دانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Reanalyzing the Trend of Long-term Spatial Changes in the Minimum Temperature of Iranواکاوی روند تغییراتفضایی بلندمدت دمایحداقل ایران117193510.22126/ges.2021.6938.2453FAراضیهفناییگروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران.یوسفقویدلگروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران0000-0003-1929-155Xمنوچهرفرج زادهگروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران.Journal Article20210921Today, climate change has become a major challenge for human societies. The widespread concern of environmental scientists and researchers about global warming has prompted them to study the time series of climatic parameters, especially temperature to reveal the trends of these parameters over the past few decades. Due to the importance of this issue, the present study was conducted to investigate the trend of spatial changes in the minimum temperature of Iran. In this regard, V20CR networked minimum temperature data with daily time resolution and 1 in 1 degree spatial resolution during the period 2019-1836 have been used. First, the trend of minimum temperature changes was calculated using Mann-Kendall test. Then, using spatial statistical methods such as global and local Moran index and hot spot analysis, the patterns governing Iran's minimum temperature and their dispersion were identified. The results of this study showed that in the cold months of the year the zone is trendless, while the decreasing trend is dominant in Iran in the warm months of the zone. An increasing trend has been observed in the southern regions and to some extent in the southeast of Iran. A declining trend occurred in northwestern Iran in January and is gradually spreading throughout Iran in other months. In May, it reaches its maximum expansion in Iran. According to the World Moran Index, the minimum temperature in Iran has a pleasant pattern. The local Moran index also showed that the northwest of Iran follows the pattern of low cluster and the southern regions of Iran follow the pattern of high cluster. The findings from hot spots indicate that the northwestern regions have cold-temperature spots (negative spatial autocorrelation) and the southern regions have hot-temperature spots (positive spatial autocorrelation).
<strong>Extended Abstract</strong>
<strong>1-Introduction</strong>
Climate change and temperature fluctuation are among the most important issues in human life. By examining the trend of the changes in the average air temperature, it is possible to trace the climatic changes of a region. Temperature is an indicator of heat intensity and an important climatic element. Significant changes in temperature or global warming have been considered as the most important manifestations of climate change in the present century (Alijani and Ghavidel, 2005). Air temperature at the surface and different layers of the atmosphere are important climatic parameters that affect other parameters such as humidity and evaporation. Therefore, changes in the amount or pattern of temperature at different time intervals are of great importance. The rate of surface temperature increase and different layers of the atmosphere is different in different regions. The results of studies conducted in Iran also point to an increase in temperature. The results of extensive studies conducted at the national, regional, and global levels indicate an increase in temperature in many parts of the world and generally an increase in average air temperature.
<strong>2-Materials and Methods</strong>
In this study, the minimum gridded temperature data of V20CR, whose temporal resolution is daily and its spatial resolution is 1 in 1 degree for the whole planet, were used during the statistical period 1836-2019. First, the trend of the studied data was calculated by Mann-Kendall test. Then, we used spatial statistics methods including spatial autocorrelation of local and global Moran’s index and hot spot analysis. The Moran’s index is the most common index used to measure spatial autocorrelation between phenomena and events. If the value of the index is +1 or close to +1, the studied variable has autocorrelation and a cluster pattern. If the value of the index is -1 or close to -1, the data are discrete and have a scattered pattern. In addition, if the value of the index is zero, the data have a random pattern.
<strong>3- Results and Discussion</strong>
The trend of Iran minimum temperature during the study period was calculated to reanalyze the changes. Increasing trends have occurred mainly in the southern regions as well as parts of central Iran. Decreasing trends have also been observed in the northwestern, central, western and eastern regions of Iran. During the cold months of the year, the trendless zone is more prominent in the country and the size of the decreasing and increasing trend zones has been reduced. In March, April, May, June and July, most of Iran is covered by a declining trend. The graphic output from the global Moran’s index showed that the minimum temperature in Iran in all months has a cluster pattern. According to the results of the local Moran’s index, in almost all months, there is a zone without a significant pattern throughout the central regions of Iran. The northwestern corner of Iran has a low cluster pattern in all months and the southern and southeastern parts of Iran have a high cluster pattern. In March, April and May, in addition to the southern parts of the country, a high cluster pattern has been observed in parts of eastern Iran located in the south of South Khorasan province and northeast of Kerman province. In May, June, July, August, September and October, a high cluster pattern can be seen even in small part of the west of the country located in the west of Khuzestan province. Hot spot analysis showed that there are cold spots in northwestern Iran in all months and its significance decreases as it progresses towards the center of Iran. Hot spots are also observed in the southern regions of Iran, located on the shores of the Persian Gulf and the Oman Sea, and by moving towards the center of Iran, their level of significance decreases. Central regions of Iran also lack a significant pattern.
<strong>4- Conclusion</strong>
In the present study, for the first time, the trend of temporal-spatial changes in the minimum temperature of Iran during the long-term period of 1836-2019 has been reanalyzed. Therefore, spatial statistics methods including global and local Moran and hot spot analysis have been used. The results of trend analysis using Mann-Kendall test showed that in September, October, November, December and January, the trend-free zone is dominant in Iran. A decreasing trend was observed in western Iran in January and it gradually spread throughout the country in other months and reached its maximum expansion in May, covering 90.22% of Iran's area. An increasing trend has also occurred in the southern regions and parts of southeastern and eastern Iran. The results of the global Moran’s index showed that the minimum temperature of Iran in all months follows the cluster pattern. According to the results of the local Moran’s index, northwestern Iran follows the low cluster pattern in all months and the southern regions of Iran follow the high cluster pattern in all months. It should be noted that the high cluster pattern has been observed in some parts of eastern and central Iran in some months. According to the results of hot spot analysis, cold temperature spots have occurred in all months in northwestern Iran. So that in East and West Azarbaijan provinces, cold temperature spots are observed at a significance level of 99% and its significance level decreases as we move towards the central regions of Iran. Warm temperature spots at the level of 99% significance are also observed in the southern regions of Iran in all months and by moving towards the central regions of Iran, its significance level decreases. In some parts of eastern Iran, hot temperature spots have been observed at a significance level of 90%.