Alireza Abbasi Semnani
Volume 22, SEPEHR , July 2013, , Pages 96-104
Abstract
Iran is located in one of the most critical geopolitical positions of the world, so that active geopolitical areas with extensive functions have surrounded Iran and changed it into an attraction for regional and global diplomacies. This has resulted in Iran effectiveness in regional and global transitions.
Yet, ...
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Iran is located in one of the most critical geopolitical positions of the world, so that active geopolitical areas with extensive functions have surrounded Iran and changed it into an attraction for regional and global diplomacies. This has resulted in Iran effectiveness in regional and global transitions.
Yet, foreign policy and governments’ performance depends on geopolitical environment and provision of necessary factors for interacting in a collection of spatial and temporal behaviors. Islamic Republic of Iran needs to design and implement its foreign policy based on its geopolitical position (position, situation, energy resources, ideology, and cultural territory) with efficient interaction and a mutual understanding of time and space. Thus, the present article seeks to reach a deep and precise understanding of favorable and unfavorable geopolitical contexts in Iran. It defines approaches used for the realization of opportunities and confronting different challenges facing Islamic Republic of Iran’s effective presence in international changes with lowest expenses and highest profits.
Mohammad Reza Pourmohammadi; Kiomars Maleki; Farhad Barandkam; Arezou Shafa'ati
Volume 21, Issue 83 , November 2012, , Pages 97-107
Abstract
Providing security, decreasing crisis in cities, and observing some principles and arrangements of passive defense with the aim of decreasing the consequences of such crisis are some of the most important issues which should be considered in urban designing and planning. Iran has always witnesses many ...
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Providing security, decreasing crisis in cities, and observing some principles and arrangements of passive defense with the aim of decreasing the consequences of such crisis are some of the most important issues which should be considered in urban designing and planning. Iran has always witnesses many catastrophes (war, etc.) and suffered many financial damages and casualties. Thus, theoretical and practical position of defense against crisis has gained significant importance in this area. Passive defense and considering its principles in urban designing and planning can decrease the damaging effects of such crisis. The ability to integrate data for modeling, locating and determining land suitability by rating territory are some of the most important capabilities of GIS, based of which it stands out as a special and exclusive system. GIS identifies hazardous points by integrating and combining benchmarks. The present article clarifies the relation between passive defense and urban planning, especially urban land use planning and assessing. Moreover, it considers the role of passive defense in protecting citizens and urban infrastructures across Sanandaj city using Arc GIS10 and AHP.
Maryam Shafiei; Zahra Arzjani
Volume 22, Issue 87 , November 2013, , Pages 99-102
Abstract
The present article investigates this research question: “why rainbows are only visible between 40 to 42 degrees in a spherical drop of water?” When a ray of sunlight enters a water drop, some of the light will be reflected, some will pass through the drop and some will be refracted according ...
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The present article investigates this research question: “why rainbows are only visible between 40 to 42 degrees in a spherical drop of water?” When a ray of sunlight enters a water drop, some of the light will be reflected, some will pass through the drop and some will be refracted according to Snell law and exit the drop and create a rainbow.
Two main factors, refractive index and wave length create different colors of rainbows and play an important role in the occurrence of rainbows. Every water drop can create one of the rain bow colors in human visual range. Then, we explain why rainbows are curved.
According to the investigations performed and mathematical formulas proved, we conclude that the intensity of light rays exiting the drop from different angles is not the same, and most of the exiting colorful light make an approximately 42 degree angle with the solar radiation. Yet, this angle depends on the light color. For red to purple, this angle is about 40 to 42 degrees.
Mahdi Ahmadi; Omid Ebrahimi; Arman Gheisvandi
Volume 21, Issue 82 , September 2012, , Pages 104-109
Abstract
Tourism development in deprived regions with necessary tourism potentials is a strategy which has recently attracted the attention of different countries of the world. In different parts of the world, preserving the environment, ecosystem and the wildlife are considered important. The present article ...
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Tourism development in deprived regions with necessary tourism potentials is a strategy which has recently attracted the attention of different countries of the world. In different parts of the world, preserving the environment, ecosystem and the wildlife are considered important. The present article investigates geomorphology of Ilam Province in Western parts of the country emphasizing on geotourism resources. Geomorphologic diversity of the province have created unmatched resources in geotourism which are able to turn the Province into a tourist attraction. The present study identifies geologic and geomorphologic characteristics of straits in this Province and provides necessary solutions for using this valuable resource. Moreover, it identifies unique features of this landforms emphasizing on their attraction.
Taghi Tavusi
Volume 21, Issue 81 , April 2012, , Pages 104-112
Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah; Hava Hasanvand; Hassan Akbari
Abstract
Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable ...
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Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.
Materials and Methods
The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.
Results and Discussion
Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.
Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).
Conclusion
The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.
Mohammad Hossein Ramesht; Roghayyeh Nikbakht
Volume 20, Issue 78 , August 2011, , Pages 89-93
Abstract
The city of Golpayegan and the surrounding area include metamorphic rocks and igneous masses produced by the performance of internal forces due to its location in the Sanandaj-Sirjan zone and also river sediments over the fourth era of geology. The existence of these igneous masses has led to the creation ...
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The city of Golpayegan and the surrounding area include metamorphic rocks and igneous masses produced by the performance of internal forces due to its location in the Sanandaj-Sirjan zone and also river sediments over the fourth era of geology. The existence of these igneous masses has led to the creation of diverse mineral resources in the city, such as the lead and zinc mines of Saleh-e-Peighambar, Godar-e-Sorkh china stone, Konjedjan marble, Haj Gharav plaster and ... . These mines are economically important for the people of this city and due to these conditions, Golpayegan and its area are strong in terms of mineral potential and therefore can play an effective role in regional development. In this research, we tried to investigate the role of geomorphology as one of the important branches of geography science. Geomorphology, on the one hand, studies units and forms such as highs and lows of areas such as mountains, plains…, and, on the other hand, the internal and external processes that form these tectonic units. These units, which have been transformed by various physical, chemical and biological factors from the first period of geology on until they have reached their current shape in modern times, are considered as one of the most valuable natural resources for humanity that are scattered in different geographic regions on the basis of geological and geomorphological characteristics. The method of examination and analysis in this paper relies on the geographic information system and the descriptive method. Using field observations and geological and topographic maps of the region we integrate the sediments of the region and existing mines based on ground surfaces (convex, concave, or flat) On the topographic maps, determine which types of mines and ores are located, how is the spatial distribution of mines based on the land levels, and assess the environmental capability of the region by preparing a map of mineral and ore mining processes.
Bahman Ramezani; Mohammad Taleghani
Volume 21, SEPEHR , February 2013, , Pages 90-94
Abstract
All over the world, coastal areas have always been exploited more than any other area because of their rich resources. Coastal areas are among the most dynamic and productive areas of the world and a context for immerse economic and social activities. On the other hand, population growth and development ...
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All over the world, coastal areas have always been exploited more than any other area because of their rich resources. Coastal areas are among the most dynamic and productive areas of the world and a context for immerse economic and social activities. On the other hand, population growth and development of activities put a lot of pressure on these areas and now coasts face different pollutions and destructions. Iranian coasts are no exception and especially northern coasts have faced different pollutions and land use development much more than their tolerance and strength during last years. Therefore, efforts to correct the dominant procedures and situations began. With the enactment of the fourth development plan (act 63) and its regulation and considering executive mechanisms and executive organization responsibilities, it is possible to organize some of current problems in coasts, and especially northern coasts. This organization must be based on researches and planning in the context of integrative environmental management. Accordingly, the present article briefly address some issues and problems and finally some suggestions are provided.
Hasan Afrakhteh; Farhad Azizpur; Roghayyeh Shamsi
Volume 22, Issue 87 , November 2013, , Pages 103-112
Abstract
Urbanism and transformation of villages near urban areas are among the issues that have influenced urban systems in developing countries like Iran during the last decades. Integration of marginal villages in the urban texture is considered to be one of the most significant physical changes in Iranian ...
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Urbanism and transformation of villages near urban areas are among the issues that have influenced urban systems in developing countries like Iran during the last decades. Integration of marginal villages in the urban texture is considered to be one of the most significant physical changes in Iranian cities, especially in the fifties. Under direct influence of large cities, especially Tehran metropolis, rural areas have experienced a deeper and faster process. So that, many are integrated into urban texture and some others are separated from their rural nature despite being far from urban texture.
The present article seeks to investigate the present position of urban villages in development plans. Descriptive-analytic and secondary data collection methods were used. Results indicate that urban development plans attend to the position of urban villages by adopting and implementing policies such as conserving natural and environmental features, developing tourism functions, protecting and organizing historical and cultural contexts.
Hosseyn Hataminejad; Musa Pajoohan; Nooshin Pakdust
Volume 22, SEPEHR , July 2013, , Pages 105-112
Abstract
Nowadays, commercial complexes and specially shopping centers as the most obvious and the latest type of these complexes have changed into an active commercial nucleus of large and modern cities due to their function and importance in daily life of citizens. Different forms of shopping centers provide ...
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Nowadays, commercial complexes and specially shopping centers as the most obvious and the latest type of these complexes have changed into an active commercial nucleus of large and modern cities due to their function and importance in daily life of citizens. Different forms of shopping centers provide diverse goods and services for their customers. Apart from economic and commercial functions, they have accepted social-cultural and recreational functions. They have changed into a place for dynamic social and cultural interactions and a place for spending free time along with shopping. As if social-cultural roles of old Iranian bazaars are recreated in a modern format. Considering the extent of commercial complex role, function and importance in social, economic and recreational activation of urban areas, strategic planning for construction and development of these complexes is especially sensitive. The present article introduces principles and foundations of drafting strategic document and planning such complexes using global literature and experiences. Descriptive-analytic research method is used. Information is collected using documentary method. Results indicate that there are four strategic areas in strategic planning of these complexes: locating area, design and tenure area, research and consultation area, financial area and each of these areas have their own goals, strategies, guidelines.
Ghaffar Fallahtabar
Volume 21, Issue 83 , November 2012, , Pages 108-112
Abstract
Researches indicate that around one third of lands are located in arid areas and Islamic republic of Iran is also located in arid and semi-arid area of the world. Apart from aridity, a significant part of the country, i.e. around 25 million hectares are wastelands. But beyond this most permanent, seasonal ...
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Researches indicate that around one third of lands are located in arid areas and Islamic republic of Iran is also located in arid and semi-arid area of the world. Apart from aridity, a significant part of the country, i.e. around 25 million hectares are wastelands. But beyond this most permanent, seasonal and temporal rivers and many important inland lakes have saltwater which worsen this sad situation. On the other hand, more water is extracted from groundwater sources to satisfy ever increasing demands. For example, 79837 million m3 water was extracted from ground water resources in 2002-2003 water year which compared to 2001-2002 water year has increased up to 2.5 percent. Inappropriate and wasteful use of groundwater also results in salinization of freshwater resources.
Southern provinces and cities bordering the desert have lots of saltwater. Water shortage and water saltiness along with salt salinization have even reached agricultural villages and lands are no longer profitable. Most aqueduct, especially those in southern parts of the country and those bordering the desert have dried. There were around 40000 aqueduct which reached 26307 in 2004. Many lands are now barren and desolate. Irregular and unplanned extraction of water from aqueducts, springs and freshwater resources by deep and semi-deep wells have decreased freshwater resources to a great degree and have gradually increased saltwater, water shortage crisis and drought crisis. This crisis is an alarm indicating a massive crisis of water shortage. Planners and authorities should see this crisis as an important religious and divine responsibility and try to find a compassionate and responsible solution. Before it is too late, they should manage and protect water resources, try to preserve rural agriculture and avoid wasting water and polluting its resources, which are shortly discussed in the present article.
Parivash Karami
Volume 21, Issue 82 , September 2012, , Pages 110-112
Abstract
According to the Greek scholar, Eratosthenes (194 BC), geography is the study of Earth as the place for human beings. In studying the Earth, geography faces different physical and human factors.Human-environment relation is the underlying concept of geography. Environment consists of natural, human and ...
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According to the Greek scholar, Eratosthenes (194 BC), geography is the study of Earth as the place for human beings. In studying the Earth, geography faces different physical and human factors.Human-environment relation is the underlying concept of geography. Environment consists of natural, human and social environments in which human beings have multiple actions and roles to play. Understanding the environment and using geographic capacities and capabilities like seas, lakes, ponds, waterfalls, forests, plantations, mountains, and sacred, ancient and cultural centers, it is possible to attract tourists and endeavor for development and income increase. As the main element of tourism in spatial crystallization, environment plays an important role in attracting tourists and provide diverse tools. Generally, the focus of geographic environment is on society and it is not possible to separate physical and spatial conditions. Every geographic perspective reveals human consistency and alignment with spatial conditions. Since every geographic area has its specific features, natural and cultural attractions are formed in accordance with them and thus there are different areas of tourism.
Geographic Data
Ali Sadeghi; Amir Reza Khavarian-Garmsir; Maryam Zareei
Abstract
Extended Abstract
Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. ...
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Extended Abstract
Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. For example, poverty can lead to unemployment, homelessness, crime, and increased disease rates. Therefore, eliminating poverty in cities plays a very important role in creating healthy and sustainable societies. Due to population growth and the influx of Afghan immigrants in recent years, some neighborhoods in District 11 of Isfahan municipality have experienced poverty due to inequality and unfair distribution of services and facilities. In order to organize the current situation and overcome the existing conditions, the spatial distribution of poverty spots must first be identified and then, with regular planning, this problem can be solved to prevent the consequences of poverty at the regional level. The aim of the current research is to analyze the spatial distribution of urban poverty indicators in the 11th district of the municipality and the social gap among the residents of this neighborhood.
Materials & Methods: The research was applied in terms of purpose and descriptive-analytical in terms of method. Based on the data of the statistical block of the 11th district of Isfahan municipality, hotspot analysis and Moran's spatial autocorrelation were performed in the GIS environment. Excel software was used for urban poverty indicators. SPS software is used for the factor analysis of the defined indicators.
Results & Discussion: The results showed that weak in the 11th region of Isfahan municipality has a cluster distribution pattern and spatial autocorrelation. According to the zoning, the parts of the center, east, northeast, and parts of the southeast and south. The west is surrounded by poor and very poor blocks, and in the north, northwest and west parts of region 11, there are very prosperous and prosperous blocks. However, in district 11 of Isfahan municipality, we see a class divide. On the other hand, I can say that having poor space in the 11th district of Isfahan city follows the characteristic pattern, in such a way that as we approach from the south to the north and from the east to the west, the poverty be decreases.
