Journal of Spatial Analysis

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Investigation and evaluation of dust and microstrip phenomena is one of the important values ​​in the management of climate and environmental hazards in the Middle East, especially in the arid, western, southern and central parts of Iran. Methods and plans for studying this phenomenon and its management are of great importance and great value. According to studies on dust phenomena based on predictive methods with low error, contradictory and low, the evaluation of the characteristics of dust and its prediction will reduce the irreparable damage that results from it. To do this, in this research, dust monitoring and assessment of its prediction in Ardebil province was performed using the ANFIS model. The data used in this study is the amount of dust in the relevant statistical period to each station from its inception until 2016. The dust phenomenon was used in the observed and predicted time intervals to assess the dust and the ANFIS model for predicting dust phenomena. According to the findings of this study, in the monitoring and prediction of dust situation, the frequency of occurrence in observed years in the maximum amount of dust in Ardabil station with 74% and the lowest in Mashgin is 8%. In the years to come, the maximum amount of dust at Khalkhal Station was 61.67% and the lowest was 10% in Mashgin. In terms of amount of dust, the Ardebil station is more intense than the rest of the stations. In terms of the severity of drought that has been studied, each of the 5 stations studied has a dust concentration of more than 74%. For the 5 stations studied for the next 18 years using manually generated codes, the stations were divided in time series, with the highest average error of training at Pars-Abad Moghan Station with 0.091% and less The highest value was obtained at the Grammy station with a value of 0.001%.
 

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The most important role that the managed areas will play to attain sustainable development goals would be protecting ecosystem and genetic diversity to achieve the scientific, aesthetics, social and economic potential benefits in future. Proper management of protected areas requires a full understanding of the present conditions, detailed and exact implementation, planning, regular monitoring and risks changes detection in protected areas to understand how are they, how they would effect on nature, recovery and rehabilitation processes and to protect them in long term is very important. Karkhe National Park and protected area is one of the most valuable and most strategic areas in the country that can be protected. This study aimed to identify and analyze threatening risks in Karkhe protected area and national park. The Study area is located with an area of ​​15828 hectares (sum of national park and protected area) on both side of Karkhe river in Khuzestan province. In this research based on field visits and using the Delphi technique, that there were 15 experts and specialist joint it, 28 risks in two terms of the natural and anthropogenic environment (physicochemical, biological, economical, social and cultural) are identified. Then to order the identified risks, The TOPSIS method was used according to the three fectors, severity, probability and sensitivity of the host environment. The results showed that the risk of lack of conservative officer by closeness coefficient (CC) 1 is the highest risk in the area and The risk of soil pollution with heavy metals by closeness coefficient 0.149 is the lowest priority. The most obtain risks has been socio-economic risks. After ordering the environmental risks was found that existing risks in the region has been in a considerable level. Finally, strategies to control risk in the region was presented. As a result, management solutions should be provided to reduce, control, or eliminate the most important risks. In the meantime, strengthening the existing environmental laws and the necessary guarantees for their implementation seems necessary.

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As 7 Stations include; Ardebil, Sarab, Shahrekord, Ahar, Takab, Zanjan, and Saghez were experiments on average every year less than 30 days with thermal stress. From these 7 stations, Ardebil and Sarab regions, having 3 and 7 days with thermal stress, respectively, have the least amount of days with heat stress. All the days with the heat stresses obtained for these stations have been the days of the first class of heat stress map, and all of them were randomly distributed over the warm period of the year.
But in contrast to this stations that had the fewest days of thermal stress, southern Iranian stations, especially those stationed at the Persian Gulf and the Gulf of Oman Sea coasts, were the most frequent days of heat stress.
The two Jask and Chabahar stations with the annual average of 304 and 301 days, with the highest thermal stress, were the most frequent regions of Iran. The lower latitudes, lower elevation, higher temperatures and relative humidity are factors that make the conditions for having the most frequencies of days with heat stress in this part of Iran.
The spatial pattern of five classes this index also show different patterns in comparison with each other so that as all stations in Iran experience at least 3 days of thermal stress in the first class during the year. But with increasing intensity classes, the number of stations that experience the conditions of these five classes over a year will be reduced. As for the second class, 16.2% of the stations, for the third class, 55.4% for the fourth class, 83.7 %, and finally for the fifth class, 90.5% of stations, do not experience comfort in any way during one year. Finally, with regard to the important role of the elevations in the spatial distribution, the relationship between the total frequency of days with thermal stress and elevation was modeled using classical linear regression model. The results of this model showed that per 100 meters above sea level, 9 days from the total frequency of days associated with Iran's thermal stress is reduced. This downward trend is such that there is no thermal stress in Iran at 2300 m above sea level. In other words, the height of 2300 meters is the elevation border between the occurrence and absence of days with thermal stress in Iran.
 

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Introduction

Trend of increasing natural resource degradation in many parts of the world, is a serious threat to humanity. Desertification is one of the manifestations of the damage that has already suffered as a scourge of many countries, including developing countries are. At present, remote sensing is one of technologies with timeliness data and accuracy suitable for monitoring land use changes in the areas of natural resources. Desertification monitoring and tracking changes, which seeks to desertification that the change could be for any reason and also collect and analyze data from activities, projects, plans and programs that may desertification range condition assessment and reporting to provide them. The purpose of this study was to evaluate changes in land use on desertification monitoring using remote sensing techniques to the agricultural lands around zayandeh rood in the East region of Isfahan.

materials and methods

In this study, the image sensor of TM to date 1987, 1998, ETM⁺ to date 2002 and  OLI to date 2014 related to the Landsat 5,7 and 8 to obtain the land use map used and then was performed radiometric and geometric correction.Then was used the color combination, the main component analysis, vegetation index and supervised classification method for detection of complications and the maximum likelihood algorithm as the most appropriate method for supervised classification in classes 9 of land cover. After production the land use map correctness evaluating operations with calculation error matrix and then was performed detection operations for these maps. Finally, for desertification of monitoring, land use years 27 changes around zayandeh rood  using the comparison method  is paid changes to identify and was obtained the area of each use.

Results and discussion

For investigate the the process of desertification, land use changes in the period of 27 years. In order to select the appropriate bands in supplying the best color composite satellite images and operations classified in order to reconstruct the images, index optimization factor was applied. The results of accuracy assessment shows that For all the images above the 80% overall accuracy and Kappa statistics indicate that almost 80 percent. Generally good agreement between the classification and classes of users on the ground there. By comparing bit images specified land use changes in the period of 27 years, riverbank has the greatest changes during this period. So during these 27 years the river high Zayandehrood degradation, which could be due to the expansion of agricultural activities in rivers. This degradation is generally represents gradual drying of the river and go surrounding cultivated by farmers. This degradation process in the margins of the river and the gradual drying of the river towards the desertification situation in the region shows.

