Application of Segmentation Methods to Recognition and Separation of Alluvial Fans in Yazd-Ardakan Basin

Document Type : Full length article


1 PhD Candidate in Geomorphology, Department of Geography, Kharazmi University, Tehran, Iran

2 Associate Professor, Department of Geography, Kharazmi University, Tehran, Iran


This research addresses the automatic extraction of alluvial fans using four methods of segmentation from satellite data. This segmentation method divides images into partitions. It is typically used to recognize objects or other relevant purposes in digital images (Fu, 2013:3260). Alluvial fans have always been a landform that attracts human because they are suitable areas for living due to freshwater and appropriate soil for drinking, cultivation, making pottery, making mud-brick and other activities (Maghsoudi and Azizi, 2012: 23). Therefore, alluvial fan extraction is significant in the planning of engineering geomorphology and other related disciplines.
During the recent years, many segmentation techniques have been developed (Ranasinghe, 2008). In this research, the most popular segmentations are presented and then those that are appropriate to identification of alluvial fans of geomorphology were introduced. In general, land-surface segmentation has demonstrated to have a great potential to improve geomorphological mapping with better representations of geomorphological objects. Segmentation divides land-surface into relatively homogeneous areas, by polygons based on input criteria. Segmentation results are used to identify objects and their classification (Drăguţ et al., 2013). The main objective of this research is to introduce and implement algorithms for geomorphological landforms segmentations that the target landforms are alluvial fans and bahada in this research. The selected study area is in Yazd Basin and to test the ability for generalization of the selected methods, the similar alluvial fans of the central city of Yazd province have been selected. Briefly, importance of segmentations in geomorphology is in the extraction of landform objects, landform classification, landform isolation and identification details of landforms.
The methodology of this study is based on processing segmentation on high-resolution images of Geoeye-1 as well as the ASTR-1 multispectral satellite images within the E-Cognition Developer© software from Trimble company. Arc Catalog and ArcGIS are used for production of the required layers in the proposed flowchart. In this study, two main approaches have been used in the construction segmentation. In the Top-down Segmentation, the objects of image are divided into smaller parts. Top-down approaches are approximately implemented by three algorithms: 1. Chessboard segmentation, 2. Quadtree-based segmentation, and 3. Contrast Split Segmentation. The forth method for segmentation is called multi-resolution segmentation that is the most popular method in the bottom-up segmentation approach (Baatz and Schäp, 2000).
We have described the four methods, and then each of those methods has been executed on the satellite images within the mentioned platforms. The outputs of each segmentation processing have been evaluated based on visual interpretation of the images. According to the flowchart proposed, outputs of segmentation have been separately overlapped on the high-resolution Geo-eye images that are used in ArcGIS environment. The existing map of geomorphology was used to improve visual interpretation. In this study, we used not only the top-down segmentation but also Bottom-up Segmentation approaches.
Results and Discussion
The segmentation results of the four methods in the E-Cognition Developer© software from Trimble company was as follows:
In short, the first method converts the image into a square shape that its output is a chessboard image. In the second method, the entire image is divided into four squares of the same size using the standard deviation or other criteria as a separate factor, and then each square is also divided into four smaller parts until to a defined threshold. These divisions continue until the objects are separated from each other based on shape and color homogeneity. This method will produce narrow strip initial segments for features with a large length-width ratio (e.g. roads, waterways, strip erosion types.), that is suitable for extraction of narrow objects in the context images. In the third method, the objects are separated by polygons from each other based on threshold values. This indicates the degree of difference between darkness and brightness. We were able to extract details of the alluvial fans (e.g. Shadow of gully erosion, oued,) using contrast segmentation method. The forth method, the image pixels or small objects are combined based on the criterion of homogeneity in successive with neighboring pixels or objects to lead to the production of larger objects. Therefore, the objects with homogeneous color and shape are combined to form a larger one. This technique is based on region growing concepts, in other words one or some known pixels are developed by the rest of unknown pixels based on a criterion.
On the basis of the results, it was concluded that two algorithms are popular and applicable in geomorphology: A) The multi-resolution algorithm is precise and high performance to identify the geometry of the alluvial fans of Yazd Basin; B) Contrast split segmentation has been successful to identify details on the body of the alluvial fans like to gully erosion, shadows, roads, Oued. Finally, in order to examine the testability of the selected methods, the multi-resolution algorithm has been executed in the similar fans in other parts of the central city of Yazd province. Its results have proved the generalizability of these methods, because the algorithm is repeated four times to identify and extract the boundaries of the alluvial fans, as the outputs appeared quite similar in the morphology of alluvial fan.


