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Javad Sadidi, Hamed Ahmadi,
Volume 2, Issue 3 (10-2015)
Volume 2, Issue 3 (10-2015)
Abstract
The term "Game GIS” implies to real spatially enabled games in which a special part of the world is virtually simulated, represented and managed. In fact, game GIS is an integrated system consists of video games and geographical information systems, aimed to simulate and representing spatially enabled environment. The achieved result of implementing a game GIS service can be exploited before a crisis for wise designing of a city and diminution of the aftermath casualties. As the decision making process plays the key role to reduce the losses, the need arises for using the models as much as close to the reality. By this, it is possible to use the virtual world in in the form of a game rather than experiencing the real world with real wounded and killed persons in. This enables us to recognize and manage a test environment for promoting the managing the real environment of a city during and before a natural hazard disaster like an earthquake. The game GIS may be counted as a service for sharing and dissemination of spatial information as well as online GIS to have a visual and synoptic management of the earth plant facing various disasters. The current research is aimed to design and implement a software architecture for an earthquake game in Tabriz city (Iran).
The study area is district 10 of Tabriz located within a fault zone. According to field surveys, 82.1 percent of buildings in the study area may be vulnerable against earthquake in terms of the quality of building construction.
Methodology of the research to design, program and implement the game GIS service are undertaken as the following processes: data collection, database creation and software production.
The collected data includes master plan maps of the district 10, building quality, number of floors, building façade materials, age of building, street network (adopted from the master plan of Tabriz) and population of each parcel. Also, some regions are assumed as hospital, relief-rescue center and treasury money.
To design the software, 2D environment of MapControl and for implementing the game into the 2D environment, ArcEngine of ArcGIS have been exploited. The mentioned engine gives us possibility to use of analysis and modelling capabilities as much as closer to the ground reality which are compatible with available geometry of the terrain (Amirian, 2013, 17-19). The MapControl is a framework in which the map and game area are displayed. Symbology is used to show the persons as well as equipments. Briefly, the stages undertaken during the current research can be explained as the following:
- Data collection based on available sources via field surveying.
- Data processing and creating a database from street networks and building owned the age, materials, floors fields.
- Calculation of vulnerability rate for each building separately as well as the amount of deconstruction damage per Richter.
- Drawing the street and alley network to prepare network analysis dataset.
- Preparing special network analysis database and evaluation in various situations.
- Using the gained layers and implementation of the scenario.
After that, the conceptual architecture of the software has been designed based on the scenario.
The game GIS services has been designed with 6 different classes offer numerous functionalities responsible for displaying program commands and different views of the game. Finally, the service is designed and implemented in a real schema for crisis management application. The resulted game is played in 4 stages. In the first stage, the player starts with a 5 Richter magnitude earthquake and ends while the player gets to 8 Richter. The designed software simulates the destruction rate of buildings based on the influential factors, wounded transfer routing and rescue operation. The game player gains credit according to his quickness and agility. The player would go to the next stage with one Richter magnitude higher, if gains enough credits during each stage. The result of the current research as a Game GIS service, can be used in earthquake simulation happens in various magnitudes for management of decreasing the effects of earthquake, quick reaction, maneuver and education. Considering the achieved results, designing and performing the game GIS service over the web based on open source technologies rather than being desktop and commercial service, can be suggested as a new research frontier for the future researchers.
Mohammad Javanbakht, Hosein Hoseini,
Volume 6, Issue 3 (9-2019)
Volume 6, Issue 3 (9-2019)
Abstract
Abstract
Introduction
Vulnerability is the limitation of a society to a risk and to dominate it for all physical, economic, social and political factors, which adversely affects the ability of the community to respond to those events. An earthquake is a natural phenomenon that will be irreversibly damaged. It has caused severe humanitarian earthquakes in the minds of a compilation of an infrastructure program to reduce the risks and damage caused by it. The country's geostationary characteristics have suggested earthquakes as one of the most destructive factors in the destruction of human life. Historical surveys show that vast areas of our country suffered financial and financial losses due to this natural disaster. According to the United Nations, in 2003, Iran ranked first in the number of earthquakes with a high intensity of 5.5 millimeters among the countries of the world.
Earthquake is a natural hazard that often causes too many losses and casualties. Iran is country with a lot of earthquakes and and Khorasan Razavi province which is studied in this study also experiences a large number of this natural hazard and 71% of the surface area of this province is in the range of medium, high and too high hazard of earthquake. One of the important sectors in which the effect of the earthquake damage very large is power transmission lines. Transmission of oil and gas products by pipelines is one of the most appropriate, inexpensive, fast and reliable methods. These lines are mostly buried in terms of safety and social considerations. In engineering collections, such structures are considered as vital arteries. Due to the fact that pipelines are spreading widely, therefore, due to the vulnerability of the transmission lines, it can damage the economy of the country.