Conclusion

In year 27 time period, Zayandeh Rood neighboring rivers has changed dramatically, so 86.43% of neighboring rivers was destroyed due to the expansion of agricultural activities vicinity of the river and drying river. Another significant changes, loss of agricultural land is notable such that 64% of this land has been reduced compared to 1987. Of reasons for the loss of agricultural land will be noted the region drought and Zayandeh Rood river drying up and Low rainfall, land use change and the proximity of the region desert. Also, has become about hectares 324.99 Of salt marsh lands to agricultural land. Moreover, the developed urban areas to its development contributed agricultural land and rangeland. Bayer lands around Zayandeh Rood Increase and also in region of rangeland lands Low and has increased Bayer lands  and somewhat until agricultural land which inappropriate use of this land shows in order to the agricultural. That this is the desertification progress in the region. Generally desertification process in this period years 27 has been a growing trend.Therefore multi-temporal and multi-spectral satellite data for enhancement, especially for desertification monitoring was large capability and classification after comparison method is helpful for determine the type and direction of changes occurred. Since the development of desertification, limited to a small area and is not recommended range is therefore more effective, in addition to work sheets, other sheets around the area also evaluate the process of desertification is to allow for planning and management in the field of combating desertification exist.

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Sea surface temperature is one of the most effective physical parameters that affects the health of coral reefs communities.High frequency of the bleaching phenomenon has extensively occurred in the Persian Gulf in the recent years due to the increase in temperature and increased changes in the sea surface temperature (SST) resulting in great mortality in the coral communities. The aim of this research is to determinate a temperature threshold which may function as a warning for the incidence anticipation of this phenomenon. Data on the variation of the SST that has been taken from National Oceanic and Atmospheric Administration (NOAA). Information related to bleaching in the regions of the southern Persian Gulf was extracted from the published papers and reports. Each of these sources also has been extracted for a 35-year statistical course (1980-2015) and by the index of degree heating weeks (DHWs) determined for the same statistical course in this research for the assessment and anticipation of bleaching phenomenon. For reviewing of the work accuracy, Peirce Skill Score (PSS) technique was used to quantify the accuracy of previous and subsequent anticipations. According to the derived results, DHWs threshold for the study region was determined to be 7.13. the threshold 7.13 for DHW is suggested as a caution threshold for bleaching incidence in southern regions of the Persian Gulf that is whenever the values of weekly positive temperature DHW show number 7.13 and higher, there is an expectation of bleaching phenomenon incidence of corals for these regions. And the score of  PSS= 0.72 derived from the amounts of H= 7/8= 0.87 for the Hit rate and F= 4/26= 0.15 for the False alarm rate of the bleaching was obtained for the southern regions of Persian Gulf and study region. In northern regions of the Persian Gulf the threshold 5.3 for DHW is suggested as a caution threshold for bleaching incidence. The rate of pss = 0.62 derived from the   amounts of     (3/4 = 0.75) for   the  Hit rate   and ( 3/23 = 0.13) for the  False alarm rate of the  bleaching was obtained  for the northern regions of  Persian Gulf and study region. Difference in DHWs values of the south and north of Persian Gulf shows more resistance of the corals of south Persian Gulf against DHW changes and SST anomalies. Also the amounts of DHW alongside SST can help more completely to the anticipation of bleaching phenomenon.

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Introduction: Wetland ecosystems, especially marine coastal wetlands of the most important and also the most vulnerable are the world's environmental resources. Which has always been sensitive to the fragility of coastal areas, high population density and intensive human activities are faced with the threat of destruction. Based on this, monitoring the trend of the changes in wetlands and their surrounding lands can be effective in the management of these valuable ecosystems. Investigating the environmental risk is a suitable instrument for evaluating and ensuring understanding of the relationships between stressor factors and environmental effects especially in wetland ecosystems. In general, application of methods of evaluating environmental risk is one of the important tools in studying environmental management along with identifying and mitigating potential environmental damaging factors in wetland regions in order to achieve sustainable development. Today, multi-criteria decision-making methods are employed in evaluating the risk in many studies.This study is based on multi-criteria decision-making methods to identify and analyze the risks threatening Tyab- Minab International wetland located in Hormozgan province was conducted.
Materials and methods: Based on the methodology to identify and prioritize risks Delphi, AHP and TOPSIS techniques were used to determine the risk priority number. In the first phase of this study, to identify and screen the main criteria of project selection, Delphi method was used. In this study, the panel of interest was determined based on a combination of experts with different expertise and out of a sample of 20 individuals, in which experts with various expertise gave a score from 1 to 5 (Likert scale) to each criterion. In this way, 32 criteria were identified as the most important and considerable risk for Minab Wetland and further proceeded to the second phase for prioritization and analysis. In this stage, multi-criteria decision-making methods were used, in which hierarchical analysis process was employed for prioritizing the criteria using Expert Choice 11 software. The indices of risk evaluation including the impact intensity, incidence probability, and the sensitivity of the receptive environment in environmental risk evaluation of wetlands do not have an equal value and significance. For this purpose, to weight the factors effective in estimating risk level and for prioritization of risk options, the technique for order of preference by similarly to ideal solution (TOPSIS) and Excel software were benefited from for calculations. The spectrum of scoring to each of the indices of incidence probability, impact intensity, and the sensitivity of the receiving environment was chosen from very low (1) to very high (9) based on hour spectrum. Following investigation of the types and frequency of indices along with the method of score determination of these indices, three indices of risk intensity (C1), risk incidence probability (C2), and the sensitivity of the receiving environment (C3) were chosen for risk ranking using TOPSIS model. Next, after determination of risk priority number using TOPSIS, the risk levels were calculated and evaluated using normal distribution method for each risk. To determine the degree of risk-taking, risks are organized in a descending order, where the elements of the number of the class and the length of the class are determined based on Relations 1 and 2 (n is the number of risks). Next, the risks are categorized based on these classes. Considering the concept of ALARP, the risks under investigation are divided into high risks, medium risks, and low risks. In this study, considering the number and length of classes, the studied risks were categorized in six levels (critical, intolerable, considerable, medium, tolerable, and trivial risks).
 

(2)

(1)

the number of classes=1+3.3 log (n)
the length of the classes= the greatest risk value - the smallest risk value/the number of classes
Results and discussion: In the first step, the final indices of the wetland's environmental risk were identified and the development of hierarchical tree and classification of the risks threatening wetlands along with their incidence probability in two groups of natural and environmental criteria was performed. Eventually, the final weight of criteria resulting from paired comparisons was obtained in Expert Choice 11 to achieve the score of incidence probability of each risk. Based on the results, among the natural, social, economic, physiochemical, biological, and cultural criteria, drought and climate change, increase urban and rural development, Smugling of fuel, oil pollution, reduce the density of vegetation, indiscriminate exploitation of groundwater were of high priority. The results obtained from ranking the the risks threatening Minab Wetland using TOPSIS suggest that oil pollution, dam construction upstream, persistent drought and climate change, and sometimes alcohol and fuel smuggling and illegal overfishing the priorities are first to fifth. Also Results showed that the respectively based on (Cj+) oil pollution (0/9109), dam construction (0/8121), the drought and climate changes (0/8063) and the smuggling of fuel (0/7520) are in Unbearable level.
Overall, the results indicated that same as this research, wetland ecosystems are subject to many threatening factors, resulting in ecological imbalance and abnormal appearance of the wetland, putting the wetland entity into danger of extinction in terms of fauna and flora.
Conclusion: Nowadays, for assessment of environmental risk, various methods are used, each of which has positive and negative points given the studied environment and the conditions governing it. Therefore, one cannot reject or approve one method with total confidence. By employing novel methods in risk evaluation, the intensity of risk incidences and, in turn, the damages and losses incurred to the environment can be prevented or at least mitigated. Further, it is also possible to move in line with proper and optimal management of environmental resources, especially wetlands and with sustainable development. Undoubtedly, understanding and recognition of the factors threatening wetlands, according to the importance and the impact of them, Prevent and cope with the threats and accurate project preparation and implementation of wetland conservation plans and environmental management.