<strong>امروزه تغییرات آبوهوا به چالشی بزرگ برای جوامع بشری تبدیل شده است. نگرانی گستردۀ دانشمندان و محققان علوم محیطی از گرمایش جهانی، آنان را برانگیخت تا سریهای زمانی پارامترهای اقلیمی، بهویژه دما، را مطالعه کنند و در پی آن، به آشکارسازی روندهای این پارامتر</strong><strong>ها</strong><strong> طی چند دهۀ اخیر بپردازند. با توجه به اهمیت این موضوع، پژوهش حاضر با هدف بررسی روند تغییرات مکانی دمایحداقل ایران انجام شده است. در این راستا، از دادههای دمایحداقل شبکهبندیشدۀ </strong><strong>V20CR</strong><strong> با تفکیک زمانی روزانه و توان تفکیک فضایی یک در یک درجه طی مقطع زمانی ۱۸۳۶ تا 2019 استفاده شده است. ابتدا روند تغییرات دمایحداقل با استفاده از آزمون منـکندال محاسبه شد. سپس با استفاده از روشهای آمار فضایی، از جمله شاخص موران جهانی، محلی و تحلیل لکههای داغ، الگوهای حاکم بر دمایحداقل ایران و پراکندگی آنها شناسایی شدند. نتایج حاصل از این پژوهش نشان داد در ماههای سرد سال، پهنۀ بدون روند و در ماههای گرم، پهنۀ روند کاهشی بر سطح ایران غالب است. روند افزایشی در مناطق جنوبی و تا حدودی در جنوب شرق ایران مشاهده شده است. روند کاهشی در ماه ژانویه در شمال غرب ایران رخ میدهد و در سایر ماهها بهتدریج درحال گسترش در کل ایران است، بهطوری که در ماه مه به حداکثر گسترش خود در سطح ایران میرسد. طبق شاخص موران جهانی، دمایحداقل ایران دارای الگوی خوشهای است. شاخص موران محلی نیز نشان داد که شمال غرب ایران از الگوی خوشهای پایین و نواحی جنوبی ایران از الگوی خوشهای بالا تبعیت میکنند. یافتههای حاصل از لکههای داغ نشاندهندۀ آن است که مناطق شمال غربی دارای لکههای دمایی سرد (خودهمبستگی فضایی منفی) و مناطق جنوبی دارای لکههای دمایی داغ (خودهمبستگی فضایی مثبت) هستند. </strong>https://ges.razi.ac.ir/article_1935_981faf8fc643f23df3b66965c9b2c668.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Identifying the Factors Affecting the Competitiveness of Cities through Tourism in Kermanshah Cityشناسایی عوامل موثر بر رقابتپذیری شهری از طریق گردشگری در شهر کرمانشاه1936193210.22126/ges.2021.6720.2431FAفرانکبهدوستگروه جغرافیای انسانی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران.کرامت الهزیاریگروه جغرافیای انسانی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران.حسینحاتمینژادگروه جغرافیای انسانی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران.حسنعلیفرجی سبکبارگروه جغرافیای انسانی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران.Journal Article20210730Today, due to global competition increase, cities are looking for the ways to increase their competitive position among other cities Tourism is a factor in urban competitiveness. Tourism, as the factor that improves urban competitiveness through tourism in national and international markets, caused to facilitate planning for the development of cities. This is an applied which is based on the exploratory factor analysis (Q) method as a mixed or combined method and 50 experts were selected by purposive sampling on the subject of research. In the qualitative stage and based on the summary of the discourse space from 50 propositions, finally 36 propositions were selected for the Q sample. In the qualitative stage after collecting the information obtained from the Q-sorting, the Q-factor analysis method was used; it is the main method to analyze the Q-data matrix. They were analyzed by varimax rotation method. SPSS software was used to discover the existing mental patterns identifying the influential variables by calculating factor scores. Using research indicators, 5 different mental models or factors with a total variance of 82.27% were calculated including political-institutional, economic, socio-cultural, physical and environmental factors. The variables were categorized in order of priority; in political-institutional factors, the variable of macro-government policies regarding the region, in economic factors, the appropriateness of the price of tourism services, in socio-cultural factors, increasing the culture of tourism, in physical factors, development of tourism facilities and services and in the environmental factor, climate change had the highest scores in each factor, respectively. Generally, the physical factor has a more specific value than other factors and is more important.
<strong>Extended Abstract</strong>
<strong>1-Introduction</strong>
As global competition increases, urban destinations are always looking for ways to increase their competitiveness, which strengthens their position in competition with other urban areas. Urban competitiveness is an economic concept and includes business improvement, employment rate, business environment, raising capital with the aim of increasing income, unemployment decrease leading to the increase in quality and living standards. Tourism development is an important determinant of competitiveness. Kermanshah city has unique features at the regional and national level including strategic location, efficient cultural and tourist features, and centrality of medical facilities in the west of the country, diversity of tourist attractions, border and ease of communication with neighboring countries. However, despite the great potential in the field of tourism, the city has not yet found its place as it is not in a good economic position. Understanding the factors that improve urban competitiveness in national and international markets helps to more effectively identify the current situation of cities and to formulate and implement more effective development policies in a targeted manner. Therefore, due to the nature of the study, this study was conducted to identify key factors affecting the future of competitiveness in Kermanshah through tourism.
<strong>2-Materials and Methods</strong>
This research is fundamental and applied according to theoretical approaches and research objectives. Its method is mixed and the research strategy is deductive. Data were collected in two forms including library and field. The sampling method was purposeful. The statistical population is 50 experts; the Q technique was used to obtain the required data. First, by using library studies and interviews, the influential factors in tourism and urban competitiveness were identified. Then, through surveys and interviews with academic elites and urban affairs experts, which is a specialized survey to predict the future, a cognitive map was determined to identify and evaluate the effective factors. At the end of this step, each factor was entered as one of the sample Q propositions on cards with the same appearance. Participants ranked the Q cards on the chart in order of importance based on their point of view. For statistical analysis of sorting data, Q factor analysis method was used to measure the correlation among the participants. Individuals were categorized according to their attitudes. The identified factors were interpreted based on the rotated factor matrix and the highest factor scores were calculated by comparative analysis.
<strong>3- Results and Discussion</strong>
This research has analyzed the issue with its exploratory nature using the indicators of urban competitiveness and sustainable development. It has had a comprehensive approach to tourism competitiveness. Factors were divided into 5 categories including political-institutional, economic, socio-cultural, physical and environmental factors. These factors have been supported in separate studies. Political-institutional factors, Economic factors, Socio-cultural factors, Physical factors and environmental factors have been studied. However, power and rent cause competition among the regions and cities in the political-institutional dimensions by mobilizing resources and attracting capital, although the current study has not considered such an issue. Physical factors have more special advantages than other influential factors. Infrastructure in physical indicators causes permanence, loyalty and the desire for tourists to visit the city of Kermanshah again. The development of appropriate medical infrastructure and intelligence affects the competitiveness of tourist destinations and the central and historical context of the city expresses the identity of the city. Besides, urban competitiveness is considered in the physical factors, which is promoted through the creation of the creative city.
In general, the findings from this study showed that knowledge of the effective factors obtained in this study have a great impact on the present and future of the city. Moreover, it revealed that it provides the necessary grounds for achieving urban competitiveness through tourism in the study area by planning and using the resources and capabilities of the city and controlling operations.