Conclusion: Some social and cultural values can perpetuate poverty and social inequality, and people in poverty may have different beliefs, attitudes, and behaviors that exacerbate their economic problems. In addition to individual and social factors, institutional factors such as housing policies, zoning laws, and land use regulations can also play a role in the spatial distribution of poverty and social inequality in urban areas. For example, discriminatory housing policies can lead to the concentration of low-income individuals in specific areas, while deprivation zoning policies can limit their access to affordable housing and employment opportunities. Today, poverty exists in various dimensions of human life and has brought with it problems and challenges. Therefore, in order to reduce poverty and implement human and sustainable development, it is essential to identify scientific and specialized methods, the geography of poverty-stricken areas, and important indicators in this field. The successful implementation of strategies and policies to reduce poverty requires the identification of all factors and needs of residents in the geographical area affected by this problem, so that programs can be developed to reduce poverty and improve conditions. This research contributes to the development of knowledge in the field of poverty and urban social planning. Its results can provide the necessary information to make decisions in addressing the urban poor problem.
Finally, the following recommendations are proposed to improve the current conditions in District 11 of Isfahan city:
Implementing neighborhood-based projects to achieve sustainable urban redevelopment with people's participation.
Establishing neighborhood development offices to identify the specific problems of each neighborhood and provide solutions.
Conducting research on poverty with the support and participation of organizations such as the Imam Khomeini Relief Committee and municipal authorities to align their results and find the best solution to address urban poverty.
Considering that the main reason for the migration of native residents of District 11 is the presence of Afghan immigrants in this area, and as a result, many social problems have arisen, it is essential to address this issue with appropriate policies; otherwise, we will face more serious problems between native residents and Afghan immigrants in the future.
Providing facilities and loans for renovation and reconstruction in the area, especially in the central, eastern, and northeast parts.
Creating social justice for the use of facilities.
Improving environmental conditions in District 11 of Isfahan, especially in the Sajjad Square neighborhood, which has an unfavorable situation. Municipal officials can address the environmental problems of this area by creating parks and green spaces, paving the streets, removing environmental pollution, collecting garbage, and organizing the vacant lands.
Creating a space for the education of working children, supporting them, and providing suitable employment opportunities for them.
Improving the physical condition of the area through redevelopment programs, and more.
Sa'di Mohammadi
Volume 20, Issue 78 , August 2011, , Pages 93-101
Abstract
Today, the colossal tourism industry, especially domestic tourism, has a special place in the countries and has an active and effective role in promoting the economic, social and cultural structure of the countries, especially in the developing countries. In this regard, rural tourism is also part of ...
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Today, the colossal tourism industry, especially domestic tourism, has a special place in the countries and has an active and effective role in promoting the economic, social and cultural structure of the countries, especially in the developing countries. In this regard, rural tourism is also part of the tourism industry, which can play an important role in empowering local people and diversifying their economic growth as well as creating new employment opportunities in close connection with other economic sectors. Rural tourism is one of the relatively good rural development grounds that can provide opportunities and facilities especially for rural employment and income, and have an effective role in the revival and renovation of rural areas. The optimal utilization of rural tourism potential as a complementary strategy for rural development can be considered as a sure step for the promotion and development of rural spaces of the country in all economic, social, cultural and environmental dimensions. Therefore, the present paper illustrates the potentials of rural areas for rural tourism and the impact of tourism on rural development.
Rahim Sarvar; Sharareh Nourani
Volume 21, SEPEHR , February 2013, , Pages 95-101
Abstract
Theory of sustainable development was proposed in 1970s, and its foundations were implemented gradually by international organizations and national governments. Emphasis on protecting the environment, reducing energy use, preserving environmental values, along with helping local societies in their development ...
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Theory of sustainable development was proposed in 1970s, and its foundations were implemented gradually by international organizations and national governments. Emphasis on protecting the environment, reducing energy use, preserving environmental values, along with helping local societies in their development are among principles emerged in designing and managing hotels and accommodations, so that since 1990s eco-hotel acts as a distinctive brand in the global market and especially in countries with an active economy in tourism. Eco-tourism management with a particular emphasis on “essential preservation for development and essential development for preservation strategy” seeks to play an active role in the realization of sustainable development according to the agenda agreed by governments. Plans implemented in the process of designing and managing by eco-hotel brand have been so significant that other countries now view its principles a special necessity.
The present article seeks to investigate the basic requirements of approaching eco-hotel in Iran. The results of this documentary-analytic study indicate that Iran environmental values and geographic diversity, along with principled utilization of environmental values and creating maximum adaptability between hotels and residences with the characteristics of every place and area requires the application of particular guidelines
Geographic Data
Hamed Asghari; Mohammad Reza Fallah Ghanbari
Abstract
Extwnded Abstract
Abstract
Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria ...
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Extwnded Abstract
Abstract
Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria such as social, economic, environmental, quality and Quantities and other goals are always noticeable to investors and managers.
Materials & Methods: Since decision-making in this field is strategic and as a result, the incomplete information of experts in conditions of uncertainty may reduce the success of future exploitation; Therefore, researchers have introduced different methods to choose the right place; D number theory as an extension of Dempster-Shafer theory in locating, while solving the deficiencies in Dempster-Shafer theory, takes into account the lack of expert information in forecasting. In this research, due to the significant amount of demand and sensitivity in the correct direction of capital resources, considering the high amount of capital required and the great importance in choosing the right place in the geography of Iran to achieve success, and that investing in this industry has always been attractive, while choosing criteria with The importance of investigating the selection of a suitable location for the construction of an edible oil refinery in thirty-one provinces of the country with the combined method of Analytical Hierarchy Process and D-Number Theory (D-AHP), due to its ability to analyze data under conditions of uncertainty that can provide a more realistic estimate , has been investigated.
Results & Discussion: the factors affecting the research problem of this research in the form of a combined method (D-AHP) and based on the consensus of the opinions of ten experts and experts have been helped with the help of brainstorming, which include: access to Raw materials, provincial demand, fixed capital costs such as land, etc. and the production capacities (factories) in the region and the frequency of consumption in the neighborhood of the province and the potential threat to the industry in case of a favorable focus are based on the behavior of consumers and political and social factors. Based on the hierarchical structure, the paired relations of D numbers for the criteria, sub-criteria (1 to 17) and options at different levels of investigation and weights have been calculated with this method, and the criteria of access to raw materials (crude oil) and provincial demand are the most important criteria. Finally, the important weights and ranks of places (provinces) in relation to the overall goal have been calculated and prioritized. Important criteria include: access to primary oil raw materials (distance from ports), fixed capital costs such as land, etc., the amount of demand in the provinces, the amount of previously created production capacities, the frequency of consumption in the neighborhood of the provinces, the lifespan of the industry in The future and political and social factors have been investigated and evaluated for 31 provinces of the country with the combined method (D-AHP) and with the consensus opinion of ten experts in the field of Iranian oil industry.
Conclusion: Therefore, the suitable place for investment in the future according to the importance coefficient of the criteria and sub-criteria and in the order of priority are as follows: provinces; Tehran (first priority), Semnan (second priority), Alborz (third priority), Central (fourth priority), Mazandaran (fifth priority), Isfahan (sixth priority), Qom (seventh priority), Fars (eighth priority), Lorestan (priority 9th), South Khorasan (10th priority), Khuzestan (11th priority), Kahkiloyeh and Boyar Ahmad (12th priority), Zanjan (13th priority), Hormozgan (14th priority), Kerman (15th priority), Yazd (16th priority), Chaharmahal and Bakhtiari (17th priority), Bushehr (18th priority), Qazvin (19th priority), East Azerbaijan (20th priority), Razavi Khorasan (21st priority), Hamadan (22nd priority), West Azerbaijan (23rd priority) ), Gilan (24th priority), Kurdistan (25th priority), North Khorasan (26th priority), Ardabil (27th priority), Sistan and Baluchistan (28th priority), Ilam (27th priority) 9th), Kermanshah (30th priority), Golestan (31st priority). Finally, the important weights and ranks of the places (provinces) have been calculated and prioritized in relation to the overall goal, which will facilitate optimal decision-making and appropriate selection for new investment and prevent waste in the consumption of capital resources and strategic planning in the long term and prevent It helps and prevents the crisis of reduction of national gross product and reduction of capacity or closure of factories, which will lead to unemployment of many employees and activists in this field and social consequences. And it shows the rational policy making to reach the desired situation.
Maryam Jaberi
Volume 21, SEPEHR , February 2013, , Pages 102-112
Abstract
Sand hills are formed by erosion in very large mega-cusps (200 meter along the coast). This erosion is caused by returning currents. In very large mega-cusps, coasts reach their narrowest limit. So progressive waves of huge storms and high tides can reach the claws of the coastal hills undercutting them, ...
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Sand hills are formed by erosion in very large mega-cusps (200 meter along the coast). This erosion is caused by returning currents. In very large mega-cusps, coasts reach their narrowest limit. So progressive waves of huge storms and high tides can reach the claws of the coastal hills undercutting them, which finally results in coastal hills being eroded. Measurements and field observations of dunes, sandy beach and returning current morphology were performed in an 18 kilometer coast line of Monterey Bay in California. This part of sandy coast line is uplifted more than 40 meters due to the spread of sand dunes. Under the cape and toward the gulf center, waves converge due to their breakdown on the Monterey underground canyons and their height increase significantly. Large gradient of wave height in coast length creates a continuous gradient in morphodynamic scale. Thus, strong returning waves and narrow mouth of the bay have resulted in the development of returning currents throughout the coast.
With 95% confidence level, longitudinal coastal changes which happen due to the volume of eroding hills have a significant correlation with longitudinal changes occurring due to coast line cusps. Moreover, longitudinal changes in coasts caused by the cusps in the coast line has a very significant relation with longitudinal changes of the coast in the range of rip currents. Therefore, it is possible to say that very large cusps are related to rip currents and the position of eroding hills is also related to the range of mega-cusps.
Mohammad Reza Zand Moghaddam
Volume 20, Issue 78 , August 2011, , Pages 102-108
Abstract
By expanding the application of the Global Positioning System (GPS), this method became increasingly inexpensive, light and easy. The accuracy of the GPS was improved, and apparently, the constant control of the morphological changes on the surface of the earth altered slowly and modestly. The purpose ...
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By expanding the application of the Global Positioning System (GPS), this method became increasingly inexpensive, light and easy. The accuracy of the GPS was improved, and apparently, the constant control of the morphological changes on the surface of the earth altered slowly and modestly. The purpose of the study is to measure morphology of ditches in a pond near Loyd in the Shankxi province using GPS. there are three main types of ditches within the area of study: the side ditches, valley ditches, hill slope ditches. This research focuses mainly on ditches of the hill slopes. The ditches of the slope in the research area are largely discontinuous and develop rapidly. In the area of research, the density of the ditch is very large. The regress rate of the slopes of the hill is 0.16-2.0m per year. The relationship between the drainage area with high slope (A) and the main slope (S) of the ditches of hill slopes is S = 0.1839 A. The 2AS values in the main cuttings of the hill slope ditches are in a range of 41 to 814 square meters and most of them range from 100 to 300 square meters. Both of these relationships are considered as indicators of the position of the ditch’s main cutting.
Geographic Data
Zahra Heydari monfared; Seyed Hossein Mirmousavi; Hossein Asakereh; Koohzad Raisipour
Abstract
Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis ...
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Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.
Materials & Methods: The studied area with an area of about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.
Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.
Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given.
Sohrab Askari
Volume 20, Issue 78 , August 2011, , Pages 107-113
Abstract
Persian Gulf has belonged to Iran since the dawn of history. The presence of Iranians in this aquatic zone is a historical and geographical fact. In some periods of history, the Persian Gulf was considered as amongst inland waters. The lawful presence of Iran in the Persian Gulf dates back to the year ...
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Persian Gulf has belonged to Iran since the dawn of history. The presence of Iranians in this aquatic zone is a historical and geographical fact. In some periods of history, the Persian Gulf was considered as amongst inland waters. The lawful presence of Iran in the Persian Gulf dates back to the year 1923. At that time, despite the opposition of major powers such as Britain and the United States, attempts were made to make the presence of Iranian navy in the Persian Gulf and the Oman Sea organized. By adopting the law of determining the extent of the coastal waters and the area of state control in the seas, on July 15, 1934, Iran's actions became operational. In the year 1956, the oil law was ratified and exploration operations began in the territories of Iran. Iraq, without specifying its maritime territory, claimed that Iran's operations had entered the territory of that country. It went on to take unilateral measures to determine the scope of his maritime area, which faced Iran’s non-acceptance. Over the past five decades, the land and border issues and political differences between Iran and Iraq have been effective in the lack of sea division between the two countries. The geographic position of the coasts of Iran and Iraq relative to each other and the exact location of the Tripartite Point, which is the crossing point of the maritime borders of Iran, Iraq and Kuwait, will have a great influence on the future border-determination process of the two countries. Currently, Iran's sea border with Iraq is under the influence of issues such as the re-signing of Algeria treaty in 1975, marking the borders with bars, the dredging of the Shatt al-Arab River (Arvandroud), the payment of war damage inflicted by Iraq, and so on. This article, while explaining the above-mentioned issues, tries to explain the necessity of settling the Iranian sea border with Iraq.
Remote Sensing (RS)
Samaneh Bagheri; Mahmoud Soorghali; Hassan Emami
Abstract
Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems ...
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Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions.
2- Methodology
The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health.
3- Results and discussion
The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage.
4- Conclusion:
Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage.
Remote Sensing (RS)
Mohamad Fathollahzadeh; Mojtaba Yamani; Abolghasem Goorabi; Mehran Maghsoudi; Mernoosh Ghadimi
Abstract
Extended Abstract
Introduction:
The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic ...
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Extended Abstract
Introduction:
The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic indices, using topographic maps, satellite images aerial photographs, and field visits. Coastal deltas are part of landforms and landscapes that, due to the proximity of two environments, land, and water, leave visible effects against tectonic activities, such as changing the pattern and location of deltas due to the change in the course of coastal rivers, the formation of unbalanced coastal terraces in parts of the coast, and the emergence of cut beaches in the form of seawalls.
One of the methods of identifying and measuring land changes is using radar remote sensing. The principles of this technique were first described by Graham in 1974 (Pacheco et al., 2006). Interferometry using radar images with an artificial window or SAR is a precise method based on the use of at least two radar images of the same area, which measures the height displacement changes in wide areas and during different time intervals with a significant accuracy of millimeters (Dong et al., 2018).
The coastal areas of northern Iran are of great importance due to the high population density and the ability to grow and develop economically and agriculturally, so monitoring geomorphic changes in the direction of sustainable development of these areas is particularly important.
In this research, the eastern coast of the Caspian Sea from Gomishan to Joibar is investigated in terms of subsidence and uplift using radar remote sensing techniques to determine the active tectonic zones of the coast in terms of temporal and spatial changes.
