KaydedilenlerDerginizi ekleyin

Dergiler / Coğrafya Dergisi / 2026 / Sayı: 46

Makro Yerşekillerinin Tanımlanmasında Ölçek ve Örneklem Pencere Boyutuna İlişkin Belirsizlikler

Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms

Neslihan Dal(ISTANBUL UNIVERSITY)0000-0003-2372-4960
Tolga Görüm(İSTANBUL TEKNİK ÜNİVERSİTESİ, AVRASYA YER BİLİMLERİ ENSTİTÜSÜ, YER SİSTEM BİLİMİ (DR))0000-0001-9407-7946
Sayfa
157–171
Yayın Tarihi
2023-05-25
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Seçili alanlarda sahayı en iyi temsil eden ölçek ve pencere boyutlarının kombinasyonlarını içeren test sonuçları, pencere boyutunda yapılan değişikliklerle genelleştirme kapasitesi arttıkça tanımlanan makro yer şekli birliğinin farklı bir haritayla sonuçlanabileceğini göstermektedir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışma makro yer şekillerinin tanımlanmasında temel alınan pencere örneklem boyutlarının istatistiksel önemi ve tanımlamalarda meydana getirdiği farklılıkların üzerinde durmaktadır.
📊
Temel Bulgular & Veri:Yapılan değerlendirmeler, farklı çözünürlüklerde sayısal yükseklik modelleri Global Multi-resolution Terrain Elevation Data–GMTED2010 ve Multi-Error-Removed Improved–Terrain DEM kullanarak yapılmıştır.
💡
Sonuç & Katkı:Seçili alanlarda sahayı en iyi temsil eden ölçek ve pencere boyutlarının kombinasyonlarını içeren test sonuçları, pencere boyutunda yapılan değişikliklerle genelleştirme kapasitesi arttıkça tanımlanan makro yer şekli birliğinin farklı bir haritayla sonuçlanabileceğini göstermektedir.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2000 Verileri
⚙️Değişkenler:Jeomorfometri · Makro Yer Şekilleri · Dağ Sınıfları

Özet

Bu çalışma makro yer şekillerinin tanımlanmasında temel alınan pencere örneklem boyutlarının istatistiksel önemi ve tanımlamalarda meydana getirdiği farklılıkların üzerinde durmaktadır. Yerşekillerinin otomatik olarak sınıflandırılmasında, optimum ölçeğin belirlenmesi sorunu önemini korumaktadır. Bu nedenle, ölçek faktörü ile örneklem pencere boyutu arasındaki ilişkiler yer şekillerinin tanımlanmasında dolayısıyla sınıflandırılmasındaki ilk aşamayı oluşturmaktadır. Yapılan değerlendirmeler, farklı çözünürlüklerde sayısal yükseklik modelleri Global Multi-resolution Terrain Elevation Data–GMTED2010 ve Multi-Error-Removed Improved–Terrain DEM kullanarak yapılmıştır. Dağ-plato ve dağ-ova arasındaki sınır belirsizliklerinin farklı ölçek ve analiz pencerelerinde tanımlamalarda getirdiği farklılıklar, UNEP-WCMC 2000 (K1) sınıflama algoritması kullanılarak Türkiye özelinde tartışılmıştır. Bu alanlara ilişkin yükseklik, eğim, topoğrafik röliyef gibi sayısal yükseklik modeli türevleri ve bunlara ait tanımsal istatistikler kullanılarak veri matrisleri oluşturulmuştur. Seçili alanlarda sahayı en iyi temsil eden ölçek ve pencere boyutlarının kombinasyonlarını içeren test sonuçları, pencere boyutunda yapılan değişikliklerle genelleştirme kapasitesi arttıkça tanımlanan makro yer şekli birliğinin farklı bir haritayla sonuçlanabileceğini göstermektedir. Buna göre makro yer şekillerinin tanımlanmasında, çalışmamızda değişen oranlarda yapılan pencere boyutu testlerinde belirlenen 2.5 km’lik komşuluk analiz penceresi boyutu üst sınırı ile daha anlamlı sonuçlar ortaya çıkmıştır. Yerşekli sınıflamasında dağ sınır ilişkilerinin, SYM çözünürlüğünden ziyade komşuluk analiz pencere boyutuna daha duyarlı olduğu görülmüştür.