<strong>4- Conclusion</strong>
Based on the results of the analysis, 36 statements were divided into five factors including political-institutional, economic, socio-cultural, physical and environmental factors. Accordingly, the first group includes 10 people, the second group includes 9 people, the third group includes 10 people, the fourth group includes 16 people and the fifth group includes 5 experts. In the political-institutional factor, the variable of "macro-government policies regarding the region", in the economic factor, the variable of "appropriateness of the price of tourism services", in the socio-cultural factor, the variable of "increasing the culture of tourism", in the physical factor, the variable of "development of facilities and Tourism services", and in the environmental factor, , the variable of "Climate Change", had the highest factor score among the existing propositions in each factor. The results of this study showed that in general, the physical factor had a higher score than other factors in urban competitiveness through tourism. Because in today's competitive world, it is not possible to attract tourists just by having tourist areas and cultural and natural attractions. The issue of sustainability and satisfaction of tourists through physical factors is more important than other categories. Therefore, it is suggested to the managers to provide the necessary ground for the competitiveness of this city and the development of tourism and its infrastructure through strategic planning and long-term and short-term plans.<strong>امروزه بهدلیل</strong><strong> </strong><strong>افزایش رقابت جهانی، شهرها بهدنبال شیوههایی برای بهبود وضعیت رقابتی خود در میان سایر</strong><strong> </strong><strong>شهرها</strong><strong> </strong><strong>هستند. گردشگری بهعنوان یک عامل در رقابتپذیری شهری مطرح است.</strong><strong> </strong><strong>شناخت عوامل بهبوددهندۀ رقابتپذیری شهری</strong><strong> </strong><strong>از</strong><strong> </strong><strong>طریق گردشگری در بازارهای ملی و بینالمللی موجب تسهیل برنامهریزی برای توسعۀ شهرها میشود. این پژوهش از نوع کاربردی است و روش آن مبتنی بر روش تحلیل عاملی اکتشافی (کیو) بهعنوان روش آمیخته یا ترکیبی است که در آن، پنجاه نفر از خبرگان در خصوص موضوع پژوهش بهروش نمونهگیری هدفمند برگزیده شدند. در مرحلۀ کیفی و بر اساس جمعبندی فضای گفتمان، از میان پنجاه گزاره، در نهایت 36 گزاره برای نمونه کیو انتخاب شد</strong><strong>.</strong><strong> در مرحلۀ کمّی، پس از جمعآوری اطلاعات حاصل از مرتبسازی کیو، از روش تحلیل عاملی کیو که اصلیترین روش برای تحلیل ماتریس دادههای کیو است، بهره گرفته شد و بهشیوۀ چرخش</strong><strong> </strong><strong>واریماکس تحلیل شد. سپس با استفاده از نرمافزار اسپیاساس (</strong><strong>SPSS</strong><strong>) برای کشف الگوهای ذهنی موجود، متغیرهای تأثیرگذار با محاسبۀ امتیازهای عاملی شناسایی شد. با بهرهگیری از شاخصهای پژوهش، پنج الگوی ذهنی یا عامل متمایز </strong><strong>با مجموع واریانس 27/82 درصد </strong><strong>محاسبه شد که شامل عوامل سیاسیـنهادی، اقتصادی، اجتماعیـفرهنگی، کالبدی و محیطی است. متغیرها بهترتیب میزان اولویت در دستهها قرار گرفت، بهطوری که </strong><strong>در عامل سیاسیـنهادی متغیر سیاستهای کلان دولت در مورد منطقه، در</strong><strong> </strong><strong>عامل اقتصادی مناسب بودن قیمت خدمات گردشگری، در</strong><strong> </strong><strong>عامل اجتماعیـفرهنگی افزایش فرهنگ گردشگرپذیری، در عامل کالبدی توسعۀ امکانات و</strong><strong> </strong><strong>خدمات گردشگری و در عامل محیطی تغییر اقلیم بهترتیب بیشترین امتیازات را در هر عامل به خود اختصاص داد. در مجموع، </strong><strong>عامل کالبدی مقدار ویژۀ بیشتری را </strong><strong> </strong><strong>نسبت به سایر عوامل داشته و دارای اهمیت بیشتری بوده است</strong>https://ges.razi.ac.ir/article_1932_a22c6dc3657cb6e52e51e9f0e334d560.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Heterogeneity of the thermal environment and its ecological evaluation in the urban region of Karajبررسی الگوی ناهمگنی محیط حرارتی شهر و ارزیابی اکولوژیک آن در منطقۀ شهری کرج3758193110.22126/ges.2021.6654.2418FAزهرامختاریگروه برنامه ریزی و طراحی محیط، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران.شهیندختبرق جلوهگروه برنامهریزی و طراحی محیط، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایرانرومیناسیاح نیاگروه برنامه ریزی و طراحی محیط، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایرانJournal Article20210922Temperature is one of the critical environmental parameters which is spatially heterogeneous, affecting biological, physical, and social interactions within an urban ecosystem. Therefore, the main purpose of this article is to investigate the contribution of various land covers in forming urban heat islands (UHIs) and urban cool islands (UCIs) and determining the pattern of UHI-UCI in an arid and semi-arid urbanization region of Karaj, Iran in July 2020. To achieve the goal, initially, the land surface temperature (LST) was retrieved using mono-window algorithm; maximum likelihood method was applied to generate the land cover/land use (LULC) map using Landsat 8 OLI -TIRS data. Then, the contribution index (CI) of each LULC in creating UHI and UCI was calculated. The thermal environment of the city was evaluated using Urban Thermal Field Variance Index (UTFVI). The results showed that built-up surface (0.2) and green space (0.76) contributed the most in creating UCI, while the barren cover played a major role (1.53) in creating UHI. In addition, the urban hot spots were seen in the industrial zone and bare land (adjacent to the Payam airport) in the southwest of the region. The UTFVI analysis showed that the central areas of the city (old and dense residential areas) were ecologically better as compared to the urban periphery. Therefore, it is necessary to implement mitigation strategies in the marginal areas of the study area. In general, the results of this research can be helpful in urban planning to moderate urban temperature in ecologically stressed zones.<br /><strong>Extended Abstract</strong><br /><strong>1-Introduction</strong><br />Temperature is one of the critical environmental parameters affecting biological-physical-social interactions of the urban ecosystem. In urban areas, the temperature is spatially heterogeneous due to extreme variation of land cover. One of the most well-known temperature-related phenomena in cities is the formation of urban heat islands (UHI), which endangers thermal comfort and ecological status. Moreover, determining hotspots with the aim of reducing the temperature is one of the important measures to maintain the temperature balance of the city. Current knowledge of the urban thermal environment is principally based on land surface temperature (LST) maps retrieved from satellite thermal sensors. Therefore, the main purpose of this paper included; 1) extraction of land surface temperature (LST) using mono-window (MW) algorithm from Landsat 8 satellite image; 2) determination of urban heat island and urban cool island (UHI and UCI) and thermal hotspots based on LST; 3) investigation of the relationship between land cover/ land use (LU/LC) with UHI and UCI by calculating contribution index, and 4) ecological assessment of the thermal environment using Urban Thermal Field Variance Index (UTFVI). <br /><strong>2-Materials and Methods</strong><br />Urban region of Karaj includes a group city, a climatically arid and semi-arid region, which was taken as the study area in current research. Since the effects of the thermal environment are more important in the hot season, this study was conducted in July 2020. The land cover/ land use map was generated using the maximum likelihood method in five classes of built-up, agriculture, orchards and green space, rangeland, bare land, and forest. The LST was retrieved using a mono-window algorithm to identify UHI-UCI. In addition to the identification of UHI and UCI, thermal hot spots were also determined across the city. Then, the contribution index was calculated in order to quantify the role of the three major land cover classes of built-up, vegetation, and bare land in forming UHI-UCI. After all, we used the Urban Thermal Field Variance Index to evaluate the ecological circumstance of the environment. Based on the thermal threshold of the UTFVI in terms of ecology and thermal comfort, the area was divided into three classes called good, normal, and poor. <br /><strong>3- Results and Discussion</strong><br />The overall accuracy and kappa coefficient of the land cover map were 87% and 83%, respectively. The accuracy of the LST map was evaluated using the data of two meteorological stations, indicating MAE and RMSE of 0.4 and 0.44, respectively. The results of this research showed that built-up cover (0.2) and agriculture and green space (0.76) contributed the most to creating UCI. However, the barren cover and abandoned farmlands played a major role (1.53) in creating UHIs due to the lack of shading effects of the building and evapotranspiration compared to built-up area and vegetation cover. Surprisingly, UCIs were formed in the densely built-up area and green spaces. The Urban Thermal Field Variance Index analysis showed that the inner and central areas of the city (old and dense part) were better in terms of ecological circumstances and thermal comfort than the newly-developed