Davood Akbari; Abdolreza Safari; Saeid Homayouni
Abstract
Abstract Recently, a new approach, based on the Hierarchical SEGmentation (HSEG), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of hyperspectral images. The HSEG algorithm, which combines region object finding with ...
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Abstract Recently, a new approach, based on the Hierarchical SEGmentation (HSEG), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of hyperspectral images. The HSEG algorithm, which combines region object finding with region object clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. This paper aims at improving this approach by using image segmentation to integrate the spatial information into the marker selection process. In this study, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. The watershed algorithm is used in parallel and independently to segment the image. It is a powerful morphological approach to image segmentation. Moreover, the class’s pixels, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Then, a marker-based HSEG algorithm is applied. Each region from the segmentation map is classified by applying a majority vote rule over the pixel-wise SVM classification results. Three benchmark urban hyperspectral datasets are used for our comparisons: Pavia, Berlin and DC Mall. The results of our experiment indicate that, compared to the original hierarchical approach, the marker selection using segmentation algorithm leads in more accurate classification maps. Indeed, the proposed approach achieves an approximately 4%, 6% and 5% kappa coefficient higher than the original hierarchical-based algorithm for the Pavia, Berlin, and DC Mall datasets, respectively.
Alireza Safdarinezhad; Mahdi Mokhtarzadeh; Mohammadjavad Valadanzouj
Abstract
Abstract
3D point clouds obtained by Airborne Laser Scanner Systems provide a varied and unique geometric information of the physical terrain surfaces due to advantages such as relatively high geometric accuracy and high spatial density of the points. Classification ...
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Abstract
3D point clouds obtained by Airborne Laser Scanner Systems provide a varied and unique geometric information of the physical terrain surfaces due to advantages such as relatively high geometric accuracy and high spatial density of the points. Classification and separation of cloud point data to environmental constructive terrains plays an important role in the process of 3D modeling of terrains. In this procedure, point cloud clustering is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper, a novel method is proposed for supervised classification of LiDAR cloud of points based on contextual analysis of LiDAR points. The proposed method consists of three main steps. In the first step, a set of features based on contextual analyses are produced for each point in LiDAR data. In the second step, the optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted by a simple k-means clustering in the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluate the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients were 93.15% and 0.89 respectively.
Kamal Omidvar
Abstract
Abstract
In most parts of the country, especially at high altitudes, the predominant form of precipitation is snow, which can be considered as the main source of water for rivers, springs, underground watersheds and Qanats. The heights of Yazd province are also one of the snowy areas of the country ...
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Abstract
In most parts of the country, especially at high altitudes, the predominant form of precipitation is snow, which can be considered as the main source of water for rivers, springs, underground watersheds and Qanats. The heights of Yazd province are also one of the snowy areas of the country because of geographical location and topographic conditions. The purpose of this research is to synoptically-dynamically identify, study and analyze the snowfall in Yazd Province. For this purpose, 12 snowfall periods were identified during the statistical period of 1999 to 2011 after identifying the geographical location of the area and with regard to the required data. To identify the motion of the air mass and to study the synoptic systems of the province, daily snow and rainfall data and synoptic maps of the land surface and upper levels of the atmosphere, jet stream, omega, circulation and humidity advection were extracted from the NCEP/NCA base and the related maps were plotted in the GrADS environment. Three synoptic and pressure patterns were extracted after conducting climatic thermodynamic researches affecting Yazd province and studying dynamic properties in terms of strengthening and weakening of pressure systems and their synoptic analysis. In the first pattern, the low-pressure combinative systems of the Eastern Mediterranean and Sudan along with the deepening of the Eastern Mediterranean troughs and the penetration of cold weather cause snowfall, especially on the highlands of Yazd province. In the second pattern, the emergence of the blocking phenomenon causes snowfall and severe cold in Yazd province. This phenomenon occurs along with deep troughs of the Eastern Mediterranean and the Red sea leading to cold weather and snowfall for several days. In the third and final pattern, with the establishment and high pressure penetration of the Caspian Sea and the Siberia and the creation of a cut-off low-pressure phenomenon in the central and northern parts of Iran, a snowfall occurs in the central regions of Yazd province.
