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.
Military and police geography
Mehdi Safari Namivandi; Sara Kiani; Amir Saffari; Hossein Rabiee
Abstract
Extended Abstract
Introduction
The security of the borders is considered as a strong support for the security of the internal areas, and any insecurity in the border areas can cause a disturbance in the economic, social, cultural and military situation of the country. Various natural (geomorphological, ...
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Extended Abstract
Introduction
The security of the borders is considered as a strong support for the security of the internal areas, and any insecurity in the border areas can cause a disturbance in the economic, social, cultural and military situation of the country. Various natural (geomorphological, hydro climatic and geological) and human factors (ethnic and religious situation of the border dwellers) are effective in the security and stability of these areas. In order to turn threats into opportunities and benefit from conditions and situations in order to maintain security and secure national interests, we must have a deep and comprehensive understanding of the level of border areas and its surrounding spaces. In the meantime, one of the most important measures is planning according to the geomorphological capabilities of the border areas. In fact, geomorphological factors are one of the most important factors that determine the type of economic activities in border areas. Also, these factors are the main determinants of the weaknesses and strengths of the border areas, so that these factors have played a dual role in many areas, including the borders of Kurdistan province. Examining the geomorphology of the border areas of Kurdistan province shows that a large part of this border strip is covered by the mountain unit. The mountainous borders of Kurdistan province have weak and strong points, and therefore it is important to pay attention to the geomorphological strength of these borders for various military purposes. Considering the importance of the subject, in this research, the potential of the Kurdistan border strip for military purposes has been discussed.
Materials and methods
This research is based on descriptive-analytical methods. In this research, the SRTM 30-meter height digital model as well as digital information layers (natural and human parameters) have been used as the most important research data. The most important tools used in the research were ArcGIS (to prepare maps and final outputs) and Super Decisions (to implement the ANP model). According to the desired goals, this research has been done in several stages, in the first stage, the used parameters have been identified. In the second stage, according to the potential of the information layers for the intended purposes, the information layers have been standardized. In the third step, using the network analysis model (ANP), weights have been given to the information layers. In the fourth step, the information layers are integrated and combined using the fuzzy gamma operator, and in this way the desired final map is prepared.
Discussion and results
Due to the fact that parts of the border strip of Kurdistan province have a high vulnerability potential, it is necessary to pay attention to the vulnerability and geomorphology of the region in the location of military facilities and equipment. According to the importance of the topic, in this research, the areas prone to the development of military facilities and equipment in the region were identified, and based on the results, the surrounding areas of Baneh and Marivan cities, due to the low altitude and slope, proximity to communication lines, urban areas And the military bases, as well as being located in the plains and cone-shaped units, have great potential for the aforementioned purposes. Also, due to the vulnerability of the region and the possibility of enemy infiltration as well as the creation of an ambush by the enemy, it is necessary to build military bases and observation centers in the region. . According to the results, the border between the cities of Baneh and Marivan is due to the potential of high vulnerability and being exposed to ambushes, as well as being far from military bases, they need to establish a military base and observation centers. The total results have shown that parts of the border strip of Kurdistan province are susceptible to enemy infiltration and ambush by the enemy, and it is necessary to identify these areas and provide the necessary solutions to reduce their vulnerability.
Conclusion
The results of the identification of areas prone to the development of military facilities and equipment have shown that 23.2% of the area has a great and very high potential for the development of military facilities and equipment. These areas, which mainly include the surrounding areas of Baneh and Marivan cities, have great potential for the aforementioned purposes due to their low altitude and slope, proximity to communication lines, urban points and military bases, as well as being located in plains and conifers. Is. Also, 29.2% of the area has little potential for the development of military facilities and equipment. These areas, which mainly include the areas between the cities of Baneh and Mervan, have little potential for the development of military facilities and equipment due to their distance from urban areas, communication routes, and military bases, as well as due to their high altitude and slope. The results of the identification of areas prone to establishing military bases and observation centers have shown that 23.1% of the area has a great and very high potential for establishing military bases and observation centers. These areas, which include the areas between the cities of Baneh and Marivan, which require the establishment of a military base and observation centers due to their high vulnerability potential and being exposed to ambushes, as well as being far from military bases. Also, 41.9% of the area of the area has little potential to create a military base and observation centers. These areas mainly include the areas adjacent to the cities of Baneh and Marivan, which, due to the presence of military bases and less vulnerability potential, have less need to establish military bases and observation 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.
Geographic Information System (GIS)
Ahmad Mazidi; Foroogh Mohammadi Ravari
Abstract
Extended Abstract
Introduction:
Time series analysis is a suitable tool that is used in mathematical modeling, predicting future events, revealing trends, investigating diffraction in climate data, as well as reconstructing incomplete data, and expanding information. Climatic changes are mainly caused ...
