Topoğrafik Yüzey Değişimlerinin Görüntü İşleme Teknikleriyle Belirlenmesi Üzerine Bir Araştırma

Abstract

Bu çalışma, topoğrafik yüzeylerde meydana gelen hareketlerin izlenmesinde İnsansız Hava Aracı (İHA) kullanımını sunmaktadır. Çalışmada farklı zamanlarda elde edilen görüntülerde komşuluk ve korelasyon ilişkileriyle yüzey hareketlerinin ortaya çıkarılması amaçlanmaktadır. Son yılların en yeni ve etkili ölçme teknolojisi hiç şüphesiz İHA’dır. İHA görüntüleri, santimetre hassasiyette çözüm üretebilen Küresel Konumlandırma Uydu Sistemleri (GNSS) ile birlikte kullanılarak yüzey hareketlerinin tespit edilmesi için değerlendirilmiştir. Bu çalışma kapsamında belirli zaman aralığında elde edilen verilerdeki özel detaylar otomatik çıkarılarak görüntülerdeki topoğrafik yüzeyin farklı sebeplerle oluşan değişimleri gözlemlenmiştir. Aynı koordinat sistemine sahip farklı zamanlarda elde edilen ortomozaikler arasındaki benzerlik ve farklılıklardan yararlanarak görüntüler üzerindeki topoğrafik yüzey özellikleri incelenebilmektedir. Çalışma bölgesi olarak heyelan vakalarının yoğun olarak görüldüğü Taşkent (Konya) ilçesi seçilmiştir. İHA görüntülerinden elde edilen ortomozaik ve sayısal arazi modeli verileri, yüzey hareketlerinin yatay ve düşey yönde tespit edilmesinde kullanılmıştır. Planimetrik olarak 0.005 m/gün hızda yer değiştirmeler, düşeyde ise ortalama 0.004m/gün hareketler tespit edilmiştir.

Keywords

• Heyelan İzleme,Ortomozaik,İnsansız Hava Aracı,Görüntü Eşleme,Sayısal Arazi Modeli

A Study on Determination of Topographical Surface Changes by Image Processing Techniques

Abstract

This study presents the use of Unmanned Aerial Vehicle (UAV) for monitoring movements on topographic surfaces. In the study, it is aimed to reveal surface movements with neighbouring and correlation relations in the images obtained at different times. The latest and most effective measuring technology of recent years is undoubtedly UAV. UAV images were used together with Global Navigation Satellite System (GNSS), which can produce solutions with centimetre accuracy to evaluate surface movements. Within the scope of this study, the individual details which are extracted automatically on the images obtained at certain time intervals, changes caused by various reasons on the topographic surface, were observed. By using the similarities and differences between orthomosaics obtained at different times with the same coordinate system, topographic surface properties on the images can be examined. Taşkent (Konya) province where landslide cases are seen as intense in the study area was selected. The orthomosaic and digital terrain model data obtained by UAV images were used to determine the horizontal and vertical direction of the surface movements. Planimetric displacements at 0.005 m/day and 0.004 m/day movements at vertical were determined.

Keywords

• Landslide Monitoring,Orthomosaic,Unmanned Aerial Vehicle,Image Matching,Digital Terrain Model

References

1. Akgün A., Bulut F., (2007), GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region, Environ Geol, 51(8), 1377-1387.
2. Akıncı H., Doğan S., Kılıçoğlu C., Keçeci S.B., (2010), Samsun il merkezinin heyelan duyarlılık haritasının üretilmesi, Harita Teknolojileri Elektronik Dergisi, 2(3), 13-27. Aydan O., Ulusay R., Atak V.O., (2008), Evaluation of ground deformations induced by the 1999 Kocaeli earthquake (Turkey) at selected sites on shorelines, Environ Geol, 54(1), 165-182.
3. Ayoub F., Leprince S., Avouac J.P., (2017), User’s Guide to COSI-CORR Co-registration of Optically Sensed Images and Correlation. California Institute of Technology 1200 East California Blvd, Pasadena, CA 91125, USA.
4. Çan T., Duman T.Y., Olgun Ş., Çörekçioğlu Ş., Gülmez F.K., Elmacı H., Hamzaçebi S., Emre Ö., (2013), Türkiye Heyelan Veri Tabanı, TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara.
5. Çömert R., Avdan U., Şenkal E., (2012), İnsansiz Hava Araçlarının Kullanım Alanları Ve Gelecekteki Beklentiler, IV. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu (UZAL-CBS 2012), 16-19 Ekim, Zonguldak.
6. Fischler M.A., Bolles R.C., (1981), Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, 24(6), 381-395.
7. Fonstad M.A., Dietrich J.T., Courville B.C., Jensen J.L., Carbonneau P.E., (2013), Topographic structure from motion: a new development in photogrammetric measurement, Earth Surf Proc Land, 38(4), 421-430.
8. Furukawa Y., Ponce J., (2010), Accurate, dense, and robust multiview stereopsis, IEEE Trans Pattern Anal Mach Intell, 32(8), 1362-1376.