Mahsa Polroudimoghadam; Saeid Hamzeh; Madjid Vazifehdoust
Abstract
Abstract
Nowadays, considering the reduction of water resources and the existing water crisis, it is necessary and important to pay attention to the proper and integrated water resources management, especially in border areas. One of the basic measures in this field is to know the amount of rainfall ...
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Abstract
Nowadays, considering the reduction of water resources and the existing water crisis, it is necessary and important to pay attention to the proper and integrated water resources management, especially in border areas. One of the basic measures in this field is to know the amount of rainfall and runoff and the trend of its changes in the watershed basins.
However, the lack of access to sufficient field data in the border areas poses a major problem. Remotely sensing data and global land models can be used to overcome this problem. The aim of this research is to investigate the trend of rainfall-runoff changes in the Doosti dam basin - which is important to decision–makers in Iran- using the Global Land Surface Model System (GLDAS). For this purpose GLDAS data were used in 7 pixels 1.5*1.5 degree between the Latitudes of 35-36.5 N and Longitude of 59.5-67 W. The type of changes and trend of model data were investigated seasonally and annually through simulation, Pearson correlation coefficient, Mann-Kendall and Mann-Kendall sequential tests over a period of 10 years from 2004 to 2013. The results of data analysis showed that the correlation between rainfall and runoff is weaker in the East and the Southeast of the studied basin than in other areas. Also, at 95% of the confidence level for annual rainfall data, the trend for the rainfall is negative only in pixel 7 and for runoff in pixels 6 and 7. Regarding seasonal data, the trend was detected to be negative for the rainfall only in spring in pixels 5 and 7, and for the runoff in winter and summer in pixel 7. The results of this model show that the GLDAS model can be very useful and practical for studying rainfall-runoff in areas with difficult access to terrestrial data because it is possible to study vast areas at low cost.
Hamid Bahiraei; Rahim Sarvar; Bahman Karegar; Abdolreza Farajirad
Abstract
AbstractFor its residents, the neighborhood presents an opportunity to have informal interactions and create social cohesion. Identifying and distinguishing places and organizing them in ones’ mental structure not ...
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AbstractFor its residents, the neighborhood presents an opportunity to have informal interactions and create social cohesion. Identifying and distinguishing places and organizing them in ones’ mental structure not only allow individuals to have an effective performance but also are the sources of security and mutual understanding, and give them a pleasant and agreeable feeling. People seek physical clarity and places which are understandable and associated with their emotions and goals.The present article attempts to investigate the importance of the creating public spaces, especially in the highly dense neighborhoods and its effects on the increased security of the residents in that this protects human privacy in his residence. First, the concept of “privacy” and its functions are examined theoretically. Then the characteristics and nature of public space (collective life) of neighborhoods and finally the role of creating public spaces in building and protecting privacy of the families will be addressed.The research method is logical reasoning and a case study of on two highly dense residential areas of Tehran, Kazem-Abad and shams-Abad, along with field studies and a survey of the their residents through questionnaire. According to findings, in the present century (i.e., the last decade of the 14th century solar Hijri calender), a remarkably large population lives in the highly dense neighborhoods. If you look at people from the perspective of slave traders, and their spatial needs are conceptualized only in terms of their bodies, the effects of overcrowding are ignored. However, if people are considered human beings that are surrounded by invisibles bubbles (i.e., their privacy) which can be measured, the architecture of such neighborhoods can be examined from a new perspective.
Mohsen Ahadnejadroshti; Ashraf Azimzadeh Irany; Saeed Najafy
Abstract
Abstract
physical growth and development of border cities with regard to defense and security structures, migration and population movements, communication and transportation infrastructure, urban management, sources of livelihood, diversity of cultural customs and…, have undergone changes and ...
