Topografik Özellikleri Kullanarak Arazi Morfolojisi Analizi: Uşak Ulubey Kanyonu Örneği

Öz

Yeryüzü şekilleri, geçmişte fizyografik ve morfometrik haritalar el yöntemleri ile çizilerek konumsal teknolojilerin gelişmesiyle arazi formlarının otomatik üretilmesi, veri tabanlarında depolanması kolaylaşarak, jeomorfoloji, toprak, ekoloji, peyzaj mimarlığı gibi fiziki planlarla ilgilenen pek çok bilim dalı tarafından daha etkin kullanılmaya başlamıştır. Bu çalışmada Ulubey Kanyonlarının arazi formlarını Coğrafi Bilgi Sistemleri aracılığıyla Topografik Pozisyon İndeksi (TPI) ile morfolojik analizler yaparak sınıflandırmak amaçlanmıştır. Uşak Ulubey Kanyonu, Amerika Birleşik Devletleri‘nin Arizona eyaletinde bulunan Büyük Kanyon’dan (Grand Canyon) sonra dünyanın en büyük ikinci kanyonu unvanına sahiptir. Uşak‘ın Ulubey ilçesinde yer alan kanyon, Ulubey ve Banaz çayları boyunca devam eden bir ana kanyon ve buna bağlanan onlarca büyük yan kanyonlardan oluşmaktadır. Morfolojik sınıflandırmaların oluşturulmasında 30 m çözünürlükte ASTER Sayısal Yükseklik Modeli (SYM) kullanılmıştır. Arazi morfolojisinin oluşturulmasında SYM verilerinden üretilen eğim, eğrisellik, yükseklik farkı, topografik açıklık vb. morfolojik parametreler kullanılmaktadır. TPI hesaplanmasında kullanılan Jennes algoritması, en küçük kareleri kullanarak belirlenen pencere boyutuna ikinci dereceden bir polinom yerleştirerek çok ölçekli bir yaklaşım kullanmaktadır. Araştırmada farklı ölçekteki SYM verileri için 300 m ve 2000 m pencere genişliği kullanılarak sonuçlar karşılaştırılmıştır. Oluşturulan morfolojk sınıflar kanyonlar, sığ vadiler, yaylalar, tabanlı vadiler, ovalar, açık yamaçlar, dik yamaçlar, vadilerde tepeler, orta eğimli sırtlar veya ovalardaki küçük tepeler, zirveler olmak üzere 10 sınıfta toplanmaktadır.

Bu çalışmadan elde edilen bilgiler, farklı özelliklere sahip arazi değişkenleri (toprak, bitki örtüsü, yükseklik vb.) için doğal sınırlar olarak kabul edilen yüzey morfolojisinin sınıflandırılması özellikle arazi bozulması ve jeomorfolojide belirlenmesinde faydalı olacaktır.

Anahtar Kelimeler

• arazi formu,Ulubey kanyonu,topografik pozisyon indeksi,CBS

Landform Analysis using Topographic Characteristics: An Example of Usak Ulubey Canyon

Öz

Earth shapes, physiographic and morphometric maps have been drawn by hand in the past. With the development of spatial technologies, automatic production of landforms and storage in databases has become easier and has been used more effectively by many disciplines interested in physical plans such as geomorphology, soil, ecology and landscape planning. This study aims to classify the landforms of Ulubey Canyons by morphological analysis with Topographic Positions Index (TPI) using Geographical Information Systems. Usak Ulubey Canyon is the second largest canyon in the world after the Grand Canyon in Arizona, USA. The canyon, located in the Ulubey district of Usak, consists of a main canyon along the Ulubey and Banaz streams and dozens of large side canyons connected to it. ASTER Digital Elevation Model (DEM) with 30 m resolution was used to form the morphological classifications. Morphological parameters such as slope, curvature, height difference, topographic aperture, etc. are used to generate the land morphology. The Jennes algorithm used in the TPI calculation uses a multi-scale approach by placing a quadratic polynomial in the specified window size using the least squares. In this study, 300 m and 2000 m window widths were combined for landform classification. The morphological classes are classified into 10 class including i) canyons, deeply incised streams ii) mid-slope drainages, shallow valleys iii) upland drainages, headwaters iv) U-shape valleys v) plains vi) open slopes vii) upper slopes, mesas viii) local ridges/hills in valleys ix) mid-slope ridges, small hills in plains x) mountain tops, high ridges. The information obtained from this study, classification of surface morphology considered as natural boundaries for land variables (soil, vegetation, height, etc.) with different characteristics will be useful in determining land degradation and geomorphology.

Anahtar Kelimeler

• Landform,Ulubey canyon,topographic position index,GIS

Kaynakça

1. Blaszczynski, J. S. (1997). Landform characterization with geographic information systems. Photogrammetric Engineering and Remote Sensing.
2. De Reu, J., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., De Smedt, P., vd. (2013). Application of the topographic position index to heterogeneous landscapes. Geomorphology. doi:10.1016/j.geomorph.2012.12.015
3. Grohmann, C. H., & Riccomini, C. (2009). Comparison of roving-window and search-window techniques for characterising landscape morphometry. Computers and Geosciences. doi:10.1016/j.cageo.2008.12.014
4. Guisan, A., Weiss, S. B., & Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecology. doi:10.1023/A:1009841519580
5. Han, H., Jang, K., Song, J., Seol, A., Chung, W., & Chung, J. (2011). The effects of site factors on herb species diversity in Kwangneung forest stands. Forest Science and Technology, 7(1), 1–7. doi:10.1080/21580103.2011.559942
6. Ho, L. T. K., & Umitsu, M. (2011). Micro-landform classification and flood hazard assessment of the Thu Bon alluvial plain, central Vietnam via an integrated method utilizing remotely sensed data. Applied Geography. doi:10.1016/j.apgeog.2011.01.005
7. Hoersch, B., Braun, G., & Schmidt, U. (2002). Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach. Computers, Environment and Urban Systems. doi:10.1016/S0198-9715(01)00039-4
8. Ilia, I., Rozos, D., & Koumantakis, I. (2017). Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece. Bulletin of the Geological Society of Greece, 47(1), 264. doi:10.12681/bgsg.10940