9. Gili J.A., Corominas J., Rius J., (2000), Using Global Positioning System techniques in landslide monitoring, Eng Geol, 55(3), 167-192.
10. Gokceoglu M.E.C., (2002), Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach, Environ Geol, 41(6), 720-730.
11. Handwerger A.L., Roering J.J., Schmidt D.A., (2013), Controls on the seasonal deformation of slow-moving landslides, Earth and Planetary Science Letters, Vol. 377-378, 239-247.
12. Haque U., Blum P., da Silva P.F., Andersen P., Pilz J., Chalov S.R., Malet J.P., Auflič M.J., Andres N., Poyiadji E., Lamas P.C., Zhang W., Peshevski I., Pétursson H.G., Kurt T., Dobrev N., García-Davalillo J.C., Halkia M., Ferri S., Gaprindashvili G., Engström J., Keellings D., (2016), Fatal landslides in Europe, Landslides, 13(6), 1545-1554.
13. Hastaoglu K.O., Sanli D.U., (2011), Monitoring Koyulhisar landslide using rapid static GPS: a strategy to remove biases from vertical velocities, Nat Hazards, 58(3), 1275-1294.
14. Hastaoğlu K.Ö., Poyraz F., Tarık T., Koçbulut F., Şanli U., Yilmaz I., Şanli F.B., Kuçak R.A., Demirel M., Gürsoy Ö., (2014), GPS ve Ps-Insar Yöntemleri Kullanılarak Koyulhisar (Sivas) Heyelanlarının İzlenmesi: İlk Sonuçlar, Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4(2), 161-175.
15. Kaab A., (2002), Monitoring high-mountain terrain deformation from repeated air- and spaceborne optical data: examples using digital aerial imagery and ASTER data, ISPRS J Photogramm, 57(1-2), 39-52.
16. Kalkan Y., Alkan R.M., Yanalak M., Tari E., Erden T., (2003), Altaş Ambarlı Liman Tesisleri Sahasında Geoteknik ve Jeodezik Yöntemlerle Heyelan İzleme Çalışması, Teknik Rapor, İTÜ Geliştirme Vakfı AR-GE İşletmesi, İstanbul.
17. Kargel J.S., Leonard G.J., Bishop M.P., Kääb A., Raup B.H., (2014), Global Land Ice Measurements from Space. Springer-Verlag Berlin Heidelberg, 876ss.
18. Karsli F., Atasoy M., Yalcin A., Reis S., Demir O., Gokceoglu C., (2009), Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey), Environ Monit Assess, 156(1-4), 241-255.
19. Küng O., Strecha C., Beyeler A., Zufferey J.C., Floreano D., Fua P., Gervaix F., (2011), The accuracy of automatic photogrammetric techniques on ultra-light UAV imagery. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-1/C22, 125-130.
20. Leprince S., Barbot S., Ayoub F., Avouac J.P., (2007), Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements, IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1529-1558.
21. Lourakis M.I.A., Argyros A.A., (2004), The design and implementation of a generic sparse bundle adjustment software package based on the levenberg-marquardt algorithm. Technical Report FORTH-ICS / TR-340, Computational Vision and Robotics Laboratory Institute of Computer Science (ICS), Foundation for Research and Tecnology-Hellas (FORTH), Science and Technology Park, Heraklio, Crete, Greece, 23ss.
22. Lowe D.G., (2004), Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, 60(2), 91-110.
23. Lucieer A., de Jong S.M., Turner D., (2014), Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography, Prog Phys Geog, 38(1), 97-116.
24. Maas H.G., Casassa G., Schneider D., Schwalbe E., Wendt A., (2010), Photogrammetric determination of spatio-temporal velocity fields at Glaciar San Rafael in the Northern Patagonian Icefield, The Cryosphere Discussions, 4(4), 2415-2432.
25. Margottini C., Canuti P., Sassa K., (2013), Landslide Science and Practice Volume 2: Early Warning, Instrumentation and Monitoring. Springer-Verlag Berlin Heidelberg, 685ss.