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Land cover changes as a basic factor in environmental change act and has become a global threat. In this research, changes in land cover in rural tourism areas by neural networks, Markov chains in software ArcGIS, ENVI, Terrset using the TM and OLI satellite imagery, Landsat Satellite was surveyed for a period of 30 years for three periods of 1985, 2000, and 2015. The findings of the first stage show that land cover changes at the period 1985-2015, were classified in five class residential spaces, Commercial, Green, Empty and mountainous spaces and communication networks. In this study, the area of mountainous and empty spaces (13.25%) has decreased and in contrast, has decreased the amount of green spaces (6.221%), Residential (5.258%), commercial (1.264%) and communication networks (0.529%). Changing land cover as one of the most important environmental risks has been directly influenced by the Commodification phenomenon. Also, the findings of the prediction using the Markov-CA chain showed that with the continuation of the current and excessive loading on the ground, on the horizon of 2030, green cover (Agriculture, gardens and grassland, garden and residential)  and  wild land  and mountain cover have been reduced and to cover residential and commercial villas will be added. Based on research findings concluded that land cover changes in rural tourism areas in order to achieve more profits has become incompatible applications. This change in land cover, in addition to the economic, social impacts, has led to the formation of environmental hazards in the Bharang area. Developing tourism in the study area by removing agricultural land from the production cycle has led to an increase in urban activities and the formation of new activities (service, Residential Garden, residential villa) instead of traditional activities(agriculture and livestock) that are economical. And by loading too much ecological power tolerable land, while posing environmental hazards, causing incompatible activities next to each other, they do not match. Therefore, tourism, which gradually formed over the years and now it has become a part of rural texture, Spatial Conflict and heterogeneity two strains has created for them. Spatial Conflict created, due to changes in land cover and acceptance of incompatible activities that derive from human-nature relationships. This means that the rapid and unpredictable trend of tourism development, the rural landscape has encountered a problem and with changes in land cover, has led to inconsistencies between different activities and eventually has shaped the Spatial Conflict.
 

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Deforestation or vegetation degradation is one of the main drivers of global earth changes, which has significant consequences in terms of ecosystem performance and biodiversity conservation. One of the ways for studying vegetation changes as the most important indicator of land degradation is remote sensing. In this study, in order to monitor the vegetation degradation trend in Ilam Province.After obtaining and preparing the required data (410 downloaded images) in the ArcGIS and Surfer software, the multiplication, mosaic and georeferencing operations are made. Converting format of images into ASCII is the next stage of the study. By converting this format, the total number of 953552 pixels is studied within the range; after removing the lost and negative values, 328042 pixels are analyzed. Besides, using parametric statistical method of the classical linear regression and programming in R software, the trend of slope variations and significance of slope variations of vegetations are obtained for the 17-year period (2000-2016). Results of this study show that the focus of the highest trend of declining slope variations (trend of negative slop variations) is in the NDVI index across the western half of the studied area and the focus of the highest trend of increasing slope variations (trend of positive slop variations) is in the NDVI index in the center and east. Significance of the trend of slope variations also approves this claim. Thus, the focus of the highest trend of slope variations (negative) in the west and southwest of the studied area along with the highest trend of slope variations (positive) in the center and east is significant at the probable level of 0.05
 

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 Investigation of the daily minimum visibility meteorological conditions using RVR data at IKA airport during 2013-2014
Hatami, J. ¹, Sabetghadam, S. ^2*, Ahmadi-Givi, F. ^3
1M.Sc. Student, Institute of Geophysics, University of Tehran
²Assistant Professor, Institute of Geophysics, University of Tehran
³Associate Professor, Institute of Geophysics, University of Tehran
 
Abstract
Atmospheric visibility is defined as the greatest distance at which an observer can see a black object viewed against the horizon sky, which is usually known as visual range. It shows the degree to which the atmosphere is transparent to visible light, therefore its impairment results from light scattering and absorption that can originate from natural or anthropogenic sources.  Visibility is an important atmospheric parameter in landing and takeoff of an aircraft. Reduced visibility due to snow, rain, fog, and haze is an important consideration in the landing and takeoff of aircraft. Visibility and the related quantity Runway Visible Range (RVR) are meteorological parameters that are crucial for the operations at an airport. The Runway Visible Range is defined as the range over which the pilot of an aircraft on the centre line of a runway can see the runway surface marking or lights delineating the runway or identifying its centre line. A large number of aviation accidents are happened cause many passengers to die. Today, safety is very important in aviation. In fact, it is a competitive factor among aviation companies. Measuring the exact visual range is one of the most important factors in flight security. According to the international standards, whenever the visual range is less than a certain threshold for runways, take-off and landing will not be authorized, and pilots will be ordered land on an alternative airport that costs airlines a lot of expenses.
   One of the methods in determining the runway visual range is to use instruments such as transmisometer and forward scaterometer to measure the amount of scattering and absorption of light by the atmosphere. A transmissometer measures the extinction of light over an atmospheric path between an emitter and a receiver and it  is directly related to the extinction. A forward scatter meter measures the amount of light scattered by a small measurement volume. RVR instruments usually locate at three places across each runway that is mandatory for operation in international airports.
    For the first time in Iran, data obtained from the RVR system from Imam Khomeini International Airport are applied in this study to examine the circumstances under which the runway visual range reached its minimum during two years 2013 and 2014. The high accuracy of these devices is a valuable factor for researchers to get more precise results. The data used include visibility range, temperature, dew point temperature, humidity, wind speed and wind direction, which are measured using the RVR system.  The main part of this study concentrates on fast decreases of RVR, meaning a decrease of visibility to below 1500 m which takes more than 10 minutes. Therefore some cases of RVR data have been investigated in more detail utilizing one-minute observations are presented. For these cases, some meteorological parameters are investigated before and after  this fast decrease of RVR occurred. These parameters as well as RVR are plot to find out what happened before and during each specific event.
    Results show that the critical low visibilities were mainly occurred in May and March and no cases of low visibility were seen between July to September. This can be due to the impact of more atmospheric systems and variable weather conditions in the relatively cold months. The highest visibilities were mostly occurred in July-September, due to the weakness of atmospheric systems and their less frequency of occurrences. Low visibility days were usually accompanied by dust, fog, mist and precipitation events.During 2013 and 2014, categorizing the weather events that may lead to the decrease of visibility to less than 1500 meter, shows that the 45 percent of the cases with the low visibilities caused by by dust, 35 percent by haze, 15 percent by fog and 5 percent caused by haze.
    For the critical cases, case studies show that the high relative humidity and the change of wind direction were also favored in the occurrence of low visual range. Case studies of the events suggest that these factors differ from one another based on how they are formed. After the fast decreases of RVR, the relative percentage of RVR events show an increasing in relative humidity especially during fog and precipitation.
 
