Journal of Spatial Analysis

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Ultra violet radiation has some useful effects and some harmful effects on human health an d create many diseases. Nowadays not only declined but the usefulness of the therapeutic effects of the Sun in the treatment of diseases such as rickets, psoriasis and eczema have been proved. But prolonged exposure to radiation of the Sun is not always beneficial and may cause acute and chronic effects on the health of the skin, eyes and immune system. Ultraviolet radiation of the Sun is one of the most destructive waves for life on Earth. So Ultraviolet radiation index and predict its rate (1 to +11) as well as the analysis of this indicator will help people to protect themselves against the Sun

    Ozone station , global ozone measurement stations and only stratosphere in Isfahan, Iran, which is in the South and in the Northern geographical position latitude 32' 31 and 70 ' 51 is located over the East. The altitude of this station from sea is 1550 m. Also atmospheric parameters in this station which are measured daily include temperature, pressure, humidity, wind speed and direction and in the upper levels of the atmosphere at 12 GMT with the help of Joe's high temp radio instrument.

    The first step to do this research was gathering of climatic data and the statistical and quantitative analysis in order to study on the subject. Ultraviolet radiation data on the same basis of assessment, ozone station during the period January 2001-December 2010 has been collected. The second batch of data information gathered from meteorological station of Isfahan climatic elements from 2001 to 2010. This data is based on monthly averages for analysis of solar UV radiations from meteorological solidarity with the country.

Adjust the time series at the first step in the study and analysis of the data was done in order to equal intervals in these regular categories and methods of statistical analysis was carried out on them and the overall process of UV changes in the form of daily, monthly, quarterly and annually. Also part of the analysis that was carried out on the data, check how the sequence or they had over time; this way specify whether data periodically changes or trends have been or not. Once the data is based on the time of occurrence, sort and arrange the time series on them.

Annually analysis of UV index showed the general variation is a common feature of studied years but in the spring season have high variation in compared with other season. The main reason of this variation may be related to sunlight angle that can be showed atmosphere effect on received radiation. Descriptive statistic result indicated that the highest mean of UV index is 6.52 and minimum were 4.8 that have very high variations and may be it has different harmful effects. Also seasonal analysis showed highest UV index created in hot summer related to highest temperature in this season. The computational modeling of UV index against years in different season indicates there do not exist a linear relation between two factors. The correlation analysis of UV index and some climatic factors showed there are a significant relation between temperature  with 0.8570 coefficient that  can be said in relation to increase of temperature, UV rate increased and vice versa and with cloud cover correlation coefficient is  -0.393 that have significant negative relation.

    Results showed that the peak time period are output in the first half and the second half of the year, landing in the specified time series. As well as through a linear fit to all charts, increase or decrease of the radiation, changes the trend in recent years, showed that based on the ultraviolet radiation changes the average increase in the spring and summer and fall and winter shows a decline. Also according to the ultraviolet radiation in daily statistics review ozone assessment station in the studied period (2001-2011) maximum amounts of ultraviolet radiation index, (11.5) observed in the middle of the summer and the minimum amounts of radiation index (0.5) observed in mid-winter.

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Thunderstorm is one of the most severe atmospheric disturbances in the world and also in Iran, which is characterized by rapid upward movements, abundant moisture, and climatic instability. Since this phenomenon is usually accompanied with hail, lightning, heavy rain, flood and severe winds, it can cause irreparable damage to the environment. Investigation of spring thunderstorms has a great significance regarding the irreparable damages can cause by them and also because of the higher frequency of this phenomenon in the spring and the necessity for preparedness and disaster mitigation actions. To identify the locations of the major thunderstorm risk areas, the entire country with an area of 1648195 square kilometers, which is located between the 25°-40° north latitude and 44°-63° east longitude is considered.     Spatial distribution of the occurrence of hazardous spring thunderstorms was analyzed using a series of monthly thunderstorm frequency data obtained from 25 synoptic stations over a 51-year-long period (1960-2010). Ward's hierarchical clustering and Kriging methods were used for statistical analysis. Initially, total number of thunderstorms in April, May and June were considered as the frequency of occurrence of thunderstorm in different stations in the spring. Measure of central tendency and dispersion which consists of the sum, minimum, maximum, range and coefficient of variation, standard deviation, and skewness were used to clarify the changes of thunderstorms and to determine the spatial and temporal climatic distribution of spring thunderstorms. An appropriate probability distribution function was chosen to determine the distributions of the data.  Due to the large volume of data and the uneven distribution of stations, cluster analysis and kriging methods were used to classify different regions into homogeneous groups for zoning and spatial analysis of spring thunderstorms, respectively. The statistical characteristics of spring thunderstorms were reviewed and fitted with a 3-parameter Weibull distribution. Regions considered for this study were classified in four separate clusters according to the simultaneity of thunderstorms in the spring. After zoning, it was found that the highest rates of thunderstorm took place in the northwest and west of country. The northeast of Iran has the second highest number of thunderstorm occurrence. The least number of thunderstorm event had happened in the central and southern half of the country.     According to the descriptive statistics parameters, maximum number of thunderstorms occurred in May.. Based on the results of the cluster analysis, there is a similar trend in the central and eastern regions, the rest of the country was clustered into five distinct homogeneous regions, including the northwestern, western, southern, northern, central northern and northeastern regions. Zoning results indicate that the highest number of the occurrence of this phenomenon in the country is concentrated in the northwestern and western regions. Higher frequency of occurrence of thunderstorms in the northwestern and western regions may be attributed to local topographic conditions like high mountains, orientation of the terrain, solar radiation on slopes and existence instability conditions, hillside convection, the presence of water resources and specific climatic conditions in these areas. In addition, as a result of a continuous surface obtained by the method of interpolation with the least amount of systematic error and also the use of correlation functions for recognizing the spatial structure of the data and estimating the model error when using the Kriging method, the weights are chosen in order to have a more optimized interpolation function. Also the cluster analysis may significantly reduce the volume of operation without affecting the results and will help in finding a real band due to more appropriate classification of different geographic areas with greater spatial homogeneity and minimal variance within the group. Based on the results of the spatial analysis, it is clear that Kriging and Ward cluster analysis methods are appropriate for thunderstorm zoning and classification of different regions according to occurrence of thunderstorm, respectively.

