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 ...
Read More
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.
Sayyed Ahmad Hosseini; Eisa Ebrahimzadeh; Mojtaba Rafieian; Mahdi Modiri; Mohsen Ahadnejad Roshti
Abstract
Monitoring the expansion of urban areas on a macro scale is very important for planning urban development and prevention of catastrophic problems in metropolitan areas. However, in most cases, lack of basic information in this area, especially in developing countries, is one of the main obstacles to ...
Read More
Monitoring the expansion of urban areas on a macro scale is very important for planning urban development and prevention of catastrophic problems in metropolitan areas. However, in most cases, lack of basic information in this area, especially in developing countries, is one of the main obstacles to achieve this. Therefore, in order to investigate the balance in the urban system of Iran, the urban primacy index and the Rank-Sizedistribution of the cities in Iran were utilized from 1335 to 1390 using population data of urban areas in different census periods. Also, in order to monitor the dynamics of urbanization in contemporary Iran from the spatial-temporal view, the DMSP / OLS multi-temporal images of the years 1371 to 1391 were used.Considering all the indices in the urban system of Iran, the results of the research showed that the urbanprimacy phenomenon has existed in all these periods, and in general, the results derived from the Rank-size logarithmic distribution ofthe cities of Iranbetween the years of 1335 and 1390 indicate that inthe last 55 years, the distribution of the cities has tended towards imbalance over time, and indicate the most unbalanced distribution with an absolute slope of 1.142 in the year of 1385. Finally, the linear regression model was used to analyze the DMSP images in relation to urban areas and the gross national product (GDP).The results showed that there was a linear relationship between light at night and population, urban population and the GDP. The R2 value for the urban population is equal to 0.854 which shows that these images can be used as a factor for identifying the dynamics of the urban system in Iran.