areas on the outskirt of the city. In addition, hot spots were seen in the industrial zone and bare land (adjacent to the Payam Airport) in the southwest of the region. In other words, the pattern of LST in the study region, which has an arid and semi-arid climate, is different from cities located in humid climate with abundant vegetation cover such as European and tropical cities. <br /><strong>4- Conclusion</strong><br />In general, it can be concluded that the mono-window algorithm is an appropriate method to retrieve LST in urban areas. In the study area, which is climatically located in an arid and semi-arid zone, dense man-made areas were cooler than marginal and low-density man-made cover during the day in the warm season. The initial ecological assessment was performed using Urban Thermal Field Variance Index. In order to improve the ecological situation in poor areas, temperature mitigation measures such as developing green space can be used according to cost analysis and expert opinions. As a recommendation, establishing complementary research focusing on the arrangement and configuration of the different landscape elements can lead to providing a more accurate analysis of the thermal environment pattern. <strong>در اکوسیستمهای شهری، بررسی الگوی ناهمگنی محیط حرارتی و عوامل مؤثر بر آن در ارزیابی اکولوژیک و آسایش حرارتی محیط شهری ضروری است. از این رو هدف </strong><strong>پژوهش حاضر بررسی ناهمگنی مکانی جزایر حرارتی و برودتی منطقۀ شهری با تأکید بر نقش پوشش اراضی در ایجاد آن و همچنین ارزیابی اکولوژیک محیط حرارتی است. از آنجا که اثرات جزایر حرارتی در فصل گرم سال اهمیت بیشتری دارد، این مطالعه در تیرماه سال 1399 با استفاده از تصاویر ماهواره لندستـ8 در منطقۀ شهر کرج انجام شد. در این پژوهش، نقشۀ دمای سطح زمین با استفاده از الگوریتم تکپنجره و نقشۀ پوشش اراضی</strong><strong> با روش حداکثر شباهت</strong><strong> استخراج شد. سپس برای تعیین</strong><strong> نقش</strong><strong> پوشش اراضی در ایجاد جزایر حرارتی و برودتی، شاخص سهم برای سه طبقۀ عمدۀ پوشش اراضی محاسبه گردید.</strong><strong> در ادامه، از نمایۀ واریانس زمینۀ حرارتی شهر (</strong><strong>UTFVI</strong><strong>) بهمنظور ارزیابی اولیۀ اکولوژیکی استفاده شد. نتایج محاسبۀ شاخص سهم نشان داد اراضی بایر و رهاشده بیشترین نقش (53/1) را در تشکیل جزیرۀ حرارتی داشت، درحالی که کمترین سهم بهترتیب مربوط به اراضی انسانساخت (2/0) و طبقۀ باغات، فضای سبز و کشاورزی (76/0) بود. همچنین یافتهها نشان داد نقاط داغ حرارتی در شهرکهای صنعتی حاشیهای و اراضی بایر (حوالی فرودگاه پیام) در جنوب منطقۀ مورد مطالعه واقع شده است. نتایج نمایۀ واریانس زمینۀ حرارتی شهر نشان داد مناطق داخل شهر (بهویژه بافت قدیمی و متراکم) از لحاظ اکولوژیکی و آسایش حرارتی، وضعیت بهتری از مناطق تازهتوسعهیافتۀ حواشی شهر دارند. بهطور کلی از نتایج این پژوهش میتوان در فرایند برنامهریزی شهری، بهمنظور بهبود وضعیت اکولوژیک شهر از دیدگاه حرارتی، استفاده کرد. </strong>https://ges.razi.ac.ir/article_1931_c6d2a2dff6e1e854c1d30e9ab01b9e98.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Analyzing the Role of Polar Vortex on Daily Extreme Precipitation in the Northwest of Iranواکاوی ارتباط تاوۀ قطبی با بارشهای روزانۀ فرین بالا در شمال غرب ایران5982192910.22126/ges.2022.6844.2437FAنفیسهرحیمیگروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه زنجان، زنجان، ایران0000-0002-6942-2055Journal Article20210824The polar vortex oscillation is one of the prominent manifestations of troposphere and stratosphere interaction, which plays an important role on the climate extremes. Therefore, in the present study, the concept of linking the daily extreme precipitation with the polar vortex and its control effect on synoptic systems was analyzed in the temporal-spatial analysis of the polar vortex at north-west stations of Iran. In this study, according to the statistic-synoptic approach, at first, the homogeneity of precipitation data and precipitation trend of all six stations were examined by linear regression test. 6 components explaining 90% of the data variance were identified in order to analyze the companionship and awareness of the trajectory and the effect of polar vortex on the studied stations, the location of the polar vortex investigated by T-type principal component analysis. Then, the position of vortex in each of these patterns was investigated by considering the polar vertex reagent contour in the geo-potential elevation maps of 500 hPa level. 6 general patterns were recognized by analyzing temporal and spatial position of the vertex during the given days in which spatial position, the extension and depth of the ridges were different. The highest positioning of the vertex was seen in the first pattern and the ridges obtained from vertex had the highest depth and expanse on the Black Sea. In all patterns, the daily extreme precipitation was caused by the placement of the receiving vessel from the polar vortex on the required area, which was due to the establishment of the massive Rex and Omega dams on Europe.<br /><strong>Extended Abstract</strong><br /><strong>1-Introduction</strong><br />Understanding the causes and nature of climatic extremes is one of the most important goals in monitoring climatic phenomena. In extreme precipitation, due to the continuity of phenomena, in addition to troposphere, stratosphere also has an active role in atmospheric interactions. The most prominent feature of the stratosphere is the polar vortex which is a large rotational cycle that occurs in winter and in both hemispheres with 50 to 90 degree distance above the tropopause (approximately 100 hPa) to the mesosphere (above 1 hPa) due to the lack of sun radiation; it is one of the dominant dynamic forms of winter rotation in troposphere and stratosphere. Thus, by the mechanism of radiation exposure, a polar vortex is formed and oscillates between the two states: strong and weak. The displacement of this system related to its normal state leads to some changes in the air of the middle point of north hemisphere, hurricane routes, extreme events such as heavy rainfall, etc. Recognition of the polar vortex, its displacement and movements at different atmospheric levels needs precaution and predictions that help to reduce heavy rainfall damage.<br /><strong>2-Materials and Methods</strong><br />In this study, two environmental databases and an atmospheric database were used to identify the manner of atmospheric currents, then the homogeneity of the average annual rainfall data of the stations were examined by standard test (SNHT). Then the significant level of its trend was examined by linear regression test. As 177 days of extreme precipitation were identified, the position of the polar vortex was analyzed by principal component analysis with T-array and rotation by Varimex method on the geopotential elevation data of the upper atmosphere to compare the synoptic and dynamic mechanisms in different positions of the polar vortex.<br /><strong>3- Results and Discussion</strong><br />The standard normal test showed that the total annual intake during the study period is homogeneous in all stations. The use of linear regression test also showed that in Tabriz, Urmia, Khoy and Miyaneh stations, the process has significantly increasing trend and in Ardabil and Parsabad stations, it has decreasing trend. By applying the principal component analysis method with Varimax rotation and the initial matrix of 177 6 629, 6 primary factors were identified which represent the main arrangement at the level of 500 hPa. The first patterns had 34%, the second pattern had 22.5% and the third pattern had 21.1%, the fourth pattern had 4.4%, the fifth pattern had 3.6% and the sixth pattern had 3.5% data variance. The first days of heavy rainfall occurred with the formation of the first pattern (during the Black Sea) and the lowest heavy rainfall was seen in the sixth pattern (Eastern Mediterranean). In the first component, contour of polar vertex is from west Europe to black sea and main rainfall is supplied from black sea. In the second component, the ridges with Caspian position and meridional curvature and great depth and several sources play a role in providing moisture. In the third component, ridges with a Mediterranean position and orbital curvature are seen with less depth. The fourth component of ridges has generally two domains with a chain connection from Central Asia to West Asia and appears as multiple sources of moisture supply. In the fifth component, in the east of the Caspian Sea, there are generally two slopes in a farther distance from the country. The sixth component with multi-range ridges is located in the eastern Mediterranean. Although the patterns are very different from each other and different sources play a role in providing moisture, one of the common features of all patterns in the middle level of the atmosphere, during high rainfall, is a strong thermal contrast due to the activity of two types of air masses with different temperature and origin, that have created the conditions for strong front currents and strengthened divergence and air ascent.