GEOGRAPHICAL DATA
Year:2016 | Volume:25 | Issue:98|Papers Count:12

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.

High accuracy and huge density of 3D points cloud acquired by airborne Lidar makes them as a good and suitable tool in order to analyze of terrain surface. In this procedure, points 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 points cloud based on contextual analysis on LiDAR points. The proposed method consists of three main steps. In the first step, a set of contextual features are produced for each points in LiDAR data. In second step, optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted to a simple k-means clustering on the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluation of the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients was 93.15% and 0.89 respectively.

In the most areas of our country, especially in heights, the most common kind of precipitation is as snow that can be considered a main source of water for streams, springs, underground watersheds and Qanats. The heights of Yazd province is one of the snowy regions in our country because of geographical situation and topographic conditions. The purpose of this research was to understand, examine and analyze synoptically - dynamically snowfall in Yazd Province. For this purpose, 12 snowfall courses during the statistical period 1999-2011 were selected following the access to the required data and the identification of the geographical situation in the region. Daily snow and rain data and synoptic maps of surface level and upper levels of atmosphere, jetstream, omega, vorticity and moisture, NCEP/NCA base were used in order to identify air mass movement and study synoptic systems in the province and the related maps were plotted in GrADS environment. Three synoptic patterns were extracted after conducting climatic thermodynamic researches affecting Yazd province and studying dynamic characteristics in terms of strengthening and weakening pressure systems. In the first pattern, low-pressure combinatory systems of eastern Mediterranean and Sudanese lead to snowfall especially in Yazd province heights. This is accompanied with the eastern Mediterranean troughs and entrance of cold air from high pressures of high altitudes. 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 eastern Mediterranean and Red sea leading to cold air and snowfall for several days. In the final pattern, the availability of Caspian and Siberian high pressures lead to cut-off low in the central and northern regions of Iran which in turn causes snowfall in the central regions including Yazd province.

Nowadays due to water deficit and crisis, it is necessary to improve water resources management. One of the major steps is an awareness of amount of Rainfall- Runoff in basins. But this effort is face with lack of access to adequate field data in border area. Using remotely sensed data and global land models are good way to overcome this problem. The aim of this study is to investigate the trend of rainfall-runoff in the Doosti dam basin which is important for decision–makers in Iran. This basin is located between Latitudes of 35-36.5 N and Longitude 59.5-67 W. For this purpose GLDAS data in 7 pixels with 1.5*1.5 degree spatial size located in the study area were used. Then trend of rainfall and runoff changes was investigated using simulation, Pearson correlation coefficient, Man Kendal and the sequence Man Kendal test during 10 years from 2004 to 2013, seasonal and monthly. The results of data analysis showed that in the East and Southeast areas of basin, correlation between rainfall and run offices than other area. Also in 95% level of confidence for annual precipitation data only in pixel number 7 and for runoff in pixel numbers 6 and 7 there is negative trends. For seasonal data, just in the spring and in pixel numbers 5 and 7 and for runoff in the winter and summer in pixel number 7 the trend was negative. About seasonal rain data analysis only in pixel 7 and for seasonal runoff in pixels number 6 and 7 the trends were negative. Beside of this it can be consider that using of GLDAS model for study the rainfall-runoff trend analysis in the regions with rare ground data is very useful and economic.

For 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.

Growth and physical development of border cities with regard to security and defense structures, migration and population movements, communications and transport infrastructure, urban management, sources of livelihood, customs and cultural diversity under the influence of internal and external communications during the years changes and developments have been different.The main objective of this study compared physical development in East and West Borders Case Study in of Zabol and Piranshahr cities by using models and Shannon entropy is Holdren. After geo-referenced images with fuzzy method has been developed to classify changes; And using a combination of automated cell Markov chains and city development is forecast for 1409. The results show that during the 29 years studied, made of 2578.10 hectares of land in the city of Zabol in 1365 to 3419.92 hectares, 612.10 hectares to 1, 785.90 hectares in the city ofPiranshahr reached in 1394. During his time in of Zabol maximum land use changes in agricultural land with 58.76 percent and 0.42 per cent has been minimal changes Orchards. While in the city of Piranshahr highest use changes in agricultural land with 67.88 percent and any change in arid lands with 2.16 percent. Based on over 29 years of entropy suggests that the physical expansion of cities, it has been growing sporadically and condensing. But compared to has been reduced compared to the year 1365 in the city ofPiranshahr. Between the years 1365- 1394 as well as the physical growth of approximately 85% of Zabol, related to population growth and 15% growth and horizontal growth of the city’s spiral While in the city of Piranshahr years due to the negative gross per capita, population growth has all the physical growth of the city. It is Forecast that during the 15 years given the projected population in Zabol 364.49 hectares And 94.15 hectares of land were built will be addedin the city ofPiranshahr city.Comparative cities due to population growth, lead to uneven development of cities that require guidance, growth and urban development programs is desirable.

