Ilia Laaliniyat; Mousa Kamanroudi Koujori; Tajeddin Karami
Abstract
Extended AbstractIntroductionThe third millennium has been called the age of urbanization and the urban population is expected to reach about 6.25 billion by 2050. The United Nations also estimates that the urban population of developing regions will grow at an average annual rate of about 2.02 percent, ...
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Extended AbstractIntroductionThe third millennium has been called the age of urbanization and the urban population is expected to reach about 6.25 billion by 2050. The United Nations also estimates that the urban population of developing regions will grow at an average annual rate of about 2.02 percent, from 2.67 billion in 2011 to 3.92 billion in 2030. Indeed, the urbanization process is a phenomenon that has become increasingly concentrated in developing countries in recent decades. Although the pace of change varies considerably between countries and regions, in fact all developing countries are becoming increasingly urbanized. The increase in urbanization has caused many problems in urban areas. This has led to the fact that today land use management of urban infrastructure has become the main challenge of many planners and city managers. Accordingly, this study seeks to investigate the scattering around the Tehran-Eyvanekey communication axis, so Pakdasht cities with about 210 thousand people , Sharifabad with about 12,000 people and Eyvanekey with about 12,000 people, make it one of the busiest axes in the metropolitan area of Tehran. Research MethodsThe main purpose of this study is to analyze the process of space expansion and modeling in the axis of Tehran Eyvanekey between 1985 and 2020 using remote sensing data and GIS. To have a comprehensive study of spatial organization of this metropolis, a deductive or inductive approach with a practical nature has been used. The basis of the study is based on using the satellite data and images (Landsat multi-time images) related to different years. Using IDRISI, GIS and GOOGLE EARTH softwares and Fuzzy Artmap LCM, MARKOV and CA models. Discussion resultsIn this study, in order to evaluate the pattern of expansion of built areas in the corridor of Tehran to Eyvanekey, TM and ETM + images of Landsat satellite related to the years 1985, 2000, 2011, and 2020 have been used. Based on this, the amount of land use changes in the four periods is as follows: The most expansion of practical surfaces in the axis of Tehran-Eyvanekey with an area of 223250 hectares, dedicated to built areas with an increase of 30,495 hectares over the last 35 years. After identifying the urban expansion pattern of Tehran-Eyvanekey corridor, in the next stage, in order to simulate how land use changes in the axis of Tehran-Eyvanekey for the year 2031, the method of automatic cells and chains has been used. For this purpose, to simulate land use changes in the axis of Tehran Eyvanekey in 2031, land use maps in 1985 and 2020 were used. The results show that according to the trend of urban growth in the region in 2031, the land area will reach more than 50,000 hectares. Also, according to the growth rate of urban areas in this region, it can be seen that during different periods, we see a kind of exponential growth in the study area, so that for the period 1985 to 2000, about 240 hectares per year have been built. This trend of growth has expanded and in the next period, ie 2000 to 2011, this number has reached about 580 hectares, and finally in the last period, ie 2011 to 2020, we have witnessed the expansion of about 2251 hectares per year in the built lands, which can be signs of accelerative urbanization. Therefore, the strategy of increasing physical density and using related methods to guide the development of the city towards greater sustainability, should be on the agenda of planners and those in charge of urban affairs. ConclusionModeling land use changes is an effective way to obtain information about how land use changes over time as well as the factors affect it. So, in order to analyze the process of space expansion and modeling in the axis of Tehran-Eyvanekey, it was modeled over a period of 35 years. The results showed that most of the land use changes during this period are related to the built lands, which due to the location of the built areas along the main arteries has a northwest-southeast pattern that is affected by urban growth in the metropolis of Tehran. As a result, they live in these areas, which are either engaged in the urban industries of these areas or use the satellite cities in this corridor as dormitory cities. Interestingly, as we move away from the main center, the metropolis of Tehran, the rate of urban land expansion decreases, which indicates that due to the low cost of housing in satellite cities, this area is a dormitory for the metropolis of Tehran.
Geographic Data
Keyvan Mohammadzdeh; Sayyed Ahmad Hosseini; Mehdi Samadi; Ilia Laaliniyat; Masoud Rahimi
Abstract
Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms ...
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Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.
Materials and methods
Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely.
Discussion and results
The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.
Conclusion
Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.
Reza Aghataher; Mahdi Samadi; Ilia Laliniat; Iman Najafi
Abstract
Abstract
Digital Elevation Models (DEM) enable researchers to perform geographical researches on a global and regional scale such as global changes, natural disasters, environmental hazards, environmental monitoring, etc. Therefore, DEM data plays a key role in scientific researches. SRTM and ASTER ...
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Abstract
Digital Elevation Models (DEM) enable researchers to perform geographical researches on a global and regional scale such as global changes, natural disasters, environmental hazards, environmental monitoring, etc. Therefore, DEM data plays a key role in scientific researches. SRTM and ASTER GDEM are two elevation datasets that cover nearly the entire land surface of the earth and are globally available (for almost 80% of the earth). Thus, it is necessary to evaluate the vertical accuracy of such data prior to their use and to select the appropriate data considering the research target. ASTER-based digital elevation model has spatial resolution of 30 meters, which seems to provide more precise elevation data than SRTM with 90 meters spatial resolution. Several studies have been performed for evaluating the accuracy of each of these two datasets in various countries of the world. The results of such studies indicate their advantages and limitations over each other. In this study, the vertical accuracy of these two DEMs are evaluated by ground control point in three zones of Iran with different topographic characteristics which are East Azerbaijan, Sistan and Baluchestan and Bushehr. Results show that the RMSE of SRTM as the index of error for the study area in East Azerbaijan, Sistan and Baluchestan and Bushehr are 6.1, 7.4 and 2.9 meters and in ASTER GDEM are 8.7, 8.3 and 7.2 meters respectively. Therefore, the vertical accuracy of STRM is higher than that of ASTER GDEM in all three zones. In this research, the relation between vertical error and land characteristics including slope and direction of slope has been studied and the results have been presented. The final findings of the research indicate higher vertical accuracy for SRTM compared to ASTER GDEM in Iran and it is concluded that SRTM is a more appropriate choice for various applications.