<br /><strong>4- Conclusion</strong><br />Checking the homogeneity or heterogeneity of the data and determining the jump points and changing the rainfall time series of the stations by the absolute standard normal test confirmed the homogeneity of the data in the mentioned stations. Then the significance of rainfall trend in all three stations was tested by linear regression method. The findings of this study showed that the general trend of rainfall in all stations except Parsabad and Ardabil was not significant. The study of heavy rainy days showed that Urmia station with 61 days had the most and the Mianeh with 20 days had the least heavy rainfall in 21 years. Analysis of polar vortex position by principal component analysis and with 177 6 629 matrix showed that 177 days of heavy rainfall occurs under 6 general patterns, polar vortex position analysis in 177 days of heavy rainfall showed that the common feature was in the middle level of the atmosphere in most different position patterns of polar vortex and the ridges on Europe flowed with cold polar vortex from the western edge of the ridge; the ridge on the lower latitudes with the arrival of hot and humid air through the eastern edge of the ridge have caused strong fronts and strengthened divergence and air rise. However, the patterns are so different from each other that they are clearly placed in 6 different patterns (positions).<strong>نوسان تاوۀ قطبی یکی از نمودهای برجستۀ برهم کنش پوشن سپهر و وردسپهر است که در فرینهای آبوهوایی نقش بسزایی دارد. بنابراین در پژوهش حاضر، بهمنظور شناسایی پیوند بارش روزانۀ فرین بالا با تاوۀ قطبی و تأثیر کنترلی آن بر سامانه های همدید، به تحلیل زمانیـمکانی تاوۀ قطبی برای ایستگاههای واقع در شمال غرب پرداخته شد. در این پژوهش، با توجه به رویکرد آماریـهمدیدی، ابتدا همگنی داده های بارش و روند بارش در هر شش ایستگاه بهترتیب به کمک آزمونهای نرمال استاندارد مطلق و آزمون رگرسیون خطی مورد ارزیابی قرار گرفت. </strong><strong>سپس بهمنظور</strong><strong> تحلیل همدید و آگاهی از چگونگی گسترش و اثرگذاری تاوۀ قطبی بر ایستگاههای مورد مطالعه، موقعیت مکانی تاوۀ قطبی با اعمال تحلیل مؤلفههای اصلی از نوع </strong><strong>T</strong><strong> بر روی دادههای ارتفاع ژئوپتانسیل در تراز 500 هکتوپاسکال برای 177 روز بررسی و 6 مؤلفه تشخیص داده شد که 90 درصد واریانس دادهها را تبیین میکرد. با تحلیل همدید موقعیت زمانی و مکانی تاوۀ قطبی طی روزهای منتخب، شش الگوی کلی شناسایی شد که در هریک از این الگوها موقعیت مکانی تاوه، امتداد و عمق ناوه متفاوت بود. بیشترین موقعیت قرارگیری تاوه در الگوی اول دیده شد که ناوه های حاصل از تاوه بیشترین عمق و گستردگی را بر روی دریای سیاه داشت. در تمامی الگوها، بارش روزانۀ فرین بالا بر اثر قرارگیری ناوۀ حاصل از تاوۀ قطبی در نزدیکی منطقۀ مورد مطالعه ایجاد شده بود که همزمان با استقرار بندالهای عظیم رکس و امگایی بر روی اروپا بود.</strong>https://ges.razi.ac.ir/article_1929_ddd8e4fa6fb8a37e075c38ba05e1be27.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Investigating the Relationship between Thermal Islands and Green Space Areas and Detecting its Changes (Case Study: Kerman City)بررسی رابطۀ جزایر حرارتی با محدودههای فضای سبز و آشکارسازی تغییرات آن (مطالعۀ موردی: شهر کرمان)83106193310.22126/ges.2022.6836.2439FAفرحنازانجم الشعاعگروه علوم و مهندسی محیط زیست، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایرانمریممروتیگروه علوم و مهندسی محیط زیست، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران.مهدیتازهگروه مهندسی طبیعت، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران.فاطمهبهادری امجزگروه علوم و مهندسی محیط زیست، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایرانJournal Article20210911This study was conducted to investigate the effect of vegetation in the city in the form of green space on Land Surface Temperature (LST) and also to identify the thermal islands of the Kerman city. LST was calculated by inverse method Planck function using Landsat 8 in Google Earth engine. The calculated LST was calculated as the average of two images in the middle months of the four seasons of 2014, 2017 and 2020. Landsat Science Notebook and a split window were used In order to evaluate the efficiency of the method used in calculating the LST. The relationship and the effect of vegetation on the calculated LST for Kerman city have been done using correlation and selection of ring buffer at intervals of 50, 100, 150 and 200 meters. Finally, their seasonal changes were examined using Moran's I autocorrelation index and its position changes were analyzed both as a season-to-season trend and as a general trend. The results showed that the Planck function method and the Landsat Science Notebook method had more accurate results than the Split window method. There is a relationship between the area of the park and its temperature, and the lowest calculated temperatures for green spaces are related to the parks with the largest area. Correlation test analysis showed that in all seasons of the year, LST is inversely related to vegetation density index. Also, the amount and intensity of this negative correlation vary depending on different seasons. The highest negative correlation value of -0.48 was recorded for the summer in 2014. Quantifying the effect of green space on the ambient temperature fluctuation showed that, on average distance 200 meters from vegetated areas, the temperature has increased by 3 degrees resulting from increasing distance from the identified cores as green space; it is clear evidence indicating the effect of green space on the amount of measured temperature. The results of this study showed that the calculation of earth surface temperature provides reliable results in the management of urban space that can be useful in future urban decisions.
<strong>Abstract </strong><strong>Extended </strong>
<strong>1-Introduction</strong>
Urbanization has many environmental consequences whose various forms have been manifested today. The difference between surface coverage in urban and non-urban areas creates fundamental changes in the nature of the built-up urban area. Urban heat island is an example of unintended climate change affected by the changes on the earth surface and the atmosphere as a result of the urbanization process. In most conducted studies on investigating surface temperature, plants have a very important role in temperature regulation and related environmental equations related. Therefore, monitoring and revealing the role of vegetation in regulating the earth surface temperature can help understand the correct temperature distribution on the earth surface, especially in urban environments and the city of Kerman is no exception. This study was conducted to investigate the effect of present vegetation in the city in the form of green space on earth surface temperature and also to identify the thermal islands of Kerman.
<strong>2-Materials and Methods</strong>
Land Surface Temperature (LST) was calculated by inverse method of Planck function using Landsat 8 satellite imagery (TIRS Sensor) in Google Earth engine system environment. The calculated LST was calculated as the average of two images in the middle months of the four seasons, spring, summer, autumn and winter of 2014, 2017 and 2020. In order to evaluate the efficiency of the method used in calculating the temperature, the methods of Landsat Science Notebook and a split window were used for a series of images. In addition to calculating the LST, Normalized Difference Vegetation Index (NDVI) was also used to monitor the spatial changes of green space in Kerman. Since it was important to study the trend of vegetation changes in the city of Kerman in the years under study, in order to study its fluctuation using the extended presence points (1) and absence (0) of green space True Skill Statistic (TSS) threshold method was used and plants were identified in the spring from 2014 to 2020. Direction of their changes was also calculated using Directional Distribution analysis in ArcGIS10.4.1. The relationship and effect of vegetation on the calculated LST for the city of Kerman was done using the methods of correlation and selection of ring buffer at intervals of 50, 100, 150 and 200 meters. The thermal island threshold was identified using a raster profile of a 25-kilometer transect from southwest to northeast in QGIS 3.16. After calculating the LST of the earth and determining the range of the beginning of thermal islands, the trend of its changes during the period 2014 to 2020 and also their seasonal changes using Moran's I autocorrelation index were investigated and analysis of changes in their position as a change of season, season and was also examined as a general trend.
<strong>3- Results and Discussion</strong>
The results of this study showed that the Planck function inverse method and the Landsat Science Notebook method had more accurate results than the Split window method. Accordingly, the results of the average indices of LST and vegetation showed that the calculated land earth surface temperature in the city center is higher than the outskirts of the city and the highest values of the calculated temperature are related to newly added sections to the outskirts of the city. Dense urban structures in the city center have lower temperatures than in the part of the city that has more rupture. On the other hand, there is a relationship between the area of the park and its temperature, and the lowest calculated temperatures for green spaces are related to the parks with the largest area.