Earthquake is a natural disaster that provides casualties, loss of financial and environmental every year. Iran due to its location on the belt of seismicity is extremely vulnerable to earthquakes. Also a lot of facilities before design criteria of seismic have been made and unfortunately quality of construction in the country was not suitable. Therefore, with respect to suspicious behavior of networks in possible earthquakes, seismic vulnerability assessment of critical infrastructure is great importance.In the proposed model initially seismic hazard analysis of based on 2 attenuation relationship for the country done, which according to the uncertainties involved in earthquake (magnitude of earthquake, focal depth and location of epicenter of the earthquake), this operation is selected at random each time and after each hazard analysis, the output of ground shaking (Peak ground acceleration, Peak ground velocity and peak ground displacement) is calculated. If the zone have been the landslide susceptibility or liquefaction susceptibility, then need the output of the hazard analysis of ground failure resulting from the permanent displacement of liquefaction and landslide are entered for each features in to the proposed model. Then the seismic vulnerability functions that for each two hazards of ground shaking and ground failure was put for every lifeline in the model database is used. Finally, according to the available functions, loss estimation of every network would do. All of these steps are for once analysis. So based on the Monte Carlo simulation, All of these operations on 10 thousand times to consider all of the uncertainties and failure modes are repeated then averaging available outputs in the database, so that all failure modes be considered. Therefore, due to the large data attribute and geometry, while the high spatial analyzes on large data as well as mathematical equations for repeated operations, attempted to coding in Visual Studio with programming language C # using.Net Framework and Arc Engine libraries and based on spatial information systems designed and developed that could estimate damage probability of slight, moderate, extensive and complete for each lifeline separately in the form of maps and tables for each feature. In this paper, in order to get a better view this research, models was implemented and analyzed for the city of Neyshabour.

Urban and regional planning, is done with the aim of developing and hazards, whether natural or man-made obstacles in the way of development. War is one of the hazards of that have been along human and has increased in recent decades because of conflict of interest countries. The passive defense as an attempt at pre-crisis, aimed at reducing the vulnerability of the infrastructure should be considered in planning.Due to the adjacency with Tehran, Alborz Province road network, are the road link between the capital and the provinces of North and North West and transit route to their North-West neighbors. In addition, the high concentration of population and industry in the middle and eastern part around province’s road network, on the one hand makes importance and on the other hand cause of the threats and vulnerabilities. Therefore planning to reduce the vulnerability of important infrastructure in times of crisis and maintain its efficiency is of paramount importance.In the present study in order to identify areas and roads that are vulnerable to the threat of war, first, we determine the criteria and indicators of vulnerability, then ranking using IHWP and In the GIS environment vulnerability maps were prepared In the different categories, then, using the SWOT model, strategies for reducing roads vulnerability is defined.The findings indicated that The main east-west road network of Alborz province in Central and Eastern areas war will be high vulnerability to the threat of war. At the same time, the capabilities and opportunities facing the road network in the same axis. Therefore with the completion of projects under construction and planned to create parallel axes and the retrofitting of bridges and displacement some land use, to reduce the vulnerability of road network Alborz province.

Factors in SAR images can be divided into five main categories radar, radar-carrying platform, channel, imaging domain and processing of the raw data. In each of these factors, various parameters of physical, structural, hardware and software had an impact, so that you-the role of each of the factors in the final observed image is formed. Modeling, Analysis and totally understand the effect of each of these parameters, creates a better understanding of how the imaging system from this perspective. It is an important step in the design and manufacture of this type of system and it also provides commentary and analysis of these kind of images. For this reason, in this article the effect of incident angle and shape of objects that are radar and the imaging domain parameter, are simulated in SAR images. Shapes that used in this simulation are cylinders, cones and cubes, which in the real world represents the buildings, silos, boughs and so on. So there are in SAR images in abundance. For more comprehensive results, different incident angles of 30, 40, 45, 50 and 60 were chosen to simulate. With this simulation and analysis of the results, the behavioral patterns of different geometric shapes in different incident angles from the perspective of SAR imaging systems will be extracted. Thus, an important step in order to identify different shapes, one of the most important issues in the interpretation of SAR images is removed.

