Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi

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.

Geomorphometry-Automatic Landform Classification

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.

Kaynakça

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. https://doi.org/10.1016/j. rse.2011.05.007

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. https://doi. org/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. https:// doi.org/10.1029/WR022i001p00015

Batuk, F., Emem, O., Görüm, T., & Gökaşan, E. (2008, June). Implementation of GIS for landforms of Southern Marmara. Paper presented at Integrating Generations. FIG Working Week 2008, Stockholm, Sweden.

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, DE: 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. https://doi.org/10.1016/j.geomorph.2004.11.007

Brabyn, L. (1998). GIS analysis of macro landform. In Proceedings of the Spatial Information Research Centre’s 10th Colloquium, November (pp.35–48). Research Centre.

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. https://doi.org/10.1016/S0165- 0114(99)00011-1

Congalton, R., & Green, K. (1999). Assessing the accuracy of remotely sensed data: Principles and practices. New York, NY: 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. https://doi.org/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 (Ed.), 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), 1–16.

Dikau, R., Brabb, E., Mark, R. K., & Pike, R. J. (1995). Morphometric landform analysis of New Mexico. Zeitschrift für Geomorphologie, 101, 109–126.

Dragut L., & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3–4), 330–344. https://doi.org/10.1016/j.geomorph.2006.04.013

Drescher, K., & Frey, W. D. (2009). Landform classification using GIS. Position IT, Agust-Sept, 30–34. Retrieved from https://www.ee.co.za/ wp-content/uploads/legacy/PositionIT%202009/page%2030-34.pdf

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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.39 86/AGS49204

Hutchinson, M. F. (1988). Calculation of hydrologically sound digital elevation models. In Proceedings of the Third International Symposium on Spatial Data Handling (pp. 117–133). Columbus, Ohio: International Geographical Union

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. https://doi.org/10.1016/j.geomorph.2006.09.012

Iwahashi, J., Kamiya, I., Matsuoka, & Yamazaki, D. (2018). Global terrain classification using 280 m DEMs: Segmentation, clustering, and reclassification. Progress in Earth and Planetary Science, 5(1), 1–31. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.1016/j.geomorph.2014.06.011

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. https://doi.org/10.1111/tgis.12265

Keller, E. A., & Pinter, N. (2001). Active tectonics: Earthquakes, uplift and landscape. Upper Saddle River, NJ: Prentice Hall.

Kılıç, F. ve Öztürk, D. (2013, Mayıs). Yeryüzü şekillerinin sayısal yükseklik modelleri ile otomatik çıkarılması. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği V. Sempozyumu’nda sunulan bildiri, Trabzon.

Klingseisen, B., Metternicht, G., & Paulus, G. (2007). Geomorphometric landscape analysis using a semi-automated GIS-approach. Environmental Modelling & Software 23(1), 109–121. https://doi. org/10.1016/j.envsoft.2007.05.007

Kramm, T., Hoffmeister, D., Curdt, C., Maleki, S., Khormali, F., & Kehl, M. (2017). Accuracy assessment of landform classification approaches on different spatial scales for the Iranian loess plateau. ISPRS International Journal of Geo-Information, 6(11), 1–22. https://doi.org/10.3390/ijgi6110366

Kringer, K., Tusch, M., Geitner, C., Rutzinger, M., Wiegand, C., & Meißl, G. (2009). Geomorphometric analyses of LiDAR digital terrain models as input for digital soil mapping. Proceedings of Geomorphometry 2009 (pp. 74–81). University of Zurich.

Luo, W., & Stepinski, T. F. (2008). Identification of geologic contrast from landscape dissection pattern: An application to the Cascade Range, Oregon, USA. Geomorphology, 99(1–4), 90–98. https://doi. org/10.1016/j.geomorph.2007.10.014

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. https:// doi.org/10.1016/S0165-0114(99)00014-7

MacMillan, R. A., Martin, T. C., & Earle, T. J. (2003). Automated analysis and classification of landforms using high-resolution digital elevation data: Applications and issues. Canadian Journal of Remote Sensing, 29(5), 592–606. https://doi.org/10.5589/m03-031

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

Mark, D. M. (1975). Geomorphometric parameters: A review and evaluation. Geografiska Annaler Series A, Physical Geography, 57(3–4), 165–177.