Methodology
In this study the vulnerability of the network lines of power transmission of Khorasan Razavi province against earthquake were studied. The aim of the present study was applicable and method of study was descriptive-analytic. To prepare a map of the extent of the vulnerability, the fuzzy gamma method was used. Effective parameters for this research include proximity to a fault line, geological structure, land slope, population density of urban and rural areas, distance from the communication lines. One of the most important fuzzy operators for overlapping indices is the GAMMA operator. Gamma operator is the general mode of multiplication and addition operators.
Mr Mohammad Hossein Aalinejad, Pro Saeed Jahanbakhsh Asl,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Simulation of runoff from Gamasiab basin snowmelt with SRM model
Abstract
Snow cover in a basin affect its water balance and energy balance. So, snow cover variation is a major factor in climate change of a region. Study of temporal variation of snowmelt and snow water equivalent depth is very important in flood forecasting, reservoir management and agricultural activities of an area. In the most of the mountainous basins of the country, information on snow cover were not available. Also, the number of meteorological stations in high altitude areas do not match with information needed for snowmelt simulation. Therefore, indirect methods such as the analysis of satellite images to obtain the needed parameters for simulation is necessary, which is the one of the most effective methods in estimation of runoff originated from snow. Using the NOAA satellite data for zoning the snow cover of area started firstly in the USA since the 1961 and continuous until today (spatial and temporal resolution of satellite images increased by starting the MODIS work).
Gamasiab River is one of the important branches of Karkheh basin. Its basin area is about 11040 km2 between latitude 47 degrees 7 minutes to 49 degrees 10 minutes east and latitude 33 degrees 48 minutes 4 degrees 85 minutes north. The altitude of this basin is 1275 to 3680 meters above sea level. In this study, for simulation of runoff originated from melting snow, firstly snow cover in the basin of Gamasiab in 2014 to 2017 calculated by using the satellite images of MODIS in the google earth engine system. Also, air temperature and precipitation data of synoptic stations in the area of study and daily stream flow discharges of Polechehr hydrometric station, from November of 2014 to July of 2017 was used. Then, weather and snow cover area included as the input of SRM for simulation of snowmelt runoff. To obtain the information needed to the model, physiographic characteristics of the basin including the area and different classes of height obtained from the Arc-Hydro and Hec_GeoHMS in DEM maps of GIS software. Then the snow cover areas obtained from the images of MODIS in daily interval that obtained by google earth engine system.
Using the digital elevation map (DEM) and the accession of the Arc-Hydro and Hec_GeoHMS software of GIS, firstly flow direction map plotted. Secondly flow accumulation and stream flow network maps plotted, and by introducing the basin output to the program (Polechehr hydrometric station) borders of the basin identified and classification of the basin accomplished according to the three distinct height classes. Monitoring the snow surface cover during the daily time interval showed that the area covered with snow in winter season. This area decreases as the air temperature increases. The SRM model simulated the snowmelt of Gamasiab basin with good accurately, in which, the percent of volume error or Vd was lose than 2% and the R2 was above 0.9.
The results of this research showed that the using the images of MODIS yields a reasonable estimation of the snow cover area of Gamasiab with local of data. Also simulation results showed the high capability of the SRM in snowmelt runoff of the area under study. Result showed that the coefficient of determination and volume percent of error of model was 0.93 and %0.3 for 2014-2015 and it was 0.9 and 3.33 for 2015-2016 years, respectively. The results of this study, was in consistent with the previous studies fading in which in addition of model's parameters, physiographic characteristics, basin play a major role in the accuracy of the simulation. According to the calculated and observed runoff diagram, in both years of study, peak temperatures begin in March, as the weather warms and the snow melts, and will continue until April. Considering the snow cover, it can be concluded that the main runoff of March Peak is related to snowmelt, but with the change in the shape of precipitation from snow to rain and the warming of the weather, April peak is related to rain. Regardless of acceptable simulation results of the model, the lack of snow survey station in the study area, (yield the model to face with difficulty) in process. To overcome this shortcoming, we used the presumptions of the model and recommended values of the model.
Keywords: MODIS; Remote sensing; Runoff Snow; SRM; Gamasiab.
Dr Sayyad Asghare Saraskanrod, Mr Roholah Jalilian,
Volume 8, Issue 4 (3-2022)
Volume 8, Issue 4 (3-2022)
Abstract
Introduction
Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is undergoing change over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. With regard to the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost. To be prevented.