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Abstract
physical growth and development of border cities with regard to defense and security structures, migration and population movements, communication and transportation infrastructure, urban management, sources of livelihood, diversity of cultural customs and…, have undergone changes and developments under the influence of internal and external communication over the years. The main objective of this research is to compare adaptively the physical development of Eastern and Western border cities of the country with a case study of Zabol and Piranshahr cities. For this purpose, ETM, TIRS, and OLI sensor images of Landsat satellites 5, 7, and 8 were selected for the period of 1986-2015 (1365-1394), and Holdren models and Shannon Entropy were used. After geo-referencing the images, Fuzzy method has been used to classify the changes of development, and the urban expansion was foreseen for the year 2030 (1409) using the combination method of Markov chains and automated cells. The results show that during the 29 years of study, the lands constructed in Zabol city has reached from 2578.10 hectares in 1986 (1365) to 3419.92 hectares in 2015 (1394), and in the city of Piranshahr from 612.10 hectares in 1986 to 1785.90 hectares in 2015. During this period, the greatest land use changes in Zabol were observed in agricultural lands with 58.76 % and the least changes were in gardens with 0.42 %. In the city of Piranshahr, however, the highest rate of land use changes were observed in agricultural lands with 67.88% and the least changes in wastelands with 2.16%. According to the entropy model, it has been shown that in the last 29 years, the physical expansion of cities has been growing sporadically and non-densely. But the rate of shapelessness has decreased in the city of Piranshahr compared to the year of 1986. Between the years of 1986 and 2015, about 85% of physical growth in Zabol city was related to population growth and 15% of the city growth was related to the horizontal and spiral growth of the city, while in the city of Piranshahr, all the city's physical growth has resulted from the population growth during the aforementioned years due to the negative gross per capita. Considering the projected population during these 15 years, it is expected that 364.4 ha in the city of Zabol and 15.94 ha in the city of Piranshahr will be added to the urban constructed lands. The adaptive comparison of the cities with regard to the population growth has led to an uneven development of the cities, which requires the guidance, growth and development of the cities with appropriate and desirable plans.
Mohammad Eskandari; Mahdi Modiri; Babak Omidvar; Aliasghar Alesheikh; Mohammadali Nekooie; Ali Alidoosti
Abstract
Abstract The earthquake phenomenon is a natural disaster that causes many fatal, financial and environmental damages every year. Iran is extremely vulnerable to earthquakes due to its seismicity and its location on the earthquake belt. Also, a large number of facilities were built before the formulation ...
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Abstract The earthquake phenomenon is a natural disaster that causes many fatal, financial and environmental damages every year. Iran is extremely vulnerable to earthquakes due to its seismicity and its location on the earthquake belt. Also, a large number of facilities were built before the formulation of earthquake resistant standards and, unfortunately, the quality of construction in some cases in the country is not optimal. Therefore, considering the suspicious behavior of the networks regarding the occurrence of possible earthquakes, the issues of assessing the seismic vulnerability of critical infrastructure are of particular importance. In this paper, a model has been presented in which, first, the risk analysis of the area of interest (based on the two existing attenuation relations for the country) is carried out, which, given the uncertainties involving in the earthquake occurrence (including magnitude of earthquake, focal depth and position of the earthquake epicenter), this operation is randomly selected at each time of the analysis, and after each hazard analysis, the outputs resulting from the earthquake hazard including the maximum acceleration values, the maximum speed and the displacement of the ground are calculated. If the area has a landslide or liquefaction potential, then the outputs resulted from the earth fault risk, including the values of liquefaction and landslide displacements, should be introduced into the model for each feature. Then, seismic vulnerability functions are used which are placed on the model database for both ground shaking hazard and ground failure for the arteries. At the end, based on the existing vulnerability functions, the network damage analysis is dealt with. All these steps are for a single analysis. Therefore, based on the Monte Carlo simulation, all of these operations are repeated 10,000 times to include all uncertainties and failure states, and the outputs in the database are averaged to account for all failure states. For this purpose, due to the large volume of descriptive and spatial data, on the other hand, large spatial analysis of data and the high volume of mathematical equations for repetition of operations, coding in the Visual Studio environment with the C # programming language was done, using the Net Framework and Arc Engine libraries which led to the production of a software system using a database and with spatial analysis and deduction capabilities based on spatial information systems (GIS) that could assess the possible slight, moderate, extensive and complete failure rates of each artery separately in the form of maps and tables for each feature. In this paper, to better illustrate this research, the existing model for the city of Neyshabur was implemented and analyzed.
Mohammad Zanganeh
Abstract
Abstract
Urban and regional planning is done for development, and natural and man-made hazards are obstacles to development. War is one of those dangers which has always been with mankind and has become more widespread in recent decades by contributing to conflicts in the interests of countries. Therefore, ...