Materials and Methods:
The Eastern Caspian Plain is the border between the Caspian Sea and West Gorgan and includes the cities of Gomishan, Bandare Turkman, Bandare Gaz, Gulugah, Khazarabad, and Joybar. The absolute height of the Caspian Plain along the coastline is determined according to the sea level, based on the hydrographic data of the Baku station, since 1850, the Caspian sea level has varied between -25.4 and -29.4 (Abdolhi Kakrodi, 2012).
The history of seismic activity in North Alborz shows that cities like Rasht, Lahijan, Amol, and Gorgan, have been destroyed many times due to destructive earthquakes (Aqhanbati, 2013). The Alborz fault is an active fault that is stretched in a clockwise direction in the southern Caspian basin.
In this research, according to the desired goals and radar remote sensing techniques, a series of Sentinel-1 radar images with a suitable time and space difference (maximum 30 days and maximum 150 meters respectively) including 61 images in time from 2014 to 2021 were prepared and processed.
Results:
The results obtained from the SBAS model indicate that the eastern part of the Caspian coast is more affected by the uplift and this trend continues up to Gorgan Bay. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm /year.
Discussion, Conclusion:
According to the results obtained from radar interferometry, the eastern coast of the Caspian Sea is more affected by uplifting. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm/year.
To verify the results obtained, the data of the Gorgan geodynamic station was used, which shows subsidence of about 90 to 100 mm in a 6-year period, which is consistent with the values obtained from radar interferometry Based on comments Shahpasandzadeh (2013) and the reports of Nazari et al (2021), active tectonics caused by the Caspian fault that indicates the horizontal geodynamic displacement diagram of Gorgan, the small area towards the north and east during this time, which is observed in the form of numerous branches with a thrust (reverse) mechanism and a right-slip component with a slope to the south in Golestan province.
Considering that the main feature of the coast of the Caspian Sea is the Surface rivers and the use of groundwater is very little and also the extraction of gas, oil, and mining resources, which is another factor in the occurrence of land subsidence, does not exist in this area, and there isn’t also huge and heavy structure in the study area that affects the subsidence of the surface; so displacement in the study area is the result of active tectonics.
Extraction, processing, production and display of geographic data
Sara Sheshangosht; Hossein Agamohammadi; Nematollah Karimi; Zahra Azizi; Mohammad Hassan Vahidnia
Abstract
Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh ...
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Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020.
Materials and Methods
Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020.
Result and discussion
Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley.
Conclusion
The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.
Geographic Information System (GIS)
Mohammad Karimi; Parastoo Pilehforooshha; Ali Safari
Abstract
Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, ...
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Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000).The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used.Materials & Methods:The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential.In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters.Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods.Results & Discussion:At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model.In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located.Conclusion:This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps.The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods.
Geographic Information System (GIS)
Abolfazl Ghanbari; Mostafa Mousapour; Habil Khorrami hossein hajloo; Hossein Anvari
Abstract
Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of ...
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Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.Materials & Methods: The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.Conclusion:Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.
Territorial conditions and security of border areas
Sayed Mehdi Mousavi Shahidi; Bahador Zarei; Mehdi Oriya
Abstract
Extended AbstractIntroductionHydropolitics is the exploration of the role of water in the relations between countries on four scales: local, national, regional and global. With 26 border rivers and the dependence of about 30% of the country's population on the water of common watersheds, Iran is among ...
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Extended AbstractIntroductionHydropolitics is the exploration of the role of water in the relations between countries on four scales: local, national, regional and global. With 26 border rivers and the dependence of about 30% of the country's population on the water of common watersheds, Iran is among the countries that are heavily affected by hydropolitical developments and changes in the world. Additionally, the security of the country's border areas is greatly impacted due to their peripheral location and strong reliance on water from border rivers. Hence, this research investigates the hydropolitics of Iran's border rivers, the indicators and components that influence it, and the security consequences on the border areas. The research utilizes Qualitative method and descriptive-analytic approach, employing Delphi methods, cross-matrix analysis (MICMAC), and ArcGIS software to produce maps.Materials & MethodsBased on the purpose, this research is among the applied research and based on the method, it is among the qualitative research, with a descriptive-analytical approach and using the Delphi technique and the cross-matrix analysis method. In this research, the authors will analyze the issue by using library resources and written documents related to the topic, while describing and explaining the event, and then while studying the library resources from the questionnaire in order to identify and screen the most important Dimensions and hydropolitical indicators of Iran's border rivers, as well as the effectiveness of the indicators will be used. The statistical population of this research includes experts, custodians and elites of the country in the field of water. In this regard, due to the unlimited statistical population and the lack of official information on the number of experts and elites, it is not possible to use Cochran's formula, and the number of 20 people is considered as the statistical population in this research and they are questioned. . Due to the type of research and not knowing the full number of the statistical population, the sampling method is "targeted sampling" and the snowball sampling method. In order to analyze and analyze data and information, since this research is one of qualitative researches, in addition to the use of library sources and analysis with a descriptive-analytical approach, methods such as Delphi in order to identify dimensions and indicators, as well as the method Cross-matrix analysis will be used in future research of effective hydropolitical strategies. In this research, Arc GIS software is used for map preparation and Micmac software is used for data analysis.Results & DiscussionThe research findings reveal that more than 50 indicators affecting the hydropolitics of border rivers were identified through the use of library resources, the Delphi technique, and a questionnaire. Ultimately, 31 factors were confirmed in the second and third stages of the Delphi process. These 31 factors were categorized into five dimensions: natural factors, human factors, geopolitical factors, military factors, and geo-economic factors by forming an expert team and consulting with professors.The results of the cross-matrix analysis in MICMAC software have shown the indicators of influential, influenceable, target, independent, result indicators, and especially risk indicators in the hydropolitics of Iran's border rivers. Among these, target indicators and especially risk indicators are important strategic indicators. The indicators of the need for drinking water from the border rivers, unemployment, and migration due to water shortage in the areas of the common catchment basins, the relations of the surrounding countries affected by the catchment basins, the existence of a large population of people in the common catchment basins, the construction of dams and mines in the upstream countries, the defense and military situation of Iran's border rivers, the political and geopolitical exploitation of water by the upstream countries, and the activities of evil and terrorist groups in the upstream countries are the most important risk indicators in the hydropolitics of border rivers of Iran.ConclusionFinally, the results show that the most important security consequences of the hydropolitics of border rivers on border areas are in environmental, economic, political, social, and cultural dimensions. The most important of these include ethnic tensions on both sides of the border, smuggling of goods and drugs in the border areas, joining terrorist groups and striving for independence, migration from border areas, reduction of agriculture in border areas, growth of poverty in border areas, and as a result, the growth of crime and the increase in the cost of providing security. Other consequences include ethnic crises due to spatial and ethnic ties, conflicts over water, marginalization and increase in crime, air pollution, drying up of border wetlands, respiratory problems in border areas, the emptying of borders, and the destruction of the environment in border areas.
Issues of the border regions of the country
Saeed Maleki; Aghil Gankhaki
Abstract
Extended Abstract
Introduction
Coastal regions, as the intersection of two distinct ecosystems, serve as one of the most active areas worldwide for the interaction and mutual communication of marine and terrestrial organisms, while providing diverse ecosystem services to humans.The macroeconomic-political ...
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Extended Abstract
Introduction
Coastal regions, as the intersection of two distinct ecosystems, serve as one of the most active areas worldwide for the interaction and mutual communication of marine and terrestrial organisms, while providing diverse ecosystem services to humans.The macroeconomic-political approaches of nations towards coastal areas, followed by population and economic influx, have resulted in coastal cities being acknowledged as centers of population receptivity and arenas of competition among diverse groups for access to aquatic-terrestrial ecosystem services. Conflicting interests among these groups and an ineffective top-down management pattern in industrial coastal cities such as Mahshahr and Asalouyeh have exacerbated the adverse impacts of various socio-economic processes on the sustainability of coastal ecosystems, intensifying the clash between economic growth and environmental preservation.
This study endeavors to quantitatively examine the associations between the governance patterns of industrial coastal cities and environmental justice within these regions. The primary objective is to develop a model that elucidates this relationship and, based on the formulated hypotheses, establish a framework for enhancing the efficiency and efficacy of participatory decision-making processes. The ultimate aim is to foster the preservation and restoration of coastal ecosystems, ensure the sustainability of ecosystem services, and mitigate environmental justice disparities during the course of economic and social development in industrial coastal cities and coastal towns.
Materials & Methods
The present study adopts a quantitative approach grounded in the established paradigm of positivism. The target population consists of residents of industrial coastal cities. The accessible population includes the resident population of Asalouyeh (Bushehr province) and Mahshahr (Khuzestan province). Data collection was conducted through questionnaires, and data analysis and modeling of the relationships between variables were performed using SPSS 26 and SMART-PLS 4 software.
The study area encompasses the coastal cities of Asalouyeh and Mahshahr. Asalouyeh is located in the southernmost part of Bushehr province and serves as the center of Asalouyeh county. It has a long history of industrial, commercial, and fishing activities. The port of Mahshahr, on the other hand, is currently industrialized and serves as the center of Mahshahr County. It is situated on the transit routes of land, sea, and rail transportation, making it a significant and strategic port, along with the Imam Khomeini port complex.
Results & Discussion
The present study employed a three-section approach to assess model fit, including measurement model fit, structural model fit, and overall model fit. The measurement model fit was evaluated using factor loadings, average variance extracted, composite reliability, and two convergent and discriminant validity measures. Convergent validity was computed based on the extracted factor loadings and average variance values, while the Fornell-Larcker criterion was utilized to calculate discriminant validity.
The results indicated that the factor loadings of each item exceeded 0.5, indicating satisfactory reliability of the model. Furthermore, the composite reliability, average variance extracted, and Fornell-Larcker table values surpassed the acceptable thresholds, indicating a good fit of the measurement model.
The present study utilized the cross-loading validity index to assess the quality of the measurement model. The Q² values indicated that the selected tool for measuring the latent variable had an acceptable level of quality, thereby validating the measurement model of the study. The results obtained from partial least squares analysis, as presented in Figures (3) and (4) and Table (5), indicated that all path coefficients and t-values were significant, with values greater than 1.96 and p-values less than 0.05, respectively, supporting the main hypotheses based on the collected data from the study population.
Furthermore, the mediator variable of social capital was found to have a moderate effect, ranging from 20% to 80%, in the relationship between desirable governance and environmental justice, indicating partial mediation.
Conclusion
The findings of this study demonstrate a robust and statistically significant relationship between desirable governance and environmental justice. Moreover, the study introduces social capital as a significant mediator in the relationship between desirable governance and environmental justice. The significance of the association between desirable governance and social capital has been validated in previous research.
Based on these results and the substantial link between desirable governance and environmental justice, along with the mediating role of social capital, it is recommended to transition the management approach of industrial coastal cities towards desirable governance. This transition can be accomplished by implementing principles and indicators of desirable governance, such as enhancing participation, transparency, effectiveness, efficiency in decision-making and planning, and responsiveness to diverse stakeholders. These measures will establish a solid foundation for advancing environmental justice in various aspects.
Furthermore, particular attention should be given to augmenting the level of social capital through well-defined and practical planning. This strategic focus will establish the necessary groundwork for leveraging social capital to enhance the effectiveness of desirable governance in industrial coastal cities, ultimately fostering environmental justice.
Geographic Data
Mirnajaf Mousavi; Nima Bayramzadeh
Abstract
Extended Abstract
Introduction
Spatial inequalities in developing countries such as Iran are more visible due to various factors, so many Scientists (Dadashpour & Shojaei, 2022-Mosayebzadeh et al, 2021- Fotres & Fatemi Zardan, 2020- Dadashpour & Alvandipour, 2018- GhaderHajat & Hafeznia, ...
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Extended Abstract
Introduction
Spatial inequalities in developing countries such as Iran are more visible due to various factors, so many Scientists (Dadashpour & Shojaei, 2022-Mosayebzadeh et al, 2021- Fotres & Fatemi Zardan, 2020- Dadashpour & Alvandipour, 2018- GhaderHajat & Hafeznia, 2018) consider the most important feature of Iran's space organization to be spatial injustice, which is the manifestation of the country's center-periphery structure at micro-local and macro-national scales. In Iran, inequality and lack of balance in the optimal distribution of facilities as a result of unprincipled past policies in industrial-service locations, growth poles, and the trend of centralization in dominant regional cities, the spatial imbalance between national, regional, district, and local levels is one of the important issues, which has emerged under the influence of mechanisms governing economic, social and political structures, this anomaly and imbalance have increased with the increase of the government's role in the economy due to the nature of its concentration and departmentalism, and more planning has been provided to the government (Faraji et al, 2019). Finally, today, the issue of inequality in many countries is mentioned as a fundamental challenge in the path of development, So it is considered one of the main obstacles in the process of national development and disruption of regional balance, Therefore, the first step in development planning is to identify the position of each region in terms of development and inequalities (Amanpour and Mohammadi, 2021); Therefore, the main goal of this research is the spatial analysis of regional inequalities in Iran during the years 2011, 2016, and 2021.
Materials & Methods
The current type of research is applied and its research method is descriptive-analytical. The collection of data in this research is in the form of a library. The statistical population of this research is 31 provinces of the country based on the last administrative and political divisions of 2021. To evaluate the state of development, 47 indicators have been used in 3 main economic-infrastructural, educational-cultural, and health-treatment dimensions. The analysis of research data has been carried out quantitatively using GIS, EXCEL, and SPSS software. In this research, to rank the provinces from the VIKOR multi-indicator decision-making model, To weight the indices using the Shannon entropy method, For data clustering using the K-Means-Cluster method, To evaluate the changes of inter-provincial inequalities using the CV statistical method, To interpolate the development of the country using the Kriging method, To evaluate the spatial correlation and the type of clustering of the development of the provinces using the Spatial Autocorrelation method (Moran's I) and Geographically weighted regression method has been used to find the relationship between development as a dependent variable and population and area as an independent variable.
Results & Discussion
The results of this research show that in 2011 due to the strong concentration of administrative, political, economic, and industrial activities in Tehran, there was a sharp divergence between Tehran province and other provinces. The growth pole theory has entered the second stage and the degree of divergence has decreased and the degree of convergence between provinces has increased. According to the results of Moran's correlation, the clustering of the country is still multipolar and there is still regional inequality in the country, so the country's border and port provinces are in a worse situation than other provinces, despite their development potentials and capacities as border corridors. The geographic weighted regression model also shows that the influence of independent variables (area and population) is greater in the northwest of the country than in the southeast of the country, This issue is estimated at 76% in 2011, 35% in 2016 and 43% in 2021.
