Detection of Flooded Areas in Golestan Province Using VV, VH and VV + VH Polarizations of Sentile-1 and Landsat-8 Images

Subject Areas :

Leila Amini ¹ , Meysam Argany ² , Ataollah Abdollahi Kakroodi ³

1 - PhD Student, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 - Assistant Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
3 - Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

Received: 2022-02-06 Accepted : 2022-04-26 Published : 2022-04-21

Keywords: Golestan, Flood, polarization, Landsat-8, Sentinel-1,

Abstract :

Undoubtedly, one of the most destructive natural hazards is floods. Detection of flooded areas for optimal control and management of future floods can be helpful. In this study, using Sentinel-1 and Landsat-8 images to identify flooded areas and determine the sensitivity of different polarizations of VV, VH and VV + VH in separating aqueous and non-aqueous regions in flood 2019 in Golestan province. Therefore, after performing the necessary pre-processing, unsupervised K-means classification and NDWI index were applied to the radar and Landsat images, respectively, and then flooded areas was calculated. Continuous and unprecedented rains, the overflow of the dam, and the clay soil of the region are the most important causes of flooding in this region. According to the digital elevation model (DEM) of the region, the lowlands of the catchment area are known to be the main focus of flood accumulation in the region. Preparation of flood vulnerability map, establishment of flood warning system in reservoirs, dams and downstream rivers is one of the recommended measures to control and deal with floods in the future.

References:

اردستانی، م ؛گلستانه، م (1387)، بررسی خصوصیات سیلاب‌های رودخانه‌ای و نقش آن در مدیریت سیلاب (مطالعه موردی: مسیل کرکانلو)، سومین کنفرانس مدیریت منابع آب، تبریز.

رجبی‌زاده، ی؛ یوب‌زاده، س.ع و ظهیری، ع (1398)، بررسی سیل استان گلستان در سال 1398-1397 و ارائه راه‌‌کارهای کنترل و مدیریت آن در آینده، اکوهیدرولوژی، دوره 6، شماره 4، زمستان 1398، ص 942-921.

سلیمانی‌ساردو، ف؛ رفیعی ساردوئی، ا؛ مصباح‌زاده، ط و آذره، ع (1399)، استفاده از تصاویر سنتینل-1 جهت پایش خسارت سیلاب فرودین (1399)، جنوب استان کرمان براساس الگوریتم جنگل تصادفی، نشریه عملی علوم و مهندسی آبخیزداری ایران، سال پانزدهم، شماره 53، تابستان 1400.

صدرممتاز، ن؛ طبیبی، س.ج و محمودی، م (1386)، مطالعه تطبیقی برنامه‌ریزی مدیریت بلایا در کشورهای منتخب، دوره 65، شماره 13، ص 19-14.

عمادالدین، س و محمدقاسمی، م (1400)، پایش نقشه‌های گسترش سیلاب با استفاده از تصاویر راداری (SAR) (مطالعه موردی: سیل فروردین 1398، شهرستان آق‌قلا)، نشریه پژوهش‌های تغییرات آب و هوایی، فصلنامه عملی دانشگاه گلستان، سال دوم، شماره ششم، تابستان 1400، صفحات: 96-79.

کاظمی، م؛ نفرزادگان، ع؛ محمدی، ف (1398)، شناسایی پهنه‌های آبی طبیعی و محدوده‌های سیل‌زده با استفاده از داده‌های راداری ماهواره سنتیل-1، چهارمین کنفرانس بین‌المللی توسعه کشاورزی، منابع طبیعی، محیط‌زیست و گردشگری ایران، تبریز، دانشگاه آزاد هنر اسلامی تبریز.

گزارش عملکرد جمعیت هلال‌احمر استان گلستان در برف، کولاک، سیل و آبگرفتگی، بهار، 1398.

میراحسنی، م و ماهینی، س (1397)، نقش سنجش از دور و داده‌های آن در چرخه مدیریت بحرانی طبیعی و بحران‌ها. فصلنامه دانش پیشگیری و مدیریت بحران، دوره: 8، شماره: 3.

 

Ardestani, M. and Golestaneh, M. (2009). Investigation of river flood characteristics and its role in flood management (Case study: Mesil Kirkanlu). Third Water Resources Management Conference, Tabriz.

Bao, Y.; Lin, L.;Wu, S.; Deng, K.A.K.; Petropoulos, G.P. (2018). Surface soilmoisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International. Journal. Appl. Earth Obs. Geoinf. 72, 76–85.

Chan, N.W. (2015). Impacts of disasters and disaster riskmanagement in Malaysia: the case of floods. In: Aldrich, D.P., Oum, S., Sawada, Y. (Eds.), Resilience and Recovery in Asian Disasters, Risks, Governance and Society. Springer (e-Book). ISBN: 978-4-431-55022-8, pp. 239–265.