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Abstract
Urban and regional planning is done for development, and natural and man-made hazards are obstacles to development. War is one of those dangers which has always been with mankind and has become more widespread in recent decades by contributing to conflicts in the interests of countries. Therefore, the passive defense as a measure in the pre-crisis phase, with the goal of reducing the vulnerability of infrastructure, should be considered in planning. Due to its proximity to Tehran, Alborz road network, is the link between the capital and the northern and northwestern provinces as well as transit route to the neighboring countries of the Northwest. In addition, the high concentration of population and industry on the sides of the roads will create a threat to the province. Therefore, planning to reduce the vulnerability of this vital infrastructure in times of crisis and maintain its efficiency is of great importance. In the present study, in order to identify areas and roads vulnerable to the threat of war, the vulnerability indicators were first determined and ranked using IHWP method, and the vulnerability maps were prepared in the GIS environment. Then, using the SWOT tool, strategies to reduce the vulnerability of passages were defined and prioritized.
The results of the study indicated that the eastern and central parts of the main east-west axis of the province’s road network will have high vulnerability to war threats, while the most important capabilities and opportunities ahead, are the same axis. Therefore, by completing ongoing and planned projects to create parallel axes as well as bridge reinforcement and displacement of some land uses, it is possible to reduce the vulnerability of the Alborz province’s road network.
Mojtaba Behzad Fallahpour; Hamid Dehghani; Ali Jabbar Rashidi; Abbas Sheikhi
Abstract
Abstract
Effective factors in SAR images can be divided into five general categories of radar, radar-carrying platform, channel, imaging domain and raw data processing section. In each of these factors, various physical, structural, hardware and software parameters are influential, in such a way that ...
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Abstract
Effective factors in SAR images can be divided into five general categories of radar, radar-carrying platform, channel, imaging domain and raw data processing section. In each of these factors, various physical, structural, hardware and software parameters are influential, in such a way that one can see the role of each of them in the final formed image.
Modeling, Analyzing and, in general, knowing the effect of each of these parameters, will provide a better understanding of how SAR imaging systems operate, and from this point of view, it will not only be an important step in designing and manufacturing these types of systems, but also it will provide the possibility of interpreting and analyzing these types of images. For this purpose, in the present paper, the effect of the angle of incidence and the shape of the targets which are parts of the radar and the imaging domain parameters, are simulated in SAR images. The shapes used in this simulation are cylinders, cones and cubes, which represent buildings, silos, tree trunks, etc., in the real world, so they are very abundant in SAR images. Also, for more comprehensive results, different angles of incidence of 30, 40, 45, 50 and 60 degrees have been selected for simulation. With this simulation and analysis of the results, the behavioral pattern of the above geometric shapes is extracted at different angles of incidence from the perspective of SAR imaging systems. Thus, an important step in identifying and recognizing various shapes, which is one of the most important issues in the interpretation of SAR images will be taken.
Hasanali Faraji Sabokbar; Seyyed Hasan Motiee Langroodi; Hossein Nasiri
Abstract
Abstract
With the development of science and technology, a large amount of spatial and non-spatial data are stored on large databases. Analyzing these data for decision making necessitates the need for spatial data mining to discover knowledge. The use of satellite imagery, geo-statistical analysis, ...
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Abstract
With the development of science and technology, a large amount of spatial and non-spatial data are stored on large databases. Analyzing these data for decision making necessitates the need for spatial data mining to discover knowledge. The use of satellite imagery, geo-statistical analysis, and all types of spatial data are useful and practical tools in studying land use change monitoring; but, what is important is the extraction of precise rules by integrating large amounts of data in order to provider knowledge about the area of interest. Rough Set Theory (RST) is one of the data mining techniques used in various ways in modeling uncertainty in data. Therefore, in this research, the RST knowledge discovery method is used to extract rules in combination with decision tree algorithm (DT) for satellite image classification and monitoring of land use changes. The results of the research indicate that according to the changes occurred during three periods of (1986-1998, 1998-2014 and 1986-2014), it can be seen that significant increasing and decreasing changes have occurred in the constructed lands and in the water bodies, while agricultural lands have not changed much. Of course, considering the base year (1986), it can be stated that the area of the agricultural lands under cultivation has witnessed a slight change compared to the base year which coincided with the imposed war, which means that the area under cultivation during the past three decades has been the same as that of the war period. This indicates that, the crisis is taking place in the agricultural sector. Also, in terms of methodology, given the overall accuracy and Kappa ratio, derived from the DT-RST combination model, RST can be considered to be a powerful tool in data mining, reducing the redundant data from databases and extracting rules for use in the DT method.