Conclusion
In general, the most important cause of Iran's regional inequality should be sought in the structure of the planning system and the pattern of regional spatial development of Iran. The formation of the planning system in Iran is based on neoclassical economic theories, the growth pole and the intense concentration of activities in the center of Iran, and this issue is very influential in creating regional inequalities, and on the one hand, due to top-down planning and lack of attention to environmental potential in the country's provinces, Actually, spatial injustice is spreading in the country and this issue can act as a dangerous factor in the direction of sustainable development of the country.
Extraction, processing, production and display of geographic data
Zahra Soltani; Majid Goodarzi
Abstract
Extended abstract IntroductionA problem that planners often deal with is choosing the best service distribution center in cities and rural areas. The distribution of each service in a specific area will create a pattern that can be random, dense, or scattered. In addition, the development of rural areas ...
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Extended abstract IntroductionA problem that planners often deal with is choosing the best service distribution center in cities and rural areas. The distribution of each service in a specific area will create a pattern that can be random, dense, or scattered. In addition, the development of rural areas includes a wide range of profound changes in social and economic structures that seek to distribute income fairly, increase living standards, and provide superior services in these areas. Therefore, rural development is possible if the facilities and services that serve economically productive activities are concentrated in optimal rural centers with suitable conditions in terms of providing services. Rural service centers also have an essential role in providing the facilities and services needed by the villages under their influence because these centers are considered a base for mobility and the desire to live in rural areas. In this regard, actual development is realized when it provides the necessary conditions for all people, regardless of location, for their dynamism, growth, and material and spiritual excellence. To achieve this goal, in this article, we are looking for the optimal location for establishing rural service centers and assessing the distribution of facilities in Tashan District of Behbahan City.Materials and MethodsThe applied study employed a descriptive-analytical research method. The data were collected via documentary studies, i.e., libraries, books, articles, databases, theses, and survey research, i.e., the statistical data of the housing foundation organization of Khuzestan province in 2021. This research employed the Analytic Hierarchy Process (AHP) and interior point method (IPM) to have more realistic and practical results. The main focus of the hierarchical analysis process in the present study was identifying the optimal points for establishing rural service centers, and Expert Choice and Excel software were used to perform such an analysis. This work was done by completing the questionnaire by ten experts in rural affairs. Also, the IPM was used to determine the level of development in the studied rural areas. All the research maps were prepared in the ArcGIS 10.3 software and adjusted and integrated with the UTM coordinate system.Results and DiscussionThe results showed that among the selected criteria for establishing service centers, population density has the highest score of 0.167, and the topography and height criteria, access to infrastructure facilities, and access to health care services, respectively, with scores of 0.152, 0.144, and 0.128 were the most valuable and essential in the following ranks. The overlap map of the criteria illustrated that among the 49 rural points of the district, five villages are in a perfect situation with an area of 11.94 square kilometers (2.7 percent), four villages are in a good situation with an area of 36.27 square kilometers. (8.4 percent), seven villages were in a relatively suitable area with an area of 100.69 square kilometers (23.5 percent), ten villages were in an unsuitable territory with an area of 153.10 square kilometers (35.8 percent). Also, 23 villages were placed in a completely unsuitable position with an area of 124.52 square kilometers (29.1 percent). In other words, Deh Ebrahim, Sarallah, Veisi, Kalgezar, and Ab Amiri villages had the most capacity for establishing rural service centers. In the ranking obtained from the IPM, Mashhad village had the lowest value with a coefficient of 0.0081 in Si+ score, recognized as the most developed village in Tashan District. Then, Bid Boland and Piazkar villages were ranked second and third in development levels with coefficients of 0.0557 and 0.0510, respectively, in Si+ score. These villages are flat areas and are mainly in a good position compared to other villages in Tashan District regarding population density and public services to establish rural service centers.Conclusions It is necessary to design the optimal pattern of hierarchical system and stratification of villages to make easy access for small and sparsely populated villages to the facilities in the area. It should be noted that the combined application of the hierarchical process and the optimal point allows researchers to locate and evaluate maps of various criteria and help to choose the exact and optimal location for establishing rural service centers.
Geographic Information System (GIS)
Jalal Samia; Manouchehr Ranjbar Shoobi; Amer Nikpour
Abstract
Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among ...
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Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among them, passing through Kandovan road is fascinating with its beautiful natural landscapes. At the same time, this road is also known as one of the most dangerous roads of Iran due to its mountainous location and the potential occurrence of different types of climatic and geomorphologic hazards. Apart from these dangers, the occurrence of accidents in Kandovan road is one of the main concerns of tourists visiting west parts of Mazandaran province and also the local governments providing relief and rescue services and facilities to injured people. Therefore, it is crucial to identifying the dangerous sections of this road in order to minimize fatalities and socio-economic losses. The purpose of this research is to investigate the spatio-temporal density pattern of road accidents and also to identify accidents clusters along Kandovan road.
Material and methods
To this end, we used road accidents information along Kandovan road, collected by the relief and rescue bases of Red Crescent organization of Mazandaran province in the period of 2016 to 2022. Information like location, date, and the number of death and injuries in the road accidents along this road were used in this research. First, we used GIS, spatial and statistical analyses in order to get insight from road accidents distribution and statistics. In the next step, Kernel Density Estimation – a Geostatitical measure – was used to investigate the general spatial density pattern of road accidents in the period of 2016-2022 and also the spatio-temporal density pattern of road accidents in every year from 2016 to 2022. Furthermore, the hot spot analysis was implemented to the distribution of road accidents in this period in order to find out whether accidents are clustered, dispersed or randomly distributed. Both general spatial pattern and annual spatio-temporal patterns of accidents were investigated using hot spot analysis. With this, accidents clusters reflected as hot spots were identified based on the Getis-Ord Gi*index and the associated Z-score, P-value and Gi-bin statistics. In this context, the number of accident clusters, the length of road in the accident clusters and the percentage of observed accidents in the clusters were computed from 2016 to 2022.
Results and discussion
Results show that 2084 accidents were occurred in the period of 2016-2022 with 9076 injuries and 52 deaths. The most number of accidents was occurred in 2022 following the end of Corona lockdown in 2021. Also, several parts of Kandovan road indicated to contain the highest number of accidents density. Besides, the accident density pattern changes spatially and temporarily with an increasing trend in the number of accidents density from the end year of Corona disease epidemic in 2020. Results from hot spot analysis also identified several accidents clusters along this road in the period of 2016-2022. In this context, road accidents clusters were identified in Zangouleh Bridge, Majlar, Siah bisheh, Knadovan tunnel and Ushen Bridge with average Z-score value of 3.12, average P-value smaller than 0.05 and confidence interval of 90 to 99%. The total length of road in these clusters was more than 14 kilometer which contains around 60 % of the total accidents. The spatio-temporal distribution pattern of accidents clusters and also road lengths in the identified clusters change decreasingly in the period of 2016-2022. The results of this research can be used to investigate the reasons behind the occurrence of road accidents in the high accidents density sections and also in accidents clusters identified along the road. Taking proper preparation and mitigation strategies can be beneficial in proper crisis management of road accidents in order to avoid human causalities and socio-economic losses.
Conclusion
We conclude that kernel density estimation and hot spot analysis are effective geostatistical approaches to investigate the density pattern of road accidents and also to identify accidents clusters. In order to increase the safety of Kandovan road, the factors contributing to accidents occurrence in highly dense accidents sections of road and also in accidents clusters need to be identified, and with implementing proper measures, their effects can be minimized.
Mahmoud Ahmadi; Abbas Ali Dadashi Rodbari; Behnaz Nassiri Khuzani; Tayebeh Akbari Azirani
Abstract
Introduction
Cloud is a special phenomenon formed by dynamic and thermodynamic changes of the general atmospheric circulation. Through dispersion and reflection of solar radiation, cloudschange energy balance of the Earth and affect its hydrologic cycleby producing rainfall in various forms. Determining ...
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Introduction
Cloud is a special phenomenon formed by dynamic and thermodynamic changes of the general atmospheric circulation. Through dispersion and reflection of solar radiation, cloudschange energy balance of the Earth and affect its hydrologic cycleby producing rainfall in various forms. Determining the state of clouds (in terms of clouds being liquid or ice) is crucial, sinceitaffects the atmosphere feedback mechanism. Moreover, the state of clouds is related with itsheight, i.e., higher clouds tend to have an icy state. Therefore, determiningtheir statusis especially important for the accuracy of elevation estimation. The present study seeks toinvestigatetemporal and spatial variation of liquid clouds in the geographical range of Iran using information received from meteorological stations and remote sensing techniques. It aims to find the feedback of cloudsin liquid phase and theirdominant condition.
Research Methodology
Data received from MODIS Sensor of TERRA Satellite (2001-2015) and Cloud mask (CM) algorithm were used in the present study. Moreover, long-term data of 31 synoptic meteorological stations collected during the period of 1960–2015 were used to compare satellite data. Followingdata decoding and required calculations, maps of each season were produced using Kriging method.
Results and discussion
Results indicate that maximum number of liquid clouds occurs in winter, while their minimum number occurs in summer. In winter, Rasht, Ramsar, Babolsar and Gorgan stations (with cumulative frequency of 174.33 to 305.66 days) have maximum frequency of liquid clouds.This country almost lacks liquid clouds in summer. Only in the coastal zone of the Caspian Sea, Rasht, Ramsar, Babolsar and Gorganstations with 153, 93.33, 77.66 and 26 days, respectively,had the maximum frequency of liquid clouds. The average thickness of liquid clouds in Iran was calculated on a seasonal scale. In winter, spring, summer and autumn, it was 22.23, 17.13, 14.11 and 16.7 microns, respectively. Results indicate that the average thickness of liquid clouds decreases in warm seasons. Maximum thickness of liquid clouds in winter, spring, summer and autumn was 33.04, 24.56, 24.85, 22.84 and minimum thickness of liquid clouds was 13.98, 6.82, 6.27, 8.09, respectively. In winter,maximum frequency of liquid clouds occurred in western Iran and the Caspian coastline, while maximum thickness of liquid clouds occurredin northwestern and western Iran.Moving from north to south and west to east,the frequency of liquid and icy clouds decreases. In contrast, maximum frequency of liquid clouds occurs in summer.
Conclusion
Results indicated that maximum frequency of winter and autumn liquid clouds mainly occur in high latitudes of northern regions, southern hillside of Alborz(west to east direction), and northwestern and western regions of the country. Maximum frequency of summer liquid clouds occurs in the Caspian Coasts, while maximum frequency of spring liquid clouds occursin the northern half and southeast regions of the country. This is well-justified due toactivities of the expected systems and local factors in each season. Liquid clouds of Iran have a nonlinear and possibly complex relationship, and factors such as hillside orientation, precipitation systems, distance from sources ofmoisture, lack of ascending factor, lack of sufficient moisture and many other factors contribute to this relationship.Evaluation of liquid clouds thickness indicated that elevated regions of central and western Zagros have the highest amount of liquid clouds in cold seasons, since low-pressure systems, fronts and mid-latitudewaves of atmosphere play a decisive role in the growthof cloud numbers in these seasons. This is also in consistencywith Masoudian (2011) results. Northwestern Iran and the Alborz belt are almost always affected by the western winds. Western winds pass over the Mediterranean Sea and its sufficient moisture resource, which play a significant role in the cloudiness of this area. Results are consistent with Alijani’sstudy(2010) that reported 120 cloudy days in Alborz Mountains, Khorasan and northern Azerbaijan altitudes. Increased cloudiness of southern and southeastern Iran during warm seasons is related with the monsoon system in July-September,which is also confirmed by Ghasemifar et al. (2018) and its mechanism is discussed by Yadva (2016). Results are also in consistency with the results of Ahmadi et al. (2018), which examined the cloud optical thickness (COT) and the total cloud cover (TCC) of Iran. In other words, results of Ahmadi et al.(2018) also confirm our findings.
Kosar Kabiri; Sayyed Bagher Fatemi
Abstract
Extended Abstract Introduction Different image fusion methodsprimarily seek to improve spectral and spatial content of the final result. However, the final fused image often suffers from some spectral distortions. Moreover, some image fusion methods are too slow. Image fusion using IHS transformation ...
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Extended Abstract Introduction Different image fusion methodsprimarily seek to improve spectral and spatial content of the final result. However, the final fused image often suffers from some spectral distortions. Moreover, some image fusion methods are too slow. Image fusion using IHS transformation is known as a fast image fusion method. Unfortunately, the resulting image fused with IHS also suffers from some spectral distortions and therefore several versions of this method have been developed. Defining weights of each band for generation of the intensity component is one of the main problems discussed in the literature. Spectral response curves are used as one of the major sources for defining relative weight of each spectral band. Scientific reports indicate that spectral response curves can improve the quality of the final fused image. Weights of each individual band is often calculated based on the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands. But, information like the non-overlapping areas of the curves are also considered to play a role in the calculation of the weights. The present comparative studyinvestigatesthe potential of using this information. Materials & Methods A multi-spectral Geoeye-1 satellite image with 2 meter spatial resolution, four spectral bands and the corresponding panchromatic band with a spatial resolution of0.5 meter were used to test the idea. Seven variants of the FastIHS fusion method have been developed based on different approaches of intensity component estimation using the information obtained from spectral response curves. The test methods have been compared with the original FastIHS image fusion method. The only difference of these methods was in the way they calculate the weights of each band. The seven tested methods included: 1) ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and multispectral response curves, 2) the ratio of the area of the multispectral band’s response curves and the area of the panchromatic band’s response curve, 3) the inverse of the distance between the central wavelength of the panchromatic and multispectral response curves, 4) the ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and the area of the panchromatic response curve, 5) the ratio of the non-overlapping area of the panchromatic and multi-spectral response curves and the area of the multispectral response curves, 6) ratio of the overlapping area of both panchromatic and multi-spectral response curves and the area of the panchromatic response curve minus the area of the multispectral response curves, 7) the ratio of the panchromatic and multispectral response curves’non-overlapping area and the area of the multispectral response curves multiplied by the ratio of the area of the multispectral response curve and the area of the overlapping regions of the panchromatic and multispectral response curves. Results & Discussion In order to evaluate the fused images, four criteria were used, including ERGAS, RMSE, Correlation Coefficient, and edge correlation with panchromatic band. In order to calculate edge correlation Coefficient, a Sobel filter was applied on the panchromatic and fused bands. Then, the correlation coefficient between the individual filtered spectral bands and the filtered panchromatic bands was calculated. All eight methods were ranked based on the four evaluation criteria. Because of the inconsistencies in the ranking results, the four criteria have been merged and a new ranking method was obtained based on the final results. Based on this final ranking, the fifth method is in the first rank and the second method is in the eighth rank. Therefore, the sorted list of the methods based on the final ranking is: IHS5, IHS3, IHS6, IHS1, IHS4, IHS7, FastIHS, and IHS2. As the ranking shows, almost all tested methods have a higher level of accuracy as compared to the base method (FastIHS). Conclusion The results indicates that using the information obtained from the spectral response curves can improve the final results of the FastIHS image fusion. This information can improvethe fusion speed and reduce spectral distortions of the final fused image. Unfortunately, the spectral feature of the data is preserved and the total number of detected edges is decreased. Spectral response curves are directly tied with the physics of the imaging, therefore using their information can produce some natural fused images with better visualization and enhanced spatial contents.
