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Makro Yerşekillerinin Tanımlanmasında Ölçek ve Örneklem Pencere Boyutuna İlişkin Belirsizlikler

Yıl 2023, , 157 - 171, 25.05.2023
https://doi.org/10.26650/JGEOG2023-1265064

Öz

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.

Kaynakça

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

Yıl 2023, , 157 - 171, 25.05.2023
https://doi.org/10.26650/JGEOG2023-1265064

Öz

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.

Kaynakça

  • 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
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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • İzbırak, R. (1955). Sistematik Jeomorfoloji, Harita Umum Müdürlüğü, Ankara google scholar
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Beşeri Coğrafya (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Neslihan Dal 0000-0003-2372-4960

Tolga Görüm 0000-0001-9407-7946

Yayımlanma Tarihi 25 Mayıs 2023
Gönderilme Tarihi 14 Mart 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Dal, N., & Görüm, T. (2023). Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms. Journal of Geography(46), 157-171. https://doi.org/10.26650/JGEOG2023-1265064
AMA Dal N, Görüm T. Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms. Journal of Geography. Mayıs 2023;(46):157-171. doi:10.26650/JGEOG2023-1265064
Chicago Dal, Neslihan, ve Tolga Görüm. “Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms”. Journal of Geography, sy. 46 (Mayıs 2023): 157-71. https://doi.org/10.26650/JGEOG2023-1265064.
EndNote Dal N, Görüm T (01 Mayıs 2023) Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms. Journal of Geography 46 157–171.
IEEE N. Dal ve T. Görüm, “Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms”, Journal of Geography, sy. 46, ss. 157–171, Mayıs 2023, doi: 10.26650/JGEOG2023-1265064.
ISNAD Dal, Neslihan - Görüm, Tolga. “Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms”. Journal of Geography 46 (Mayıs 2023), 157-171. https://doi.org/10.26650/JGEOG2023-1265064.
JAMA Dal N, Görüm T. Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms. Journal of Geography. 2023;:157–171.
MLA Dal, Neslihan ve Tolga Görüm. “Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms”. Journal of Geography, sy. 46, 2023, ss. 157-71, doi:10.26650/JGEOG2023-1265064.
Vancouver Dal N, Görüm T. Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms. Journal of Geography. 2023(46):157-71.