AFET RİSK DEĞERLENDİRMELERİNDE CBS, İHA VE JEOFİZİK YÖNTEMLERİNİN ENTEGRASYONU: BUCA/İZMİR ÖRNEĞİ

Öz

Bu çalışma, Coğrafi Bilgi Sistemleri (CBS) ve İnsansız Hava Araçları (İHA) teknolojilerinin yerbilimleri ve özellikle jeofizik uygulamalardaki kullanımını incelemektedir. CBS, mekânsal verilerin yönetilmesini ve analiz edilmesini sağlarken, İHA teknolojisi yüksek çözünürlüklü yüzey verilerinin hızlı ve ekonomik bir biçimde elde edilmesini mümkün kılmaktadır. Bu iki teknolojinin entegrasyonu, yerbilimsel süreçlerin daha doğru modellenmesini ve risk analizi çalışmalarının etkinliğini artırmaktadır. Literatürdeki uygulamalar ışığında değerlendirilen bu bütünleşik yaklaşım, İzmir-Buca’da yürütülen bir örnek çalışma ile somutlaştırılmıştır. Söz konusu çalışmada, fotogrametrik yöntemlerle üretilen sayısal yüzey modeli ve eğim haritaları CBS ortamında analiz edilerek potansiyel heyelan/göçme alanları belirlenmiş, ardından gerçekleştirilen jeofizik ölçümler ile yer altı yapılar modellenmiştir. Sonuçlar, CBS ve İHA teknolojilerinin jeofizik mühendisliğinde hızlı, hassas ve entegre analiz süreçleri sunduğunu ortaya koymaktadır. Ayrıca bu çalışma, doğal afetlerin önlenmesi ve arazi kullanım planlamasında söz konusu teknolojilerin nasıl etkin kullanılabileceğine dair uygulamalı bir örnek sunmaktadır.

Anahtar Kelimeler

• Coğrafi Bilgi Sistemleri (CBS)
• İnsansız Hava Araçları (İHA)
• Jeofizik Uygulamalar
• Sayısal Yüzey Modeli
• Eğim Analizi
• Risk Haritalama
• Yer Altı Görüntüleme
• Fotogrametri
• Afet Yönetimi

INTEGRATION OF GIS, UAV, AND GEOPHYSICAL METHODS IN DISASTER RISK ASSESSMENTS: THE CASE OF BUCA/IZMIR

Öz

This study investigates the integration of Geographic Information Systems (GIS) and Unmanned Aerial Vehicle (UAV) technologies in geosciences, focusing on geophysical applications. While GIS enables the management and spatial analysis of various data layers, UAVs allow for the rapid and cost-effective acquisition of high-resolution surface data. The synergy between these technologies enhances the modeling of geophysical processes and improves the effectiveness of risk assessment efforts. Supported by relevant literature, this integrated approach is exemplified through a case study conducted in the Buca-İzmir, Turkey. This application used photogrammetric methods to produce a digital surface model (DSM), followed by slope analyses in a GIS environment to identify potential landslide and ground failure zones. Subsequently, geophysical surveys were conducted to model the subsurface structures associated with these risks. The findings demonstrate that integrating GIS and UAVs provides a fast, precise, and holistic framework for geophysical engineering applications. Furthermore, the study presents a practical example of how these technologies can be effectively utilized in disaster risk reduction and land-use planning.

Anahtar Kelimeler

• Geographic Information Systems (GIS)
• Unmanned Aerial Vehicles (UAVs)
• Geophysical Applications;
• Digital Surface Model
• Slope Analysis
• Risk Mapping
• Subsurface Imaging
• Photogrammetry
• Disaster Management

Kaynakça

1. Bignardi, S., Mantovani, A., & Zeid, N. A. (2016). OpenHVSR: imaging the subsurface 2D/3D elastic properties through multiple HVSR modeling and inversion. Computers & Geosciences, 93, 103–113.
2. Burrough, P. A., & McDonnell, R. A. (1998). Principles of Geographical Information Systems. Oxford University Press.
3. Casagli, N., Cigna, F., Bianchini, S., Righini, G., & Casagli, M. (2017). Remote sensing techniques for landslide hazard and risk assessment. Geoenvironmental Disasters, 4(1), 9. https://doi.org/10.1186/s40677-017-0073-1
4. Casana, J., Laugier, E. J., & Cothren, J. (2017). Thermal imaging for archaeology and geophysics from UAVs. Journal of Field Archaeology, 42(4), 401–412. https://doi.org/10.1080/00934690.2017.1338513
5. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
6. Demir, G., & Yomralıoğlu, T. (2017). The role of GIS in geological engineering applications. Turkish Journal of Geographic Information Systems, 1(1), 1–10.
7. Farr, T. G., et al. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2). https://doi.org/10.1029/2005RG000183
8. Kavzoglu, T., & Yildiz, M. (2021). Integration of remote sensing and GIS for geological hazard mapping: A case study from Turkey. Environmental Earth Sciences, 80(6), 245. https://doi.org/10.1007/s12665-021-09529-z

9. Kılcı, F., Özcan, A., & Çelik, Y. (2020). Integration of geophysical data with GIS: A case study. Geophysical Bulletin, 28(2), 55–70.
10. Lee, S., Choi, J., & Min, K. (2011). Landslide susceptibility mapping using GIS and the weights-of-evidence model. International Journal of Geographical Information Science, 25(12), 2151–2169. https://doi.org/10.1080/13658816.2011.556118
11. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic Information Science and Systems (4th ed.). Wiley.
12. Malehmir, A., et al. (2017). The potential of unmanned aerial vehicles for geophysical data acquisition. Geophysics, 82(4), H13–H28. https://doi.org/10.1190/geo2016-0454.1
13. Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. https://doi.org/10.1080/13658810600661508
14. Nakamura, Y. (1989). A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. Railway Technical Research Institute, Quarterly Reports, 30(1).
15. Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15. https://doi.org/10.1007/s12518-013-0120-x
16. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping and 3D modeling – current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(1/C22), 25–31.
17. Van der Meer, F. D., van der Werff, H. M., van Ruitenbeek, F. J., Hecker, C. A., Bakker, W. H., Noomen, M. F., ... & van der Meer, S. D. (2012). Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112–128. https://doi.org/10.1016/j.jag.2011.08.002
18. Zhou, C., Lin, G., & Tang, J. (2015). 3D geological modeling using GIS and its application in land subsidence analysis. Engineering Geology, 187, 118–130. https://doi.org/10.1016/j.enggeo.2014.12.016