Hadi Ghafourian; Seyed Hossein Sanaei Nejad; Mahdi Jabbari Nowghabi
Abstract
Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, ...
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Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, and industrymanagement. Due to the problems related to generalization of point precipitation to regional precipitation, alternative methods have been proposed forthe measurement of this variable. In many cases, short reference period, inadequate density of stations and poor quality of data collected from precipitation measurement networks have challenged the analysis of this climate variable. In order to overcome these problems, it is necessary to identify alternative sources, evaluate and use them to estimate the amount of precipitation. The present study primarily seeks to evaluate precipitation data from the TMPA and provide calibration data for arid, semi-arid, Mediterranean, humid, and very humid regions of Iran on a monthly scale. Materials and Methods In the present study, monthly precipitation data of 15 synoptic stations in 5 regions of Iran (arid, semi-arid, Mediterranean, humid and very humid) were selected as reference data and monthly precipitation data from the TMPA (3B43-v7) were corrected based on them. To ensure reliability of results and reduce errors,stations were selectedrandomly from 15 separate provinces with different topographic conditions. A 20-year reference period (1998-2017) was selected for the study. Collected satellite data have a monthly temporal resolution and a spatial resolution of 0.25 degrees covering 50th parallel south to 50th parallel north. Table 1 shows features of the selected stations and their corresponding pixels. Pre-processing included quality control, homogeneity test, and data accuracy test. Usinga long-term reference period of 20 years, different statistical criteria to evaluate satellite data and a correction relationindependent from ground data are among the advantages of this research. In this study, a more efficient method is used to determine errors and one of the most modern methods of calibration is also used. Followingthe application of log transformation and multiplicative model, monthly C parameter was calculated to rectify satellite data collected from different climates. Results were evaluated using R2 (Coefficient of Determination), MBE, MAE and RMSE. Results and Discussion Findings indicated that the distribution of initial data obtained from TMPA satellite in a monthly scale is similar to the distribution of pattern obtained from ground data (due to a correlation of above 75% (R2>0.6)). Satellite data collected from arid areas are usually overestimated, while data collected from humid areas are generally underestimated. However, determination coefficients (R2) of different climates show a strong correlation between these two sources of data. The initial TMPA data have estimated the monthly precipitation of Bam, Piranshahr and Abali stations with the least amount of error. The highest level of errors were obtained from Marivan, Bandar Anzali, and Koohrang stations. In other words, the highest level of errors have occurred in the very humid region. Calibration of TMPA data collected from the 5 different climates indicated that correction of TMPA monthly data would improve valuesestimated from satellite images. Mean bias error (MBE) was reduced by 88.7, 95.3, 68.4, 38.4 and 63.9 percentin arid, semi-arid, Mediterranean, humid and very humid climates, respectively. Values of the correction parameter (C) in the arid climate indicate that a reduction factor has been applied to rectify satellite data collected in each month of the year. In the semi-arid climate, reduction factorswere obtained for each months of the year. A reduction factor is also required to rectify data collected in the warmest months of the year (June, July, and August) in the Mediterranean climate. Due to the low precipitation of these months, overestimation seems reasonable in these areas. A reduction factor should also be applied in the humid climate for 6 months of spring and summer. Considering the precipitation rate in these areas, decreasing precipitation rate in these seasonsresults in overestimation and error. Due to the significant precipitationrate in the cold months of the year (autumn and winter), decreasing factorand underestimation are expected to occur. In the very humid climate, a reduction factor should be appliedin the warmest months of the year (June, July, and August). Due to the low precipitation rate of these months and higherfrequency of cloudy days, overestimation will be reasonablein these areas. Due to underestimationin the coldest months of the year (autumn and winter), coefficients higher than one must be corrected. Conclusion Based on the results, the model used to correct precipitation in all 5 climates have reduced errors in precipitation measurement. However, this improvement was more obvious in arid and semi-arid climates. Sincea large part of Iran havean arid and semiarid climate, this calibration model is highly recommended. In addition, the final correction model does not depend on ground data and thus, applying the calibration modelto areas other than the specified stations will also be useful.
Saeid Mahmoodizadeh; Ali Esmaeily
Abstract
Extended Abstract Introduction Information obtained from change detection processes in urban regions has a remarkable effect on urban planning and management. Due to the variety of land coversin urban regions, they are considered as a complex region extracting information from which is quite challengeable. ...
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Extended Abstract Introduction Information obtained from change detection processes in urban regions has a remarkable effect on urban planning and management. Due to the variety of land coversin urban regions, they are considered as a complex region extracting information from which is quite challengeable. Hence, independent application ofoptical and radar data in changedetection may result in improper recognition of some altered regions and falsification ofobtained results. These two sensors record different kinds of information from different phenomenonat the earth’s surface, and thus can be considered as complementing each other. So, the fusion of these two data sources (radar and optical) can improve the detection of altered area. Radar data do not depend on the sun and atmospheric conditions and has thus gained much attention. In fact, radar data provide information on the spatial and geometrical characteristics of the geographical features, while optical sensors are sensitive to the reflectance of different surfaces at visible and infrared wavelengths.Therefore, the surface reaction is different in optical and radar data. Application of radar data in urban regions is limited merely due to the dependence of the intensity data (i) on the incidence angle and the speckle noise.On the other hand, independent application of optical data cannot produce accurate results in urban regions due to the spectral similarity of materials. And since the nature of these two types of images is different, it seems that their fusion improves and increases the accuracy of the information collectedfrom urban areas. Materials and Methodology Considering thebenefits of optical and radar data integrationas well as the application of unsupervised techniques in change detection studies, the present research has developed an unsupervised method for the integration of optical and radar data in order to detect changes. The area under study is a region located in the northwestof Mashhad city in northeastern Iran which has experienced considerable changes in its land cover from 2016 to 2018. Optical and radar dataare used toevaluate the proposed method. Optical data consists of a pair of multispectral imagesacquired from Sentinel-2 in 9/2016 and 9/2018. Radar data consists of a pair of SAR imagesacquired from Sentinel-1 in 9/2016 and 9/2018. The proposed method was used to integrate radar and optical data with the aim of obtaining a single band image with a higher information content. This method is an effective solution used to integrate data and reduce data dimensions from n to one dimension. In this method, necessary preprocessing was first performed on the radar and optical data, and then the characteristics extracted from optical and radar images were integratedpixel-to-pixel. technique was used to integrate these characteristics and detect changes. Generally in this method, input is divided into two categories of radar and optical data. The optical characteristics include spectral indices calculated from different bands at t1 and t2. These indices include NDVI, ARVI, SAVI, NDWI, NDBI, which are efficient for studying and identifying three types of land cover: vegetation, water and residential areas. In fact, to reduce the effects of topography and image brightness and to increase the possibility of detecting and segregating geographical features, the spectral indices were used as the input of optical part. Normalized ratio images obtained from the VV and VH polarizations of the radar images at t1 and t2 were considered as the input of radar data part. Then, a weight was estimated for each feature entering the segment using the PSO algorithm. Since the present study seeks to estimate the optimal weight of characteristics extracted from optical and radar images and ultimately to combine these features and obtain a single-band image, each particle in this algorithm contains the n weight of the extracted features from the images. OTSU thresholding techniquewhich is the relation used for inter-class variance maximization is also used as thecost function to assess the particles. In this function, the weight of each characteristic should be selected in a way that the inter-class (two classes of altered and unaltered regions)variancereaches its maximum value and the most optimal threshold limit can be estimated. The output of the proposed method will be a single-band image with higher information content. After applying the OTSU threshold limit, two classesof altered and unaltered regions are formed. The proposed method was also compared with other unsupervised change detection methods. Results Findings of the present study indicate high efficiency and accuracy of the method developed for changedetection. In this method, the ratio of pixels wronglydetected to the total number of evaluated pixels was 9.21% which is the lowest value. The overall accuracy and Kappa coefficients of the classification were respectively 90.79 and 0.819, which were the highest values compared to the other methods used in the present study. Conclusion Considering the benefits of optical and radar data integration, as well as unsupervised techniques application in change detection study, the present research has developed an unsupervised method for integration of optical and radar data andchangedetection. This unsupervised method for data integration is usedto achieve a single band image with higher information content. The technique makes it possible to integrate the optical and radar data and reduce data dimensions from n to one. For all input characteristics entering section, a weight was estimated using PSO algorithm. Since the proposed method is unsupervised, OTSU thresholding technique which is the relation used for inter-class variance maximization, is also used to assess the particles. The results have revealed high capability of the proposed method todetectchanges witha higher accuracy.
Saeed Farzaneh; Reza Shahhoseini; Iman Kordpour
Abstract
Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most ...
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Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most significant problems in drought monitoring is lack of proper spatial coverage for the collected data (due to unavailibility of field data in some regions) and also lack of a suitable time scale (observations and thus drought estimation is not always possible). Since satellite observations do not face challenges like lack of spatial scale which is quite common in field observations, remote sensing satellites can provide a better estimate of droughts. However, satellite observations alone are not capable of accurately estimating the occurrence of droughts. Therefore, a combination of field and satellite observations has been used recentely to reach a better estimate of hydrological problems. Materials & Methods Temporal and spatial complexity of droughts have made a new global index combining ground-based and satellite-based observations quite necessary. Given the kind of data used in MDI index, we cannot expect it to be global. However, its performance is still acceptable in similar environments and climates, and thus it has been used in the United States (Texas). Datasets selected for the present study have different temporal and spatial scales and thus, a common scale must be found before calculating the index. Data received from GRACE satellite and MODIS sensor were downloaded monthly, but precipitation data were collected on a daily basis. Thus, aritmatic mean of precipitation data was calculated to reach a monthly avarage. Regarding the spatial scale, one-degree precipitation data were received from GRACE and MODIS while precipitation data extracted from synoptic stations had a point-based nature. Therefore, Inverse Distance Weighting (IDW) method was used to produce a one-degree network. Three types of observations were used in the present study including data received from synoptic stations of Iran meteorological organization, GRACE mission satellite-based gravity data and MODIS remote sensing satellite-based data. These were selected to identify droughts over a 14-year time series. Results & Discussion The present study has calculated MDI drought index on a one-degree spatial scale and monthly temporal scale for 168 months using Precipitation, NDVI, and TWS data. Severe droughts in northwestern and central areas of Iran from 2004 to 2014 have led to a shortage of water in reservoirs. In addition to drought, too much water harvesting in northwestern Iran has resulted in a decrease in groundwater level and thus, increased water harvesting from rivers and canals leading to the Urmia Lake and reduced water level in this lake. The results of MDI drought index calculated for Iran over the period of 2000 to 2014 show a high correlation with the results of standardized precipitation-evapotranspiration drought index. According to the type of data used to calculate MDI index, it is expected to have a strong correlation with PDSI index due to its sensitivity to precipitation, area temperature and soil moisture content. Since GRACE and MODIS satellite-based data, and data received from synoptic stations were used, a strong correlation with MDI is also expected. It should be noted that PDSI index is higher than MDI index in Iran, although both show the drought trends accurately. For example according to PDSI index, the worst drought of the last two decades in Iran has occurred in 2008, and MDI index shows the same year. Conclusion The present study has introduced a new drought index using a combination of precipitation data, GRACE_TWS and NDVI. These data were selected because of their high sensitivity to drought. GRACE_TWS observations monitor hydrological drought and include surface and subsurface water sources. NDVI observations are mostly used to identify photosynthetic activities of vegetation cover and are therefore very useful for detecting agricultural drought. Precipitation value shows the amount of surface water in the study area. Precipitation can have relatively rapid effects and is therefore useful for monitoring meteorological drought. MDI index has identified several droughts in each region of the country in the period of 2003 to 2016. These identified droughts have generally covered the country over time. However, each drought has had a different impact on ecosystem. In Iran, the most severe droughts have occurred during 2008 to 2009 and 2011 to 2012. Since MDI correlates well with PDSI, both show a drought in these years. In order to develop the proposed algorithm, the effect of different zoning of the study area on MDI index can be studied.
Geographic Information System (GIS)
Sakine Koohi; Asghar Azizian
Abstract
Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts ...
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Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). Materials & MethodsASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. Results & DiscussionFindings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. ConclusionResults indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.
Alireza Taheri Dehkordi; Seyyed Mohammad Milad Shahabi; Mohammad Javad Valadan Zouj; Mahmood Reza Sahebi; Alireza Safdarinejad
Abstract
Extended Abstract
Introduction
Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, ...
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Extended Abstract
Introduction
Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, identification of thermal islands, monitoring of urban vegetation, and assessment of air pollution are just a few examples of areas related to urban management that remote sensing technology is the basis for indirect measurement of the related quantities. Maps of urban structures such as building blocks are commonly used in crisis management, urban design, and urban development studies.
Materials
In this study, the production of urban building block maps using Sentinel 1 and 2 satellite images has been conducted. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index ( NDBI ) for three consecutive months, the slope feature derived from the 30-meter Shuttle Radar Topographic Mission (SRTM)Digital Elevation Model of the study area, along with two Vertical – Vertical (VV) and Vertical - Horizontal ( VH ) polarization in both ascending and descending orbits, form the set of input features.
Methods
The proposed method of this paper relies on the use of a generalizable trained classifier. Initially, the classifier is trained in 2015 using training samples obtained from a new rigorous refining process using different remote sensing and spatial products. This rigorous refining process uses a reference urban map of 2015. In the first step, the corresponding areas related to the ways and roads are removed using the OpenStreetMap data layer. Areas suspected of vegetation with NDVI greater than 0.2 are then discarded. Also, due to the high backscattering of buildings in Synthetic Aperture Radar images, areas with a value less than the average backscattering coefficient of the remaining areas are eliminated. Finally, the residual map is refined using the Mahalanabis distance and the Otsu automatic thresholding method. The trained classifier is then used to generate a map of building blocks at similar time intervals for the three target years (2018, 2019, and 2020). Due to the diversity of texture and density of building blocks in the metropolis of Tehran, the proposed method has been evaluated in this area. Due to the concentration of political, welfare, and social facilities, Tehran has experienced more unplanned and irregular expansion and urbanization than other cities in Iran, which has lead to changes in buildings and constructions. Also, due to the availability of free satellite images and various online processing operations, the Google Earth Engine platform has been used in this study. The performance of three different classifiers including Random Forest (RF), Minimum Mahalanabis Distance (MD), and Support Vector Machines (SVM) are examined in this process. In order to evaluate the proposed method, reference samples obtained from visual interpretation of high-resolution satellite images (Google Earth) in all three target years have been used.