26. Messerli A., Grinsted A., (2015), Image georectification and feature tracking toolbox: ImGRAFT, Geoscientific Instrumentation, Methods and Data Systems, 4(1), 23-34.
27. Niethammer U., James M.R., Rothmund S., Travelletti J., Joswig M., (2012), UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results, Eng Geol, 128, 2-11. R Core Team, (2016), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/, [Erişim 21 Haziran 2019].
28. Ressl C., Brockmann H., Mandlburger G., Pfeifer N., (2016), Dense Image Matching vs. Airborne Laser Scanning – Comparision of two methods for deriving terrain models, Photogrammetrie, Fernerkundung, Geoinformation, 2016(2), 57-73.
29. Sanlioglu I., Zeybek M., Karauguz G., (2013), Photogrammetric Survey and 3d Modeling of Ivriz Rock Relief in Late Hittite Era, Mediterr Archaeol Ar, 13(2), 147-157.
30. Sanlioglu I., Zeybek M., Yigit C.O., (2016), Landslide Monitoring with GNSS-PPP on Steep-Slope and Forestry Area: Taşkent Landslide, 2nd International Conference on Engineering and Natural Sciences (ICENS 2016), 24-28 May, Sarajevo, Bosnia and Herzegovina.
31. Scaioni M., Longoni L., Melillo V., Papini M., (2014), Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives, Remote Sensing, 10.3390/rs60x000x, ss.53.
32. Scaioni M., (2015), Modern Technologies for Landslide Monitoring and Prediction. Springer-Verlag GmbH Berlin Heidelberg, 249ss.
33. Scambos T.A., Dutkiewicz M.J., Wilson J.C., Bindschadler R.A., (1992), Application of Image Cross-Correlation to the Measurement of Glacier Velocity Using Satellite Image Data, Remote Sens Environ, 42(3), 177-186.
34. Singh R., Bartlett D., (2018), Natural Hazards: Earthquakes, Volcanoes, and Landslides. CRC Press, 506ss.
35. Snavely K.N., (2008), Scene reconstruction and visualization from internet photo collections, PhD Thesis, University of Washington, https://www.cs.cornell.edu/~snavely/publications/thesis/thesis.pdf, [Erişim 21 Haziran 2019].
36. Turner D., Lucieer A., de Jong S.M., (2015), Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV), Remote Sens, 7(2), 1736-1757.
37. Türk T., (2018), Determination of mass movements in slow-motion landslides by the Cosi-Corr method, Geomatics, Natural Hazards and Risk, 9(1), 325-336.
38. van Westen C.J., Castellanos E., Kuriakose S.L., (2008), Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng Geol, 102(3-4), 112-131.
39. Zeybek M., Şanlıoğlu İ., (2013), Heyelanların izlenmesinde yersel lazer tarama tekniklerinin kullanılması üzerine araştırmalar, Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu (TUFUAB’2013), 23-25 Mayıs, KTÜ, Trabzon.
40. Zeybek M., Şanlıoğlu İ., Genç A., (2015), Yüksek Çözünürlüklü Yersel Lazer Tarama Verilerinin Filtrelenmesi ve Filtrelemelerin Heyelan İzlemeye Etkisi, Doğal Afetler ve Çevre Dergisi, 1(1-2), 11-20.
41. Zeybek M., (2017), Yüksek Çözünürlüklü Topoğrafik Veriler Yardımıyla Heyelanların İzlenmesi, Doktora Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Konya, 169ss.
42. Zeybek M., Şanlıoğlu İ., (2018), Landslide Monitoring and Assessment for Highway Retainingwall: The Case Study of Taşkent(Turkey) Landslide, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W4, 603-608, 10.5194/isprs-archives-xlii-3-w4-603-2018.
43. Zeybek M., Şanlıoğlu İ., (2019), Point cloud filtering on UAV based point cloud, Measurement, 133, 99-111.
44. Zhang Z.L., Li J., Guo Y.L., Lid X., Lin Y.B., Xiao G.B., Wang C., (2019), Robust procedural model fitting with a new geometric similarity estimator, Pattern Recognition, 85, 120-131.