Application of landsat imageries for mapping post-earthquake landslide, case study: 2012 Ahar-Varzegan earthquake, NW Iran

الموضوعات :

Leila Khodaei Geshlag ¹ , Shahram Roostaei ² , Davood Mokhtari ³ , Kalil Valizadeh ⁴

1 - Department of Geomorphology, University of Tabriz, Iran
2 - Department of Geomorphology, University of Tabriz, Iran
3 - Department of Geomorphology, University of Tabriz, Iran
4 - Department of Geomorphology, University of Tabriz, Iran

تاريخ الإرسال : 01 الإثنين , رمضان, 1440 تاريخ التأكيد : 25 السبت , شعبان, 1441 تاريخ الإصدار : 14 الخميس , صفر, 1442

الکلمات المفتاحية: Google Earth, Landsat-5 and 8, Earthquake, Ahar-Varzegan, landslide,

ملخص المقالة :

The 2012 Ahar-Varzegan earthquake and its aftershocks have not only caused huge damage with a severe loss of life and property but also induced many geo-hazards with the major type of collapse, creep, slip, debris flow, and fallings that are generally considered as landslide in this study which can cause continuous threats to the affected region. in this study, a semi-automated geo-hazard detection method has been presented to determine the Landslides due to 2012 Ahar-Varzaghan earthquake in area from Ahar to Varzaghan by the use of bi- temporal Landsat images from before and after the earthquake. The accuracy of the results was checked out using field observations, Google Earth images and the error matrix. The results of the visual validation with the Google Earth images showed that the used method can detect landslids with relatively high accuracy.The images of landsat5 and 8 Because of their multispectral advantages can be used as a suitable data source for research on Instabilities. Finally, the validating results obtained by using the error matrix showed the total accuracy of 92.1% and kappa coefficient was 0.99. So based on the results obtained from the above method, the landslides were distributed mainly in slopes between 15 and 40 degrees and the height distribution of instabilities of 1420 to 2000 meters. Also based on vegetation indices, density of landslides have been increased after the earthquake. Generally unstabel slopes are located along river valleys and roads in mountain regions with deep valleys and steep slopes. According to the nature of present study, the obtained result can be useful for environmental planners and project developers.

المصادر:

1. Callaghan K, Engelbrecht J, Kemp J (2015) the Use of Landsat and Aerial Photography for the Assessment of Coastal Erosion and Erosion Susceptibility in False Bay, South Africa. South African Journal of Geomatics 4: 65-79.


2. Ciampalini A, Federico R, Bianchini S, Frodella W, Bardi F (2015) Remote Sensing As A Tool For Development Of Landslide Databases: The Case Of The Messina Province (Italy) Geodatabase. Geomorphology 249:103-118.


3. Djamour Y, vernant P, Nankali H R, Tavakoli F (2011) NW Iran-Eastern Turkey Present-Day Kinematics: Results From The Iranian Permanent GPS Network. Earth and Planetary Science Letters 307: 27-34.


4. Dou J, Chen S, Chang K T, Yunus A P (2015) Automatic Case-Based Reasoning Approach For Landslide Detection: Integration Of Object- Oriented Image Analysis And A Genetic Algorithm. Remote Sensing 7: 4318- 4342.


5. Huang Y, Chen W, Liu J (2012) Secondary Geological Hazard Analysis in Beichuan after the Wenchuan Earthquake and Recommendations for Reconstruction. Environmental Earth Sciences 66: 1001-1009.


6. Jaboyedoff M, Oppikofer T, Abellán A, Derron MH,  Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in Landslide Investigations: A Review. Natural Hazards 61: 5-28.


7. Keefer DK (1984) Landslides caused by earthquakes, GeologicalSociety of America Bulletin, 95: 406-421.


8. Lacroix P (2016) Landslides Triggered by The Gorkha Earthquake In The Langtang Valley, Volumes And Initiation Processes Earth, Planets Space 68: 1- 46.


9. Lacroix P, Zavala B, Berthier E, Audin L (2013) Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images And Application to The Earthquake-Triggered Landslides Of Pisco (Peru, 2007, Mw8. 0). Remote Sensing 5: 2590-2616.