Keywords: runway visual range, scattering and absorption of light, low visibility.
 
 

 

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. Caspian Sea south coast future climate change estimations through regional climate model
many physical of the procedures related to climate change are not perceived thoroughly. Scientific knowledge used to show those procedures completely, and to analyses forecasts is so complex, since most current studies about climate physical model have been done through semi experimental and random models and most of the current analysis techniques are still going through early stages. One of the important aspects of this study is modeling physical procedures of sea level rise geographical pattern, which is used practically for SLR threat evaluation of special geographical location, meaning Caspian basin. Since Caspian basin is a closed sea, it is heavily influenced by climate change and meanwhile is changing due to physical level and environmental change. It is necessary to define Caspian coast climate change possibility with specific focus on climatology and meteorology fine data, also to define the scale of sea level fluctuations for the sake of exact planning in different fields. This study aims at presenting a new dynamic method, via using an integrated model system named SIMCLIM, which can clarify SLR satellite changes well.
According to scientific examination existing in this study, based on scatter scenario 4.5 RCP and 8.5 RCP for the following years, until 2100, temperature and precipitation change proposal have been presented. On one hand, Caspian coastal climate change analysis and estimation were based on climate patterns and water flows in the form of regional climate statistical model in order to simulate and forecast, on the other hand surveying chronological changes of Caspian sea coast slope with satellite height measurement was done to measure sea surface height fluctuations The present study has used SIMCLIM model for the first time in order to clarify Caspian sea level changes, elements, and effective climate reasons, all simultaneously in one project. The project base is according to coastal systems and procedures. Coast line shore change simulations are based in Bruun law.
In future the frequency and intensity of extreme events temperature and precipitation will increase. Extreme events illustrate changes in extreme temperature and precipitation measures, in comparison with the base period of 1981-2010 which convey precipitation sum or the temperature beyond 95 percentile of base period. Temperature and precipitation coefficient of variation for the whole Caspian basin is positive and it varies from 25 to 88 percent. A disordered pattern is dominating south basin of the sea. Sea level changes, considering vertical earth movements, which is 2 mm in a year, resulted from subsidence of Caspian pit seabed have been obtained for both scenarios. In general, annual sea level average while ignoring seasonal changes, is increasing consistently and it was calculated 1.22 cm each year according to high estimation procedure in scenario 8.5 RCP and it was 0.93 cm based on scenario 4.5 RCP. Predicted results were compared with real results of base20-year period from 1995-2015. Base period results in three levels of sensitivity of low, mid, high shows 8.4, 10.1, and 11.8 cm rise; after comparing them with model forecast results, meaningful coordination at the level of 95 percent was found out. In both scenarios, all over the Caspian shoreline water advance and destruction will exist. In the worst case scenario of 8.5 RCP of 2030, current coast will decrease about 23 meters and in 2060 it will be about 53 and in 2100, there will be 117 meters advance towards land.
Precipitation and temperature percent for 2030, 2060, 2100 will change increasingly. Spatial variability and annul coefficient of variation are various in different regions. North, western north, eastern north and east will include the least temperature fluctuations, and the highest percent of precipitation with the highest coefficient of variation all convey chronological period precipitation distribution with disordered accumulation and more local difference in this region in comparison with other regions. Then Caucasus mountainous region will have the highest increase in precipitation with a suitable scatteredness, during a year. The southern part of Caspian Sea will be with the highest increase in temperature and the least amount of increase in precipitation in percent. High coefficient of variation in this area illustrates abnormal and disordered pattern on the threshold of precipitation for both scenarios.
 fluctuations in sea level based on subsidence of Caspian pit seabed was calculated.In general, average annual sea level is increasing which will be 1.22 cm, per year for scenario RCP 8.5 and 0.93 cm for scenario 4.5. Due to incapability of world community in decreasing releasing greenhouse gases, it is expected scenario that 8.5 RCP to come to reality.
 Caspian Sea shoreline is influenced by water advance and destruction. The difference between two scenarios in 2060 will be 3 meters and in 2100 will be 12 meters. Instinctually, such advances in coasts with less depth and less slope will be more. This study suggests that coastal changes are inevitable. However, this region inhabitant owns no systems or no systems have not yet developed to aid them be able to adopt with the climate changes.
 
Keywords: Sea level rise, South Caspian basin, Extreme event, Coefficient of variations, shoreline.

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ite selection for Temporary Earthquake Shelter Compounds, Using Analytic Hierarchy Process and Weighted Linear Combination based on GIS; Case Study: Shahrood
 
Abstract
Natural disasters are unpredictable and unavoidable and their occurrence in human settlements has led to catastrophes in many cases. Therefore, it is necessary to prepare prior the occurrence of these events. A prompt response can be a solution to this goal. As long as assurance is provided to the affected population that there is no lack of assistance and facilities, their ability to return to pre-disaster conditions and recovery will be increased. Selecting the right accommodation according to the needs of people after natural disasters, as well as planning to meet the needs of the victims, not only reduces risks and expedites recovery operations during reconstruction, but also strengthens the protection structures and advances the safety aspects of communities prior to any kind of incident. Increasing concerns arising from high occurrence of natural disasters, especially earthquakes, and witnessing the unpleasant consequences, will emphasize the need for proper habitation conditions and facility provision. Iran is a country that is prone to disasters. In fact, no country in the world is immune to natural disasters. In this research, urban areas of Shahrood located in Semnan Province, have been studied with regard to seismic potential and proximity to the southeastern part of Alborz Mountain and Shahrood’s faults as a sample for selecting appropriate post-earthquake shelters. The overall purpose of this research is to propose a decision making process for efficient and safe spatial planning in the wake of crises. In the first step, the structural vulnerability of buildings in terms of foundation quality and their vulnerability measures is estimated at 24% of the city, which is equivalent to 12778 buildings prone to destruction, even facing a mild earthquake. After analyzing the possible damage to the city, according to the average household size of 3.43 people in Shahrood, 43829 people are estimated to be homeless. The amount of space needed to accommodate these people, with an estimated per capita of 30-45 square meters per person, was estimated at 132 to 198 hectares, which could be used in a few distinct and scattered spaces. After determining the area needed for settlement, the criteria influencing the location of temporary shelters were identified based on scientific literature and analysis of previous experiences. According to these studies, the criteria for influencing this concept are classified into nine groups including access, location, cultural, economic, compatible and incompatible neighbors, infrastructure conditions, land quality as well as space area. The subsets of these nine criteria can be categorized into two groups: constraint factors and classification possibilities. Factors such as distances from faults, high voltage electric power lines, vulnerable zones, gas stations and chemical storage facilities are known to cause limitations. The concept of the facility in this research, in addition to proximity to residential areas, main roads and storage depots, includes access to compatible applications, medical centers, security centers, fire departments and outdoor spaces; hence it is necessary to accommodate people at the minimal distance from these facilities. Parameters such as area, surface water, infrastructures and available energy sources are some of the criteria that need to be measured in terms of their quality in proposed options and decisions are to be made on the basis of their existence and accuracy. Since each parameter has a different effect on the location of temporary accommodation therefore, the list of priorities is sorted accordingly. In this article, a methodology for locating shelter after an earthquake has been recommended by using hierarchical analysis, weighted linear combination and GIS. In this multi-criteria decision-making process, the weighting process was performed on each parameter by paired questionnaires that were provided to 40 experts, and analyzed according to the principle of hierarchy (AHP) and arranged in Expert Choice software. Then, all of the data layers in GIS software were combined with WLC method according to the criteria and standardization. The Geographic Information System (GIS) has been used as one of the most useful tools in allocation and land use planning. To analyze the data in the software, after converting the data into the Raster structure and classifying the layers in appropriate categories and in accordance with the functional radius, a conclusion was made in the Arc GIS environment. The output, obtained by overlapping the collected items, is a list of land plots suitable for post-disaster shelters, sorted according to the aforementioned priorities.
 