Correlation test analysis showed that in all seasons of the year, earth surface temperature is inversely related to vegetation density index. Also, the amount and intensity of this negative correlation varies depending on different seasons. The highest negative correlation value of -0.48 was recorded in the summer of 2013. The detected threshold in the ROC curve was identified as 0.1489 and the values of 96.6% and 94.5% were calculated as sensitivity and specificity for this value, respectively, which indicates the appropriate strength of the threshold in separating vegetation from other structures of the urban environment. Quantifying the effect of green space on the ambient temperature fluctuation showed that the temperature has increased by 3 degrees on average distance of 200 meters from vegetated areas as a result of increasing distance from the identified cores as green space which is clear evidence proving the effect of green space on the amount of temperature has been measured.
The results of the analysis of space changes show that there is a growing trend in the area and number of green spaces in Kerman during the period of this study and the direction of changes in green space was to the west. Moran index analysis also showed that the thermal islands in the city of Kerman have change depending on the season under study and the most stable area has thermal islands in the common areas between zones 1 and 2 in the north of the city.
<strong>4- Conclusion</strong>
This study highlights the role of vegetation in regulating earth surface temperature in Kerman. The results of this study showed that the calculation of earth surface temperature provides reliable results in the management of urban space which can be useful in future urban decisions.<strong>هدف از این مطالعه، بررسی تأثیر پوشش گیاهی موجود در شهر، در قالب فضای سبز، بر دمای سطح زمین و همچنین شناسایی جزایر حرارتی شهر کرمان است. محاسبۀ دمای سطح زمین (LST) بهروش معکوس، تابع پلانک با استفاده از تصاویر ماهوارۀ لندستـ8 در محیط سامانۀ گوگل ارث انجین انجام گرفت. دمای سنجیدهشده بهصورت میانگین دو تصویر در ماههای میانی چهار فصل بهار، تابستان، پاییز و زمستان سالهای 1392، 1395 و 1399 محاسبه شد و به منظور ارزیابی کاراییِ روش مورد استفاده در محاسبۀ دما از روشهای دفتر علوم لندست و پنجره مجزا برای شماری از تصاویر استفاده گردید. ارتباط پوشش گیاهی با دمای سطح زمین، محاسبهشده برای شهر کرمان، و اثر آن بر این دما به روشهای همبستگی و انتخاب بافری حلقه ای، در فواصل 50، 100، 150 و 200 متری بررسی شد. در نهایت تغییرات فصلی آنها با استفاده از شاخص خودهمبستگی موران بررسی شد و تحلیل تغییرات موقعیت آن، هم بهصورت روند تغییر فصلبهفصل و هم بهصورت روند کلی، انجام گرفت. نتایج این مطالعه نشان داد که روش معکوس تابع پلانک و روش دفتر علوم لندست نسبت به روش پنجره مجزا نتایج دقیقتری داشتند. بین مساحت پارک و مقدار دمای آن ارتباط وجود دارد و کمترین دماهای محاسبهشده بهازای فضاهای سبز مربوط به پارکهای با بیشترین وسعت است. تحلیل آزمون همبستگی نشان داد که در تمام فصول سال، دمای سطح زمین با شاخص تراکم پوشش گیاهی رابطۀ معکوس دارد؛ همچنین مقدار و شدت این همبستگی منفی بسته به فصل متفاوت است. بیشترین مقدار همبستگی منفی، 48/0- بود که در تابستان سال 1392 ثبت شد. کمّیسازی تأثیر پوشش گیاهی بر نوسان دمای محیط پیرامون نشان داد که با افزایش فاصله از هسته های شناساییشده بهعنوان پوشش گیاهی، دمای سطح زمین افزایش مییابد، به نحوی که به طور متوسط در فاصلۀ 200 متری از مناطق دارای پوشش گیاهی، دما 3 درجه افزایش داشته است که گواه آشکاری بر تأثیر پوشش گیاهی بر مقدار دمای اندازه گیریشده است. نتایج این مطالعه نشان داد که محاسبۀ دمای سطح زمین نتایجی قابل اتکا در زمینۀ مدیریت فضای شهری ارائه میکند که می تواند در راستای تصمیم گیریهای آتی شهری مفید واقع شود.</strong>https://ges.razi.ac.ir/article_1933_cc5fc6d3d78e2e530efbea9bf7fb410a.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Thermal Monitoring and Evaluation of Precipitation Data Applying TRMM and GPM Satellites (Case Study: Bandar Abbas City)پایش و ارزیابی زمانی دادههای بارش توسط ماهوارههای TRMM و GPM (مطالعۀ موردی: شهر بندرعباس)107124193810.22126/ges.2022.6929.2450FAهادیسیاسرگروه کشاورزی، دانشگاه پیام نور، ایرانامیرسالاریگروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، میناب، ایرانمریمحیدرزادهگروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، میناب، ایرانJournal Article20210923Precipitation plays a decisive role in meeting the water needs of various crops, dam reserves, feeding surface and groundwater resources, and the occurrence of floods and droughts. Lack of access to long-term daily rainfall data and the high cost of setting up ground meteorological stations necessitate the replacement of low-cost methods with high-precision, high-volume data such as satellite data. The aim of this study was to evaluate the accuracy of precipitation data predicted by TRMM and GPM satellites in Bandar Abbas metropolis during a 17-year period from 2000 to 2017. Hourly precipitation data of TRMM and GPM satellites were obtained from databases. After analyzing the data format in MATLAB environment, hourly precipitation information was extracted. The results showed that the accuracy of both TRMM and GPM models in precipitation forecasting was appropriate and close to each other; it was often overestimated so that 75% of TRMM model precipitation forecasts and all forecasts GPM models were overestimated. The results showed that the TRMM model was more accurate than the GPM model in accurately predicting the occurrence of rainfall events and had less error in predicting unrealistic rainfall and the highest accuracy of the TRMM and GPM models is on a monthly, annual and daily scale, respectively. The value of EF index in TRMM model varies from -284.52 to 0.71 and in GPM model from -25514 to -1.25. The value of the EF index in the TRMM model predictions was positive in 42% of events, while, in the GPM model, it was not positively predicted in any event. The general conclusion of the research is that TRMM satellite is a suitable tool for monitoring and forecasting precipitation.<br /><strong>Extended Abstract</strong><br /><strong>1-Introduction</strong><br />Precipitation plays a decisive role in meeting the water needs of various crops, dam reserves, feeding surface and groundwater resources, and the occurrence of floods and droughts. Lack of access to daily and long-term data in different regions, high costs of setting up ground meteorological stations, as well as measurement errors have led researchers to seek new, inexpensive, up-to-date methods, available and accurate. In this regard, one of the practical ways to comprehensively estimate global precipitation is the use of satellites. Research has been done in the field of evaluation of satellites in estimating precipitation in different time scales; different results have been obtained regarding the degree of accuracy and more or less of their estimation. The aim of this study was to evaluate and monitor TRMM and GPM satellite data in Bandar Abbas metropolis in daily, monthly and annual time intervals in the period 2000-2017.<br /><strong>2-Materials and Methods</strong><br />The coastal city of Bandar Abbas is the capital of Hormozgan province and is located in southern Iran. The coordinates of the area include to North and to East. For this study, daily precipitation data of Bandar Abbas synoptic station over a 17-year period from 2000 to 2017 were used. Hourly precipitation data of TRMM and GPM satellites were obtained from databases. After analyzing the data format in MATLAB, hourly precipitation information was extracted. TRMM is the first precipitation radar space system still in orbit and uses its information. The TRMM satellite was launched on November 27, 1997 and the GPM satellite on February 28, 2014. The TRMM satellite provides systematic, multi-year, visible, and infrared and microwave rainfall measurements in the tropics as the main inputs for climate and climate projects; the products of this rainfall source are now covered by 60 degrees worldwide. North up to 60 degrees south has been available to everyone since 2000. The GPM signal is also a combination of the two warnings (GMI) and the two-pronged airborne radar (DPR). Mean Absolute Difference (MAD), Root Mean Square Error (RMSE), relative deviation (BIAS), Index of Agreement (IA) and Efficiency Index (EF) ) were applied in order to evaluate the accuracy of satellite data relative to the ground station from the statistical coefficient of determination (). Correlation indices, including Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI) and True Skill Statistic (TSS) were also used to validate the computational results.