Document Type : Research Paper


1 Graduate Master of Remote Sensing and Geographic Information System, University of Tabriz

2 Ph.D. in Geography and urban planning, Sistan and Baluchestan University

3 Ph.D. student in Remote sensing and Geographical Information System, Faculty of Geography, University of Tehran

4 PhD student in Geography and urban planning, Kharazmi University

5 PhD in Geomorphology, University of Tabriz


Extended Abstract
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.
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.


Main Subjects

  1. پورباقر کردی، قنواتی، کرم، صفاری؛ سیدمهدی، عزت‌الله، امیر، امیر (1394) کاربرد روش‌های قطعه‌بندی تصاویر طیفی در شناسایی و جداسازی مخروط افکنه‌های حوضه یزد-اردکان، مجله پژوهش های جغرافیای طبیعی، دوره 47، شماره 3، پاییز 1394، صفحه 367-383.
  2. رعیتی شوازی، کرم، غفاریان مالمیرا، سپهر؛ منیره، امیر، حمیدرضا، عادل. (1395). مقایسه کارایی برخی الگوریتم‌های طبقه‌بندی در مطالعه تغییرات لندفرم‌های بیابانی دشت یزد-اردکان. پژوهش‌های ژئومورفولوژی کمّی. سال 8، شماره 1. صص 73-57.
  3. شایان، یمانی، فرج‌زاده، احمدآبادی؛ سیاوش، مجتبی، منوچهر، علی. (1391)، طبقه‌بندی نظارت‌شده لندفرم‌های ژئومورفولوژیکی مناطق خشک با استفاده از پارامترهای ژئومورفومتریک (مطالعه موردی: منطقه مرنجاب)، فصلنامه سنجش از دور و GIS ایران، سال چهارم، شماره 2 (پیاپی 14). صص 19-28.
  4. مکرم، درویشی بلورانی، نگهبان؛ مرضیه، علی، سعید. 1396. ارتباط ویژگی‌های مورفومتری حوضه‌های آبخیز و فرسایش‌پذیری در سطوح مختلف ارتفاعی با استفاده از شاخص موقعیت توپوگرافی (TPI) مطالعه موردی: حوضه آبخیز نازلوچای. فصلنامه علمی پژوهشی اطلاعات جغرافیایی سپهر. دوره 26. شماره 101. صص 131-142.
  5. Baatz, M., Hoffmann, C., & Willhauck, Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P. & Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114.
  6. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I. & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of photogrammetry and remote sensing, 58(3), 239-258.
  7. Bishop, M. P. Shroder Jr, J. F. & Colby, J. D. (2003). Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 55(1-4), 345-361.
  8. Blaschke T & Strobl J 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS – Zeitschrift für Geoinformationssysteme 6:12-17.
  9. Chaudhuri, B. B. & Sarkar, N. (1995). Texture segmentation using fractal dimension. IEEE Transactions on pattern analysis and machine intelligence, 17(1), 72-77.
  10. Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
  11. Drăguţ, L. & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3), 330-344.
  12. Drăguţ, L., & Eisank, C. (2012). Automated object-based classification of topography from SRTM data. Geomorphology, 141, 21-33.
  13. Drguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859-871.
  14. ECognition.2012. Ecognition User Guide and Reference book. (Munich, Germany: Definiens Imaging) Published by: Trimble Germany GmbH, Arnulfstrasse 126, D-80636 Munich, Germany.441p.
  15. Etzelmüller, B., Sulebak, J.S., 2000, Developments in the Use of Digital Elevation Models in Periglacial Geomorphology and Glaciology, Physische Geographie, Vol. 41, PP. 35–58. Baatz, M., Hoffmann, C., & Willhauck,
  16. Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P. & Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114.
  17. Hall, O.,Hay, G. J., Bouchard, A., and Marceau, D. J. . 2004. Detecting dominant landscape objects through multiple scales: an integration of object-specific methods and watershed segmentation. Landscape Ecology, 19:1. 59–76.
  18. Hammond, E.H., 1964. Analysis of properties in land form geography: an application to broad-scale land form mapping. Annals of the Association of American Geographers 54, 11–19.
  19. Huggett RJ (2007): Fundamentals of Geomorphology. Routledge, London, UK .448p.
  20. Huggett RJ (2007): Fundamentals of Geomorphology. Routledge, London, UK
  21. Kaushal, A., & Singh, Y. (2006). Extraction of geomorphological features using radarsat data. Journal of the Indian Society of Remote Sensing, 34(3), 299-307.
  22. Klingseisen, B. Metternicht, G. Paulus, G. 2008. Geomorphometric landscape analysis using a semi-automated GIS-approach. Environmental Modeling and Software 23, 109-121.
  23. Klingseisen, B., Warren, G., & Metternicht, G. (2008). LANDFORM-GIS based generation of topographic attributes for landform classification in Australia. na.
  24. Korzeniowska, K. (2017). Object-based image analysis for detecting landforms diagnostic of natural hazards.
  25. Lees, B. 2006. The spatial analysis of spectral data: Extracting the neglected data. Applied GIS, 2:2. 14-1.
  26. Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928-4943.Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. CRC press.
  27. Piloyan, A., & Konečný, M. (2017). Semi-automated classification of landform elements in Armenia based on SRTM DEM using k-means unsupervised classification. Quaestiones Geographicae, 36(1), 93-103.
  28. Saha, K., Wells, N. A., & Munro-Stasiuk, M. (2011). An object-oriented approach to automated landform mapping: A case study of drumlins. Computers & geosciences, 37(9), 1324-1336.
  29. Szuster, B. W., Chen, Q. & Borger, M. (2011). A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31(2), 525-532.
  30. Uzar.M, 2014, Automatic Building Extraction with Multi-sensor Data Using Rule-based Classification. European Journal of Remote Sensing.47:1-18. doi: 10.5721/EuJRS20144701.
  31. Zhang, Y. & Maxwell, T. (2006, May). A fuzzy logic approach to supervised segmentation for object-oriented classification. In ASPRS 2006, Annual Conference Reno, Nevada May (pp. 1-5).