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Extended Abstract
Introduction:
Time series analysis is a suitable tool that is used in mathematical modeling, predicting future events, revealing trends, investigating diffraction in climate data, as well as reconstructing incomplete data, and expanding information. Climatic changes are mainly caused by fluctuations, fluctuations, or changes in climatic elements, especially temperature and precipitation. These developments leave undeniable effects on local phenomena, hence the evidence of the past climate can be traced in all wet and dry, hot and cold environments, and biological areas (Ghayour, 2006:85). The temperature of the earth's surface is an important parameter for evaluating the energy budget of the earth's surface (Trigo et al, 2008:1). With the change in climate (temperature and rainfall), many changes are made on the surface of the earth, including vegetation. In fact, with the increase in temperature and decrease in rainfall, vegetation in the region decreases. Considering the importance of the issue and the relationship between climatic indicators and vegetation, by determining the relationship between them, one can predict the changes based on the other, which leads to an increase in the speed and accuracy of the work. Therefore, it seems important to use satellite images and extract and investigate the relationship between temperature and rainfall factors as well as vegetation in different areas, especially watersheds (Zhu et al, 2016:792). With the expansion of satellite technology, satellite images have widely provided access to information on land resources, and remote sensing tools have taken an important role in obtaining information about climate phenomena, because multi-spectral satellite images have important advantages, including They have the availability and ability of digital interpretation (Lillesand and Kiefer, 1994:750).
Materials & Methods:
In this research, using monthly rainfall data from a CHIRPS sensor with a spatial resolution of five kilometers, NDVI vegetation index from a MODIS sensor for 16 days, with a resolution of 250 meters, and day and night surface temperature of 8 days from a MODIS sensor with a resolution of one kilometer, to analyze the changes in surface temperature and its relationship with climatic factors in Kerman province during a statistical period of 22 years (2001-2022) were studied. In the investigation of the annual precipitation fluctuations of Kerman province, standardized values of Z have been used, and these values have varied between -1.5 and +1.5. After receiving the data, first the CHIRPS images, then the NDVI and LST images were processed in the ArcGIS software environment and the values were extracted for Kerman province and then analyzed in the Excel software environment.
Results & Discussion:
According to SPI results, drought is observed in 2010, 2016, 2018, and 2021, and drought in 2004, 2009, 2017, 2019 and 2020. In the rest of the years, the SPI index has been normal. Also, the seasonal rainfall showed that the highest rainfall was in the winters of 2005, 2017, and 2019 with an amount of 90 mm and more and the lowest rainfall was in the summer of 2019 with an amount of less than 1.04 mm. The value of the vegetation cover index (NDVI) is also in the spring season with a value of 1.05, which has an increasing trend, and the lowest value of the vegetation cover index (NDVI) in the autumn and winter seasons, whose lowest value is 0.35 and 0.42 on December 19 and November 17 with a trend A decrease is shown. The seasonal vegetation also shows that as we move from the west of the region to the east, the amount of vegetation decreases. The seasonal changes in the temperature of the surface of the earth during the day in Kerman province show that the hottest seasons are summer and spring and the coldest season is winter. The seasonal changes in the earth's surface temperature at night also show that the highest surface temperature is related to summer and spring, and the lowest is in autumn and winter.
Conclusion:
In general, the results show that according to temperature fluctuations, there is a positive and significant relationship between the temperature of the earth's surface and vegetation (P-value at the 0.01 level). And there is a negative and significant relationship between the temperature of the earth's surface and precipitation. So precipitation has the greatest effect on the variability of the earth's surface temperature and vegetation has the least effect on the surface temperature changes. The increase in day and night temperatures in the spring and summer seasons causes an increase in evaporation and a subsequent decrease in water resources throughout the province and pressure on underground water. On the other hand, with the increase in temperature, the amount of evaporation and transpiration (plants' water needs) will also increase and will lead to a potential decrease in water resources, especially in the eastern regions of the province, but the presence of vegetation can almost reduce the temperature of the earth's surface. In the autumn and winter seasons, during the last decades, with the increase in temperature, the amount of precipitation and vegetation has decreased. Also, an increase in temperature can increase the water demand, which in turn leads to more extraction of surface and underground water resources. This means that the surface temperature has increased significantly in the mentioned statistical period. Also, the different conditions of each region are important factors in determining the type of relationship between temperature, vegetation, and precipitation. The results of this research on the relationship between the earth's surface temperature and climatic factors with the research of Mianabadi et al (2023), and Mazidi et al (2023) based on the method of the experimental relationship between surface temperature and other factors are consistent. According to the findings, the temperature trend in Kerman province is significant and the possibility of heat stress will increase in the future.
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.