Main Subjects

1. اختصاصی، م.، احمدی، ح.، فیض‌نیا، س. و بوشه، د. (1383). «فرسایش بادی، رخساره‌ها و خسارات آن در حوضة دشت یزد- اردکان». مجلة منابع طبیعی ایران. ج57. ش4. ص567-581.
2. بهرامی، ش. و بهرامی، ک. (1390). «ارزیابی تکنیک‌های ژئومورفولوژیکی جهت شناسایی مخروط‌افکنه‌های قدیمی و جدید به‌منظور تعیین مناطق مستعد سیل‌خیزی در چهار مخروط‌افکنه در زاگرس چین‌خورده». مجلة جغرافیا و توسعه. ش22. ص89-106.
3. شایان، س. و زارع، غ. (1390). «تبیین مفهوم فرسایش از دیدگاه ژئومورفولوژی و مقایسة آن با دیدگاه منابع طبیعی».
فصلنامة پژوهش‌های فرسایش محیطی. س1. ش1. ص77-92.
4. شایان، س.، یمانی، م.، فرج‌زاده، م. و احمدآبادی، ع. (1391). «طبقه‌بندی نظارت‌شدة لندفرم‌های ژئومورفولوژیکی مناطق خشک مرنجاب». مجلة سنجش از دور و GIS ایران. ش2. ص19-28.
5. فیضی‌زاده، ب. و هلالی، ح. (1389). «مقایسة روش‌های پیکسل‌پایه و شیء‌گرا و پارامترهای تأثیرگذار در طبقه‌بندی پوشش/کاربری اراضی استان آذربایجان غربی».پژوهش‌های جغرافیای طبیعی. ش71. ص73-84.
6. گورابی، ا. و کریمی، م. (1391). «روشی جدید در استخراج مخروط‌افکنه‌ها از مدل رقومی ارتفاع». مجلة پژوهش‌های ژئومورفولوژی کمی. ش3. ص89-100.
7. محمدی،ن.، آل‌شیخ، ع.، صداقت، ا. و ملک، م. (1389).«روشی جدید در قطعه‌بندی خودکار تصاویر ماهواره‌ای با دقت بالا برای استخراج خطوط ساحلی».مهندسی دریا. ش11. ص25-35.
8. مقصودی، م.، فاضلی نشلی، ح.، عزیزی، ق.، گیلمور، گ. و اشمیت، ا. (1391). «نقش مخروط‎افکنه‌ها در توزیع سکونتگا‌ه‌های پیش از تاریخ از دیدگاه زمین‌باستان‎شناسی». پژوهش‌های جغرافیای طبیعی. ش4. ص1-22.
9. Argialas, D. P. and Tzotsos, A. (2004). "Automatic Extraction of Alluvial Fans from Aster L1 Satellite Data and A Digital Elevation Model Using Object-Oriented Image Analysis". in ISPRS Congress. Pp.1-6.
10. Asselen, V. and Seijmonsbergen, S. (2006). "Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM". Geomorphology. 78. Pp. 309–320.
11. Australian curriculum (2008). "Oxford big ideas geography, chapter 1: landforms and landscapes". Pp. 40-53.
12. Baatz, M. and Schäp, A. (2000). "Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation". Heidelberg. Pp. 12-23.
13. ــــــــــ. (1999)."Object-Oriented and Multi-Scale Image Analysis in Semantic Networks". Proc. of the 2nd International Symposium on Operationalization of Remote Sensing August 16th – 20th. Enschede. ITC.
14. Baatz, M., Hoffmann, C. and Willhauck, G. (2008). progressing from object-based to object-oriented image analysis. availablein online with ISBN: 978-3-540-77057-2.
15. Bahrami, sh. and Bahrami, k. (2011). "Geomorphological techniques assessment to identify old and new fans in order to suitable areas most prone to four fans in the Zagros folded". Geography and Development. 22. Pp.89-106. (In Persian).
16. Definiens, AG. (2006). "User Guide for Definiens Professional 5" . München. Germany.
17. Dragut, L. and Blaschke, T. (2006)."Automated classification of landform elements using object-based image analysis". Geomorphology. 81. Pp. 330–344.
18. Ekhtesasi, M.R., Ahmadi, H., Feiznia, S. and Busche D. (2005). "Wind Erosion, Facies and Damages in Yazd –Ardakan Plain". Iranian Journal of Natural Resource. Vol. 57. NO. 4. Pp. 567-581. (In Persian).
19. Feizizadeh, B. and Helali, H. (2010)."Comparison of pixel-based and object-oriented and classification parameters in landuse/landcover map Case study:West Azerbaijan Province". physical geography researches. NO. 71. Pp. 73-84. (In Persian).
20. Fu, G., Zhao, H., Li, Cong. And Shi, l. (2013). "Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique".Remote Sensing. 5. Pp. 3259-3279.