Abstract

This study focuses on the statistical significance of sampling window sizes, which are used to define macro landforms and the differences they cause in definitions. In the automatic classification of landforms, the problem of determining the optimum scale remains important. Therefore, the relations between the scale factor and the window size constitute the first step, thus classifying landforms. The evaluations were carried out using GMTED2010 and MERIT DEM at different resolutions. The differences in the definitions of different scales and analysis windows caused by the border uncertainties between mountainplateau and mountain-plain that are specific to Türkiye were discussed using the UNEP-WCMC 2000 classification algorithm. Data matrices were created using DEM derivatives such as elevation, slope, and topographic relief for these areas and their descriptive statistics. The test results, which include the combinations of scale and window sizes that best represent the area in selected fields, indicate that the defined macro landform units can result in a more different map as the generalization capacity increases with the changes made in the window size. More meaningful results emerged with the upper limit of the 2.5 km NAW size determined in our study’s window size tests performed at varying rates. In landform classification, mountain boundary relationships were more sensitive to NAW size than DEM resolution.

🔖

Bu Makaleye Atıf Yap

Dal, N., Görüm, T. (2023). Makro Yerşekillerinin Tanımlanmasında Ölçek ve Örneklem Pencere Boyutuna İlişkin Belirsizlikler. Coğrafya Dergisi, Sayı: 46, 157–171. https://doi.org/10.26650/JGEOG2023-1265064

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (63)