<br /><strong>3- Results and Discussion</strong><br />The results showed that the accuracy of both GPM and TRMM models are close to each other and acceptable. According to the findings from this study, both TRMM and GPM satellites can be used to estimate precipitation in Bandar Abbas. The highest accuracy of GPM and TRMM models is related to monthly, annual and daily scales, respectively. The highest value of monthly R coefficient in both TRMM and GPM models was 98%. However, comparison of other statistics showed that the TRMM model has a higher accuracy than the GPM model. According to the results of POD statistics, TRMM model (with POD = 0.638) compared to GPM model (with POD = 0.531) has more accurately predicted rainfall events among all events. False warning ratio (FAR) also showed that TRMM model with FAR = 0.773 has less unrealistic rainfall forecast than GPM model with FAR = 0.897. The TRMM model predicts precipitation at 11% more accurate points, while the GPM model fails to pinpoint precipitation at almost half of the points. The lower accuracy of the TRMM model than the GPM model (13% difference) also confirmed the higher accuracy of the TRMM model. TSS statistics also showed that the accuracy of the TRMM model is better than the GPM model and with more appropriate confidence can be applied to the application of the TRMM model in hot and humid areas of Bandar Abbas metropolis. In the TRMM model, 25% of the data had an above 0.5, and in the GPM model, in any of the months, the forecast was not above 0.5. The value of EF index in TRMM model varies from negative 284.52 to positive 0.71 and in GPM model varies from negative 25514 to negative 1.25, the value of this index in TRMM model is positive in 5 months. However, it is not positive in the GPM model in any month. The Index of Agreement (IA) in the TRMM model varies between 0.087 and 0.911 and in the GPM model varies between 0.009 and 0.672. The value of BIAS index in TRMM model varies between negative 12.8 and positive 21.58 and in GPM model varies between positive 22.64 and positive 78.86. In TRMM model, 25% of BIAS negative data was obtained. In other words, 25% of the data are underestimated. Due to the positive BIAS of all GPM model data, the data in this model were overestimated. The highest value of explanation coefficient () equal to 1.54 related to June on TRMM satellite and the lowest value of coefficient of determination () equal to 0.004 related to October on GPM satellite. The TRMM model was overestimated in 75% of the estimates and the GPM model was overestimated in 100% of the estimates, and the accuracy of the satellites in predicting the occurrence of precipitation in the rainy months (winter) decreased.<br /><strong>4- Conclusion</strong><br />The general conclusion of this study showed that the accuracy of both TRMM and GPM satellites in estimating precipitation in the southern metropolis of the country with suitable hot and humid climate was evaluated. Due to the higher accuracy of TRMM satellite than GPM satellite, it is recommended to use TRMM satellite data on a monthly scale to estimate precipitation.<strong>بارش نقشی تعیین کننده در تأمین نیاز آبی محصولات مختلف، تأمین ذخایر سدها، تغذیۀ منابع آبهای سطحی و زیرزمینی، وقوع سیلابها و خشکسالیها دارد. دسترسی نداشتن به دادههای بارش روزانه و طولانیمدت و هزینۀ بالای ایجاد ایستگاههای هواشناسی زمینی، جایگزینی روشهای ارزان با دادههایی دارای دقت بالا و به تعداد زیاد، مانند دادههای ماهوارهای، را ایجاب میکند. پژوهش حاضر با هدف ارزیابی دقت دادههای بارش پیشبینیشده توسط ماهوارههای </strong> <strong> و </strong> <strong> در کلانشهر بندرعباس </strong><strong>طی یک دورۀ هفدهساله، از سال 2000 تا 2017، انجام شد. دادههای ساعتی</strong><strong> </strong><strong>بارش</strong><strong> </strong><strong>ماهوارههای </strong> <strong> و </strong> <strong> از</strong><strong> </strong><strong>پایگاههای اطلاعاتی دریافت و</strong><strong> </strong><strong>پس</strong><strong> </strong><strong>از</strong><strong> </strong><strong>تحلیل</strong><strong> </strong><strong>فرمت</strong><strong> </strong><strong>دریافتی</strong><strong> </strong><strong>دادهها</strong><strong> </strong><strong>در</strong><strong> </strong><strong>محیط</strong><strong> </strong><strong>متلب، اطلاعات</strong><strong> </strong><strong>بارش</strong><strong> </strong><strong>ساعتی</strong><strong> </strong><strong>استخراج</strong><strong> </strong><strong>گردید</strong><strong>. نتایج نشان داد که دقت هر دو مدل </strong> <strong> و </strong> <strong> در پیشبینی بارش، مناسب و نزدیکبههم و غالباً دچار بیشبرآوردی است، بهطوری که ۷۵ درصد پیشبینیهای بارش مدل </strong> <strong> و تمامی پیشبینیهای مدل </strong> <strong> دچار بیشبرآوردیاند. یافتهها مشخص کرد که مدل </strong> <strong> نسبت به مدل </strong> <strong>دقت بیشتری در پیشبینی صحیح وقایع بارندگی اتفاقافتاده و خطای کمتری در پیشبینی بارندگیهای غیرواقعی دارد و بیشترین دقت مدلهای </strong> <strong> و </strong> <strong> بهترتیب مربوط به مقیاسهای ماهانه، سالانه و روزانه است. در مدل </strong> <strong>، ۲۵ درصد دادهها دارای </strong> <strong> بالاتر از ۵/۰ هستند، حال آنکه در مدل </strong> <strong>، در هیچیک از دادهها، </strong> <strong> پیشبینی بالاتر از ۵/۰ قرار نگرفت. مقدار شاخص </strong> <strong> در پیشبینیهای مدل </strong> <strong> در ۴۲ درصد وقایع، مثبت شد، درحالی که در مدل </strong><strong>GPM</strong><strong> در هیچ واقعۀ پیشبینیشدهای مثبت نگردید. نتیجهگیری کلی تحقیق این است که ماهوارۀ </strong><strong>TRMM</strong><strong> ابزاری مناسب برای پایش و پیشبینی بارش است.</strong>https://ges.razi.ac.ir/article_1938_6147a3f3696a6ec1b423d6c980d395d2.pdfدانشگاه رازیجغرافیا و پایداری محیط2322-319711420211222Monitoring the Drought Effects on Vegetation Changes using Satellite Imagery (Case Study: Ilam Catchment)پایش تاثیر خشکسالی بر تغییرات پوشش گیاهی با استفاده از تصاویر ماهوارهای (مطالعه موردی: حوزه آبخیز ایلام)125143196810.22126/ges.2022.7130.2472FAنسرینعوض پورگروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایرانمرزبانفرامرزیگروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران0000-0003-2817-0928رضاامیدی پورگروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایرانحسینمهدی زادهگروه کارآفرینی و توسعه روستایی، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایرانJournal Article20211110Drought is a natural phenomenon that has a significant impact on agriculture and also influences different aspects of people's lives in both arid and semi-arid regions. Vegetation cover is one of the living components of the ecosystem and plays an important role in many ecosystem processes that are strongly affected by climatic events such as drought. In the present study, the status of vegetation changes in relation to drought index has been investigated in the Ilam catchment (Ilam city). In this research, the 30-year precipitation of synoptic stations of Ilam, Mehran, Dehloran, Sarableh, Ivan, Darrehshahr and Abdanan were applied to calculate the standardized precipitation index (SPI). The Landsat satellite images were used to extract the standard vegetation difference index (NDVI) and also to investigate the detection of vegetation changes in the study area. Besides, the relationship between the SPI drought index and NDVI vegetation index was performed by the Pearson correlation method between raster layers of NDVI and SPI. The results of the SPI index showed that the drought in the years 2003 and 2008 occurred with more intensity than other years. Moreover, the vegetation classification map obtained from the NDVI index showed a decreased trend in the level of vegetation from 1988 to 2018 which is occurred mostly in the dense vegetation category (15959 ha in 1988 and 6492 ha in 2018). Based on the results of the present study, it is concluded that the changes in vegetation over time is directly related to the severity of drought, which should be considered by managers and decision-makers in the natural resources for vegetation management.