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Forest fire is one of the important problems in Iran which is caused by different factors such as human and natural factors. One of these factors is climate conditions that can be created by heat wave and special circulation of atmospheric phenomena. Occurrence of forest fire in north of Iran have different impacts on environment such as destruction of natural. According to the position of Iran in the dry climate zone provides required conditions for this hazard. Unfortunately,every year thousands of hectares of precious green cover is burned. Forest fires have harmful effects on human life directly,or in directly and lead to environmental destruction and pollution, global warming, loss of vegetation, and dry soil erosion. As a result, research on forest fires will become necessary. The study region is Mazandaran province forests located  in north of Iran with area of  23756.4 square Kilometers.The main object of this study is to detect the forest fires using satellite data and associated analysis with synoptic approach based on weather maps.

To detect fire in the study area different satellite data such as  synchronized and geostationary satellite data were used. In this study, MODIS satellite imagery and global algorithm detection of fire to detect fire in the forest and meadows of Mazandaran province were used. The climate data including weather station data and weather map were used. Other data include data of LST and vegetation products of MODIS. In order to downscale the global data an appropriate threshold was defined. In the proposed method,  After geometric correction and radiometric the cloud mask was removed, And then fire potential was identified with different thresholds and tests. Three fire episodes of  Savadkooh 2006, Noor , 2009 , and Behshahr, 2010 were selected for study.

Results showed  a threshold value of 310 ° K for MODIS sensor band 22 is good for a global scale. Cold and small fires are not detected, Therefore Local threshold was used. In addition, surface temperature and vegetation mapping , chlorophyll amount of vegetation were used before and after the fire episode.It became apparent that the amount of chlorophyll was reduced and the temperature was increased after the fire.

   The synoptic maps of the fire day showed a low pressure over the region and mid level systems indicated the advection of warm air over the area. Surface stations showed the increase of temperature and reduction of moisture during the fire days over the long period mean values.

According to the results of the study the ground level data accompanied the upper level images and pressure patterns.

Universal high performance of fire detection algorithm was used  to identify areas of forest fires Using MODIS satellite images and global algorithm modified to suit the characteristics of the study area fire detection. Then three of the fires were identified with this method. The algorithms with MODIS images and weather data together indicated the validity of the study and performance of this algorithm to identify the location of fire in the study region. Therefore the method of this study can be used in other areas to detect forest fires.

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Evaluation of land use changes in coastal cities of Khuzestan province using GIS and RS
 

Abstract:
Today, the expansion of human societies and greater environmental dominance have led to faster and wider environmental change than ever before. The speed and variety of this change in urban environments is greater than in other areas. The purpose of this study was to investigate the temporal and spatial variability of four coastal cities of Khuzestan province (Bandar Imam Khomeini, Bandar Mahshahr, Abadan and Khorramshahr) using land use measures over a period of 20 years 1997-2009 to accurately determine spatial-temporal pattern of changes. is. The method of the present research is quantitative and its dominance is dichotomous. To extract the land cover map data through Landsat satellite imagery from 1977 and 1998 taken by OLI and MSS5 sensors, the images were divided into four main classes (residential), vegetated areas, wetlands (rivers). And Bayer were categorized. After preparing land cover maps from TerrSat software was used to analyze land use changes and finally using the Markov chain to predict urban development trend in the study areas. The results show that Abadan and Khorramshahr have the most changes in vegetation use, while in the two cities of Imam Khomeini (Rah) and Mahshahr the most changes were related to the use of Bayer. Added to the timeline.
Keywords: Spatio-temporal changes, Land use, TerrSat software, Coastal citie
 

