climate divisions (1), in zone 3-4 and zone 5, cultural-historic monuments indicator (1), zone 6, distance of road indicator (1) and finally in zone 11, is devoted to isothermal spots, inhabitant crowd, and lands use (1) Respectively. In prioritization the resulting zone, maximum priority includes, zone 2 (%54/49), zone 11 (%33/17), zone 6 (%8/49), zone 3-4 (%2/26) and zone 5 (%1/16). Regarding to priority due weight of each zone, the region contains high percent of protection zone and also to the corridor between two protected area and gorge of worthful ovis orientalis species, It can be said that the region was of high value for a higher level of protection.

Touran Biosphere Reserve is located in the North east of Semnan Province and Iran which is the most important core area for critically endangered (Cr) Asiatic cheetah in the country. Identifying suitable habitats and mapping of species distribution area have an important role for appropriate conserving of the endangered species and designing management planning. In this study we applied Maxent software to model the distribution of suitable habitats, identification of the most important environmental, climate and geographical factors for the species in Touran BR which is the second largest habitat of the species in Iran. Cheetah presence data are from 2001 to 2015. Our results showed that 20 percent of Touran is suitable for cheetah and water resources and the perpendicular vegetation index have provided unique information. Moreover, cheetah suitable habitats are plains and hilly areas which have highly overlap with the distribution of the Jebeer Gazelle, Wild sheep and Goitered Gazelle. Moreover, according to the results, cheetah avoid mountainous habitats where is larger competitor, Persian leopard, territory.

Dust particles are important atmospheric aerosol compounds. The particles are resulting performance of strong winds at the soil surface desert areas. Sources of dust are 2 types: 1- Natural Resources 2- Human Resources. Iran is located in the desert belt which this problem cause increased the frequency of dust storms, especially in South East (Sistan) and South West. China Meteorological Administration Center classifies storms based on particles type, visibility and speed storms to 4 kind: Floating Dust, Blowing Dust, Sand/Dust Storm and Sever Sand/Dust Storm. In general, the effects of dust storms in 7 of Environment (particles into remote areas, the effect of dust particles on the material, climate, oceans and deserts), public health and health (increase of respiratory diseases , cardiovascular problems, digestive, eye, skin, reduced hearing, infections, reduced life expectancy and premature death, etc.), economic (unemployment, road accidents, damage to communication lines, air, land, sea, increase water turbidity in water utilities, creating uncertainty for all economic activities, etc.), Agriculture and Livestock (negative effect on the growth of plants and animals, reduced productivity and diversification, intensification of plant and animal pests and diseases, rising costs maintenance of livestock, etc.), socio-cultural (poverty and the loss of local jobs, destruction of subcultures, rural migration to the cities, closure of educational premises, industrial units, services, etc.) and military-security (disabling weapons, food and beverage contamination, the threat of sensitive electronics and power transmission systems, and reduce the useful life sitting on warehouse equipment, logistics cargo weight gain, etc.) can be evaluated. One way to identify, evaluate and forecast dust storm modeling. Dust cycle consists of 3 parts, dust emissions, dust and subsidence transfer dust that can be simulated by models.
In this study using the WRF_Chem model with FNL[1] input data and GOCART schema, sever dust storm in Sistan region was simulated to date 14 & 15 July 2011. Satellite images of the event was received by the MODIS sensor. Dust concentration data was received from the Department of Environment. The dust storm code, minimum visibility data and maximum wind speed data was received from the, Meteorological Organization.
The results of the simulation for dust concentration which peak amount of dust was for 21Z14July2011 and 03Z15 July 2011. Model output showed maximum wind speed 20 m/s with North to South direction in the study area. The model predicts maximum dust concentration for the latitude 31 degree North and longitude 54 degree East to 66 degree East (Within the study area). MODIS sensor images showed clearly the sever dust storm. Simulated time series in Figure 3-1 Changes in dust concentration during the event show in the Sistan region. As can be seen from the peak of the concentration of dust in 21 hours on 14 July (350 micrograms per cubic meter) and 03 hours on 15 July (425 micrograms per cubic meter) 2011 was created. Model simulation and satellite images indicated which the Sistan region, especially dry bed of Hamoun wetland in East of Iran was main source of sand and dust storm. Also, based on the model output blowing wind direction from North to South on Iran which converging these currents in East Iran caused by strong winds in the lower levels (According to the meteorological data), arise dust, increasing the dust concentration (According to Department of Environment data), increasing the dust and being transferred to the Southern regions, especially Oman sea. To identify the source of the sand and dust storm, the path of the particle and anticipated this event cant actions and warned to stop and reduce effects its. . Simulation of dust particles in the resolution of 10 and 30 kilometers, the plains of Sistan in Iran's East region as the main source screen. The findings suggest that compliance with the maximum concentration limits on known sources of particles (especially Sistan plain dry bed of plain wetlands) is. Check drawings wear rate showed that the source of dust in the Sistan region, particularly the high potential of our wetlands dry bed of soil erosion in wind activity 120 days during the hot and dry conditions, and silt and clay up to thousands of kilometers away from their source transfers. Vector lines on maps wear rate, indicative of converging flow north-south and severe dust storms in history is this. It is better than models forecast dust events and rapid alert