Keywords: site selection, temporary sheltering, AHP, WLC, GIS
 
 

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Analysis and Measurement of Environmental Resilience of Villages in Gorganrud Watershed against Flood (Golestan province, Iran)
 
Abstract
Environmental hazards are inevitable phenomena that always place serious risks on the development of human societies, especially rural development. In the recent years, however, significant changes have been made in crisis management approaches, and the prevailing view has shifted from the "reduction of vulnerability" approach to "resilience improvement". Resilience is a new concept often used in the face of unknowns and uncertainties. Therefore, along with this change of attitude, it is important to examine and analyze natural hazards in terms of resilience. According to global statistics, floods, as one of the most devastating natural disasters, have caused the greatest losses and casualties to human settlements, which is true both in our country and in Golestan province. Investigations show that only in the statistical period of 1991-2014, 106 rainfall cases have led to the occurrence of floods in this province. These floods have damaged natural resources, the environment and the prevalence of environmental pollution; In addition, other natural and human factors have contributed to the heightened risk of flood damage. But if it was planned for the restoration of villages, then the damage could be reduced. Therefore, this research was conducted with the general purpose of determining the relationships between environmental factors and factors of rural communities of Gorganrud watershed on their resilience and numerical values. Finally, the residual spatial analysis of rural limited settlements was studied. Accordingly, the research questions are as follows: a) What is the relationship between environmental factors and factors in the villages of Gorganrud watershed in Golestan province with the resilience of the communities living in them in the face of flood? b) What are the resiliency values ​​of these communities in the environmental dimension and which zones? This is an applied research with descriptive-analytical method. A library of researcher-made questionnaires was used for collecting data using library resources. The statistical population consisted of 106 villages with 22,942 households. First, 31 villages were selected by cluster sampling. Then, using Cochran formula, 318 families were selected as sample size and selected by simple random sampling method. Also, for assessing the validity of the questionnaire, using Delphi collective wisdom methods, it was determined by using historical studies and opinions of experts in rural areas. The reliability of the questionnaires was also determined by using the Cronbach's alpha coefficient in the pre-test method. The value for the household questionnaire was r_a1=0.841 and r_a2=0.862, respectively. All steps for statistical analyzes have been performed by Excel and SPSS software. Additionally, the development of mapping, risk-taking, risk and resilience was also done with the help of ArcGIS software and the weight of each criterion was determined by the Super Decision tool; Then, using the weighted and linear overlapping methods, each of the sub-criteria of the main indexes was multiplied in its weights. The study area is divided into two distinct sections in terms of geological and geomorphological structure. The southern and eastern parts of it are the ripples of the eastern Alborz mountains, which are taller in the southern part and extend along the east-west direction. Also, the northern part of the studied basin is the Gorgan plain, in which the main branch of Gorganrud flows from east to west and all branches of the south and east are drained. Following the general slope of the main branch and its long-standing walls in the mid-east, it is usually not flooded; but as far as the west is concerned, its slope is very low and one of the flood plains is considered as the basin. The results of the research show that there is a significant relationship between the environmental factors of the studied basin villages and the resilience of the communities inhabited by them in the face of floods. Also, the average environmental resilience of the whole region was lower than the average (2.76 average), rural households in the sub-basins of TilAbad and ChehelChai with an average of 3.24 and 3 had relatively good environmental resilience, But most of the rural households in the sub-basins of Ghurechai and Lower of Gorganrud, Mohammad Abad-Zaringol, Madarsoo and Sarisoo, with an average of 2.89 to 1.85, had a poor environmental resilience. In addition, According to the flood risk resilience map, it can be said that of the total 31 sample villages studied, about 29 percent of sample villages have "medium upward" resilience in facing flood risks; conversely, most of these villages (71%) also have relatively low degree of resilience. Also, comparing the findings of this study with the results of most other researches, such as the studies of Olshansky and Kartes (1998) regarding the necessity of considering the environmental factors of settlements, observing the necessary environmental standards and the necessity of using proper land use management tools to reduce risk hazards and improve resilience, Center of Emergency Management Australia (2001) on the need to consider the state of the infrastructure, including the level of communications and accesses, biological conditions, including the status of pollution, as well as geographical characteristics, such as distances and proximity, climate, topography, as well as the general results of studies by Rafiean et al. (2012) in special selection of the most suitable model of resilience based on the combination of carter and socioeconomic model due to the simultaneous attention of this model to its geographical features and its comprehensiveness, as well as attention to the local communities' participation, Rezaei (2010), Shokri Firoozjah (2017) and Anabestani et al. (2017) Regarding the low value of the calculated population, the resiliency number of the society is consistent and consistent with the lack of attention to infrastructure issues, locations, etc., which is below the baseline (3). As a result, all of the aforementioned components of the resilience of inhabitants of sample societies have been affected by its environmental dimension, which is often due to insufficient attention and insufficient handling of them, which reduces resilience of rural residents to flood risks.
 
Keywords: Environmental hazards, Flood, Vulnerability, Resilience, Spatial analysis, Golestan Gorganrud basin.
 

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Analysis of Rural Settlements Resilience against Earthquake
Case Study: Marivan County
 
