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Geomorphometry-Automatic Landform Classification

Year 2018, , 15 - 26, 22.06.2018

Fatmagül Kılıç Gül

https://doi.org/10.26650/JGEOG409177

Abstract

In the past, landforms were represented in physiographic and morphometric maps by hand drawing. With developments in digital elevation models (DEM), geographic information systems (GIS) and image analyses, automatic extraction of landforms from morphological parameters and data storage in databases is now possible and are actively utilized in various fields, such as geomorphology, soil science, and ecology. In the above scopes, DEM data forms the database of morphometric parameters, such as, relief, slope, curvatures, and topographic openness. Presently, calculation of parameters, implementation of relationships with landforms, scaling, classification methods, topological relations, homogeneity, and generalizations during the transformation of 3D components of landforms, such as mountains, peaks, slopes, valleys, and plain, into 2D geometric elements in computers are being investigated. In this study, the methods and applications for the automatic extraction of the landforms developed by researchers across different disciplines were reviewed. Classification methods were grouped as combined parameters method and unsupervised/supervised classification methods based on pixel/object. This paper emphasizes the importance of adopting machine learning to implement new models applicable to all terrains.

Keywords

Landforms DEM Classification

References

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  • Iwahashi, J., Kamiya, I., Matsuoka, M. et al. (2018). Global terrain classification using 280 m DEMs: Segmentation, clustering, and reclassification. Progress in Earth and Planetary Science 5(1), 1-31.. doi:10.1186/s40645-017-0157-2
  • Jamil, A., Bayram, B. (2018). Tree species extraction and land use/cover classification from high-resolution digital orthophoto maps, IEEE Journal of Selected Topics In Applied Earth Observations and Remote Sensing, 11(1), 89-94. doi: 10.1109/JSTARS.2017.2756864
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Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi

Year 2018, , 15 - 26, 22.06.2018

Fatmagül Kılıç Gül

https://doi.org/10.26650/JGEOG409177

Abstract

Yeryüzü şekilleri, geçmişte fizyografik ve morfometrik haritalarda elle çizilerek gösterilirken, jeomorfometri, sayısal yükseklik modelleri (SYM), görüntü işleme ve coğrafi bilgi sistemleri (CBS) alanındaki gelişmeler şekillerin otomatik çıkarılmasını, veri tabanlarında depolanmasını ve jeomorfoloji, toprak bilimi, ekoloji vb. pek çok alanda daha etkin kullanımını sağlamıştır. Bu tür çalışmalarda temel veri SYM ve ondan hesaplanan eğim, eğrisellik, yükseklik farkı, topografik açıklık vb. morfolojik parametrelerdir. Yeryüzünde bileşenleri üç boyutlu (3B) olan yamaç, düzlük, vadi vb. şekillerin sınırlarının, iki boyutlu (2B) geometrik elemanlara yazılımlar ile dönüştürülmesinde parametrelerin hesaplanması, şekillenme ile ilişkilerinin kurulması, ölçek, sınıflandırma yöntemi, yeryüzü şekillerinin doğada birbirlerine göre topolojik ilişkileri, homojenlik, genelleştirme halen araştırılan konular arasındadır. Bu çalışmada, farklı disiplinlere mensup araştırmacılar tarafından yeryüzü şekillerinin otomatik belirlenmesine yönelik geliştirilen yöntemler ve uygulamalar incelenmiş, yöntemler; parametrelerin kombinasyonu ile yapılan denetimsiz sınıflandırma; piksel tabanlı denetimsiz/denetimli sınıflandırma ve obje tabanlı sınıflandırma şeklinde ayrıştırılmıştır. Örüntüler, öğrenme tabanlı modeller vb. algoritmalar ile her tür araziye uygulanabilecek modellerin geliştirilmesinin önemi vurgulanmıştır.