Mahmood Ahmadi; Hasan Lashkari; Ghasem Keikhosravi; Madjid Azadi
Abstract
Abstract
The present study was conducted to simulate precipitation and temperature with the RegCM4 and LARS dynamic model in two states, with and without using the statistical post-processing technique of direct model output in the north-east of Iran (Great Khorasan) and the statistical period of 1987-2011 ...
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Abstract
The present study was conducted to simulate precipitation and temperature with the RegCM4 and LARS dynamic model in two states, with and without using the statistical post-processing technique of direct model output in the north-east of Iran (Great Khorasan) and the statistical period of 1987-2011 in the annual time period. Based on the results, the annual bias average raw precipitation is equal to 53.63 millimeters and the post-processed is -11.25 in the LARS model in the study area during the 2007-2013 verification period. In summary, performing post-processing technique has been effective at 84% of the study stations in annual time scale and has reduced severely the bias error rate in most stations. Based on the results obtained from the RegCM4 model, the annual bias average raw rainfall of the RegCM4 model is calculated to be 85.3 millimeter and the post-processed to be 61.04 during the 2006-2011 verification period. Therefore, error values in most stations are very high before and after processing and the model results are not acceptable. In summary, performing post-processing technique has been effective at 75% of the research stations in annual time scale. Therefore, the absolute value of the bias error of the average annual rainfall post-processing of the LARS and RegCM4 models are equal to 13.6 and 61 respectively. The annual bias average raw temperature of the LARS model is equal to 0.096 degrees Celsius and the post-processed is -0.432. Practically, this is larger than the bias without post-processing, so post-processing operation is not effective in all stations and is only well defined in 46% of the stations. Simulation of 2 meter temperature data at the meteorological stations using the RegCM4 model as well as MA operations showed high efficiency.The annual bias average raw temperature of the RegCM4 model was -2.78 degrees centigrade which fell to -0.05 after applying post-processing technique. At all stations, the modelled annual temperature is different from observational data less than 0.1 ° C. Therefore, in the simulation of annual rainfall data, the LARS model is even more responsive than the RegCM4 model. And, in simulating the annual temperature data, the RegCM4 dynamic model shows a much better reality than the LARS statistical model.
Bakhtiar Feizizadeh; Khalil Didehban; Khalil Gholamnia
Abstract
Abstract
Land Surface Temperature (LST) is one of important criteria in regional planning and management. LST can be used in many practical programs of environment, agriculture, meteorology and relevant surveys. Due to the limitations of meteorological stations, remote sensing can be used as the basis ...
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Abstract
Land Surface Temperature (LST) is one of important criteria in regional planning and management. LST can be used in many practical programs of environment, agriculture, meteorology and relevant surveys. Due to the limitations of meteorological stations, remote sensing can be used as the basis of many meteorological data. One of the most important practical aspects of remote sensing in climate studies is the estimation of surface temperature. In this regard, the split window algorithm is considered as an effective method for extracting surface temperature, which provides the highest accuracy based on scientific resources. In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to estimate the land temperature in Mahabad catchment. To accomplish the goal, modeling and analyzing of the images were performed after radiometric corrections. The vegetation index, the vegetation shortage, the temperature of the satellite illumination, the emissivity of the land surface, the column water vapor (CWV) are of effective criteria for estimating the land surface temperature by the method of split window algorithm. The values necessary to calculate the land surface temperature were obtained by performing mathematical relation computation. Eventually, the land surface temperature was accurately estimated with an error of 1.4 degrees Centigrade. Areas with high vegetation cover and covered with water show low temperatures and, areas with low vegetation cover and bare soil show a high temperature, all of which are effective in temperature variations in the studied area. The results of the research indicate that the method of split window algorithm provides exact and reliable results in the estimation of land surface temperature, which can be used in environmental studies and geosciences.