Iran is a seismic prone country located over the Himalayan-Alpine seismic belt. Striking earthquakes during the past years and decades are strong proofs for vulnerability of rural areas in this country; loss of lives, damage to buildings, even demolishing villages have been experienced in Iran rural areas. All these fatal effects are evidences to make villages more resilience and strengthen their structures because in the case of vulnerable structures, earthquake can be tremendously destructive. Therefore, losses of live and property can be avoided through making resilience rural social, economic and physical structure like construction of buildings that sway rather than break under the stress of an earthquake. Making villages resilience are directly related to saving rural residents lives and their property. Briefly, reaching or maintaining rural areas capacities to an acceptable level are the main purpose of this study by analyzing mentioned structures. This study conducted in Marivan rural settlements which exposed to earthquake.
According to Morgan Table, 310 samples responded to the questionnaires. The samples of this study were selected by chance from 6 districts and 18 villages. The main methods for analysis of collected data were Dimatel, ANP and Statictical analysis by SPSS. The results of ANP and Dimatel analyses led to the determination of relation among the factors. It should be noted we used Delfi method for this part. Moreover, for the final part ANOVA analysis is used by the authors. 
All around the world, countries have different approaches to deal with hazards in order to mitigate fatal affects. In fact, the goal of all management practices is to reduce hazard impacts. Iran faces a variety of hazards because of placing in a special geographical position; in this regard earthquake is the most important one. Resiliency approach can improve the flexibility of rural settlements through strengthen the capabilities of them and reduce their vulnerability. In the present study, analysis of rural settlements resilience against earthquake has been investigated. The results show that the resiliency is lower than the average in the studied villages. Also, there was a significant difference among the studied villages in terms of the resiliency against earthquake. The findings are consistent with the results of Nouri and Sepahvand in 2016 and Rezaei et al., in 2014.
Considering the analysis of data and ANP analysis of the internal and external factors in a general and separate way, the studied villages of Marivan city can be considered as non-resilience structures; in this regard, the most important reason is the inappropriate condition in the internal factors of rural settlements. The poor quality of construction and the inadequate structure of buildings must be considered, as well. Another obvious reason is the existence of eroded texture in this area. According to external factors, relief does not cover rural areas and led to reduce the resilience of rural settlements. Investigating the resilience of rural settlements based on external factors not only indicates the inappropriate situation of rural structure in this analysis, but also it proves a more favorable situation than internal factors. The findings show that structure and the amount of structure confinement in decrease the tissue texture of rural settlements play a profound role; changing these factors requires a long time and long-term planning. Regarding the post hoc test, variance analysis suggests the highest resiliency in Zarivar with an average of 2.99 and the lowest survival rate in KhavumirAbad rural district with an average of 1.87. Moreover, according to the one-sample T-Test, the socio-cultural dimension with a mean of 3.05 has the best situation in terms of resiliency against earthquake in the studied villages. For improving resiliency in the studied villages, authors’ suggests are including: managing and organizing preparation measures and response along with effective actions to reduce the risks of earthquake and providing a crisis management department; strengthen scientific and research studies to identify and reduce the risks; applying the rules to retrofit the buildings and increasing the safety factors in new construction; mapping the vulnerabilities in rural areas; increasing people participation and preparing them to deal with an emergency situation caused by an earthquake.
 
Keywords: Resiliency, Rural Settlements, Earthquake, Marivan County
 
 

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Agricultural crops have damaged a lot due to the aftermath of late spring frost, and because low temperatures have damaging effects on agricultural production, it is essential to anticipate and prevent potential damages. Often, atmospheric temperature variations are very urgent due to the high temperature of the systems and the plants cannot adapt themselves with severe oscillations and, have been damaged. The aim of this study was to analyze the climate of the spring frost in Kermanshah, identifying the sea level equations and the late spring freezing frost of the period from 1990 to 2015. This survey has been done to determine the period of the freezing phenomenon, determine the minimum daily temperature of 7 stations placed in Kermanshah,  Hamedan, and Ilam. After analyzing the data of spring frost freezing of Kermanshah province using the main component analysis technique and hierarchical clustering method, the most common 10 patterns of late spring coldness of the area were studied and determined. In 10 resulting cluster, 8 clusters were related to the high-pressure pattern of Siberia. From the total 91 days of spring frost freezing in Kermanshah province (79% (72 days)) is due to the high rainfall of Siberia, 12% (11 days)  is due to the Mediterranean climate and 9% (8 days) is due to the Van lake climate. These pressure patterns were named according to the location of their deposition, which caused the loss of the environment and the freezing frost of the spring.

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Investigating the threats of mangrove forests
with the help of remotely sensed data
 
Behzad Rayegani: Assistant Professor of College of Environment, Department of Environment
 
 
Mangroves are a group of trees and shrubs that live in the coastal intertidal zone. Mangrove forests are very important because they are known as natural heritage and crucial in protecting coastal ecosystems. Mangrove forests stabilize the coastline, reducing erosion from storm surges, currents, waves, and tides. The intricate root system of mangroves also makes these forests attractive to fish and other organisms seeking food and shelter from predators. So, they are ideal places to support the elements of seafood networks. However, these forests are in danger of degradation because of rapid population growth, poor planning and unsustainable economic development. In the process of regenerating an ecosystem, it is necessary to identify the precursors of the threat, to consider the means to eliminate these threats. Therefore, identifying the threatening factors of the mangrove forest ecosystem is the first step in the restoration and protection of the ecosystem.
This study aims to investigate the change and the destruction in Mangrove forests and to identify threatening forces in the Hara Protected Area. Remote sensing is now widely used in studies of ecosystem changes because its information is available for the past, and there are many highly-developed techniques for change detection through remote sensing. Therefore, in order to identify the threatening factors of mangrove forests, remote sensing techniques were used to identify changed areas during a 15-year period. Images of ETM+ and OLI sensor from 2001 to 2015 were collected in the Hara Protected Area (Khorekhoran International Wetland). Given that we have used the multiple-date remote sensor data in this study, it was necessary to use absolute atmospheric correction methods for radiometric harmonization of data. So, with the aid of the ERDAS IMAGINE 2014 software, the Atmospheric and Topographic Correction (ATCOR) model was applied to all data. Subsequently, due to the difference in radiometric resolution of the OLI sensor with the ETM+ sensor, the output of ATCOR of both sensors was stretched into 8-bit data in order to eliminate the existing divergence in radiometric resolution. Also, based on spatial information, one of the image of OLI sensor at the current time was corrected geometrically, and then other images were registered to this image to eliminate geometric errors. There are many ways to detect changes with the help of remote sensing data, but we used two widely used techniques in this study: 1) post-classification comparison; 2) Change detection techniques of Algebra. Totally four different change detection methods were applied to these images. Change detection techniques of Algebra image method include image difference, image ratio, regression and post-classification comparison were used. At first, with the knowledge of the studied area, by combining the two supervised and unsupervised classification (hybrid method), the pixels that were known as mangrove forests were identified in both time periods of study. Then pixels with decreasing trend were determined by post-classification comparison method. From the image of the mangrove forests with the logic of Boolean (OR), a mask of mangrove was obtained, which showed the areas of mangroves during the two periods. This mask was used to make the second group of methods for determining changes (Algebra method) applied to the data. By doing this, in all algebra methods, the histogram showed the normal distribution. Finally, the vegetation spectral indices were applied to the data and their coefficient of variation was obtained in the Boolean mask area. Among these indices, NDVI showed better performance, so the algebra operation was used for this index. Accordingly, areas with decrease, increase and no change trends were visited and then overall accuracy and kappa coefficients were determined.
The results showed that the method of post classification comparison has the highest accuracy in the monitoring of vegetation changes in mangrove forests. This method with a total accuracy of over 93% and a kappa of more than 0.9 showed the highest accuracy in the detection methods of the changes, therefore, in the final examination and prioritization of the regions, this method was used. The surveys showed that the smuggling of fuel due to pour gasoline into the water and camel grazing are the most important destructive factors in the mangrove forest. After determining the rate degradation in four regions, these regions were ranked in order to carry out reclamation and restoration projects.
In the case of intelligent use of the capabilities of remote sensing, one can easily identify the threatening factors of an ecosystem. In the case of mangroves, the only limiting factor is tidal conditions. It is therefore recommended that, as in this study, images are chosen to determine the changes that are in a same tidal state
 
 
Keywords: Remotely Sensed change detection, Image Algebra Change Detection, Post-classification comparison, Determination of thresholds
 
 
 
 

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Risks assessment of forest project implementation in spatial density changes of forest under canopy vegetation using artificial neural network modeling approach
 