Cian, F.; Marconcini, M.; Ceccato, P. (2018). Normalized Difference Flood Index for rapid flood mapping: taking advantage of EO big data. Remote Sens. Environ. 209, 712–730.

Cui, J.; Zhang, X.; Wang,W. (2020). Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method. International Journal of Agricultural and Biological Engineering 13 (1), 178–190.

Dadhich, G.; Miyazaki, H. and Babel, M. (2019). Applications of sentinel-1 synthetic aperture radar imagery for floods damage assessment: A case study of Nakhon Si Thammarat, Thailand. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. 1927-1931.

Gaillard, J. (2017). “Natural Hazards and Disasters,” International. Encycl. Geogr.

Gric, J. and Hepplewhite E. (1983). Design and construction of the Thames barrier cofferdams. Proceedings of the Institution of Civil Engineers. 74(2): 191-224.

Gstaiger, V., Huth, J., Gebhardt, S., Kuenzer, C., Wehrmann, T., (2012). Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data. International. Journal. Remote Sens. 33, 7291–7304.

Hahmann, T.; Martinis, S.; Twele, A.; Roth, A. and Buchroithner, M. (2008). Extraction of water and flood areas from SAR data. International 7th European Conference on Synthetic Aperture Radar. 1-4.

Huang, M.; Jin, S. (2020). Rapid flood mapping and evaluation with a supervised classifier and change detection in shouguang using sentinel-1 SAR and sentinel-2 optical data. Remote Sens. 12 (13), 2073.

Jongman, B.; Wagemaker, J.; Romero, B.R. (2015). Early flood detection for rapid humanitarian response: harnessing near real-time satellite and Twitter signals. ISPRS International .Journal. Geo International. 4 (4), 2246–2266.

Jonkman, S.N. (2005). Global perspectives on loss of human life caused by floods. Nat. Hazards 34 (2), 151–175.

Mahdavi, S.; Salehi, B.; Granger, J.; Amani, M.; Brisco, B.; Huang,W. (2018). Remote sensing for wetland classification: a comprehensive review. GIScience Remote Sens 55, 623–658.

Mather, P.M. (1999). “Computer Processing of Remotely Sensed Images”, 2^ndEdition, John Wiley &Sons.

Mirahsani, M.S. and Mahini, S. (2019). The role of remote sensing and its data in the cycle of natural crisis management and crises Quarterly Journal of Crisis Prevention and Management Knowledge, Volume: 8, Issue: 3. https://civilica.com / doc / 793828.

Montz, B. E.; Tobin, G. A.; Hagelman, R. R. Natural hazards. (2017). explanation and integration. Guilford Publications.

Rimba, A.; Besse, M.; Fusanori, (2017). Evaluating the extraction approaches of flood extended area by using ALOS-2/PALSAR-2 images as a rapid response to flood disaster. Journal of Geoscience and Environment Protection 5 (1), 40–61.

Roy, R., Gain, A., Hurlbert, M.A., Samat, N., Tan, M.L., Chan, N.W. (2020). Designing adaptation pathways for flood-affected households in Bangladesh. Environ. Dev. Sustain.

Ruzza, G.; Guerriero, L.; Grelle, G.; Guadagno, F.M, and Revellino, P. (2019). Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery. Water, 11(11): 2289.

Shen, X., Wang, D., Mao, K. (2019b). Inundation extent mapping by synthetic aperture radar: a review. Remote Sensing. 11 (7), 879.

Shen, X.; Anagnostou, E.N.; Allen, G.H. (2019a). Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar. Remote Sensing. Environ. 221, 302–315.

Skoufias.; E. Strobl.; E. Tveit, T. B. (2017). “Natural disaster damage indices based on remotely sensed data: an application to Indonesia”.

Takeuchi K. Flood management in Japan from rivers to basins. Water International. 2002; 27(1): 20-26.

Tanguy, M.; Chokmani, K.; Bernier, M.; Poulin, J.; Raymond, S. (2017). River flood mapping in urban areas combining Radarsat-2 data and flood return period data. Remote Sensing. Environ. 198, 442–459.

Tong, X.; Luo, X.; Liu, S.; Xie, H.; Chao, W.; Liu, S.; Jiang, Y. (2018a). An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery. ISPRS J. Photogramm. Remote Sensing. 136, 144–153.

Twele, A.; Cao,W.; Plank, S.; Martinis, S. (2016). Sentinel-1-based floodmapping: a fully automated processing chain. Int. J. Remote Sens. 37 (13), 2990–3004

Withagen L. and Feenstra E. Delta )2000.( inventory of the current situation of the Deltawerken. Rapport RIKZ Netherlands.

Zhang, X.; Chan, N.W.; Pan, B.; Ge.X.; Yang, H. (2021). Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1 time series. Science of the Total Environment 794148388.