21. Gerçek. D. (2010). "Object-Based Classification of Landforms based On Their Local Geometry and Geomorphometric Context". PhD thesis in GIS. Sup: Dr. Vedat. M.E.T University.
22. Goorabi A. and Karimi M, (2013). "New Method for Extraction of Alluvial Fans from Digital Elevation Model" quantitative geomorphological researches . NO. 3. Pp. 89-100. (In Persian).
23. Haralick, R., Shanmugan, K. and Dinstein, I. (1973). "Textural features for image classification". IEEE Trans. Systems Man Cybernetics. 3. Pp. 610–621.
24. Hoffmann, A. and van der Vegt, J.W. (2001). "New sensor systems and new classification methods". laser and digital camera-data meet object oriented strategies. 6. Pp. 16–23.
25. Macmillan R., Pettapiece W., Nolan, S. and Goddard, T. (2000)."A generic procedure automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic". Fuzzy sets and Systems. 113. Pp. 81–109.
26. MacMillan, R.A., Pettapiece, W.W., Nolan, S.C. and Goddard, T.W. (2000). "a generic procedure for automatically segmenting landforms into landform elements using dems, heuristic rules and fuzzy logic". Fuzzy Sets and Systems. Pp. 81-109.
27. Maghsoudi, M., Fazeli Nashli, H., Azizi, GH., Gillmore, G. and Schmit, A. (2012)." Geoarchaeology of Alluvial Fans:A Case Study from Jajroud and Hajiarab Alluvial Fans in Iran". physical geography researches. NO. 4. Pp. 1-22. (In Persian).
28. Martin, k., Schroeder, W. and Lorensen, B. (2012). "Reference Book for eCognition® Developer 8.7.2". Trimble Germany press. Munich.
29. Meinel, G. and Neubert, M. (2004). "A comparison of segmentation programs for high resolution remote sensing data". In: Conference of the ISRPS 2004. Istanbul. Turkey. Pp. 19-23.
30. Miliaresi, G. (1999). "Automated segmentation of alluvial fans to regions of high to intermediate Flood hazard from landsat thematic mapper imagery". 2nd International Symposium on Operationalization of Remote Sensing. Netherland. Pp. ?-6.
31. Mohammadi, N., Aalesheikh, A., Sedaghat, A. and Malek M.R. (2010). "A novel segmentation approach for coastline extraction from high resolution Sattelite images". Marine Engineering. NO. 11. Pp. 25-35. (In Prsian).
32. Prima, O.D.A, Echigo, A., Yokoyama, R. and Yoshida, T. (2006). "Supervised landform classification of Northeast Honshu from DEM-derived thematic maps". Geomorphology. NO. 78. Pp. 373–386.
33. Ranasinghe, A. (2008). "Multi Scale Segmentation Techniques in Object Oriented Image Analysis". proceeding in ACRS2008. Pp. 1-6.
34. Richards, J.A. and Jia, X. (2006). "Remote Sensing Digital Image Analysis: An Introduction". Springer-Verlag. New York.
35. Shayan, S., Yamani, M., Farajzadeh, M. and Ahmadabadi A. (2013)."A Supervised Classification of Geomorphometric Parameters to Extract the Geomorphologic Landforms in Dry Regions. (Case study: Maranjab region)". Iranian Journal of Remote Sensing & GIS. NO. 2. Pp. 19-28. (In Persian).
36. Shayan, S. and Zare, GH. (2011). "Geomorphologic Explanation of Erosion Concept and Comparing it to Natural Resources Science Aspects". Environmental Erosion Researches. Pp. 77-92. (In Persian).
37. Strobl, J. (2007). "Segmentation-based Terrain Classification". Springer-Verlag. Pp. 125-141.
38. Sulebak, J.R and Vind, H. (2003). "Multiresolution Spline Models and Their applications in Geomorphology". Concepts and Modelling in Geomorphology. Tokyo. Pp. 221–237.