With the development of science and technology, the tremendous amount of spatial and non-spatial data have been stored in large data bases. Analyzing these data for decision is seriously in need of spatial data mining and knowledge discovery to provide knowledge. Using satellite images, geo-statistical analysis, and all kind of spatial data are useful and applicable tools in land use changes monitoring; but what’s important among them is how to extract rules throughout big data for knowledge discovery. Rough Set Theory (RST) is one of data mining techniques which is applied for uncertainty modeling in different ways. Hence, this paper applies an integrated method of RST-DT for satellite images classification and land use changes monitoring. The RST algorithm is used in order to extract reasonable rules.From the results of research, regardless of the changes occurred during three periods (1986-1998, 1998-2014 and 1986-2014) can be found that the land use changes has drastically occurred into residential and water bodies classes by the rate of increasing and decreasing, respectively. Compared to former classes, farm lands have changed a little during the periods. With respect to the base year (1986 or 1365), the area of agricultural lands compared to the base year, coincided with the war period, has shown little changes. This means currently the area of agricultural lands is similar that of during the war. It could be attributed to a tragedy that has been happening in the agricultural sector. Accuracy and Kappa ratio from hybrid model of DT-RST display the RST as a powerful tool in data mining, to reduce superfluous data from the database and extracting rules in order to apply in DT method.

In this study, rainfall and temperature simulated annual in north - east of Iran (Great Khorasan) In the period 1987-2011. Based on the results, in LARS model during verification period (2007-2013), average annual rainfall raw bias is equal to 53.63 millimeter and post- processed is -11.25. Shortly, in annual period, in 84% of studied stations, post-processing techniques have been effective and bias error rate has decreased heavily in more stations. Based on the results, in Reg-CM4 model during verification period (2006-2011), average annual rainfall raw bias is equal to 85.3 millimeter and post- processed is 61.04. Shortly, in annual period, in 75% of studied stations, post-processing techniques have been effective and MA technique is more effective. So absolute bias error after processing an average annual rainfall of LARS equal to 6.13 and RegCM4 model is 61.average annual temperature raw bias is equal to 0.096 millimeter and post - processed is -0.432. This is more than bias without post - processing hence, post-processing is not effective in all stations and only in 46% of stations are good. Simulation of 2-- meter temperature data in climate stations with RegCM model and MA technique show high effectiveness. Average annual temperature raw bias in RegCM4 model is equal to -2.78 millimeter and post- processed is -0.05. In all stations modeled annual temperature with Observed data has difference less than 0.1. Also in rainfall simulation, LARS_WG model is more better than RegCM model and in simulation of annual temperature data, dynamic RegCM4 model show more clear Objectivity of researched area rather than LARS_WG model.

land Surface Temperature (LST) is an important criterion for 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 base of meteorological data. One the practical approach of remote sensing is this important that it can be used for monitoring of LST. For this goal, the split window algorithm is well known technique among the remote sensing based methods for evaluating of LST. According to relevant literature this method has offered minimum error for evaluating the LST. In this survey LST of Mahabad’s catchment was derived, by using multi-spectral and thermal bands of Landsat 8. For this goal, after the atmospheric/ radio-metrical correction, image analysis process was performed.Normalized difference vegetation index, fraction of vegetation cover, satellite brightness temperature, land surface emissivity and column water vapor (CWV), are of the vital criteria in land surface temperature in split Window algorithm. For the task of LST modeling, the temperature was calculated based on relevant mathematical equations. Eventually, we managed to evaluate the LST being derived with an error of 1.4 Centigrade. And regions with high vegetation index and covered with water low temperature and regions with low vegetation index, and bare shows a high temperature which are all effective in temperature changes in researching area. Results of this research indicated, the method of split window algorithm is presenting exact, and reliable results for the scientific environmental researches and geoscience.