9. Ilia I, Rozos D, & Koumantakis I. (2013). LANDFORM CLASSIFICATION USING GIS TECHNIQUES. THE CASE OF KIMI MUNICIPALITY AREA, EUBOEA ISLAND, GREECE. Bulletin of the Geological Society of Greece.
10. Jenness, J. (2006). Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x, v. 1.2. Jenness Enterprises.
11. Jones, K. B., Heggem, D. T., Wade, T. G., Neale, A. C., Ebert, D. W., Nash, M. S., vd. (2000). Assessing landscape condition relative to water resources in the western united states: A strategic approach. Içinde Environmental Monitoring and Assessment. doi:10.1023/A:1006448400047
12. Mac Millan, R. A., Martin, T. C., Earle, T. J., & Mc Nabb, D. H. (2003). Automated analysis and classification of landforms using high-resolution digital elevation data: Applications and issues. Canadian Journal of Remote Sensing. doi:10.5589/m03-031
13. Martín-Duque, J. F., Pedraza, J., Sanz, M. A., Bodoque, J. M., Godfrey, A. E., Díez, A., & Carrasco, R. M. (2003). Landform Classification for Land Use Planning in Developed Areas: An Example in Segovia Province (Central Spain). Environmental Management. doi:10.1007/s00267-003-2848-2
14. McNab, W. H. (2007). A topographic index to quantify the effect of mesoscale landform on site productivity. Canadian Journal of Forest Research. doi:10.1139/x93-140
15. Mert, A., Şentürk, Ö., Güney, C. O., Akdemir, D., & Özkan, K. (2013). Mapping of Some Distal Variables Available for Mapping Habitat Suitabilities of The Species . A Case Study from Buldan District. Içinde 3rd International Geography Symposium - GEOMED 2013 (ss. 489–497).
16. Mokarram, M., Roshan, G., & Negahban, S. (2015). Landform classification using topography position index (case study: salt dome of Korsia-Darab plain, Iran). Modeling Earth Systems and Environment, 1(4), 1–7. doi:10.1007/s40808-015-0055-9
17. Mokarram, M., & Sathyamoorthy, D. (2018). A review of landform classification methods. Spatial Information Research, 26(6), 647–660. doi:10.1007/s41324-018-0209-8
18. Oruç, M. S., Mert, A., & Özdemir, İ. (2017). Modelling Habitat Suitability for Red Deer ( Cervus elaphus L .) Using Environmental Variables in Çatacık Region, Eskişehir. Bilge International Journal of Science and Technology Research, 1(2), 135–142.
19. Özdemir, S., & Özkan, K. (2016). Ecological properties of Turkish Oregano (Origanum onites L.) and balsamic sage (Salvia tomentosa Miller) in the Ovacık Mountain district of the Mediterranean region. İstanbul Üniversitesi Orman Fakültesi Dergisi, 66(1), 264–277. doi:10.17099/jffiu.39407
20. Rigol-Sanchez, J. P., Stuart, N., & Pulido-Bosch, A. (2015). ArcGeomorphometry: A toolbox for geomorphometric characterisation of DEMs in the ArcGIS environment. Computers and Geosciences. doi:10.1016/j.cageo.2015.09.020
21. Seif, A. (2014). Using Topography Position Index for Landform Classification (Case study: Grain Mountain). BEPLS Bull. Env. Pharmacol. Life Sci, 311(311), 33–39. http://www.bepls.com/october2014bepls/6.pdf
22. Skentos, A., & Ourania, A. (2017). Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece). Annals of Valahia University of Targoviste, Geographical Series, 17(1), 90–97. doi:10.1515/avutgs-2017-0009
23. Şentürk, Ö., Negis M.G. & Gülsoy, S. (2019). Alpha Species Diversity and Ecological Site Factor Relations in Brutian Pine Forests: A Case Study From Gölhisar District. Bilge International Journal of Science and Technology Research, 3(2), 178-188.
24. Tekin, S. & Çan, T. (2019). Slide Type Landslide Susceptibility Assessment of the Ermenek River Watershed (Karaman) Using Artificial Neural Network. Bilge International Journal of Science and Technology Research, 3(1), 21-28.
25. Tagil, S., & Jenness, J. (2008). GIS-based automated landform classification and topographic, landcover and geologic attributes of landforms around the Yazoren Polje, Turkey. Journal of Applied Sciences. doi:10.3923/jas.2008.910.921
26. Verhagen, P., & Drâguţ, L. (2012). Object-based landform delineation and classification from DEMs for archaeological predictive mapping. Journal of Archaeological Science. doi:10.1016/j.jas.2011.11.001
27. Weiss, A. (2001). Topographic position and landforms analysis (Poster presentation). Içinde ESRI User Conference, San Diego, CA, July 9-13.