Results
The performance of the aforementioned classifiers has been investigated using 3 different criteria: overall accuracy, user accuracy, and F-score of building blocks. The RF method with an overall accuracy of over 93% in all three target years has shown the best performance. The SVM method ranks second with an accuracy of about 91% every three years. However, the MD method with an overall accuracy below 85% in all three target years has not performed well.
Discussion
The results show better performance of the RF method in all three target years with an overall accuracy of over 93%. It should be noted that the MD classifier with higher user accuracy than other methods, has shown better performance in detecting the class of building blocks. However, the RF method is the best classifier in terms of the user accuracy of the background class. The effect of using two VV and VH polarization and also the slope derived from the SRTM Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a more than 10% reduction in overall classification accuracy.
Conclusion
Based on calculated overall accuracies which are above 80% in the majority of investigated cases, two different results can be concluded. First, the trained classifier has shown good temporal generalization and has achieved acceptable accuracy in the target years. Second, due to the different collection processes of training and evaluation data, the proposed rigorous refining method for the preparation of training data has shown good performance. The effect of using two VV and VH polarization and also the slope derived from the SRTM Digital Elevation Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a tangible decreasein overall classification accuracy.
Mina Mohammadi; Abbas Kiani
Abstract
Extended Abstract
Introduction
DEMs (digital elevation models) are of critical importance in different areas such as land use planning, infrastructural project management, soil science, hydrology and flow direction studies. Across greater spatial scales, their usage is the key for contouring topographic ...
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Extended Abstract
Introduction
DEMs (digital elevation models) are of critical importance in different areas such as land use planning, infrastructural project management, soil science, hydrology and flow direction studies. Across greater spatial scales, their usage is the key for contouring topographic and relief maps. A DEM represents the bare surface, eliminating all natural and artificial features, while the digital surface model (DSM) captures both natural and artificial features of the environment. DSM is of significant interest for applications such as environmental planning, map updating, or building detection. Ground filtering is the removal of the points belonging to the above-ground objects in order to retrieve ground points to be used in generating DEM. DEM can be effectively obtained from LIDAR or digital photogrammetry. Lidar point clouds have great success in representing the objects they belong to; but since the Lidar data acquisition is still a costly process, using point clouds generated by the photogrammetric process to produce DSM is a reasonable alternative. Since DSM represents the information of surface of the land objects and is also affected by ground slope, it cannot be useful lonely for interpreting the data; therefore, to make optimal use of it, a distinction is required between the land and non-land pixels. On this basis, due to the large volume of the high-resolution images and with regard to complex urban structure, a fast yet simple and accurate method is desirable.
Material & Methods
Based on the filtering algorithms, the provided digital surface model is classified into ground and off-ground pixels. For all the off-ground pixels, the closest ground point is assumed to be the relevant low point, thus, through the height difference of the off-ground point with the assigned ground point, the so-called normalized height is computed. However, most of the filtering algorithms are mainly developed to filter Lidar data and will require the adjustment of a number of complex parameters to achieve high accuracy. At the same time, the processing time, degree of effectiveness in different scenes, and degree of automation of these methods are also important. Scene details and topographical complexity, for example in urban areas, make the filtering process even more challenging. For optimal results, users should try to adjust various parameters until they find the desired filtering result, which is a time-consuming and costly process. Due to the lack of a comprehensive study on the efficiency, automation, and computational complexity of different filtering methods on the points cloud obtained from photogrammetry, in this study, different and most widely used algorithms in this field of study were compared with each other. The studied methods were analyzed in terms of class filtering quality, processing time (execution time), scene complexity, and number of algorithm parameters (indicating the degree of user involvement in data processing to determine the amount of automation). Results of this analysis can be useful in order to better understanding the performance of filtering methods on the DSM obtained from high resolution images (dense point clouds from aerial and UAV images). In addition, it can be suitable for different users according to the parameters of time, hardware, scene type, and output accuracy.
Result & Discussion
Ground filtering is essential for DEM generation. In this paper, for ground filtering, at first, a suitable algorithm was selected and, after setting the initial parameters, they were applied to the point clouds. Comparing the obtained results, it can be seen that in the building class with sloping roofs, Morph and ATIN methods performed better, but in buildings with flat roofs, only Morph method had good accuracy. In the mono-tree class, the Morph and ATIN methods in Metashape software were able to perform the separation well, and in the tree row class, both methods performed well. The ATIN method in Metashape software was able to differentiate the road class more accurately than other methods. It also performed well in the river class. Therefore, according to the results of this study, if the goal is to identify high tolls in urban areas, due to the lower computational cost of the Morph method than the ATIN method, the Morph method is recommended. But if the goal is to produce good quality DTM, the ATIN method will be the priority.
Conclusion
In this research, ATIN, ETEW, MLS, MORPH1D, and MORPH2D algorithms for land extraction were evaluated. Thus, first the algorithms were examined on the test data and, then, the results were analyzed with the ground true images. In this study, five filtering methods were examined and compared on three images of urban areas, which included various natural and human-made features, including streets, trees, and buildings. The data were related to the digital aerial imagery taken by Intergraph/ZI DMC sensor in Vaihingen city, Germany. DSM data sets were defined on the grid with the ground resolution of 9°cm. Comparing the results of all the three data sets, it can be seen that the difference in accuracy between the one- and two-dimensional morphology algorithms was very small and they had similar performance. In terms of processing time, the ATIN method had longer execution time than other methods and the ETEW method had shorter execution time than other algorithms. Also, the number of algorithm parameters indicated the degree of user participation in data processing. Therefore, due to the point that the ETEW algorithm had fewer parameters, its degree of automation was higher than other algorithms. Comparing and reviewing the results obtained from the test data demonstrated that MLS and ETEW algorithms had the lowest efficiency in the urban area. On the other hand, in features such as buildings with sloping roofs, single trees, and tree rows, two ATIN and Morph algorithms provided favorable results. According to the obtained results, the suitable algorithm was Morph algorithm for flat-roofed buildings and ATIN algorithm for road and parking. In general, it is recommended to use the Morph algorithm for urban and small areas due to time savings and less effective parameters.
Sara Haghbayan; Behnam Tashayo; Mehdi Momeni
Abstract
Extended Abstract
Introduction
Today, one of the most complex issues in most countries is the high crime rate and the increase in social anomalies in them. One of these anomalies is residential burglary, which is one of the most widespread crimes in most countries of the world. Because spatial and ...
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Extended Abstract
Introduction
Today, one of the most complex issues in most countries is the high crime rate and the increase in social anomalies in them. One of these anomalies is residential burglary, which is one of the most widespread crimes in most countries of the world. Because spatial and time play a very important and undeniable role in the formation of hot crime spots such as residential burglary therefore, by identifying the spatial and temporal of hot crime spots can be largely prevented. Previous studies have focused more on identifying and analyzing spatial crime hotspots and performing temporal analysis of crimes independently of spatial crime hotspots. However, in order to prevent the occurrence of these crimes in the future, a combination of time and spatial hot crime spots is needed to provide a more complete and accurate analysis. The aim of this study is to provide a systematic method for combining spatial and temporal information of residential burglary. The proposed method is based on spatial analysis and allows investigating the temporal distribution of events in hot crime spots. For this purpose, GIS capabilities have been used to perform statistical and graphical tests to identify and display crime hotspots. The results showed that hotspots follow a spatially clustered and temporally focused pattern. The research findings showed that the highest frequency of burglary is in hot spot No.4 in 2016 August, on Wednesday at 8 am, and the lowest frequency of burglary is in hot spot No.1 in 2018 January, on Sunday at 4 am.
Materials & Methods
The statistical tests used in this study include mean center, standard deviation ellipse test for clustering. The first step in identifying crime hotspots is to use the tests for clustering. For this purpose, in this study, the method of the average nearest neighbor is used. The results of residential burglary test for clustering showed that this crime is a cluster pattern in the study area. After proving to be clustered, graphical methods including point map display and kernel density have been used to display the hot crime spots. The results of the kernel density test cause to the identification and display of four spatial the hot crime spots in the study area.
The data used in this research include information on the time, place and type of crimes in the years 2015, 2016, 2017, 2018. The total number of crimes is 319073, of which 5573 were related to residential burglary, which was used as a statistical population in this study.
Results & Discussion
Statistical analysis was performed over a period of four years, which is equivalent to 48 months and 35064 around the clock for each hot crime spot. The results show that the highest incidence of crime in hot spot No.4 is equivalent to 1172 cases of residential burglary, which of all these four hot spot has a smaller area equivalent to 1117 hectares. Temporal analyzes of hot crime spots were performed annually, monthly, weekly and hourly. The results of the annual analysis of all four hot spots show that the highest rate of residential burglary is in 2016 and the lowest rate is in 2018.
The findings of this study show that the combination of spatial and temporal of hot crime spots analysis lump-sum by temporal analysis regardless of the spatial hot spots in monthly, daily and hourly intervals is significantly different. The combination of spatial and temporal of hot crime spots in the monthly interval shows that the maximum and minimum rates of residential burglary per month are different in these four hot spots. The highest number of residential burglary respectively occurred in hot spot No. 1 in October, in hot spot No. 2 in August, in hot spot No. 3 in June and in hot spot No. 4 in August. However, the results of the statistical analysis of time without considering the spatial hot crime spots show that August is the highest and April is the lowest. Daily statistical analysis shows that the highest number of residential burglary occurs in hot spot No. 1 and hot spot No. 3 on Friday, while in hot spot No. 2 it is Thursday and in hot spot No. 4 it is Wednesday. This analysis is different with a general daily analysis that shows Friday as the highest number of occurrences. Hourly analysis also shows that the peak of residential burglary in all four centers is at different hours; Thus, the peak of residential burglary areas in the study area is in the hot spot No. 1 hour 22, in the hot spot No. 2 hours 17, in hot spot No. 3 hours 12, in the hot spot No. 4 hours 8. However, statistical analysis of the time without considering the spatial hot spot shows the peak of residential burglary at 12 noon.
Conclusion
In this study, a new framework for the simultaneously displaying the pattern of crimes in two dimensions of spatial and time was presented, which can be used to identify the pattern of distribution of spatial and temporal of hot crime spots. The results of kernel density estimation analysis are four spatial-temporal crime hotspots where the spatial hotspot distribution pattern is clustered and the temporal of hot crime spots distribution pattern is focused. The results show that 78% of burglaries occur in these four crime hotspot, which cover only 25% of the total area of the study area. Therefore, by identifying the spatial and temporal of hot spots, crime can be largely prevented. This method is used to identify and display any type of crime in each study area and allows the identification and display of the combination of spatial and temporal hot crime spots.
Hossein Bagheri; Mohammad Hassan Zali
Abstract
Extended Abstract
Introduction
The concentration of particulate matters has recently increased in the metropolitan area of Tehran resulting in many severe hazards for both the environment and citizens. Particulate matters (PM) with a diameter less than 2.5 microns (PM2.5) are considered to be one ...
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Extended Abstract
Introduction
The concentration of particulate matters has recently increased in the metropolitan area of Tehran resulting in many severe hazards for both the environment and citizens. Particulate matters (PM) with a diameter less than 2.5 microns (PM2.5) are considered to be one of the most dangerous types of pollution. Estimating the concentration of these particles in Tehran is challenging due to the existence of various sources of pollution and the lack of sufficient ground stations. Aerosol optical depth (AOD) data retrieved from satellite imagery can be an alternative. However, AOD are not easily convertible into surface pollution and requires the development of appropriate models such as those based on data-driven approaches and machine learning techniques. Thus, the present study seeks to create a model to estimate the concentration of PM2.5 in Tehran employing deep generative models and in-situ measurements, meteorological data, and AOD data extracted from MODIS satellite imagery. Reviewed literature has proved the ability of deep learning techniques to solve regression and classification problems. Deep learning techniques are divided into various categories, one of which is based on the generative models seeking to reconstruct the input features. In this way, high-level and efficient features can be employed to explore the relationship between PM2.5 and AOD. Thus, the present study has investigated the potential of deep generative models for estimating PM2.5 concentration from high resolution AOD data retrieved from satellite imagery.
Materials and Study Area
As a metropolitan area suffering from air pollution particularly in winters, the capital city of Iran, Tehran was selected as the study area. PM2.5, the main source of pollution in Tehran, is mainly emitted from vehicles and especially old urban public transport fleet.
Aerosol data collected by Aqua and Terra sensors of MODIS and retrieved by Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm were used in the present study. Meteorological data were obtained from the global ECMWF climate model, and the concentration of PM2.5 was measured at air quality monitoring stations. Data were collected for a time interval of January 2013 to January 2020.
Methods
The present study has investigated the potential of deep generative models used to provide an estimate of PM2.5 concentration based on satellite AOD data. To reach such an aim, three types of deep generative neural networks, deep autoencoder (DAE), deep belief network (DBN) and conditional generative adversarial network (CGAN) were developed. Moreover, the performance of deep generative modes was compared with linear regression techniques as typical models used to explore the relation between PM2.5 and AOD data. Finally, the most accurate model for the generation of high resolution (1km) PM2.5 maps from AOD data was selected based on the performance of models.
Results and Discussion
The accuracy of each developed model was evaluated using the test data and the obtained results were compared with results obtained from other basic linear regression models. Accuracy evaluation indicated that the developed deep autoencoder (DAE) combined with support vector regression led to the highest correlation (R2 = 0.69) and lowest RMSE (10.34) and MAE (7.95) and thus, can be potentially used for high resolution estimation of PM2.5 concentration. Next was the developed deep belief network which with a performance close to DAE demonstrated its potential capability to estimate PM2.5 concentration from satellite AOD data. The CGAN network acted less accurately in the estimation of PM2.5 concentration as compared to other deep generative models, but outperformed the linear regression algorithms on the test data. To sum up, findings indicated that deep generative models have outperformed classical linear regression techniques used for high resolution estimation of PM2.5 from satellite AOD data. Among the linear methods, the highest accuracy was achieved by the Lasso algorithm with an RSME of 12.14 and MAE of 9.46 on the test data which showed the significance of regularization for the improvement of performance in linear regression algorithms. Nevertheless, the accuracy of linear regression techniques was much lower than deep generative models.
Conclusion
Finally, DAE was selected as the best model for the estimation of PM2.5 concentration across the study area and high resolution maps of PM2.5 concentration were generated using the developed model. Investigating the daily PM2.5 maps generated for two days with different air quality conditions (clean and polluted) demonstrated the efficiency of the developed DAE for PM2.5 modeling.