Iran location in the desert belt is accompanied with frequency increasing the sand and dust storms, especially in the eastern and southern areas and created adverse environmental effects. One of ways to study this event is dust simulation and forecasting. Aim of this study, Weather Research & Forecasting-Chemistry coupled model (WRF-Chem.3.6.1) is used to simulate, forecast and design alerting system for sand and dust storm events (East of Iran). In addition, dust concentration data that were collected by environmental protection organization (EPO), wind speed and direction data from the Meteorological Organization and MODIS images are used to investigate transport path of storms and have better forecast and right time alerting. True color images by software ERDAS IMAGIN queries to identify dust source. Results showed that the main dust and sand emission source in Sistan is dry bed of Hamoun wetland. Also during the storms that are investigated in this study, transport of dust clouds are observed to southern part of Iran up to Oman sea because of converging currents north-south winds on Eastern part of Iran (especially in spring and summer) that create strong winds in lower levels of atmosphere. This wind are responsible of increasing dust emission. WRF-Chem model had reasonable estimation related to spetial and temporal scale in the study area. Due to use of global forecasted data as model input data, it was expected to observe bias in concentration estimation from real situation. The model was run for 30 km spatial resolution and results revealed storm formation in Sisitan affected by local geographical properties especially topography features.

In order to reach sound herbivore management, understanding of vegetation associations with herbivores, specifically where the populations of the species of interest are depleting is crucial. Hence, in this investigation, effort was made aiming to determine the current habitat status of roe deer species (Capreolus capreolus) for conservation purposes. During 2012 and 2013 in three distinct seasons, presence and absence of roe deer sign data were recorded on random transect lines. Moreover, vegetation parameters including: density, canopy cover of oak trees, Importance Value, diameter breast height (DBH) of the oak trees, and the edible plant species in Bozin and Markheil Protected Area were measured. Vegetation analysis was carried out using T-Square and plot sampling techniques. Data was modeled by performing Generalized Linear Model (GLM) approach and the best candidate model was chosen based on the least value of the Akaike Information Criterion (AIC). Additionally, a chi-square equivalent assumption was tested to compare the differentiation rates of roe deer presence between the seasons. From the generated models, the presence of roe deer demonstrated a strong correlation with canopy cover and DBH, but no significant correlation appeared for the other factors. At the same time, 27 edible plant species were identified using pellet group analysis. Finally, the density of oak trees and the composition of understory scrubs may potentially influence the population dynamic of roe deer in the area. Further the community based research survey and conservation measures need to be accomplished for enhancing the viability of this overlooked species.

Nowadays, climate change, drought and habitat fragmentation threaten biodiversity throughout the world. Beside of natural incidents, anthropogenic factors are the most important reasons for disconnecting genetic flow between habitat patches. Hence establishing suitable habitat corridors is very vital for migratory species or wide-ranging animals. Such animals need to migrate repeatedly to their recognized home ranges, but are often faced with many natural physical barriers or man-made obstacles in their habitats which resulted stress and forced dispersion of animal groups. If a suitable habitat is not provided, wildlife groups will inevitably become scattered and we will be faced with isolated meta-populations and biodiversity degradation over the long-term and this event may lead to species extinction in the future. In this research, at first four factors including topographic position, digital elevation model (DEM), distance to road and land cover layers were selected to assign habitat suitability. At the next step, habitat patches were extracted through weighting these factors and their characteristics into the GIS environment. Then, the suitability and ecological resistance maps were used to calculate the cost-distance factor to extract potential habitat corridors with considering the least distant-cost path criterion. These alternative corridors were then corrected for accuracy by field visits and consideration of probable land-use conflicts and desired corridors were finally selected. In the end, the selected corridors were ranked Considering annual allocated budget for installing and priority of corridors.