Martins, F. M. G., Fernandez, H. M., Isidoro, J. M. G. P., Jordán, A., & Zavala, L. (2016). Classification of landforms in Southern Portugal (Ria Formosa Basin). Journal of Maps, 12(3), 422–430. https://doi.org/10.1080/17445647.2015.1035346

Mashimbye, Z. E., De Clercq, W. P., & Van Niekerk, A. (2014). An evaluation of digital elevation models (DEMs) for delineating land components. Geoderma, 213, 312–319. https://doi.org/10.1016/j. geoderma.2013.08.023

Miller, B. A., & Schaetzl, R. J. (2015). Digital classification of hillslope position. Soil Science Society of America Journal, 79(1), 132–145. https://doi.org/10.2136/sssaj2014.07.0287

Milne, J. D. G., Clayden, B., Singleton, P. L., & Wilson, A. D. (1995). Soil description handbook. Landcare, AU: Manaaki Whenua Press.

Minar, J., & Evans, I. S. (2008). Elementary forms for land surface segmentation: The theoretical basis of terrain analysis and geomorphological mapping. Geomorphology, 95(3–4), 236–259. https://doi.org/10.1016/j.geomorph.2007.06.003

Moravej, K., Karimian Eghbal, M., Toomanian, N., & Shahla Mahmoodi, S. (2012). Comparison of automated and manual landform delineation in semi detailed soil survey procedure. African Journal of Agricultural Research, 17(7), 2592–2600. https://doi. org/10.5897/AJAR11.728.

Morgan, J. M., & Lesh, A. (2005). Developing landform maps using ESRI’s model builder. Proceedings of the 2005 ESRI International User Conference, (pp. 25–29). USA.

Norini, G., Zuluaga, M. C., Ortiz, İ. J., Aquino, D. T., & Lagmay, A. M. F. (2016). Delineation of alluvial fans from digital elevation models with a GIS algorithm for the geomorphological mapping of the Earth and Mars. Geomorphology, 273(15), 134–149. https://doi. org/10.1016/j.geomorph.2016.08.010

O’Caliaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer Vision, Graphics and Image Processing, 28(3), 323–344. https://doi.org/10.1016/ S0734-189X(84)80011-0

Pennock, D. J., Zebarth, B. J., & DeJong, E. (1987). Landform classification and soil distribution in hummocky terrain, Saskatchewan, Canada. Geoderma, 40(3–4), 297–315. https://doi. org/10.1016/0016-7061(87)90040-1

Peucker, D., & Douglas, H. (1975). Detection of surface-specific points by local parallel processing of discrete terrain elevation data. Computer Graphics and Image Processing, 4(4), 375–387. https:// doi.org/10.1016/0146-664X(75)90005-2

Pike, R. J. (1988). The geometric signature: Quantifying landslideterrain types from digital elevation models. Mathematical Geology, 20(5), 491–511.

Pelfini, M., & Bollati, I. (2014). Landforms and geomorphosites ongoing changes: Concepts and implications for geoheritage promotion. Quaestiones Geographicae, 33(1), 131–143. https://doi. org/10.2478/quageo-2014-0009

Piloyan, A., & Konečný, M. (2017). Semi-automated classification of landform elements in Armenia based on SRTM DEM using k-means unsupervised classification. Quaestiones Geographicae, 36(1), 93–103. https://doi.org/10.1515/ quageo-2017-0007

Prima, O. D. A., Echigo, A., Yokoyama, R., & Yoshida, T. (2006). Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology, 78(3–4), 373–386. https://doi.org/10.1016/j.geomorph.2006.02.005

Romstad, B. (2001). Improving relief classification with contextual merging. In J. T. Bjørke & H. Tveite (Eds.) Proceedings of the 8th Scandinavian Research Conference on Geographical Information Science (pp. 3–14).