Nowadays, environmental risk assessment has been defined as one of the effective in environmental planning and policy making. Considering the position and structure of vegetation on the forest floor, the main role of forest under canopy vegetation cover can be noted in attracting and preventing runoff in the forest floor and reducing subsequent environmental risks. The purpose of this article is forest under canopy vegetation density changes modeling considering forest ecosystem structure and forest management activities as an environmental risk. The main objectives of this study were to: (1) model forest under canopy vegetation density in forest ecosystem to elucidate the ecological and management factors affecting on under canopy vegetation density; (2) prioritize the impacts of model inputs (ecological and management factors) on under canopy vegetation density using model sensitivity analysis and (3) determining the trend model output changes in respond to model variables changes.
In this study, Land Management Units (LMUs) were formed in the region considering ecological characteristics of land. LMUs were mapped out based on Ian McHarg’s overlay technique by ARC GIS 9.3 software. Ecological factor classes of an LMU differ from ecological factor classes of adjacent LMUs (at least in one ecological factor class). The following types of data were solicited for each LMU:
(1) Ecological variables: Altitude or elevation (El), Slope (Sl), Aspect (As), soil depth (SD), Soil Drainage (SDr),Soil Erosion (SE), Precitipation (Pr), Temprature (Te), trees Diameter at Breast Height (DBH), Canopy Cover (CC), and forest Regeneration Cover (RC).
(2) Management variables: Cattle Density (CD), Animal husbandry Dsitance (AD), Road Dsitance (RD), Trail Dsitance (TD), logs Depot Dsitance (DD), Soil Compaction (SC), Torist impacts (To), Skidding impacts (Sk), Logging impacts (Lo), Harvested trees volume (Ha), artificial Regeneration (Re) and Seed Planting (SP).
(3) Forest under canopy vegetation density: The percentage of under canopy vegetation density in each LMU was estimated by systematic random sampling method. In each LMU, a one square meter sample was taken. The average percentage of under canopy vegetation density in sample units of each LMU was calculated and used in the modeling process.
ANN learns by examples and it can combine a large number of variables. In this study, an ANN is considered as a computer program capable of learning from samples, without requiring a prior knowledge of the relationships between parameters. To objectively evaluate the performance of the network, four different statistical indicators were used. These indicators are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²).
Various MLFNs were designed and trained as one and two layers to find an optimal model prediction for the under canopy vegetation density and variables. Training procedure of the networks was as follows: different hidden layer neurons and arrangements were adapted to select the best production results. Altogether, many configurations with different number of hidden layers (varied between one and two), different number of neurons for each of the hidden layers, and different inter-unit connection mechanisms were designed and tested.
In this research, 129 LMUs were totally selected, then ecological and management variables were recorded in them. In the structure of artificial neural network, ecological and management variables were tagged as inputs of artificial neural network and the percentage of under canopy vegetation density was tagged as output layer. Considering trained networks (the structure of optimum artificial neural network has been summarized in Table1), Multilayer Perceptron network with one hidden layer and 4 neurons in each hidden layer created the best function of topology optimization with higher coefficient of determination of test data (which equals 0.857) and the lowest MSE and MAE (which are 0.866 and 0.736 respectively). Considering the results of sensitivity analysis, ecological and management variables like the forest canopy density, cattle density in forest, soil erosion and soil compaction respectively show the highest impact on forest under canopy vegetation density changes (Fig1).
 
Table1. The structure of optimum artificial neural network in forest under canopy vegetation density

Output Layer	First Hidden Layer	Network features
Linear	Hyperbolic tangent	Transmission Layer
Gradient descent	Gradient descent	Optimization Algorithm
0.7	0.7	Momentum
1	4	Number of Neurons
-0.9 up to 0.9	-0.9 up to 0.9	Normalization

 
Table2. The structure of optimum artificial neural network in test data

MSE	MAE	RMSE	R2	Data	The structure of network( the number of neurons)-epoch
0.716	0.678	0.846	0.931	Trainning	Tanh(4)-160
0.793	0.703	0.891	0.894	Validation
0.866	0.736	0.931	0.857	Test

 

 
Fig1. The results of sensitivity analysis of artificial neural network model
 
Nowadays, artificial neural network modeling in natural environments has been applied successfully in many researches such as water resources management, forest sciences and environment assessment. The results of research declared that designed neural network shows high capability in forest under canopy vegetation density modeling which is applicable in forest management of studied area. Sensitivity analysis identified the most effective variables which are influencing under canopy vegetation density.
So, to identify hazardous LMUs in study area, we should pay attention to the canopy density of LMUs as the variable with high priority in determination of under canopy vegetation density. We believe that, in hazardous LMUs in forests, we should pay attention to some modifiable factors of LMU, which is cattle density in forest, by timely plan for livestock elimination. The forest under canopy vegetation density assessment model, in forest projects impact assessment, could be a solution in decision making about forest plan structure and implementation of similar projects in similar locations. 
 
Keywords: Forest plan, Environmental impact assessment, Multilayer perceptron, under canopy vegetation, artificial neural network
 

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A new Approach to Urban livability, Thermal Comfort as the Primitive Condition to enhance the livability: Case study, District 22 of Tehran.
 
 
Ahmad Porahmad: Professor of Urban Geography and Planning, University of Tehran
Hossain Hataminezhad: Professor of Urban Geography and Planning, University of Tehran
keramatollah Ziyari: Professor of Urban Geography and Planning, University of Tehran
Saeideh Alijani^*: PhD candidate of Urban Geography and Planning, University of Tehran
 