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Objective: in recent decades, population growth, urbanization development, and change in land use have led flooding as one of the most destructive natural disasters in the world. Therefore, our goal is to identify flood areas and the synoptic patterns that lead to it, which are among the most important issues in preventing and reducing the effects of flooding and dealing with it.
Methods: In this study, in order to prepare a map of flooded areas, the extent of the floodwater that occurred in June (2024) in Ardabil province, were processed SAR radar images before and after the flood. Then, to identify synoptic patterns, daily maps of geopotential height at 500 hectopascals, sea level pressure at 1000 hectopascals, omega pressure at 500 hectopascals, and relative humidity at 700 hectopascals with a spatial resolution of 2.5 degrees in 2.5 degrees latitude were received and analyzed from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) of the United States.
Results: The flood area study indicated that in the studied province, Bilehsavar city with an area of 593 hectares, Parsabad city with 505 hectares, Meshkin-shahr with 245 hectares, and Germi city with 192 hectares were flooded due to the waterlog. The analysis of the flood zones also showed that the largest volume of flood entering Ardabil Province during the studied period was related to the northern cities of the province, where the provision of all moisture conditions and instability at the full depth of the troposphere layer led to the occurrence of heavy flood-causing rainfall in these areas.
Conclusions: The results of this study indicate that the use of radar data, due to its outstanding capabilities, is a useful tool in detecting and continuously monitoring of floods. Therefore, by detecting flood-prone areas and synoptic conditions that produce floods, executive managers can make the best decisions to deal with possible future floods.
 

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introduction: Resilience is the key to urban sustainability, and by reducing vulnerability, it creates a sustainable environment for cities and plays a fundamental role in reducing urban vulnerability to environmental hazards.
Methods: The present research method is descriptive-analytical and applied in terms of purpose. The aim is to measure the resilience level of District 2 of Karaj and identify its strengths and weaknesses in facing environmental hazards. The research combines data from statistical documents, written reports, and questionnaire data obtained from surveys. The statistical sample consists of 30 experts related to crisis management in District 2 of Karaj, selected through the snowball sampling method. Data analysis was performed using mean statistics, standard deviation, and factor analysis.
Results: The results indicate that enhancing the resilience of this area through improving the quality of physical infrastructure, better construction management, adherence to architectural standards, and urban planning principles can reduce vulnerability and create a sustainable and resilient environment. Additionally, the evaluation of building sustainability shows that the average index in District 2 was 4.45. The average indices for spatial organization were 4.43, geographical characteristics were 4.57, and infrastructure sustainability was 4.8. This indicates a favorable status of resilience and sustainability indices in District 2 of Karaj.
Conclusions: Factor loadings show that the coefficients are above 0.6, confirming the validity of the resilience indices. Therefore, the urban resilience and sustainability of the region can be evaluated as favorable to highly favorable.
 

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Objective: During a landscape, it is not facile to discriminate land parts that have dissimilar amounts and types of vegetation. Plant Ecological Units (PEUs) are known as management units and are a reflection of the management actions and natural disturbances in the region. This research aims to fuse different resolutions of satellite images to increase the PEUs classification accuracy.
Methods: For this purpose, the Marjan-Borujen watershed in Chaharmahal va Bakhtiari province was selected. After field monitoring and surveys, four dominant PEUs groups were identified in the study area. In this study, bands from the Landsat_8 satellite images with 30 m  spatial resolution (bands 7_2) and a 15 m panchromatic band (band 8) were used, as well as the Sentinel_2 satellite images including panchromatic bands (8, 4, 2, 3) with 10 m spatial resolution. First step, using the Landsat panchromatic band, the 30-m bands were upgraded to 15 m through the pen-sharpening process; so the 15 m  data set was prepared from the Landsat_8 satellite. Then, to increase the spatial resolution of the 15-meter data set to 10 m, the Sentinel_2 panchromatic bands were used. In this way, the Sentinel_2 panchromatic bands were geometrically matched with the Landsat_8 15 m data set, and the Co-Registration process was performed with the minimum RMSE(0.05). Finally,  two data sets (2 to 8 bands) of the Landsat_8 satellite images with 15 m and 10 m spatial resolution, the PEUs classification maps were prepared using the RF classification algorithm, and the maps' accuracy was displayed as an error matrix.
Results: The results show that increasing the spatial resolution significantly enhances the accuracy of PEUs classification maps. The 15 m set shows an overall classification map accuracy of 66%, while increasing the spatial resolution to 10 m enhances the overall accuracy to 82%. As well as, the error matrix results show that the classification map procured from the 10 m set, all four PEUs groups have improved the producer accuracy, user accuracy, and kappa agreement index. So, in this map, PEU 2 and PEU 3 have the highest kappa agreement coefficient (83 percent).
Conclusions: This study shows that using the Gram-Schmidt fusion algorithm and consequently increasing the spatial resolution of Landsat 8 images from 30 m to 10 m reduces mixed pixels and increases pure pixels, which in turn improves the quality of PEU classification maps.
 