<strong>Extended Abstract</strong>
<strong>1-Introduction</strong>
Drought is one of the factors that destroys natural ecosystems such as deforestation, desertification, and rangeland destruction. One of the predictions of drought-related climate change is to affect species extinction and change vegetation productions. Drought can alter plant natural conditions for survival, reducing overall plant populations and ecosystem productivity, and even threatening regional biodiversity. Since Ilam province is located in the arid and semi-arid region in Iran; therefore, it is very important to be aware of the climatic situation, its changes and also its effects on vegetation. Hence, the main purpose of this study is to investigate the changes in vegetation in relation to drought indicators in Ilam catchment.
<strong>2-Materials and Methods</strong>
In this research, the study area is Ilam catchment which is located in Ilam province. This catchment is one of the vast watersheds of the province with an area of about 11,800 hectares. Within this catchment, the cities of Ilam and Chavar as well as rural centers are located with a total population of about 280,000 people. The average rainfall and temperature of the study area are about 575.5 mm and 16.7 ° C, respectively, so the study area is defined as a semi-arid climate according to the de Martonne climatic classification (de Martonne, 1926). In the present study, the standard precipitation index (SPI) was used to investigate the occurrence and severity of climatic drought. For this purpose, the precipitation data from seven synoptic stations of Ilam, Ivan, Sarableh, Dehloran, Darhshahr, Abdanan, and Mehran were used. The SPI index is a normalized value with a mean of zero and a standard deviation of one. Positive SPI values indicate wetness greater than the average precipitation, but, negative values show dry conditions less than average precipitation. Furthermore, Landsat satellite data were used to study the changes in coverage in the study period (1988-2018) and its relationship with the standard precipitation index. After extracting the NDVI map, vegetation classification methods were used to separate different vegetation classes. Then, the trend of their changes during the study period was investigated. The NDVI index values are in the range of -1 and +1, which tends to be one for dense vegetation. To achieve the research objectives, a Raster-based regression analysis was used to obtain the regression relationship between SPI and NDVI indices.
<strong>3- Results and Discussion</strong>
The results showed that in 2013 and 2018, the value of the SPI index for most of the studied stations indicates a negative number showing the occurrence of drought stress in these areas. On the other hand, in 2019 and 2014, the value of the SPI index in all studied stations was positive, which indicates more rainfall than the long-term rainfall of each station. The classification of the NDVI index during the given time shows a decrease in the level of vegetation from the past to the present occurred mostly in the dense vegetation category. The correlation results between NDVI and SPI indices were positive for all five years. In all studied years, the SPI index with NDVI index had an almost high correlation coefficient, so the highest correlation was in 2008 with a correlation coefficient of 0.48 and the lowest correlation was in 1988 with a correlation coefficient of 0.36. Therefore, it seems that the above results are sufficient to monitor the drought situation in Ilam province.
<strong>4- Conclusion</strong>
This study aimed to investigate the trend of vegetation change and climatic drought and to find the relationship between these changes. The study of drought periods with SPI index in the study area indicates the increase of droughts and their frequency. The findings from the NDVI vegetation index show a decrease in vegetation level, especially in the dense cover of the region. On the other hand, there was a significant relationship (α <0.05) between drought and vegetation index. In other words, with increasing drought (reduction of available plant moisture), plant growth and production will be decreased. Similar research is carried out in this area. For example, Baagideh et al. (2011) showed that the quarterly SPI index had the highest correlation with vegetation changes using the NDVI index and chose this index as the basis of their calculations. Therefore, vegetation management should be considered by managers and decision-makers in the field of natural resources, especially in periods of drought.<strong>خشکسالی رخدادی طبیعی است که تأثیر قابل توجهی در کشاورزی، اقتصاد و در نتیجه ابعاد مختلف زندگی مردم در مناطق خشک</strong><strong> و نیمهخشک</strong><strong> </strong><strong>دارد</strong><strong>.</strong><strong> پوشش گیاهی یکی از اجزای زندۀ اکوسیستم است و نقش مهمی در بسیاری از فرایندهای اکوسیستمی دارد که بهشدت تحتتأثیر</strong><strong> رخدادهای اقلیمی، از جمله</strong><strong> </strong><strong>خشکسالی، است. </strong><strong>در پژوهش حاضر، وضعیت تغییرات پوشش گیاهی در رابطه با شاخص خشکسالی در محدودۀ حوضۀ آبخیز ایلام (شهرستان ایلام) بررسی شد. در این تحقیق، از آمار ۳۰سالۀ (1367ـ1397) بارش ایستگاههای سینوپتیک (شهرستانهای ایلام، مهران، دهلران، سرابله، ایوان، درهشهر و آبدانان) برای محاسبۀ شاخص بارش استاندارد (</strong><strong>SPI</strong><strong>) و از دادههای تصاویر ماهوارهای لندست (سنجندههای </strong><strong>OLI</strong><strong>، </strong><strong>ETM</strong><strong> و </strong><strong>TM</strong><strong>) برای تهیۀ شاخص تفاضل پوشش گیاهی استاندارد (</strong><strong>NDVI</strong><strong>) و بررسی تغییرات پوشش گیاهی منطقۀ مورد مطالعه، استفاده شد. واکاوی ارتباط بین شاخص خشکسالی (</strong><strong>SPI</strong><strong>) و شاخص پوشش گیاهی (</strong><strong>NDVI</strong><strong>) </strong><strong>با</strong><strong> </strong><strong>استفاده از روش همبستگی پیرسون بین لایۀ رستری پوشش گیاهی و خشکسالی انجام شد. نتایج نشان داد که بر اساس شاخص </strong><strong>SPI</strong><strong> محاسبهشده، در سالهای 1382و 1387 پدیدۀ خشکسالی با شدتهای بیشتری نسبت به سایر دورههای مورد بررسی (1367ـ1397) اتفاق افتاده است. نقشۀ دستهبندی پوشش گیاهی حاصل از شاخص </strong><strong>NDVI</strong><strong> در طول سالهای 1367 تا 1397 بیانگر کاهش سطح پوشش گیاهی از گذشته تا حال بوده، بهطوری که این کاهش بیشتر در دستۀ پوشش گیاهی متراکم (از 15.959 هکتار در سال 1367 به 6492 هکتار در سال 1397) رخ داده است. بر اساس یافتههای تحقیق حاضر، نتیجه گیری میشود که تغییرات پوشش گیاهی در طول زمان با شدت خشکسالی ارتباط مستقیمی دارد که مدیران و تصمیمگیران عرصۀ منابع طبیعی، بهمنظور مدیریت پوشش گیاهی، باید به این موضوع مهم توجه کنند.</strong>https://ges.razi.ac.ir/article_1968_068613b3410ef87261ddb95bc5b769f3.pdf