Romstad, B., & Etzelmüller, B. (2012). Mean-curvature watersheds: a simple method for segmentation of a digital elevation model into terrain units. Geomorphology, 139–140, 293–302. https://doi. org/10.1016/j.geomorph.2011.10.031

Ruhe, R. V. (1960). Elements of the soil landscape. In Transactions of the 9th Congress of the International Society of Soil Science (pp. 165–170). Madison, Wisconsin: International Soil Science Society.

Schmidt, J., & Hewitt, A. (2004). Fuzzy land element classification from DTMs based on geometry and terrain position. Geoderma 121(3–4), 243–256. https://doi.org/10.1016/j.geoderma.2003.10.008

Seijmonsbergen, A. C., Hengl, T., & Anders, N. S. (2011). Semiautomated identification and extraction of geomorphological features using digital elevation data. In M. J. Smith, P. Paron, & J. S. Griffiths (Eds.), Geomorphological mapping (pp. 297–335). Amsterdam, NLD: Elsevier.

Shary, P. A. (1995). Land surface in gravity points classification by a complete system of curvatures. Mathematical Geology, 27(3), 373–390.

Shary, P. A., Sharaya, L. S., Mitusov, A. V. (2002). Fundamental quantitative methods of land surface analysis, Geoderma, 107(1–2), 1–32. https://doi.org/10.1016/S0016-7061(01)00136-7

Skidmore, A. K. (1990). Terrain position as mapped from a gridded digital elevation model. International Journal of Geographical Information Systems, 4(1), 33–49. https://doi.org/10.1080/02693799008941527

Speight, J. G. (1974). A parametric approach to landform regions. In Progress in geomorphology (pp. 213–230). Oxford, UK: Alden Press.

Speight, J. G. (1990). Landform. In R. C. McDonald, R. F. Isbell, J. G. Speight, J. Walker, & M. S. Hopkins (Eds.), Australian Soil and Land Survey field handbook (pp. 9–57). Melbourne: Inkata Press.

Stepinski, T. F., & Bagaria, C. (2009). Segmentation-based unsupervised terrain classification for generation of physiographic maps. IEEE Geoscience and Remote Sensing Letters, 6(4), 733–737. https://doi. org/10.1109/LGRS.2009.2024333

Tarboton, D. G., & Ames,D. P. (2001, May). Advances in the mapping of flow networks from digital elevation data. World Water and Environmental Resources Congress, Orlando, Florida

Tunçay, T., Bayramin, İ., Öztürk, H. S., Kibar, M., & Başkan, O. (2014). The use of remote sensing and geographic İnformation system techniques to determine relationships between land use and landform. Toprak Su Dergisi, 3(2), 124–136.

van Asselen, S., Seijmonsbergen, A. C. (2006). Expert-driven semiautomated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology, 78(3–4), 1309–1320. https:// doi.org/10.1016/j.geomorph.2006.01.037

Ventura, S. J., & Irvin, B. J. (2000). Automated landform classification methods for soil landscape studies. In J. P. Wilson & J. C. Gallant (Eds.), Terrain analysis principals and applications (pp. 245–294). New York, NY: John Wiley & Sons.

Verhagen, P., Dragut, L. (2012). Object-based landform delineation and classification from DEMs for archaeological predictive mapping. Journal of Archaeological Science, 39(3), 698–703. https://doi. org/10.1016/j.jas.2011.11.001

Weiss, A. (2001). Topographic Position and Landforms Analysis. ESRI User Conference. San Diego, CA

Wysocki, D. A., Schoeneberger, P. J., & LaGarry, H. E. (2000). Geomorphology of soil landscapes. In P. M. Huang, Y. Li, & M. E. Sumner (Eds.), Handbook of soil sciences: Properties and processes (pp. 5–40). Boca Raton, FL: CRC Press.

Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12, 47–56. https://doi.org/ 10.1002/esp.3290120107

Zhao, W. F., Xiong, L. Y., Ding, H., & Tang, G. (2017). Automatic recognition of loess landforms using Random Forest Method. Journal of Mountain Science, 14(5), 885–897. https://doi. org/10.1007/s11629-016-4320-9

Wood, J. (1996). The geomorphological characterisation of digital elevation models (Doctoral dissertation, University of Leicester, England). Retrieved from https://lra.le.ac.uk/handle/2381/34503

Kaynak Göster