Extraction, processing, production and display of geographic data
Fatemeh Ahmadi; Yasser Ebrahimian Ghajari; Abbas Kiani
Abstract
Extended AbstractIntroductionUrbanization can be defined as social and economic development and thus, urban planners need timely information for service provision and management in urban areas. Due to the difficulties of traditional methods, automatic and semi-automatic methods have gained special importance. ...
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Extended AbstractIntroductionUrbanization can be defined as social and economic development and thus, urban planners need timely information for service provision and management in urban areas. Due to the difficulties of traditional methods, automatic and semi-automatic methods have gained special importance. Therefore, remote sensing data and image classification techniques have been used to help identify different types of land use. Nighttime light emission data can help researchers effectively identify human activities and urban areas. These satellite images are collected from the surface of the earth at night and can clearly separate nighttime light emission of urban areas from the surrounding dark areas. Thus, it can be concluded that various types of data with different nature and capabilities (spectral, nighttime light, etc.) are available for any specific area each of which has its own advantages and limitations. As a result, using a combination of these data types will increase accuracy and reduce uncertainty. Algorithms and scientific methods enabling this combination are thus of great importance. The present study applies a combination of nighttime light emission and daytime multispectral images to produce automatic and high-quality optimal training samples and locate built-up areas.Material & MethodsTwo study areas (in Babol and Kerman) with two different climates have been investigated in the present study. Also, DMSP and VIIRS nighttime light emission images and Landsat 5, 7 and 8 images collected during the statistical period have been used.Research MethodsThe present study has proposed an approach consisting of four main phases of pre-processing, feature extraction and production of initial training samples, selection of optimal training samples and finally classification and evaluation. Nighttime light emission images were corrected and primary samples including two classes of built and unbuilt areas were produced using the limit of automatic thresholds. Nighttime light emission is generally related with human activities, and thus, built-up areas usually have a higher nighttime light emission value compared with unbuilt areas which have a lower or zero value. Due to the saturation and blooming problems occurring in DMSP images and the relatively low spatial resolution of nighttime light emission data, training samples extracted from built areas using these data still include unbuilt areas such as water bodies and vegetation cover. Therefore, an index has been developed using features extracted from nighttime light emission and Landsat images. Considering the inverse relationship between various features of urban and rural areas (vegetation cover and soil) in LST images obtained from the thermal band of Landsat images and the NDVI vegetation index obtained from Landsat and features of urban areas in nighttime light emission image, an index was provided which maintains the main characteristics of urban areas in nighttime light emission images while minimizing saturation and blooming. Finally, time series of classified images was investigated and urban expansion was analyzed.Result & DiscussionFollowing nighttime light emission data correction, an upward trend was observed for the values of pixels collected from each city which verifies the pre-processing stage. Then, an appropriate automatic threshold limit was selected in accordance with the features of each nighttime image and applied to produce the initial training samples. Nighttime light emission images were corrected using the introduced index to minimize saturation and blooming in urban and suburban areas. Training samples thus optimized were used for final classification. Due to the low quality of initial training samples, classified pixels obtained from urban areas did not confirm to reality. Thus, classification faded in Kerman city in some years and practically no classification was performed which shows the low quality of initial training samples. Due to the low spatial resolution of nighttime light emission images, the size of samples collected from built-up areas was falsely detected to be large, and thus, there were definitely samples related to vegetation, soil, and etc. in the specified range. In the next step, classification was performed using optimal training samples in which built-up regions were modified. In this way, results got closer to the reference data and reality. In fact, using a combination of nighttime light emission and Landsat data can overcome the limitations of both methods.Conclusion Selection of training samples is considered to be the main and fundamental challenge of classification. With a valid training sample, classification is precisely performed. Since, traditional and manual methods of obtaining training samples are costly and time-consuming, automatic and semi-automatic methods have become specifically important. Therefore, the present study has classified and extracted built-up areas using satellite images. The initial training samples can be obtained automatically from nighttime light emission images, however high saturation and blooming of these images have reduced their quality. To solve this problem, a nighttime light index has been developed based on the relationship between the characteristics of urban areas in optical images and nighttime light emission images which has minimized related problems in both study areas with two different climates to a great extent. This shows the flexibility and effectiveness of the proposed method. High-quality training samples thus obtained were highly effective in the final classification phase. Investigating urban expansion time series has shown that urban growth and expansion have generally occurred around the city.
Extraction, processing, production and display of geographic data
Maryam Kouhani; Abbas Kiani; Yasser Ebrahimian Ghajari
Abstract
Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of ...