  1. Arrell, K. E., Fisher, P. F., Tate, N. J., & Bastin, L. (2007). A fuzzy c-means classification of elevation derivatives to extract the morphometric classification of landforms in Snowdonia, Wales. Computers & Geosciences, 33(10), 1366-1381. doi:10.1016/j. cageo.2007.05.005 google scholar
  2. Atalay, I. (1987). Türkiye Jeomorfolojisine Giriş. (Introduction to geomorphology of Türkiye). Turkish. E.U. Edebiyat Fak. Yay. No.8, Izmir. google scholar
  3. A-Xing, Z., Burt, J. E., Smith, M., Rongxun, W., & Jing, G. (2008). The impact of neighborhood size on terrain derivatives and digital soil mapping. In Zhou, Q., Lees, B., & Tang, G. A. (Eds.). Advances in digital terrain analysis (pp. 333-348). Berlin, Heidelberg: Springer. google scholar
  4. Couclelis, H. (2003). The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge. Transactions in GIS, 7(2), 165175. doi:10.1111/1467-9671.00138 google scholar
  5. Danielson, J. J. & Gesch, D. B. (2011). Global multi-resolution terrain elevation data 2010. US Department of the Interior and US Geological Survey, Open-File Report 2011-1073, 1-35 google scholar
  6. Dehn, M., Gartner, H., & Dikau, R. (2001). Principles of semantic modeling of landform structures. Computers & Geosciences, 27(8), 1005-1010. doi:10.1016/s0098-3004(00)00138-2 google scholar
  7. Deng, Y., Wilson, J. P., & Bauer, B. O. (2007). DEM resolution dependencies of terrain attributes across a landscape. International Journal of Geographical Information Science, 21(2), 187213. doi:10.1080/13658810600894364 google scholar
  8. Deng, Y.X., Wilson, J.P., & Gallant, J.C., (2018). Terrain Analysis. In: Wilson, J.P., Fotheringham, A.S. (Eds.). Handbook of Geographic Information Science. (pp. 417-435). Oxford: Blackwell Publishers. google scholar
  9. Ehsani, A. H., Quiel, F., & Malekian, A. (2010). Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map. GeoInformatica, 14(4), 405424. doi:10.1007/s10707-009-0085-4 google scholar
  10. Evans, I. S. (2012). Geomorphometry and landform mapping: What is a landform? Geomorphology, 137(1), 94-106. doi:10.1016/j.geomorph.2010.09.029 google scholar
  11. Evans, I.S. (1975). The effect of resolution on gradients calculated from an altitude matrix. Report 3 on Grant DAERO-591-73-G0040, ‘Statistical characterization of altitude matrices by computer’, (Appendix: Stationarity). Department of Geography, University of Durham, England, 1-6. google scholar
  12. Fisher, P. (2000). Sorites paradox and vague geographies. Fuzzy Sets and Systems, 113(1), 7-18. doi:10.1016/s0165-0114(99)00009-3 google scholar
  13. Fisher, P. F., and Wood, J., 1998. What is a Mountain? or The Englishman who went up a Boolean Geographical concept but realised it was Fuzzy. Geography 83 (3), 247- 256 google scholar
  14. Fisher, P., Wood, J., & Cheng, T. (2004). Where is Helvellyn? Fuzziness of multi-scale landscape morphometry. Transactions ofthe Institute British Geographers, 29(1), 106-128. doi:10.1111/j.0020-2754.2004.00117.x google scholar
  15. Fisher, P., Wood, J., & Cheng, T. (2004). Where is Helvellyn? Fuzziness of multi-scale landscape morphometry. Transactions of the Institute of British Geographers, 29(1), 106-128. doi:10.1111/j.0020-2754.2004.00117.x google scholar
  16. Florinsky, I. V., & Kuryakova, G. A. (2000). Determination of grid size for digital terrain modelling in landscape investigations— exemplified by soil moisture distribution at a micro-scale. International Journal of Geographical Information Science, 14(8), 815-832. doi:10.1080/136588100750022804 google scholar
  17. Gallant, J. C., & Hutchinson, M. F. (1997). Scale dependence in terrain analysis. Mathematics and Computers in Simulation, 43(3-6), 313321. doi:10.1016/s0378-4754(97)00015-3 google scholar
  18. Goodchild, M. F. (2001). Metrics of scale in remote sensing and GIS. International Journal of Applied Earth Observation and Geoinformation, 3(2), 114-120. doi:10.1016/s0303-2434(01)85002-9 google scholar
  19. Goodchild, M.F. (2011). Scale in GIS: An overview. Geomorphology, 130(1-2), 5-9. doi:10.1016/j.geomorph.2010.10.004 google scholar
  20. Görüm, T. (2018). Tectonic, topographic and rock-type influences on large landslides at the northern margin of the Anatolian Plateau. Landslides, 16, 333-346. doi:10.1007/s10346-018-10977 google scholar
  21. Grohmann, C.H., & Riccomini, C. (2009). Comparison of roving-window and search-window techniques for characterising landscape morphometry. Computers & Geosciences. 35, 2164-3169. doi:10.1016/j.cageo.2008.12.014 google scholar