10. Li Z, Shi W, Myint S W, Lu P, Wang Q (2016) Semi-Automated Landslide Inventory Mapping From Bi-temporal Aerial Photographs Using Change Detection And Level Set Method. Remote Sens Environ 175: 215 - 230.


11. Martha TR, Govindharaj KB, Kumar K V(2015) Damage and Geological Assessment Of The 18 September 2011 M W 6.9 Earthquake In Sikkim, India Using Very High Resolution Satellite Data. Geosciences 6: 793-805.


12. Milillo P, Fielding EJ, Shulz WH, Delbridge B, Burgmann R (2014) COSMO-Skymed Spotlight Interferometry Over Rural Areas: The Slumgullion Landslide In Colorado, USA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7: 2919-2926.


13. Moosavi SH, Ranjbar A (2014) Monitoring and Tracking of Land Use Changes in Cloud Mountain Basin Using Satellite Images. Journal of Geographical Information: 25( 97) : 130- 145 .


14. Moosavi V, Talebi A, Shirmohammadi B (2014) Producing a Landslide Inventory Map Using Pixel-Based And Object-Oriented Approaches Optimized By Taguchi Method. Geomorphology 204: 646-656.


15. Nichol J, Wong M S (2005) Satellite Remote Sensing For Detailed Landslide Inventories Using Change Detection And Image Fusion. Int. J. Remote Sensing 26: 1913-1926.


16. Petschko H, Bell R, Glade T (2015) Effectiveness Of Visually Analyzing Lidar DTM Derivatives For Earth And Debris Slide Inventory Mapping For Statistical Susceptibility Modeling. Landslides 13: 1-16.


17. Rastner P, Bolch T, Notarnicola C, Paul F (2014) A Comparison of Pixel and Object-Based Glacier Classification with Optical Satellite Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3: 853-862.


18. Roostaei Sh (2000) A Study On The Dynamics Of Landslides and The Causes Of Their Occurrence Using Morphometric Methods In Aharchay Basin. Ph.D. Thesis, Rajaei, Abdolhamid. Tabriz University.


19. Seed HB (1968) Landslides during earthquakes due to soil liquefaction, Jurnal of the soil mechanice and foundations division, American society of civil engineers 93(SM5): 1053-1122.


20. Shepherd J, Dymond J (2003) Correcting satellite imagery for the variance of reflectance and illumination with topography. Remote Sensing 24: 3503–3514.


21. Shi B, Liu C (2015) Uav for Landslide Mapping and Deformation Analysis. Proc. International Conference on Intelligent Earth Observing and Applications 9808:12.


22. Szantoi Z, Simonetti D (2013) Fast and robust topographic correction method for medium resolution satellite imagery using a stratified approach.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(4): 1921–1933.


23. Travelletti J, Allemand P, Delacourt Ch, Malet JP (2012) Correlation Of Multi-Temporal Ground-Based Optical Images For Landslide Monitoring: Application, Potential And Limitations. ISPRS J. Photogramm. Remote Sensing 70: 39-55.


24. Vermote E, Justice C, Claverie M,Franch B (2016) Preliminary analysis of the performance of the landsat 8/OLI and surface reflectance product. Remote Sensing Enviroment 185: 46-56.


25. Wei Y, Wei X, Chen Y (2014) Analysis of Distribution Regularity and Development Tendency of Earthquake Secondary Geo-Hazards in Yingxiu - Maoxian Section along the Minjiang River. Remote Sensing. Land Retour 26: 179-186.


26. Xu C (2015) Preparation of Earthquake-Triggered Landslide Inventory Maps Using Remote Sensing And GIS Technologies: Principles And Case Studies, Geoscience Frontiers 6: 825-836.


27. Yazdi P, Escribanoa J, Santoyo M (2017) Stress transfere and statistical analysis of 2012 aharvarzeghan. Seismic sequnce, northwestern Iran. Primer Congreso en Ingeniería Geomática. Valencia, 5 - 6 de Julio 2017.


28. Zhan Z, Lai B (2015) a Novel DSM Filtering Algorithm for Landslide Monitoring Based On Multiconstraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8: 324-331.


29. Zhao W, Li A, Nan X, Zhang Z, Lei G (2017) Postearthquake landslides Mapping from landsat-8 data for the 2015 Nepal earthquake. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(5): 1758-1768.