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Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of Iran is also highly considered important. Research suggests that the destruction and change of vegetation cover and forests are among the most important factors influencing natural hazards such as floods, erosion, and earthquakes. In addition to processing and presenting well-known spatial data, remote sensing can also be used to improve human understanding of annual changes in vegetation cover, from a local to a global scale. In this regard, the anomaly evaluation criterion with high differentiation can separate and display anomalous areas in order to recognize the change process and reveal the areas with anomalies over time. Thus, medium-resolution images, vegetation indices, and anomaly criteria can be used to evaluate long-term vegetation changes. Therefore, a positive step in reducing the environmental effects of a region can be made by locating the urban areas that have experienced changes over time and making decisions related to future planning.Material and methodsThis study utilized a time series of Landsat 5, 7, and 8 images downloaded from the Google Earth engine. To get the best representation of the vegetation in this study, spring and summer were chosen because vegetation at this time is at its greenest. The main focus of this study was on the evaluation of vegetation changes over time quantitatively and qualitatively, using remote sensing data from Google Earth Engine to prepare a map of vegetation changes over time. The general process of implementing this research can be summarized in 7 phases. The first phase involves taking Landsat images and preparing statistical meteorological data. In the second phase, the time series images were collected according to the specific period and in the third phase, the obtained images were corrected and pre-processed. As a next step, the EVI index is extracted from all Landsat images, and then to determine the anomaly of changes, a series of statistical analyses, including the mean and standard deviation, are applied. The next step involves generating the map of anomalous time series changes and extracting the map of vegetation changes to improve understanding. The end of the process also includes evaluating the results obtained from this research. Results and DiscussionSince vegetation and drought changes are non-uniform depending on location and distance from the sea and humid areas, and vegetation is destroyed to build villas, residential areas, commercial areas, and towns, several study areas were divided into smaller pieces. Then each area was analyzed and evaluated separately for its changes. It has been observed in the first and third study areas that vegetation has generally been on the rise in the past 36 years, although sometimes there have been anomalies and fluctuations in EVI value. It was significant to see the reduced vegetation in 2008 in both regions. For example, 262.5 mm of precipitation in the first region fell this year, indicating a rain shortage. The results obtained from the second region, considered one of the coastal regions, indicate that the anomaly graph in the region during the period had a downward slope in the direction of decreasing vegetation, and EVI values reached 0.14 in 2005 and 0.09 in 2013. The 4th and 5th regions have shown a lot of fluctuations in anomalous changes and EVI values, although the trend has generally been downward. Results obtained in the 4th region show that vegetation cover peaked in 2004 and 2011. Rainfall in the 5th region, a highland region, in 2008 was deficient, with 259.8 mm reported by the meteorological station. The anomaly value in this year was -1.96. According to the Department of Meteorology in Mazandaran province, most droughts that have affected the underground water in the province have taken place in coastal and plain areas in the province's east and center, and in western cities, they have mostly affected mountainous areas.ConclusionThirty-six years of EVI time series images obtained from Landsat images were utilized in this study to investigate the changes and identify anomalies. In order to conduct a more detailed investigation, the study area was divided into several different regions, and each region was evaluated separately. The results obtained with existing meteorological statistical data were analyzed because vegetation can be affected by climatic and environmental conditions such as weather conditions. According to the results from study area )4(, vegetation cover has consistently decreased over the last three decades due to various factors like tree cutting, landslides, or land use changes. As shown in the map showing the obtained changes, there appears to be an increase in the value of the vegetation index in some northern areas of Chalus city until around 2002, indicating an improvement in greenness. While In some areas close to the Caspian Sea and the coastline, because of the construction of villas and commercial areas, there has been a loss of vegetation, such as in area (2) based on the changed map, a major part of the vegetation in that area has been destroyed because of the establishment of a settlement and construction of a road. As a result of comparing the evaluation of two anomaly approaches, it has also been concluded that both modes show almost the same trend of changes, but the graphs in "Anomaly compared to the overall average" mode compared to "Anomaly compared to the average of each set" display the change process better.
Yasser Ebrahimian Ghajari
Abstract
Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have ...