  22. Guth, P.L., Van Niekerk, A., Grohmann, C.H., Muller, J.P., Hawker, L., Florinsky, I.V., Gesch, D., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., L6pez-Vâzquez, C., Carabajal, C.C., Albinet, C. Strobl, P. (2021). Digital Elevation Models: Terminology and Definitions. Remote Sensing, 13 (18), 3581. https:// doi.org/10.3390/rs13183581 google scholar
  23. Hagen-Zanker, A. (2016). A computational framework for generalized moving windows and its application to landscape pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 44, 205-216. doi:10.1016/j.jag.2015.09.010 google scholar
  24. Hengl, T., & MacMillan, R. A. (2009). Geomorphometry — A Key to Landscape Mapping and Modelling. In Hengl, T. & Reuter, H.I (Eds.), Geomorphometry - Concepts, Software, Applications, Series Developments in Soil Science, (pp. 433-460). Amsterdam, Elsevier. google scholar
  25. İzbırak, R. (1955). Sistematik Jeomorfoloji, Harita Umum Müdürlüğü, Ankara google scholar
  26. Jasiewicz, J., & Stepinski, T. F. (2013). Geomorphons — a pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147-156. doi:10.1016/j.geomorph.2012.11.005 google scholar
  27. Kapos, V., Rhind, J., Edwards, M., Price, M.F., & Ravilious, C. (2000). Developing a map of the world’s mountain forests. In: Price MF, Butt N (Eds.), Forests in sustainable mountain development: a state-of knowledge report for 2000. UK: CAB International, Wallingford, 4-9. google scholar
  28. Kienzle, S. (2004). The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives. Transactions in GIS, 8(1), 83-111. doi:10.1111/j.1467-9671.2004.00169.x google scholar
  29. Kuzucuoğlu, C. (2019). The physical geography of Turkey: an outline. In C. Kuzucuoğlu, A. Çiner, & N. Kazancı (Eds.), Landscapes and landforms of Turkey (pp. 7-15). Switzerland: Springer Nature. google scholar
  30. Kuzucuoğlu, C., Çiner, A. & Kazancı, N. (2019b). The geomorphological regions of Turkey. In C. Kuzucuoğlu, A. Çiner, & N. Kazancı (Eds.), Landscapes and landforms of Turkey (pp. 41-178). Switzerland: Springer Nature. google scholar
  31. Li, L., Ban, H., Wechsler, S.P., & Xu, B., (2018). Spatial Data Uncertainty. In: Huang, B. (Eds.). Comprehensive Geographic Information Systems. (pp. 313-340). Oxford: Elsevier. doi:10.1016/ b978-0-12-409548-9.09610-x google scholar
  32. Li, Y. (2015). Effects of analytical window and resolution on topographic relief derived using digital elevation models, GIScience & Remote Sensing, 52:4, 462-477, doi: 10.1080/15481603.2015.1049577 google scholar
  33. MacMillan, R. A., & Shary, P. A. (2009). Landforms and landform elements in geomorphometry. In T. Hengl & H. I. Reuter (Eds.), Geomorphometry: Concepts, software, applications (pp. 227-254). Amsterdam: Elsevier. google scholar
  34. MacMillan, R.A., Pettapiece, W.W., Nolan, S.C., & 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, 113(1), 81-109. doi:10.1016/s0165-0114(99)00014-7 google scholar
  35. Marceau, D. J. (1999). The Scale Issue in the Social and Natural Sciences. Canadian Journal of Remote Sensing, 25(4), 347356. doi:10.1080/07038992.1999.10874734 google scholar
  36. Mark, D. M. (1975). Geomorphometric Parameters: A Review and Evaluation. Geografiska Annaler: Series A, Physical Geography, 57(3-4), 165-177. doi:10.1080/04353676.1975.1187991 google scholar
  37. Meybeck, M., Green, P., & Vörösmarty, C. (2001). A New Typology for Mountains and Other Relief Classes. Mountain Research and Development, 21(1), 34-45. doi:10.1659/0276-4741(2001)021[0034:an tfma]2.0.co;2 google scholar
  38. Pain, C.F. (2005). Size Does Matter: Relationships Between Image Pixel Size and Landscape Process Scales. In International Congress ofModelling and Simulation, Proceedings of the MODSIM, Sydney, Australia, 12-15 December (pp. 1430-1436). Modelling and Simulation Society of Australia and New Zealand Inc.: Sydney, Australia. google scholar
  39. Pike, R. J. (1988). The geometric signature: Quantifying landslide-terrain types from digital elevation models. Mathematical Geology, 20(5), 491-511. doi:10.1007/bf00890333 google scholar
  40. Pike, R.J., Evans, I. S. & Hengl, T. (2009) Geomorphometry: A Brief Guide. In Hengl, T. & Reuter, H.I (Eds.), Geomorphometry— Concepts, Software, Applications, Series Developments in Soil Science, (pp. 3-33). Amsterdam, Elsevier. google scholar
  41. Rasemann, S., Schmidt, J., Schrott, L., & Dikau, R.(2004). Geomorphometry in mountain terrain. In M. P. Bishop & J. F. Shroder (Eds). Geographic Information Science and Mountain Geomorphology (pp. 101-146). Berlin: Praxis Books in Geophysical Sciences, Springer. google scholar
  42. Sainsbury R.M. (1995). Paradoxes, 2nd ed., In Vagueness: the paradox of the heap, 23-52,Cambridge, Cambridge University Press. google scholar
  43. Schmidt, J., & Andrew, R. (2005). Multi-scale landform characterization. Area, 37(3), 341-350. doi:10.1111/j.1475-4762.2005.00638.x google scholar
