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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Abstract

Climate change in one area has severe impacts on water resources and, consequently, agriculture in that area. Therefore, studying the extent of the vulnerability of regions to adopting policies to reduce or adapt to new conditions is of particular importance. One of the methods for assessing the extent of damage to agricultural activities is the calculation of the vulnerability index. In this study, with the aim of assessing agricultural vulnerability to climate change, The CVI index was calculated for 16 cities in Guilan province.

The results showed that the cities of Rasht (61.58) and Talesh (55.21) had the highest vulnerability and, accordingly, had the least adaptive power to climate change compared to other cities. And Langrood County (29.51) has the lowest number of vulnerabilities. The average value of the calculated index is 40.42 in Guilan province. In component R, the most vulnerable were Talesh (99.66) and lowest for Lahijan (2.27), In component M, the highest vulnerability was for Rudbar (97.21) and the lowest for Talesh (24.30), In component A, the most vulnerable were Rasht (89.99) and the lowest for Anzali (2.21), In component C, the most vulnerable were Shaft (66.66) and lowest for Anzali (1.89), In component U, the most vulnerable were Rasht (67.55) and the lowest for Astara (28.92), In component E, the highest vulnerability was for Talesh (76.49) and lowest for Lahijan (22.69), In component G, the most vulnerable was reported to Rasht (53.05) and the lowest vulnerability was reported for Sunnelk (23.24).

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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.
 
 
 

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Evaluation of SADFAT model performance in daily forecast of Land Surface Temperature in the city of Tehran
 
Abstract
The high spatial and temporal limitations of TIR images for use in urban climatology have been identified as a current scientific challenge. Therefore, the use of Data Fusion Algorithms in Remote Sensing has been considered. In the old methods, two bands of one sensor were used for Data Fusion. In these methods, a panchromatic band was used to increase spatial accuracy, so only spatial resolution was increased. To solve this problem, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to integrate the images of two Landsat and Modis gauges to increase the spatial and temporal resolution of the reflection. but, this algorithm is designed for pixels and unmixing areas that are the same in Modis and Landsat pixels. The use of this model was not suitable for urban areas with a different of landuse. Therefore, the Enhanced STARFM model (ESTARFM) was developed. The ESTARFM model was improved in 2014 to predict thermal radiation and LST, taking into account the annual temperature cycle and the unevenness of the earth's surface, and the SADFAT model was introduced.
In this study, the performance of SADFAT model in the use of OLI spatial resolution and MODIS temporal resolution in LST forecast in urban areas was examined. The metropolis of Tehran has different surface covers and multiple microclimates. So if the algorithm works successfully, This model can be used in other cities to improve urban heat island studies. The inputs for the algorithm are thermal radiance of Modis and Landsat   images, the red and near infrared band of Landsat for daily production of LST in 2017 in the city of Tehran. The algorithm uses two pairs of Modis and Landsat images at the same time and sets of Modis images at the time of prediction and then calculate the conversion coefficient for relating the thermal radiance change of a mixed pixel at the coarse resolution to that of a fine resolution. In this way, LST is generated in areas with a variety of landuse.
All the estimated pixels were compared to the base image pixels in that range to evaluate the results of the model. The comparison results for the autumn days with the average correlation coefficient of 0.86 and RMSE equal to 0.122, showed that the model has the highest accuracy in this season and in other seasons with the average correlation coefficient of 0.76 and RMSE about 0.4, has provided good accuracy.
Visual interpretation of the results of SADFAT showed that this model is able to accurately predict the LST of the land cover in different surface coatings and even in areas where one or more urban land uses are mixed in one MODIS pixel.
However, the borders are well separated and the features are not combined. Although the boundaries are clearly defined, in some land uses, the predicted LST is somewhat higher than the observational image.
Landsat and Modis satellites pass through an area with a small time difference, so they are suitable for combining with each other. But in predicting reflectance with the SADFAT algorithm, there are systematic and variable errors that we need to be aware of in order to increase the output accuracy. One of the systematic and unavoidable errors is the instability of the Terra and Aqua satellites passing through at any point, ie at each satellite pass, the location of the study area in Swath and the size of the pixel changes. Due to the distance of the study area from the vertical center of measurement on the ground (Nadir), the amount of this error varies on different days and should be checked for each day. The preventable error is the sudden change in one or more images used (16 days of the same pass time interval for Landsat) is high for estimating surface reflectance with spatial and temporal resolution. These changes may be due to human factors such as air pollution or natural factors. Natural factors such as clouds and dust storms are the main sources of error in using the SADFAT model because they are sudden and temporary and cover a wide area. The occurrence of these two factors has a great impact on reflectance. Therefore, a sudden change in these factors, in one or more images, causes a large error in the calculations.
The study also found minor spatial errors in the prediction, so that even on days when the results were better, points were observed where the values ​​in the predicted LST images did not match exactly with the OLI sensor. The reason for this may be due to changes in vegetation. Although there are some systematic and variable errors in the images and the implementation of the algorithm The results of this study showed that the performance of this model is reliable for predicting the daily LST with a spatial resolution of 30 meters in Tehran.
This method is able to support urban planning activities related to climate change in cities, so it is recommended that its performance be examined separately for different land cover in the city and the efficiency of this algorithm be evaluated with other sensors such as Copernicus Sentinels.
 
Key words: Spatial and Temporal Data Fusion, SADFAT, Heat island, LST, Urban climatology
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