The concept of urban livability is defined as the quality of life and wellbeing of urban residents. That is the interaction of people, environment and built environment. The residents can achieve happy life and well-being only when the nature surrounding them is happy and healthy. According to the range of welfare concept there is a spectrum of quantitative indicators that directly measure (human body temperature, heart rate, air temperature, wind speed ...) and qualitative indicators such as quality of life, pleasure and joy. The comfort and ease of environment are in the middle of the spectrum, in other words, the intrinsic concept of ambient comfort is environment. The inadequacy of natural environment will affect both indicators in the spectrum and lead to citizens' dissatisfaction and decline in social welfare and threaten the health of humans. Living in a salty marsh or very dry hot climate is never happy and satisfied. Accordingly, many concepts such as living quality, living environment, and quality of place, quality of life and sustainability are often used interchangeably with livability).
         This research is trying to weight the natural environment at least equal to the other two components of the sustainable development triangle. Among the components of natural environment, climate is playing the most important and significant role. Urban climate affects all aspects of city including building interiors, city architecture and open spaces. Thermal comfort of open spaces promote the social life and interrelations of residents. Therefore, in order to promote the social relations and economic activities especial consideration should be paid to open spaces. Accordingly, two types of data were measured for calculating the thermal comfort in the district 22. Subjective and objective evaluations which present qualitative and quantitative data. Objective data includes micrometeorological measurements with mobile instruments. Subjective data evaluated actual sensation vote or perception vote of thermal comfort by people using the urban open spaces. To this goal, questionnaires were prepared and scattered through space users simultaneously with micrometeorological measurements. Subjective data evaluated perceptual sensation vote of thermal comfort by people using the urban open spaces in three hot days of August 2018. Nine points are selected for site measuring and field survey which are representative of two types of urban open spaces in this research:1) Urban park and 2) street. Four cardinal points were chosen adjacent to the Shohadaye Khalije Fars Lake inside the park located in sidewalk pathway around the Lake.  Other five points were selected in streets with different orientation and aspect ratio through the district. The physiologically equivalent temperature (PET), mean radiant temperature (Tmrt), sky view factor (SVF) and aspect ratio (H/W) are the most important indicators in this research which were calculated for evaluating comfort in the district.
      Results showed that urban open spaces in the district are discomfort and expose people to the extreme heat stress; over 40°C. This determines that, the natural environment especially around the Shohadaye Khalije Fars is not comfort. The questionnaire also indicated that people felt warm and dissatisfied.
        There is a high linear correlation between thermal comfort and mean radiant temperature and globe temperature. Therefore, it is concluded that thermal comfort in the district, is directly affected by urban areas.  Also in the streets with low SVF and high aspect ratio, PET were calculated more comfortable than other streets. Point 5 at Naghibzade street, confirmed the effect of urban geometry on thermal comfort. Otherwise, the lack of tremendous trees for creating shade is visible especially around the lake. The high linear correlation between Tmrt and SVF around the lake confirmed the openness of the area and the high amount of solar radiation. Therefore, planting more trees for creating the shade effect is necessary. 
        The perceptual analysis of thermal comfort indicated that by increasing of PET, people felt warmer. However, in a city like Tehran, people are more resistance to the heat stress. In addition, the characteristics of human body strongly depends on psychology and individual features and is a hard issue to predict. Otherwise, the people who felt warm were more than those felt slightly warm which indicates dissatisfaction of people. To be noticed that, thermal comfort of above 40 °C in summer is an alarm to urban planner and designers to rethink about climate consideration and global warming as a most important urban challenge in the district seriously. Besides, the consideration of thermal comfort and urban geometry should be imbedded into the comprehensive plan. This research proved that the climatic consideration for improving the quality of life and livability is important and urban designers and planners should rethink and review the comprehensive plan of Tehran to make a livable and sustainable city in the future.
Keywords: urban livability, climate comfort, sustainable development, urban sustainability, urban geometry, physiologically equivalent temperature, district 22 of Tehran.
 
 
 
 
 
 
 
 

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Landslide susceptibility mapping is  essential for  land use  planning and decision-making especially in  the mountainous areas. The main objective of this  study is to produce landslide susceptibility maps (LSM) at Dalahoo basin, Iran  using two statistical models such as an  index of entropy and Logistic Regression and to compare the  obtained results. At the  first stage, landslide locations identified by Natural Resources Department of Kermanshah Province is used to prepare of LSM map. Of the 29 lanslides identified, 21 (≈ 70%) locations were used for the landslide susceptibility maps, while the remaining 8 (≈ 30%) cases were used for the model validation. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, distance to faults, distance to rivers, distance to roads, land use, and  lithology  were extracted from the spatial database. Using these factors,  landslide susceptibility and weights of each factor were analyzed by index of entropy and Logistic Regression models. Finally, the ROC (receiver operating characteristic) curves for landslide susceptibility maps were drawn and  the areas under the curve (AUC) were calculated. The verification results showed that the index of entropy model (AUC = 86.08%) performed slightly better than conditional probability (AUC = 80. 13%) model. The produced susceptibility maps can be useful for general land use  planning in the Dalahoo basin, Iran.

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The application of Extreme value analysis method in heat wave hazard climatology; case study in Mid-Southern Iran
Abstract
Greenhouse warming poses the main cause of atmospheric hazards’ exacerbation and emergence in recent years. Earth planet has been witnessing frequent and severe natural hazards from the distant past; however, global warming has strongly influenced the occurrence of some atmospheric hazards, especially the ones induced by temperature and has increased the frequency and severity of those risks. Such extreme risks arising from temperature element and being affected by global warming could be referred to hot days and their frequency more than one day which undergo heat waves. Of the studies conducted worldwide in conjunction with the phenomenon of heat waves, the following can be pointed out; Schär (2015) has focused his studies on the Persian Gulf and the worst heat waves expected in this area. The recent work revealed an upper limit of stability which enables the adaptability of human body with heat stress and humidity. If people are exposed to a combination of temperature and humidity over long periods higher than this level, they will lead to hyperthermia and death, because heat dissipation from the body is physically impossible. Paul and al-Tahrir (2015) using a high-resolution regional climate model demonstrated that such a situation can occur much earlier. In Iran, in relation to heat waves, Ghavidel (2013) analyzed climatic risk of Khuzestan province in 2000 regarding super heat waves using the clustering approach. The obtained results unveiled the establishment of a low pressure at ground level and high pressure dominance at mid-altitudes up to 500 hp as well as the increase in atmosphere thickness having led to the ground overheating. Added to that, the source of heat entering into Khuzestan is advective and hot and dry air transport through Arabian Peninsula, Iraq and Africa. Ghavidel and Rezai (2014) addressed in a study to determine the temperature-related threshold and analyze the synoptic patterns of super heat temperatures in southeast region of Iran; the results of study approved that the only pattern effective on the occurrence of super heat days in Iran’s southeast is the establishment of the Grange’s heat low-pressure at ground level and subtropical Azores high elevation dominance at 500 hPa level. In this study, absolute statistical indicators, also recognized as above-threshold values approach, were used in order to identify, classify and heat waves synoptic analysis in the warm period of the year in the southern half of Iran. To use above-mentioned indicators, firstly daily maximum temperature statistics of studied stations with the highest periods were averaged every day once in June to September and once for the months of July and September. Using statistical indicators of long-term mean and standard deviation or base period, indicators would be defined for the classification of heat waves and days with peak extreme temperatures. In such classifications, usually long-term average or base period is multiplied by 1 to 3 to 4 times standard deviation and each time is account for the factor of each class.
To select the days for synoptic analysis, averaging was performed of all classified waves into four heat wave categories of low, intermediate, strong and super heat; accordingly based on the maximum blocks in each class of heat waves, days that had the highest temperature values were selected as the class representative for mapping and synoptic analysis.
This study dealt with investigating heat waves synoptic during the year’s warm period in the southern half of Iran. Studies showed that a variety of synoptic systems in the year’s warm period affect the study area. As well as, synoptic analyses concluded that in the southern half of Iran over the year’s warm period when occurring heat waves, low-pressure status dominates the ground level (caused by Gang’s low-pressure and local radiant mode); thus high-pressure status with closed curves is prevailing in atmosphere’s upper levels that gives rise to the divergence, air fall and Earth's surface heating. Studying the status of the atmosphere thickness in the days with the heat wave in the study area indicates its high altitude and thickness that this itself implies the existence of very hot air and susceptibility of the conditions for the occurrence of heat waves. In addition, wind maps at atmosphere’s different levels well illustrate the wind of very warm and hot air masses from the surrounding areas to the southern part of Iran; therefore it can be noted that aforementioned hot air masses mainly wind from places like different regions of the Arabian Peninsula, Iraq, North Africa and the low latitudes to the study area.
 
Keywords: Synoptic analysis, heat waves, maximum blocks, southern half of Iran.