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Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have become more complicated. Earthquake is one of the most important natural hazards that takes the lives of many people every year. Although definite prediction of earthquake is not still possible, high-risk areas can be identified by zoning earthquake hazard using new technologies such as GIS, and measures can be taken to deal with the critical situation of identified regions during an earthquake. Planning of temporary accommodation with the aim of crisis management and reduction of secondary damages caused by the earthquake have always been amongmajor concerns of urban planners and managers. In the past, the policy of creating temporary accommodation centers and disaster relief sites lacked a specific program, so that locating a vacant land, with no owner was the most important principle for the creation of these centers in urban areas. It is now proved that these methods lack efficiency. However, recent advances in modern technologies such as GIS have improved planning process. This kind of planning procedure takes effective parameters and criteriainto account, many of which have spatial nature. Urban resiliency is one of the most important branches of urban crisis management, thus risk assessment and risk reduction planning, including site selection for temporary accommodation (as a principle of urban resiliency),are highly essential. Materials and methods The study area of the present research is Babol, one of the major and central cities of Mazandaran Province. Babol is located in BabolCounty, 14 km from the Caspian Sea and 10 km from the Alborz Mountains. With a total area of approximately 32 km2 and a population of250,217 (at the2016 census), it is the second most populous city in Mazandaran province.The 600 km long Caspian faults and 680 km long Alborz faults are among the effective faults of the study area. In the present study, effective measures for selectionof temporary accommodation siteswere extracted and weighted using expert opinions specialized in structural engineering, earthquake, urban planning, crisis management, passive defense, traffic and transportation. Identified criteria included distance from the river, distance from the fault, land use, distance from installations network, access to the transit network, distance from fire stations, population density, distance from tall buildings, distance from police stations and distance from health centers. Then, using GIS analytic functions, standard maps were produced and combined to identify the best areas for temporary accommodation (after a possible earthquake) in Babol. Criteria were weighted using fuzzy analytic hierarchy process and weighted overlay method was also used to combine them. Results and discussion Analyzing the results indicated that only 7% of the total study area (Babol City) is appropriate for temporary accommodation. Identified areas were examined according to other temporary accommodation standards. Finally, six sites and a total of 107 hectares (less than 4% of the study area) were identified as suitable sitesfor temporary accommodation. With a very large area (37 hectares) and full access to water, electricity and gas facilities,the first site is locatednear eastern beltway of Baboland Lotus PondRecreational Complex. The second proposed site is a 11-hectarevacant arealocated in the northeastern part of Babol City, between Ramenet and Pari Kola Villages. With a total area of 22 hectares,the third proposed site is located in the south-east of Babol City and near Babol-Qa’emShahr Road. Unlike the previous three sites, the fourth proposed site is located almost inside the city. It is a vacant 5-hectarearea in the northern side of the Motamedi Martyrs’ Cemetery. The next site, also located inside the city, is Aminian Dormitory (Noushirovani University of Technology) with a total area of 4 hectares. Although the last proposed site was ranked lower than the other five sites in the final analysis, it has the highest score among available sites inwestern side of Babol river. With a total area of 28 hectares, this site is located within a short distance of Imam Khamenei Highway. Conclusion According to the international standards, per capita area for temporary accommodation is approximately 4 m2. Therefore,with a population of about 250,217,Babol needs an average space of 100 hectares for temporary accommodation. Although, the proposed space for temporary accommodation (107 hectares) in Babol almost equals the required space (100 hectares), with the present rate of population growth inBabol, increasedconstructions, and consequently, reduction of appropriate space for temporary accommodation, Babol will definitely face a shortage of suitable space for temporary accommodation of earthquake victimsin near future. Moreover, the spatial distribution of suitable sites for temporary accommodation is not reasonable, as most of the suitable sites are located in the eastern part and within the boundaries of the city. While, these sites are expected to be scattered throughout the city with an equal access for all residents.Finally, it can be concluded that temporary accommodation of earthquake victimswas not considered in urban planning of Babol, and as a result, the city does not have a suitable status regarding temporary accommodation of earthquake victims.
Yasser Ebrahimian GhaJary; Ali Asghar Alesheikh; Mahdi Modiri; Reza Hosnavi; Morteza Abbasi
Abstract
Throughout the history, cities have neverbeen safe due to the damages caused by human and natural disasters. So that inthe past, the cities were the war’s last target, but now with the development of technology, those hindrances have turned into the first wartargets. In fact, what poses war andair ...
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Throughout the history, cities have neverbeen safe due to the damages caused by human and natural disasters. So that inthe past, the cities were the war’s last target, but now with the development of technology, those hindrances have turned into the first wartargets. In fact, what poses war andair raids as a threat, is just the problem ofencountering it and being. One major way to prepare facing such threats, is the knowledge about the degree of its vulnerability whenthey occur. So, it depends on taking up some methodsto diminish the vulnerability instability. Whatthe researchers were looking for, was the modelling the vulnerabilityof the city buildings (one of the most important urbancomponent) in one part of theTehran (region 6 of Tehran municipality). Since vulnerability is made upof various criteria, so the proposed model in this researchis a kind of multi-criteria model (multi attribute decisionmodel), and according to the spatial essence of the criteria, this model has been carried out in GIS (MCDM-GISmodel). Delphi method has been used to survey major vulnerabilityfactors with the help of urban passive defense, structure, andarchitecture experts. The modelling of the 9 criteria has resulted throughAnalytic Hierarchical Process (AHP), and it shows that about 38percent of building has low vulnerability, about 60 percent has mediumvulnerability and 2 percent has high vulnerability (over 60 percent of buildingshas plus-average vulnerability) which shows the necessity for taking actions in order todecrease vulnerability through passive defense.