Keywords

SYM Yeryüzü şekilleri Sınıflandırma

References

  • Anders, N. S., Seijmonsbergen, A. C., Bouten, W. (2011). Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sensing of Environment, 115(12), 2976-2985. doi:10.1016/j.rse.2011.05.007
  • Arrell, K. E., Peter, 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 Band, L. E. (1986). Topographic partition of watersheds with digital elevation models. Water Resources Research, 22(1), 15-24. doi: 10.1029/WR022i001p00015
  • Batuk F., Emem O., Görüm T., Gökaşan E. (2008, January). Implementation of GIS for landforms of Southern Marmara , FIG Working Week, Stokholm, Isveç
  • Blaschke, T., Strobl, J. (2003). Defining landscape units through integrated morphometric characteristics. In E. Buhmann, & S. Ervin (Eds.) Landscape modelling: Digital techniques for landscape architecture (pp. 104-113). Heidelberg: Wichmann Verlag
  • Bolongaro-Crevenna A., Torres-Rodríguez V., Sorani V., Framed D., Ortiz M. A. (2005). Geomorphometric analysis for characterizing landforms in Morelos State, Mexico. Geomorphology 67(3-4), 407–422. doi: 10.1016/j.geomorph.2004.11.007
  • Brabyn, L. (1998, November). GIS analysis of macro landform. Proceedings of the Spatial Information Research Centre’s 10th Colloquium, 35-48. Dunedin, New Zealand
  • Burrough, P. A., van Gaans, P. F. M., MacMillan, R. A. (2000). High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113(1), 37-52. doi: 10.1016/S0165-0114(99)00011-1
  • Congalton, R., Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. New York: Lewis Publishers
  • Del Val, M., Iriarte, E., Arriolabengoa, M., Aranburu, A. (2015). An automated method to extract fluvial terraces from LiDAR based high resolution digital elevation models: The Oiartzun Valley, a case study in the Cantabrian margin. Quaternary International, 364, 35–43. doi: 10.1016/j.quaint.2014.10.030
  • Dikau, R. (1989). The application of a digital relief model to landform analysis in geomorphology. In J. Raper (Eds.), Three dimensional application in geographic information systems (pp. 51-77) London, UK :Taylor & Francis
  • Dikau, R., Brabb, E. E., Mark, R. M. (1991). Landform classification of New Mexico by computer. USA- Geological Survey Open-File Report, 91(634)
  • Dikau, R., Brabb, E., Mark, R. K., Pike, R. J. (1995). Morphometric landform analysis of New Mexico. Z. Geomorphol. Suppl. 101, 109–126
  • Dragut L., Blaschke T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology 81(3-4), 330–344. doi: 10.1016/j.geomorph.2006.04.013
  • Drescher, K., Frey, W. D. (2009, Agust-Sept). Landform classification using GIS, Position IT, 30-34
  • Evans, I.S. (1980). An integrated system of terrain analysis and slope mapping. Zeitschrift für Geomorphologie, Supplementband 36, 274–295.
  • 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
  • Gallant, A. L., Brown, D. D., Hoffer, R. M. (2005). Automated mapping of Hammond's landforms. IEEE Geoscience and Remote Sensing Letters, 2(4), 384-388. doi: 10.1109/LGRS.2005.848529
  • Gökgöz, T. Moustafa Khalil, M. B. (2015). Large scale landform mapping using Lidar DEM. ISPRS International Journal of Geo-Information, 4(3), 1336-1345. doi: 10.3390/ijgi4031336
  • Gruber, F. E., Baruck, J., Geitner, C. (2017). Algorithms vs. surveyors: A comparison of automated landform delineations and surveyed topographic positions from soil mapping in an Alpine environment. Geoderma, 308, 9-17. doi: 10.1016/j.geoderma.2017.08.017
  • Hammond, E. H. (1954). Small scale continental landform maps. Annals of Association of American Geographers 44, 33-42
  • Hrvatin, M., Perko, D. (2009). Suitability of Hammond's method for determining landform units in Slovenia. Acta Geographica Slovenica, 49(2), 343-366. doi: 10.39 86/AGS49204
  • Hutchinson, M. F. (1988). Calculation of hydrologically sound digital elevation models. Proceedings of the Third International Symposium on Spatial Data Handling, 117-133. Sydney, Australia.
  • Iwahashi, J., Pike, R. (2007). Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 86(3-4), 409-440. doi: 10.1016/j.geomorph.2006.09.012
  • Iwahashi, J., Kamiya, I., Matsuoka, M. et al. (2018). Global terrain classification using 280 m DEMs: Segmentation, clustering, and reclassification. Progress in Earth and Planetary Science 5(1), 1-31.. doi:10.1186/s40645-017-0157-2
  • Jamil, A., Bayram, B. (2018). Tree species extraction and land use/cover classification from high-resolution digital orthophoto maps, IEEE Journal of Selected Topics In Applied Earth Observations and Remote Sensing, 11(1), 89-94. doi: 10.1109/JSTARS.2017.2756864
  • Jasiewicz, J., Stepinski, T.F. (2013). Geomorphons - a pattern recognition approach to classification and mapping of landforms, Geomorphology, 182(2013), 147–156. doi: 10.1016/j.geomorph.2012.11.005
  • Jasiewicz, J., Netzel P., Stepinski, T. F. (2014). Landscape similarity, retrieval, and machine mapping of physiographic units. Geomorphology 221, 104–112
  • Jenson, S.K., Domingue, J.O. (1988). Extracting topographic structure from digital elevation data for GIS analysis. Photogrammetric Engineering & Remote Sensing, 54(11):1593-1600.
  • Karagulle, D., Frye, C., Sayre, R., Breyer, S., Aniello, P., Vaughan, R., Wright, D. (2017). Modeling global Hammond landform regions from 250-m elevation data. Transactions in GIS. 21(5) 1040-1060. doi: 10.1111/tgis.12265
  • Keller, E. A., & Pinter, N. (2001). Active Tectonics: Earthquakes, Uplift, and Landscape. Upper Saddle River, N.J. : Prentice Hall
  • Kılıç F., Öztürk D. (2013, Mayıs). Yeryüzü Şekillerinin Sayısal Yükseklik Modelleri İle Otomatik Çıkarılması, TUFUAB V. Sempozyumu, Trabzon
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There are 75 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Fatmagül Kılıç Gül 0000-0003-3467-9288

Publication Date June 22, 2018
Submission Date March 23, 2018
Published in Issue Year 2018

Cite

APA Kılıç Gül, F. (2018). Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi. Journal of Geography(36), 15-26. https://doi.org/10.26650/JGEOG409177
AMA Kılıç Gül F. Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi. Journal of Geography. June 2018;(36):15-26. doi:10.26650/JGEOG409177
Chicago Kılıç Gül, Fatmagül. “Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi”. Journal of Geography, no. 36 (June 2018): 15-26. https://doi.org/10.26650/JGEOG409177.
EndNote Kılıç Gül F (June 1, 2018) Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi. Journal of Geography 36 15–26.
IEEE F. Kılıç Gül, “Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi”, Journal of Geography, no. 36, pp. 15–26, June 2018, doi: 10.26650/JGEOG409177.
ISNAD Kılıç Gül, Fatmagül. “Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi”. Journal of Geography 36 (June 2018), 15-26. https://doi.org/10.26650/JGEOG409177.
JAMA Kılıç Gül F. Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi. Journal of Geography. 2018;:15–26.
MLA Kılıç Gül, Fatmagül. “Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi”. Journal of Geography, no. 36, 2018, pp. 15-26, doi:10.26650/JGEOG409177.
Vancouver Kılıç Gül F. Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi. Journal of Geography. 2018(36):15-26.