  44. Schoorl, J. M., Sonneveld, M. P. W., & Veldkamp, A. (2000). Three-dimensional landscape process modelling: the effect of DEM resolution. Earth Surface Processes and Landforms, 25(9), 1025-1034. doi:10.1002/1096-9837(200008)25:9 3.0.co;2-z google scholar
  45. Shary, P. A., Sharaya, L. S., & Mitusov, A. V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107(1-2), 1-32. doi:10.1016/s0016-7061(01)00136-7 google scholar
  46. Skidmore, A.K. (1990). Terrain position as mapped from gridded digital elevation data. International Journal of Geographical Information Systems, 4(1), 33-49. doi:10.1080/02693799008941527 google scholar
  47. Slaymaker, O. (1991). Mountain geomorphology: A theoretical framework for measurement programmes. Catena, 18(5), 427437. doi:10.1016/0341-8162(91)90047-2 google scholar
  48. Slaymaker, O., & Embleton-Hamann, C. (2018). Advances in global mountain geomorphology. Geomorphology, 308, 230 264. doi:10.1016/j.geomorph.2018.02.016 google scholar
  49. Smith, M. P., Zhu, A.-X., Burt, J. E., & Stiles, C. (2006). The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma, 137(1-2), 58-69. doi:10.1016/j.geoderma.2006.07.002 google scholar
  50. S0rensen, R., & Seibert, J. (2007). Effects of DEM resolution on the calculation of topographical indices: TWI and its components. Journal of Hydrology, 347(1-2), 79-89. doi:10.1016/j.jhydrol.2007.09.001 google scholar
  51. Szypula, B. (2017). Digital Elevation Models in Geomorphology. In D. S. Shukla (Eds.), Hydro-Geomorphology - Models and Trends (pp. 81-112). doi:10.5772/intechopen.68447 google scholar
  52. Thompson, J. A., Bell, J. C., & Butler, C. A. (2001). Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma, 100(1-2), 6789. doi:10.1016/s0016-7061(00)00081-1 google scholar
  53. USGS. (2010). Global multi-resolution terrain elevation data. Retrieved from http://topotools.cr.usgs.gov/gmted_viewer/ google scholar
  54. Uuemaa, E., Ahi, S., Montibeller, B., Muru, M., & Kmoch, A. (2020). Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM). Remote Sensing, 12(21), 3482. doi:10.3390/ rs12213482 google scholar
  55. Van Hinsbergen, D. J. J., Maffione, M., Plunder, A., Kaymakcı, N., Ganerod, M., Hendriks, B. W. H.,. Vissers, R. L. M. (2016). Tectonic evolution and paleogeography of the Kırşehir Block and the Central Anatolian Ophiolites, Turkey. Tectonics, 35(4), 983-1014. doi:10.1002/2015tc004018 google scholar
  56. Wang, D., Laffan, S. W., Liu, Y., & Wu, L. (2010). Morphometric characterisation of landform from DEMs. International Journal of Geographical Information Science, 24(2), 305 326. doi:10.1080/13658810802467969 google scholar
  57. Wilson, J.P. and Gallant, J.C. (2000). Digital Terrain Analysis. In: Wilson, J.P. and Gallant, J.C. (Eds.). Terrain Analysis: Principles and Applications (pp. 1-27). New York, John Wiley & Sons. google scholar
  58. Wood, J.D. (1996). The geomorphological characterisation of digital elevation models( Ph.D. thesis, Department of Geography, University of Leicester, Leicester, U.K.). Retrieved from https:// leicester.figshare.com/articles/thesis/The_geomorphological_ characterisation_of_Digital_Elevation_Models_/10152368 google scholar
  59. Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research, 55, 5053-5073. doi:10.1029/2019wr024873 google scholar
  60. Zwolinski, Z., & Stefanska, E. (2015). Relevance of moving window size in landform classification by TPI. J. Jasiewicz et al., (Ed.), Geomorphometry for Geosciences In (pp. 273-277). AdamMickiewicz University in Poznan - Institute of Geoecology and Geoinformation, International Society for Geomorphometry,Poznan. Bogucki Wydawnictwo Naukowe,ISBN: 978-83-7986-059-3. google scholar
  61. Guth, P.L., Van Niekerk, A., Grohmann, C.H., Muller, J.P., Hawker, L., Florinsky, I.V., Gesch, D., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., L6pez-Vâzquez, C., Carabajal, C.C., Albinet, C. Strobl, P. (2021). Digital Elevation Models: Terminology and Definitions. Remote Sensing, 13 (18), 3581. https:// doi.org/10.3390/rs13183581 google scholar
  62. Rasemann, S., Schmidt, J., Schrott, L., & Dikau, R.(2004). Geomorphometry in mountain terrain. In M. P. Bishop & J. F. Shroder (Eds). Geographic Information Science and Mountain Geomorphology (pp. 101-146). Berlin: Praxis Books in Geophysical Sciences, Springer. google scholar
  63. Van Hinsbergen, D. J. J., Maffione, M., Plunder, A., Kaymakcı, N., Ganerod, M., Hendriks, B. W. H.,. Vissers, R. L. M. (2016). Tectonic evolution and paleogeography of the Kırşehir Block and the Central Anatolian Ophiolites, Turkey. Tectonics, 35(4), 983-1014. doi:10.1002/2015tc004018 google scholar