Soroush Motayyeb; Farhad Samadzadegan; Farzaneh Dadrasjavan
Abstract
Extended AbstractIntroduction, MaterialsImproving energy efficiency in buildings has become a major topic of interest in recent studies. Modern technologies have improved energy performance in new buildings. However, there is a growing demand for inspecting old buildings and enhancing their energy efficiency. ...
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Extended AbstractIntroduction, MaterialsImproving energy efficiency in buildings has become a major topic of interest in recent studies. Modern technologies have improved energy performance in new buildings. However, there is a growing demand for inspecting old buildings and enhancing their energy efficiency. Areas of heat dissipation are the most significant faults in insulation occurring as a result of thermal bridge, excessive heat loss, air leakage, or defective thermal insulation in building components. Heat dissipation mainly occurs on the facade. Lack of sufficient information on the energy performance and associated costs of retrofitting buildings have made visualization and determination of the heat dissipation areas crucial for improving energy efficiency. The present study primarily seeks to determine areas of heat dissipation on building facades in order to optimize energy efficiency and energy storage in buildings. A vertical flight Unmanned Aerial Vehicle (UAVs) with low altitude flight, equipped with Post-Processing Kinematic (PPK) module and MC1-640s thermal infrared camera made by KeiiElectro Optics Technology at a rate of 30 frames per second have been utilized in the present study to gather the needed data. Also, thermal infrared images of the building facade were collected from PedarSalar palace in Aliabad village, Aradan-Garmsar city with a longitude of 52.3034 and a latitude of 35.1600 in order to assess the proposed method. Methods, ResultsThe present study seeks to propose a method for visualizing and determining the heat dissipation areas in facades with the aim of increasing energy efficiency. The proposed research method was divided into two parts. The first stage involved the generation of a dense point cloud and related orthophotomosaics utilizing thermal infrared images collected by UAVs, bundles adjustment, Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms. The second stage involved converting the thermal infrared orthophotomosaic to HSV color space in order to choose the seed pixels for the Region-Growing-based segmentation algorithm. Since Hue-Saturation-Value (HSV) color space performs better when visualizing the concept of light, seed pixels were chosen from the HSV color space pixels with the highest degrees of grayscale to enter the segmentation algorithm. Then, introducing the seed pixels as input to the Region-Growing algorithm, areas of heat dissipation were automatically determined in the facade.A dense thermal infrared point cloud was produced with a density of 1779067 points per square meter, Reprojection error of 0.41 pixels and Ground Sample Distance (GSD) of 0.75 cm using 45 thermal infrared images captured by UAVs flying perpendicular to the facade of the building at a distance of 11 meters and a flight altitude of 1.70 meters. The Precision and Recall evaluation criteria have been employed to analyze detected areas of heat dissipation. Precision and recall evaluation criteria equaled 90 percent and 87 percent, respectively. Results indicated that the proposed method has improved precision and recall evaluation criteria and determined areas of heat dissipation with higher accuracy. Discussion, ConclusionGiven the critical importance of improving energy efficiency, and potential energy storage and reducing energy consumption in buildings and costs of production, obtaining related data to find optimization solutions is critical especially in older buildings. Since heat dissipation mainly occurs on the facade, the present study seeks to identify and determine areas of heat dissipation on the facade to visualize and improve energy efficiency applying the Region-Growing segmentation algorithm on the thermal infrared orthophotomosaic generated by photogrammetry UAVs. Since the HSV color space shows higher resolution in distribution of pixels used to extract areas of high temperature, seed pixels were introduced to the Region-Growing segmentation algorithm. Finally, precision and recall evaluation criteria were used to determine the accuracy of heat dissipation areas automatically detected on orthophotomosaics. Thus, the accuracy of the proposed method has been evaluated using the precision and recall criteria resulting in 90% and 87 %, respectively. Results indicated increased accuracy of the proposed heat dissipation detection method as compared to previous studies.
Farzad Moradi; Ali Reza Azmoudeh Ardalan; Parham Pahlavani
Abstract
Introduction Recently, National Cartographic Center, the Organizationfor Registrationof Deeds and Properties, and alsoon a limited scale some municipalities have developed systems to provide real-time differential positioning services. Although these systems have proved to be efficient for quick mapping ...
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Introduction Recently, National Cartographic Center, the Organizationfor Registrationof Deeds and Properties, and alsoon a limited scale some municipalities have developed systems to provide real-time differential positioning services. Although these systems have proved to be efficient for quick mapping purposes in this country, they do not provide accurate differential positioning in coastal and offshore areas and thus cannot meet the needs of navigation and exploration and extraction of marine resources in oil fields. However, Iran has long maritime boundary in its south and north, and maritime economy is considered to be a priorityin its development planning. Since site selection for permanent positioning stationsis considered to be the main step of creating a real-time differential positioning system, finding the most suitable location for permanent positioning stations in the south of the country was selected as the purpose of the present study. To reach this aim, pairwise comparison matrix of the required information layers was first constructed using Delphi methodbased on the opinion of 5 experts, and in the next step, computer coding was performedin MATLAB using Fuzzy Analytic Hierarchy Process to compute the weight of each layer and sublayer.Then, layers were classified in GIS environment based on the weights obtained from the analysis of pairwise comparison matrices for each sublayer. Finally, layers were integrated usingweighted index overlay analysis methodto select optimal sites for permanent stations based on the weights obtained for each layer. Details of the calculations and the results are presented in the article. Materials and Methods High efficiency of analytichierarchyprocess and spatial information systems in management and analysis of spatial data have led to the creation of a highly efficient environment in which various stages of different analysis such as site selection for permanent GNSS stations can be performed. One of the advantages of this procedure is that the analysis can beupdated in the shortest possible time and the result can be depicted visuallyat any stage of decision makingwith a simple changing of the values (weights) of each input data based on the expert opinion. Thisgreatly impacts experts' understanding of changes in the studyarea. Accordingly,fuzzy analytichierarchyprocess method is used within the GIS environment in the present study. Results and Discussion The present study addresses the issue of site selection for permanent GNSS stations. In the first step,pairwise comparison matrix was created for the criteria and sub-criteria and filled in by 5 experts. Then, layers were classified in GIS environment based on the weights obtained for each sub-layers of pairwise comparison matricesand the codes written in MATLAB. Finally, suitable locations for permanent GNSS stations were obtainedby integrating the layers usingweighted index overlay. Conclusion The present study has provided the results of optimal site selection for GNSS permanent stations. These selected sites meet the needsofprecise positioning in the coastal areas of the country and can be used in navigation and exploration and extraction of marine resources and oil fields. Afterthe selection of southern coasts as the study area, 7 criteria (proximity to urban areas and facilities, slope, distance from faults, distance from access roads, soil type, distance from rivers and distance from railways) were selected based on the expert opinion. A pairwise comparison matrix was createdfor these criteria and sub-criteria and 5 expert experts were consulted in this regard. Expert opinions were analyzed using codes written in MATLAB software andFuzzy Analytic Hierarchy Process method and thus, the weight of each criterion and sub-criterion was obtained. These weights were then integrated using the geometric mean method and the final weight of each layer and sublayer was determined. Using Arc map software, these weights were applied to different layers and sublayers, and finally, optimal locations for permanent GNSS stations were divided into 5 classesof very good, good, medium, bad, and very bad stations. Good and very good classes can be considered as optimal places forcontinuously operating reference stations.
Roohollah Karimi; Ali Reza Azmoude Ardalan; Siavash Yousefi
Abstract
Introduction
Components of verticaldeflection, i.e., North-South component and East-West component ,are used for accurate determination of geoid or quasigeoid. Moreover, vertical deflection components area useful source for determination of variations in subsurface density and geophysical interpretations. ...
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Introduction
Components of verticaldeflection, i.e., North-South component and East-West component ,are used for accurate determination of geoid or quasigeoid. Moreover, vertical deflection components area useful source for determination of variations in subsurface density and geophysical interpretations. Generally, there are two definitions for verticaldeflection. According to Helmert definition, vertical deflection at any given pointis the angle between the actualgravity vector (actual plumb line) and a line that is normal to the reference ellipsoid(a straight line perpendicular to the surface of reference ellipsoid). Another definition of vertical deflection is proposed by Molodensky. According this definition, vertical deflection at any given point is the angle between actualgravity vector and normal gravity vector (normal plumb line). Some relations have been introduced to convert Molodensky vertical deflection to Helmert vertical deflection. Helmert vertical deflection is estimated using astrogeodetic observations (combination of astronomical and geodetic observations).
Presently, global geopotential models (GGMs) have been expanded to the degree of2190, which is equivalenttoabout 5-min spatial resolution. Vertical deflectionat any point on the Earth can be calculated using the GGM. The resulting vertical deflection is consistent with Molodensky definition.Unfortunately, accuracy of GGMs is not sufficient for estimation of verticaldeflection.In other words, since GGMs are expanded up to a limited degree due to their resolution, omission error(or truncation error) occurs in computation of the earth’s various gravity field functionals, such as the geoidal height and verticaldeflection. Combining GGM with a digital terrain model (DTM) is a method used to reduce omission error.It should be noted that DTM has a higher spatial resolution as compared to GGM.In this method, the omitted signals of GGM can be modeled using residual terrain model (RTM) derived from subtracting high resolution DTM from a reference smooth surface. The reference smooth surface is obtained from eitherapplying average operator to DTM or expanding global topography into spherical harmonics. Fortunately, DTMs with spatial resolution of 3seconds or more,and reference smooth surface based on 2190 degree spherical harmonics are publicly available.
The present study seeks to assess vertical deflectionderived from a combination of GGM and DTM in Iran. Previously, Jekeli(1999) has studied EGM96 geopotential model with the aim of computingvertical deflection in the USA. Hirt(2010) and Hirt et al. (2010a) have assessed vertical deflection in Europe and the Alps using a combination of EGM2008 and RTM models.In Iran, GO_CONS_GCF_2_TIM_R4, a GOCE-only model, and EGM2008 geopotential model have been used toobtain vertical deflection and the results have been evaluated byKiamehr and Chavoshi-Nezhad(2014).
Materials & Methods
To implement the present study,a EGM2008 model with a spatial resolution of about 5-min is selected asGGM and a SRTM model with 3-sec spatial resolution is considered as DTM. To obtain RTM, DTM2006 model based on2190 degree spherical harmonicsis selected as the reference smooth surface.To compute the residual topography effect, prism method was used in an ellipsoidalmulti-cylindrical equal-area map projection system. First, we compute vertical deflectionusing EGM2008 model. It is also calculated using a combination of EGM2008 model and RTM(EGM2008/RTM method). In the next step, vertical deflection derived from the first method (EGM2008 model) and the second one (combination of EGM2008 model and RTM) are compared with vertical deflectionderived from astrogeodetic observations in 10 available Laplace stations in Iran.
Results & Discussion
Results indicate that there is a 1.2sec difference between North-South component of vertical deflection (i.e.) obtained from EGM2008 model and astrogeodetic observations.With RTM, this will reach 1 sec, which shows a 15% improvement. Moreover, there is a5.7secdifference between East-West component of vertical deflection () obtained from EGM2008 model and astrogeodetic observations, while this value will reach 5.6sec using RTM. Improvement in East-West component () is1.4%, which is smaller than the improvement of North-South component (). Based on the computations, we found that values of and in the Laplace stations canreach 17sec (RMS=7sec) and 15sec (RMS=8sec), respectively. Therefore, it is concluded that the relative error ofNorth-South component ()computation using EGM2008/RTM method is about 6% and the relative error ofEast-West component ()computation is about 37%.
Conclusion
The present research has studied the RTM effect on the improvement of GGM used for the determination of vertical deflectionin Iran. To performthe study, EGM2008 model with around 5-min spatial resolution was selected as GGM. RTM is also derived from subtracting the DTM2006 model (based on2190 degree spherical harmonics)from the 3-sec spatial resolutionSRTM model. Numerical findings indicate that a combination of RTM and GGM can improve the results of vertical deflectioncomputation, as compared to the results obtained from GGM-only approach. The improvement in North-South component of vertical deflection () is about15%and East-West component of the vertical deflection () undergoes about 1.4% improvement. In general, EGM2008 model and its combination with RTM have been more successful in the computation of component as compared to computationin the geographical region of Iran. There is no clear explanation for this difference, but it can be due to errors in theastronomical or geodetic observations oflongitude in Laplace stations.
Zahra Banimostafavi; Saeed Farzaneh; Mohammad Ali Sharifi
Abstract
Extended Abstract:
Introduction
Nowadays, engineering structures face many threats. Natural and human activities can result in deformation and displacement of dams, bridges, and towers. As a result, any crack in the body of these structures is important and may have dangerous consequences. To prevent ...
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Extended Abstract:
Introduction
Nowadays, engineering structures face many threats. Natural and human activities can result in deformation and displacement of dams, bridges, and towers. As a result, any crack in the body of these structures is important and may have dangerous consequences. To prevent catastrophes, the behavior of these structures should be monitored permanently during the construction phase and after opening.Nowadays,thebehavior of engineering structures such as dams, power plants, and towers is considered to be especially important. Three different methods are usually used to measure such behavior: classical, satellite and precise instruments.
Materials and methods
Modern equipment is considered to be a crucial factor in controllingpossible changes and preventing human errors. Therefore, different sensors are installed in the structure to measure tensile and shear flexibility during the construction phase. Moreover, data received from these sensors is analyzed permanently during the service life to ensure sustainability of the structure. These tools make internal analysis of these structures possible. Analyzing the behavior of engineering structures is considered to be one of the most important tasks in the field of geodesy. Inaccurate analysis of displacements can have deadly effects. Various methods are used to measure such displacements, which are divided into two categories: robust and non-robust methods based upon the results of the epoch adjustment. To find deformations, a geodetic network should be defined in the first step. If two epochs are not measured in the same datum, the results will not be reliable. Displacement can be measured in two ways: Absolute and Relative. In the absolute method, some points are considered to be stable, while in the relative network, all points are considered to be unstable, and the problem is solved based upon this hypothesis. The method of relative network is used in the present study. Regarding network geometry, displacement analysis is performed using two methods:single and combinatorial. Moreover, displacement analysis is divided into two categories of robust and non-robust methods. Iterative Weighted Similarity Transformation (IWST)and Minimum L1 norm are among robust methods which calculate the matrix of displacement by minimizing the first and second norm. Global Congruency Test (GCT) is a non-robust statistical method used to determine unstable points in geodetic networks. Robust and GCT are among classical methods used to discover unstable points in geodetic networks, while Simultaneous Adjustment of Two Epoch (SATE(is a new method used to achieve this purpose. Combinatorial methods are also considered to be a suitable alternative method used for detecting unstable points in a geodetic network. In our previous study, “evaluation of single-point methods used fordetecting displacement in classical geodetic networks”, single-point methods of detecting unstable points were investigated and the SATE method was selected as the optimal method. Unlike single-point methods, these methods examine all points of the geodetic network simultaneously to discover unstable points.