Materials & Methods
In order to classify and detect the marginal land of the river, TM and OLI image images were selected for a specific month (August, August) for the years 1987 and 2017. The purpose of this study was to investigate the changes occurring in the studied area with an emphasis on agricultural lands. To do this, the images before processing in the ENVI software took radiometric, atmospheric and geometric corrections on them. After that, the main components of the river route were extracted. Five basic algorithms were used to classify the base pixel, but eCognition software was used to classify the object. Supervised classification identifies homogeneous regions with examples of land use and land cover, in which pixels are assigned in known information classes. Education is a process that determines the criteria for these patterns. Learning output is a set of spectral signatures of proposed classes. The first step in object-oriented classification is the segmentation of the image and the creation of distinct objects, consisting of homogeneous pixels. The main purpose of image segmentation is to combine pixels or small objects to create large image objects based on the spectral and spatial characteristics of the image. In order to evaluate the accuracy and compare the resulting maps, the overall accuracy and Kappa coefficient are used. When the sampling of pixels is done as a spectral or informational class pattern, the evaluation of the spectral reflection of classes and their resolution can also be done. An algorithm with the highest accuracy and accuracy will be the basis for the detection. Detection of changes, which leads to a two-way matrix and shows variations of the main types of land use in the study area, was carried out in this study. Pixel-based cross-tabulation analysis on pixels facilitates the determination of the conversion value from a specific user class to another user category and areas associated with these changes over the given time period.
Results & Discussion
The results showed that the object-oriented method is more accurate than the base pixel algorithms for providing user-defined maps. The amount of accuracy in the method based on object-oriented classification depends largely on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. The Kappa coefficient for each image is approximately 0.90. So these maps are the basis for the discovery of change. According to the results, the agricultural and residential lands have been increased and this increase has been accompanied by a decrease in rangelands. A general overview of this 30-year period shows that the arable and dry farming, respectively, increased by 2418.79 and 719.61 hectares and the rangelands had a decrease of 2848.86 hectares. However, the residential class and human effects show an increase of 428.88 hectares or a growth of 178.87%, which indicates the importance of agriculture in the studied area.
Conclusion
Identifying and discovering land cover changes can help planners and planners identify effective factors in land use change and land cover, and have a useful planning to control them. For this reason, maps are needed with precision and speed, and object-oriented processing methods make this possible with very high precision. The results of this study, in addition to proving the precision and efficiency of object-oriented processing in land cover estimation, between 1987 and 2017, have witnessed a decrease in the area of rangeland lands and, on the other hand, agricultural and residential lands, which is indicative of the overall trend Destruction in the area through the replacement of pastures by other uses such as rainfed farming.
Keywords: Land Use, Gamasiab, Object Oriented, Pixel Base, Kappa Coefficient
Dr. Jamal Mosaffaie, Dr. Amin Salehpour Jam, Dr. Mahmoudreza Tabatabaei,
Volume 9, Issue 3 (12-2022)
Volume 9, Issue 3 (12-2022)
Abstract
Landslide risk assessment is essential for all landslide damage mitigation plans. The purpose of this research is to assess the risk of landslides in the Shahrood watershed of Qazvin province. First, the landslide susceptibility map was prepared using fuzzy operators. the landslide distribution map and also 11 effective factor layers including slope, slope direction, altitude, land use, lithology, distance to road, distance to stream, distance to fault, earthquake acceleration, precipitation, and maximum daily precipitation were first prepared. After determining the frequency ratio and fuzzy membership values for the map classes of different factors, the landslide susceptibility map was prepared using different gamma values. Then, after preparing the fuzzy map of vulnerability for different land use units, the amount of landslide risk was determined from the product of two maps of landslide susceptibility and vulnerability. In general, 104 landslides with a total area of 1401 hectares were recorded in this region, 70% of which were used for modeling (73 landslides with an area of 982 hectares) and the remaining 30% (31 landslides with an area of 418 hectares) were used to assess the accuracy. The evaluation results showed that the highest value of Qs index (equal to 1.34) belongs to the gamma equal to 0.93 and therefore this model has higher accuracy than other gamma values. The importance of features at risk ranges from 0.05 (no coverage) to 1 (residential and industrial areas). To deal with landslide damages, three general policies including suitable for development, prevention, and treatment were proposed, which should be applied based on the two factors of risk and vulnerability for different areas of landslide risk. Finally, in order to reduce landslide damages, suitable land uses for high-risk regions were introduced.
Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
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