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Landslide change detection by use of RADAR images (Case study: Takht village of Golestan province)
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Elnaz Ali Asl Khiabani *   , Mohammadjavad Valadan zoej , Mahdi Motagh |
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Abstract: (1205 Views) |
Natural phenomena in the world, such as earthquakes and heavy rains, which are sometimes combined with wind storms, can cause landslides. These landslides in one area can damage several parts and cause significant damage to natural and human infrastructure. Landslides occur in almost all countries of the world and play an important role in the changing of the earth's surface. There are different geodetic and non-geodetic methods to measure the changes caused by this phenomenon. Geodetic methods are not suitable for preparing a landslide damage map due to their limitations such as high cost and time consuming. Therefore, we have to use non-geodetic methods. Nowadays, the use of remote sensing techniques has received much attention. Radar images have been proposed as a suitable tool for monitoring landslides due to their high spatial resolution, wide view, the possibility of capturing in any kind of weather conditions and during the night, the high frequency of spatial and temporal observations, and acceptable accuracy. In addition, there are various methods for extracting information from this type of data, most of which require large initial data, and time-consuming processing. But in this research, we are trying to produce a landslide damage map by using the least input data and in the shortest possible time (no need to spend time for downloading all the required data). Therefore, the method used in this research is the use of Sentinel-1A RADAR images and processing these images in the Google Earth Engine (GEE) processing platform. In this article, we will prepare landslide damage map by examining the changes in the backscattering coefficient image (σ° ) between before and after landslide RADAR images. In this research, by having two sets of images related to before and after the occurrence of the landslide in ASC and DSC pass mode, we can produce the Iratio image between the image before and after the landslide for ASC and DSC mode. After that, we can average between IratioASC and IratioDSC to produce the IratioAverage. After producing this image and removing areas that cause errors and ambiguity, such as seas and lakes, agricultural areas, deforested areas, etc. finally, by determining a suitable threshold, it is possible to detect landslide areas. In order to evaluate the accuracy, since the lack of ground data in the study area, the generated landslide map was compared with Sentinel-2 optical sensor images and the results showed a high agreement between these two data sets, and this shows the high accuracy of the proposed method. |
Article number: 3 |
| Keywords: Landslide detection, σ° image, Ascending, Descending, Sentinel-1A, Google Earth Engine |
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Full-Text [PDF 881 kb]
(618 Downloads)
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Type of Study: Research |
Subject:
Photo&RS
Received: 2022/12/28
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1. Mozafari, M., 2021. Monitoring the movement of land in landslide areas using time series radar interferometry analysis. Master thesis, K. N. Toosi University of Technology. 2. Serey, A., Piñero-Feliciangeli, L., Sepúlveda, S.A. et al. "Landslides induced by the 2010 Chile megathrust earthquake: a comprehensive inventory and correlations with geological and seismic factors." Landslides vol. 16, pp. 1153-1165, 2019. [ DOI:10.1007/s10346-019-01150-6] 3. AliAsl Khiabani, E., Valadan Zoej, M. j., Maghsoudi, Y., 2022. Monitoring the earth-fill dams displacement using the time series radar data. Journal of Geomatics Science and Technology, 11(4): 83-96 4. Wang, F., Fan, X., Yunus, A.P. et al. "Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism." Landslides vol. 16, pp. 1551-1566, 2019. [ DOI:10.1007/s10346-019-01187-7] 5. Mondini, A.C., Guzzetti, F., Chang, K.T., Monserrat, O., Martha, T.R. and Manconi, A., 2021. Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future. Earth-Science Reviews, p.103574. [ DOI:10.1016/j.earscirev.2021.103574] 6. Bardi, F., Frodella, W., Ciampalini, A., Bianchini, S., Del Ventisette, C., Gigli, G., Fanti, R., Moretti, S., Basile, G. and Casagli, N., 2014. Integration between ground based and satellite SAR data in landslide mapping: The San Fratello case study. Geomorphology, 223, pp.45-60. [ DOI:10.1016/j.geomorph.2014.06.025] 7. Herrera, G., Notti, D., García-Davalillo, J.C., Mora, O., Cooksley, G., Sánchez, M., Arnaud, A. and Crosetto, M., 2011. Analysis with C-and X-band satellite SAR data of the Portalet landslide area. Landslides, 8(2), pp.195-206. [ DOI:10.1007/s10346-010-0239-3] 8. Zhang, B. and Wang, Y., 2020. An improved two-step multitemporal SAR interferometry method for precursory slope deformation detection over nanyu landslide. IEEE Geoscience and Remote Sensing Letters, 18(4), pp.592-596. [ DOI:10.1109/LGRS.2020.2981146] 9. Sun, Q., Zhang, L., Ding, X.L., Hu, J., Li, Z.W. and Zhu, J.J., 2015. Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sensing of Environment, 156, pp.45-57. [ DOI:10.1016/j.rse.2014.09.029] 10. Uemoto, J., Moriyama, T., Nadai, A., Kojima, S. and Umehara, T., 2019. Landslide detection based on height and amplitude differences using pre-and post-event airborne X-band SAR data. Natural Hazards, 95(3), pp.485-503. [ DOI:10.1007/s11069-018-3492-8] 11. Mondini, A.C., Santangelo, M., Rocchetti, M., Rossetto, E., Manconi, A. and Monserrat, O., 2019. Sentinel-1 SAR amplitude imagery for rapid landslide detection. Remote sensing, 11(7), p.760. [ DOI:10.3390/rs11070760] 12. Konishi, T. and Suga, Y., 2018. Landslide detection using COSMO-SkyMed images: A case study of a landslide event on Kii Peninsula, Japan. European journal of remote sensing, 51(1), pp.205-221. [ DOI:10.1080/22797254.2017.1418185] 13. Park, S.E., Lee, S.G., 2019. On the use of Single-, Dual-, and Quad-Polarimetric SAR observation for landslide detection. ISPRS Int. J. Geo Inf. 8 [ DOI:10.3390/ijgi8090384] 14. https://fa.wikipedia.org/wiki/ تخت (مینودشت) 15. Mahdavian, A.,2013. Seismic zoning of Golestan province .Geosciences Scientific Quarterly Journal,23(89), pp.165-174. 16. H. Laur1 , P. Bally2 , P. Meadows3 , J. Sanchez4 , B. Schaettler5 , E. Lopinto6 , D. Esteban4;2004," Derivation of the Backscattering Coefficient σo in ESA ERS SAR PRI PRODUCTS"; ES-TN-RS-PM-HL09 Issue 2, Rev. 5f. 17. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD. 18. Jung, J. and Yun, S.H., 2020. Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 Hokkaido landslides. Remote Sensing, 12(2), p.265. [ DOI:10.3390/rs12020265] |
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