نشریه تحلیل فضایی مخاطرات محیطی
سال ششم شماره 2 (تابستان 1398)

 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.

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.

Resilience, as a concept to confront abnormalities, surprises and unexpected changes in recent years has been raised as the ability of places, societies, and systems to respond to the dangers of tensions and pressures; so that the system can quickly return to pre-stressed situation, threats It accepts the future and confronts them. Central region of Iran according to the zoning studies of the national physical plan of Iran, including three provinces of Isfahan, Chaharmahal and Bakhtiari and Yazd, in a desert climate with many crises in the permafrost environment that has disturbed the state of resilience of the region, and as a result the scheme and target application regional resilience on policy and planning to reduce vulnerability and to cope with various trans-regional crises. Despite the fact that the concept of resilience at the level beyond the city has become apparent, there is still no clear framework for measuring this situation at the regional level. Based on this research, it is believed by the trans-regional and multi-dimensional nature of the resilience that by modifying and applying the concept of resilience to the integrated and multi-dimensional at the regional level, an appropriate framework for status measurement regional resilience in the form of a composite index and thereby risk reduction planning and promoting the resilience of the presentation To give. In this regard, the major purpose of the research is to develop an optimal framework for assessing, measuring and ranking the resilience situation in the central region of Iran. The results show that Chaharmahal and Bakhtiari province have the highest resilience and then there are two provinces of Isfahan and Yazd, respectively. In the meantime, Yazd province has the lowest resilience among the provinces of the central region; therefore, it is necessary to focus on planning and allocating resources to promote and improve priority sectors. Responding to resilience agendas requires the adoption of transregional planning and decision-making approaches such as environmental regionalism.

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

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.

Vulnerability and adaptation to climate change are local and context-specific, though connected to complex processes at multiple temporal and spatial scales. As such, there is a growing awareness that place-based studies of current and past responses to climatic stress can shed light on the capacity of a given system to respond to future climate change. There is also a growing appreciation of the importance of institutions—formal and informal—in shaping adaptation strategies and mediating the adaptive capacity of households and communities. While rural resource-dependent communities have historically coped with climatic fluctuations, whether such coping mechanisms are still successful today, and will be in the future, depends on the structure of supporting institutions and the way in which they mediate access to entitlements. Indeed, most social–ecological systems have undergone dramatic change in the last century due to climatic, landscape, and institutional shifts. Because coping mechanisms are developed in relation to particular landscapes, livelihoods, and institutions, social and ecological changes have altered relations across these elements, impacting the effectiveness of particular coping strategies. For instance, pastoralists have historically deployed a suite of coping mechanisms in response to the highly variable climate of semi-arid and arid landscapes. Yet, these capacities may be increasingly compromised in the rangelands of East Africa due to increasing exposure to climate extremes, such as flood and drought and shifting institutional environments. The mechanisms that pastoralists in East Africa historically utilized to cope with climate variability were part of a tightly coupled system where livelihoods, institutions, and landscapes were mutually reinforcing. Pastoralists’ livelihoods were co-produced with a savanna mosaic landscape managed as a common property system by formal and informal customary institutions. Farmers frequently cope with risks due to the uncertainty of climatic conditions .Population growth, changes in agricultural policies, environmental regulations and the degradation of natural resources such as soil and water also present farmers with numerous challenges. Although farmers have experience in coping with a certain degree of uncertainty, increased climate variability and changes may cause severe problems. Drought in particular is a climatic disaster that creates substantial costs for farmers and affects their agricultural systems extensively. Drought is the most complex of all natural hazards . making the arid and semi-arid regions of the world vulnerable. Although drought has not been well documented , the resource-dependent sectors such as agriculture are the most vulnerable to the impact of this phenomenon. A review of the long-term annual precipitation trends indicated that drought had a worldwide return frequency of every 20e30 years . However, in the last 50 years, some countries such as Iran and Bangladesh have experienced approximately 27 and 19 drought events, respectively. Therefore, for arid and semiarid regions, drought is a recurrent feature that could lead to the loss of crop production, food shortages and starvation if not managed appropriately. According todrought impacts could be managed at macro (national), mesa (local) and micro (village and household) levels. However, the micro-level management (i.e., what the farmers do in response to drought) is of great importance. A review of the studies of farmers’ decision-making in response to climate variability revealed that most research has focused on the decision event and not on the entire process. The main Purpose of this study was to modeling drought effects drought effects on sustainable livelihoods of small scale farmers in rural settlements. Statistical population of this study consisted of all Small-Scale Farming in Kurdistan province. Using Kerjcie & Morgan sampling table, 402 person were selected as the sample using stratified proportional sampling method. The instrument of the study was a questionnaire which its validity was confirmed by a Content validity and construct validity and its reliability was established by calculating Chronbach's Alpha and Combined reliability Coefficient (α>0.7). The results of Man- Kendall test showed that the level of aquatic and dry crops, along with the amount of crop production, has increased over time but there is no statistically significant effect on dry production. Also, the results showed that in the economic aspect, the greatest impact on distribution of income and living expenses, in the social dimension, on location affiliation and security and social welfare, the environmental dimension has had an impact on environmental pollution and land resources and on institutional aspects more on the cooperation and participation of the people. In addition, the results of structural equation modeling showed that drought had the most impact on sustainability livelihood dimensions, respectively, on social, environmental, economic and institutional dimensions.

Drought is one of the most complex and unknown natural phenomena that causes a periodic water crisis in the affected areas. Increasing water demand on the one hand and the experience of droughts in the province in recent years have led to the water crisis. Knowing the drought is one of the requirements for water crisis management. The purpose of this study was to analyze the trend of the SPI drought index in Isfahan province using nonparametric Sen’s slope test, Pettitt’s change point test and Man-Kendall test. From the monthly climatic data of 10 synoptic stations with a length of 27 years (1990-2017) for time series The results of applying Mann–Kendall and Sen’s slope tests based on SPI Index for 9, 12, 18, 24 and 48 month time periods, shows drought trend is significantly increasing for all stations out of Ardestan, Esfahan and Shahreza stations. In Ardestan station, the drought trend is significantly decreasing for 9, 12, 18, 24 and 48 month time periods and in Isahan station, the drought trend is significantly decreasing for only 48 month time period, and in Shahreza statition, the drought trend is significantly increasingonly for only 18 month time period. Despite all stations, the drought trend for one month time period, is significantly increasing just for Naein station. In addition, applying Mann–Kendall test on monthly rainfall for all station shows downward but not significant trend. Finally, applying Pettitt’s change point test based on SPI Index for 9, 12, 18, 24 and 48 month time periods indicates the existence of a significant change point. For same periods we observe no change point for the monthly rainfall in all stations. In summation, considering the SPI drought index, about 59% of all stations show significant downward trend bases on Mann-Kendall test and 60% of all stations show significant slope based on Sen's slope test and 75% of all stations show significant change point based on Pettitt's test. In general, for drought analysis using different time periods for the SPI index, in a short time period. (such as 6 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. All together, we are facing a water crisis in Isfahan province and we must manage water demand very urgently.

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.