Results and discussion
The strong dependence of these methods on the network geometry makes discovery of all unstable points impossible. Combinatorial methods are considered to be a suitable alternative method used to detect all unstable points in the geodetic network. These methods does not have a strong dependence on scale and the network geometry. Multiple Sub Sample and M-split methods are classified in this category. These methods can detect unstable points efficiently. The present study takes advantage of simulated datato evaluate combinatorial methods such as Multiple Sub Sample (MSS) Angles, MSS-distance difference, and M-split and compare them with the SATE method with the aim of choosing the optimal method. Then, unstable points in the real network of Jamishan dam in Kermanshah Province will be discovered using the identified optimal method.
Conclusion
The present study identifies the best method between single and combinatorial methods. The best method can detect most unstable points and has the lowest dependence on geometry, scale and other factors influencing the results.According to the results, Multiple Sub Sample with distance difference is selected as the best method.
Mohammad Hossein Rezaei Moghaddam; Keyvan Mohammadzade; Majid Pishnamaz Ahmadi
Abstract
Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of ...
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Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring.Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies.
Materials and methods
A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery).
Discussion and results
Different functions used in OBIA techniques,such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies.
Conclusion
In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.
Abolfazl Sharifi; Mohammad SaadatSeresht Mohammad SaadatSeresht
Abstract
Extended Absrtact
Introduction
Today, With the improvement of UAV technology as a spatial data collection platform, using the UAV photogrammetric method for mapping aims has become more popular. The advantages of this method include cost-effectiveness, speeding up the project process, ...
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Extended Absrtact
Introduction
Today, With the improvement of UAV technology as a spatial data collection platform, using the UAV photogrammetric method for mapping aims has become more popular. The advantages of this method include cost-effectiveness, speeding up the project process, high resolution of spatial data, and production of various spatial products such as orthophoto mosaic, digital surface and ground models, 3D virtual model, and 3D map. From quality point of view, in addition to the network design in UAV photogrammetry projects, the camera and its accurate calibration are essential too. Metric cameras have a strong geometry, and their calibration parameters are known and stable with the smallest possible values. In spite of high accuracy outputs of metric cameras, it is practically impossible to use them in ultra-light public drones due to their high weight, size and cost. Therefore, today, non-metric and unstable digital cameras are conventional in UAV photogrammetric systems.However, many efforts are being made to reduce this weakness by improving the geometric quality of lightweight and inexpensive non-metric cameras. Despite of these efforts, application of non-metric cameras will not yet give us acceptable products without some practical considerations such as reducing flight altitude, increasing image side lap and overlap, and using high density of ground control points, which leads to a significant increase of cost and time. The main problem with these non-metric cameras is the weak geometry of their components that makes a high instability in the camera calibration parameters. This highlights the importance of proper geometric calibration of these cameras.
Materials & Methods
So far, several distortion models have been used to calibrate the metric cameras such as Brown model with a maximum of 12 parameters, including principal distance, principal point coordinates, lens radial and decentering distortions and affinity. These parameters are simultaneously estimated in a bundle adjustment with self-calibration process. Therefore, it can be said that this model considers fixed physical parameters for geometric modeling of the camera by which many images acquired in a photogrammetric block. If non-metric camera geometry is not modeled by a dynamic model with local spatial and temporal distortion parameters, some local systematic errors remain in the image observations. These systematic errors cause the estimation of unknown parameters in the least square adjustment is biased. Though this solution significantly improves the result of non-metric cameras in UAV photogrammetry, some errors in the 3D reconstruction remain yet due to low strength of observation equations set which comes from dynamic nature of the camera distortion model.The dynamic image distortions lead to parallax in stereoscopic vision and horizontal/vertical steps in the boundaries of connected 3D models. This paper proposes a post-processing method to reduce dynamic image distortions after conventional self-calibration of a non-metric camera with Brown model. The proposed method is based on local modeling of the image residuals using a finite element method. The data used in this study are photogrammetric drone images taken by ILCE-7RM2T, FC6310 and FC300S cameras. The proposed algorithm has been implemented in MATLAB programming environment and Agisoft Metashapesoftware has been used for initial processing.
Results & Discussion
As mentioned, the proposed algorithm is a post-processing task which reduces the image residuals and increases the geometric compatibility of 3D stereovision models.One of the critical indicators in the photogrammetric mapping production line is the quality of stereoscopic vision and the study of the vertical steps between connected 3D models. Because, photogrammetric map production requires stereo vision and the amount of model steps is used as a criterion for evaluation of image geometric distortion level. It can conclude that the use of the above idea is very effective in non-metric cameras with high geometric instability. The results of our experiments performed on the UAV photogrammetry data with low camera geometric stability indicate a60% reduction in the vertical steps of the models in stereoscopic vision and a 70% reduction in image residuals. This leads to a higher geometric quality of digital-elevation, 3D model, orthophoto, and map with 3D stereoscopic vision process. On the other hand, using this algorithm for non-metric cameras with higher geometric stability has a lower effect on the results. In our experiments, it was shown the vertical steps between 3D models can be reduced by 15% to 20%. However, there are still consecutive stereo models with quick steps in this type of camera, which will improve the geometric errors in stereoscopic vision if we ignore the computational costs.
Conclusion
The results of our experiments performed on the UAV photogrammetry data with low camera geometric stability indicate a 70% reduction in image residuals and a 60% reduction in the vertical steps of the models in stereoscopic vision. In this paper, the behavior of image residuals, the rate of model step reduction, and processing time in different dimensions of the distortion grid were investigated, and the grid dimensions of 150 to 200 pixels were recommended to apply the proposed method. Suggestions for further research are summarized in three sections. First, various factors such as the weight of observations and the weight of constraint equations can affect the estimation of the distortion grade, which can be estimated from the VCE method. Another point to consider in completing the proposed solution is to apply the temporal dependence between distortion grids in consecutive images. Also, although the proposed method uses the idea of finite elements as post-processing, it is more accurate to estimate this grid of distortion at the same time as the bundle adjustment.
Mohammad Amin Ghannadi; Matin Shahri
Abstract
Extended AbstractIntroductionAir pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) ...
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Extended AbstractIntroductionAir pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran. Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases. Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data. Materials and MethodsThe present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation. Results and DiscussionThe present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%. ConclusionFindings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.
Geographic Data
Hossein Asakereh; Ava Gholami
Abstract
Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, ...
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Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & MethodsData collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & DiscussionAppropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October. ConclusionSelecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.
Behnam Ghasemzade Qurmic; Alireza Safdarinejad
Abstract
Extended Abstract
Introduction
Analyzing the image blocks captured before and after geometrical changes is known as the conventional approach for detecting them in photogrammetric applications. Developed methods can be categorized into 1- comparison of 3D models generated via the image blocks and 2- ...
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Extended Abstract
Introduction
Analyzing the image blocks captured before and after geometrical changes is known as the conventional approach for detecting them in photogrammetric applications. Developed methods can be categorized into 1- comparison of 3D models generated via the image blocks and 2- direct comparison of single images. The occurrence of radiometric differences in the geometrically changed areas can increase their discrimination and facilitate their detection. However, the occurrence of geometric changes without sensible radiometric effects is a special type of change that its identification is faced with more challenges. Slight displacement of the objects in the scene, small landslides, subsidence or uplift, the effects of local pressure and tension on objects in the industrial procedures and etc. are some examples of geometric changes that do not have a noticeable radiometric appearance in the images.
In the absence of incorrect observations, simultaneous triangulation of image blocks captured before and after geometric changes is a simple and effective way of reaching to detection of changes. In other words, by identifying the corresponding points in the fixed regions of the scene in the image blocks, the simultaneous triangulation of the image blocks captured in both epochs can align them in a unique object coordinate system. Thus, it can be possible to generate two independent and co-registered 3D models for identifying the occurred changes. However, maintaining the radiometric similarity of the changed areas leads to the identification of wrong-matched points when using automatic image matching methods.
The inclusion of an unknown 3D position for each wrong-matched point in the changed areas leads to a defect in the design of the mathematical model for the bundle adjustment. These defects result in incorrect generation of the 3D models, large and systematic errors in the residuals of observations, and incorrect estimation of the extrinsic parameters of images. The remedy to this defect is to assign two distinct unknown 3D positions for each wrong-matched point before and after changes in the bundle adjustment. Lack of prior knowledge of the wrong-matched points located in the changed areas is the cause of this problem. In this article, an iterative solution is proposed to identify and correct the effects of the wrong-matched points in the process of simultaneous bundle adjustment.
Materials and Methods
In the proposed method, at first, all the confident radiometrically matched points among all images taken before and after the geometric changes are detected via the well-known feature-based image matching methods. Their matched positions, then, are again accurately rectified and verified by the least squares image matching method. The matched points identified after refinement are classified into two categories. 1- The matched points that have been detected only in the images of one image block and 2- The matched points that have been detected at least in two images in each image block. Among the points of the second category, there probably are matched points that are geometrically changed between two epochs, but their radiometric similarities have made to incorrectly identified as the matched points between two image blocks. In this paper, these were called the wrong-matched points which are iteratively identified and their corresponding mathematical models are corrected in the triangulation process.
To do so, three different bundle adjustments are performed as the first step. Independent triangulation of the image blocks captured before and after the geometric changes and the simultaneous bundle adjustment of both blocks via the initially detected matched points of the first and second categories are the first three triangulations. Due to the existence of wrong-matched points, the initial simultaneous triangulation has a defect in the design of the mathematical model, which is gradually and in an iterative process, the wrong-matched points located in the changed areas would be identified.
Identification of the wrong-matched points is done using the relative comparisons on their residual vectors. The comparisons are designed in two consecutive statistical tests. The main idea of this method has been inspired by the well-known Baarda test in the detection of gross errors in the observations of geodetic networks. By gradual identification of the wrong-matched points, their corresponding mathematical model will be modified in the bundle adjustment.To do so, the unknown values of the 3D coordinates of these points are separated for the time before and after the change epochs.This action by modification of the mathematical model in the bundle adjustments brings back the relative equilibrium in the estimation of the residual vector of observations.
Results and Discussion
Implementation and comparison of the proposed method with a conventional geometric approach in identifying the incorrectly matched points (using robust estimation of the epipolar geometry) have shown the adequacy and superiority of the proposed method. The proposed method, on average in more than 11 different experiments, was able to achieve an average accuracy of 85.8% in identifying the changed points. Meanwhile, the proposed method shows a 34.5% improvement compared to the conventional geometric approach based on epipolar geometry.
Conclusions and suggestions
The proposed method is an effective solution for identifying the geometrically changed points in the simultaneous triangulation of image blocks before and after geometric changes when the changed areas have a stable radiometric similarity. This method is more sensitive to the occurred changes than the conventional method of identifying incorrect correspondences based on epipolar geometry. Iterative adjustment of the observations’weight matrix through the Variance Components Estimation (VCE) techniques in order to detect and eliminate the effects of wrong-matched points can be considered a future research topic in this field.
Narges Nonejad; Elham Nazemi; Hamid Saberi
Abstract
Extended Abstract Introduction The concept of place has long been considered an issue of importance for sociology, anthropology, and human geography. Geography begins with human beings and will not exist without human activities and their effects on the Earth’s surface. Humanistic geographers believe ...
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Extended Abstract Introduction The concept of place has long been considered an issue of importance for sociology, anthropology, and human geography. Geography begins with human beings and will not exist without human activities and their effects on the Earth’s surface. Humanistic geographers believe that place is a part of the geographic space occupied by someone or something in which perceived values are manifested. Studying the concept of place begins with the distinction between space and place. Sociology and human geography experts believe that space is made up of the material and human-made environment as well as the natural environment, and with the meaning added by individuals, groups or culturalprocesses, it changes into place. Since human geography examines the relations among human communities andbetween these communities and their environment, it can identify social patterns dominant in them. Definitely human communities cannot function properly in providing their memberswith a social identification without planning and providing rich and well-defined facilities tailored to their needs, up-to-date values and requirements. Therefore, considering the enormous and comprehensive transformations of the Information Age and the necessity of aligning with this global movement on one hand, and the importance of the meaning as one of the most important qualitative variables of urban spaces, exploring and recognizing the effects of cyberspace on the perception, sense of attachment and belonging to a place are of special importance. Therefore,studying factors influencing the meaning of place is necessary to improve the quality of urban space. In fact, meaning of place is aninternal emotion individual feels toward a place formed by the interaction of different factors. Many studies have been performed on the meaning of place some of which consider meaning as an inherent characteristic of the place, and others believe that meaning is induced by the individual in different circumstances. In fact, meaning is created by presence in the place and our perception of it. The continuity of space-based experiences formed by motor system, and the recognition and perception of space creates a sense of satisfaction for people in contact with the place. Therefore, the quality of urban spaces can be improved by creation ofmeaningful spaces based on appropriate space-based rules, measures and disciplines. To reach this aim, we need to investigate and realize factors influencing perceptions of place by its residents. Thus, we must inevitably understand changes in and influences ofthe values, attitudes and demands of society. Nowadays, we are witnessing rapid changes in cities which seems to reduce the effectiveness of old ideas and assumptions about urban development, planning, and management, and subsequently, question accepted concepts about the nature of space, place, time, distance and processes of urban life. The advent of the Information Age achievements has redefined space and provided us with a new experience of space. Cyberspace is considered as the main axis of development in the world, and its achievementshave different effects on various dimensions of human life. Thus,Cyberspace is replacing the real world in a way. Undoubtedly, these changes in different dimensions of human lifeare influencing the perception of space. The present study seeks to evaluate the effect of Cyberspace usage time in different users on the physical, personal, social and functional components of the meaning of space and their defining indices in urban spaces. In this study, we believe that users of this environmenthave a different understanding of their space, place, and face different dimensionsof space based on their usage time, and thus, perceive the meaning of urban space differently. Materials & Methods In order to answer the main question of the study, “How does the use of cyberspace affect the perception of meaning in traditional and modern urban spaces?”, Thus, the effect of cyberspace usage on defining components of perception including physical, individual, social and functional components was investigated. A traditional urban space (Imam Square) and a modern urban space (City Center) was selected as the study area in Isfahan and the statistical samplespresent in these places were studied. Correlationalresearch method was used. The statistical tests of Kolmogorov-Smirnov regression and Pearson correlation were used to determine the relationshipbetween independent and dependent variables, and its intensity and direction. Conclusion Results indicate that using cyberspace increasesthe users’ understanding of the meaning of place while being present in urban spaces.In this regard, the incremental effect on the four factors, the degree of correlation and the impact of cyberspace usage on the components of meaning has been extracted and analyzed in two traditional and modern urban spaces.