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Factors affecting the adaptation of rural settlements to the water crisis of Lake Urmia Case study: Miandoab County

Problem statement
In recent years, Lake Urmia, the largest lake in Iran, has faced severe water shortages, which has raised concerns in terms of economic, social and environmental consequences in the surrounding communities, especially in rural areas. Livelihood dependence of rural community stakeholders, to the natural resources and agricultural products have caused the harmful effects of drying Urmia Lake to be more visible. The drying up of Lake Urmia is not limited to this lake, but human communities have also suffered a lot from their sphere of influence. Due to the human effects of the drying of Lake Urmia,  it is necessary to analyze the effects of this phenomenon from a human perspective in research. Identifying the adaptive capacity of rural community stakeholders makes it possible to adopt appropriate management strategies to reduce the damage caused by lake drying. Therefore, despite the importance of the subject of this research, it seeks to study the factors and forces affecting the adaptation capacity of rural settlements in the face of the drying crisis of Lake Urmia in the city of Miandoab and so on.

Research Methodology
In terms of methodology, strategy and design, the present study is a combination of (mixed), sequential and explanatory exploratory, respectively. In this study, for a detailed study of community mentalities, a discourse on effective factors to increase the adaptive capacity of rural settlements in the face of drying or water retreat of Lake Urmia, the combined method of (Q) was selected. The research discourse community included local managers (governorate experts, heads and employees of government departments, districts, rural districts and Islamic councils) as well as local experts in the sample villages of Miandoab city. Targeted sampling method (snowball) was used to select the statistical sample. Q statements were also compiled using first-hand sources (expert opinions, local managers, field observations, etc.) and codified sources (books, articles, publications, etc.) using the library and field methods. The Q questionnaire was also used to assess the attitude of experts. In order to analyze the data of the Q (Q) method matrices, heuristic factor analysis based on the individual method (Stanfson method) was used.

Description and interpretation of results
 In reviewing the findings of the exploratory factor analysis model with KMO criterion, Bartlett test confirmed the sufficient number of samples and its appropriateness for the research. To investigate the most important influencing factors, the specific value and percentage of variance were calculated and the number of factors was determined by pebble diagram and Kaiser Guttman criterion. The results showed that the most important factors and forces affecting the increase of adaptation capacity to the drying of Lake Urmia in the sample villages of Miandoab are: 1) Increasing economic capital and the use of natural resources, 2) Increasing social capital and investment, 3) Developing infrastructure facilities and improving the skills of villagers, 4) Economic diversification and improving rural management .. Among these factors, the first factor with a specific value of 5.40 and a percentage of variance of 24.55 was recognized as the most important factor and effective force in increasing the adaptation capacity of the studied villages against the drying of Lake Urmia. Thus, economic and natural factors, as the most important assets of the villagers, are endangered at any time by the drying up and retreat of the water of Lake Urmia and have a direct impact on the livelihood of the villagers.

Keywords: Adaptation capacity, Lake Harumiyeh, Miandoab County.

 

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Drought is one of the most important challenges faced by pastoralists around the world. This phenomenon can have significant negative effects on livestock health, production, and livelihoods. However, pastoralists can adapt to drought and reduce its negative effects by adopting various strategies. Semi-nomadic people in Darbandi, Kalat-Naderi County, have been facing drought since 2007 due to their livestock farming. Since livestock farming has profound impacts on the lifestyle and livelihoods of these communities, the present study examines their experience in facing drought and identifies their management strategies in these conditions. The research method is qualitative and the research tool is in-depth interviews with 20 semi-nomadic people in Darbandi, Kalat-Naderi. Sampling was purposeful and carried out until theoretical saturation was reached to ensure that a wide range of perspectives and experiences were collected. The data from the interviews were analyzed using a qualitative grounded theory approach to extract key patterns and concepts. According to the findings, the semi-nomadic Darbandi people of Kalat County have adopted various strategies in the face of drought, which are classified into four categories: rangeland and grazing management strategies, livestock nutrition management, water consumption management, and livelihood diversification. These results can be used as a basis for formulating better policies in the field of crisis management and rural development. Also, these results can be used for more effective planning to reduce the vulnerability of nomads to drought.