Abstract
Natural disaster in the form of flash-flood struck Chamoli district on February 7 2021 through the Rishiganga and Dhauliganga river valley. The event was majorly caused due to the massive rockslide containing snow, ice and rock, that detached from Mrigu Dhani peak near Ronti glacier. This event substantially damaged the Hydropower project in the Rishiganga and Tapovan Vishnugad, resulting in the deaths of over 200 people. In this regard, rapid flood mapping becomes crucial in evaluating hazard. Integrating Google’s cloud-based platform algorithm with high resolution satellite imageries could prove efficient in rapidly monitoring the event. In this paper, an attempt has been made to classify the Sentinel-2 imageries for both pre-flood and post-flood period to map the flood extent using random forest supervised classification in the google earth engine. About a 30 km river stretch, with a buffer of 200 m, from the origin at Ronti Gad to Vishnuprayag was taken for the analysis. The region was classified into six different classes, namely, forest, water, built-up, barren land, snow, and shadow. The pre-flood and post-flood change were analyzed to estimate the net flooded area. The results are validated with the very high-resolution Digital Globe imageries and compared with the flood extent estimated from manual histogram thresholding of Normalised Difference Water Index (NDWI) of Sentinel-2 datasets after masking out for slope more than 20 degrees. The overall flooded area is estimated as 0.66 sq. km. The method proved reliable and was validated with an overall accuracy of 88% and a F-score of 0.85 and could be used for flood mapping during similar incidents in the future.
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References
Afshari S, Tavakoly AA, Rajib MA, Zheng X, Follum ML, Omranian E, Fekete BM (2018) Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model. J Hydrol 556:539–556. https://doi.org/10.1016/j.jhydrol.2017.11.036
Amitrano D, Di Martino G, Iodice A, Riccio D, Ruello G (2018) Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans Geosci Remote Sens 56(6):3290–3299. https://doi.org/10.1109/TGRS.2018.2797536
Benoudjit A, Guida R (2019) A novel fully automated mapping of the flood extent on sar images using a supervised classifier. Remote Sens 11(7). https://doi.org/10.3390/rs11070779
Bhattacharjee S, Kumar P, Thakur PK, Gupta K (2021) Hydrodynamic modelling and vulnerability analysis to assess flood risk in a dense Indian city using geospatial techniques. Nat Hazards 105(2):2117–2145. https://doi.org/10.1007/s11069-020-04392-z
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1201/9780429469275-8
Brunner GW (2021) HEC-RAS River Analysis System. http://www.hec.usace.army.mil/
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Celik T (2010) Change detection in satellite images using a genetic algorithm approach. IEEE Geosci Remote Sens Lett 7(2):386–390. https://doi.org/10.1109/LGRS.2009.2037024
Chevuturi A, Dimri AP (2016) Investigation of Uttarakhand (India) disaster-2013 using weather research and forecasting model. Nat Hazards 82(3):1703–1726. https://doi.org/10.1007/s11069-016-2264-6
Darabi H, Choubin B, Rahmati O, Torabi Haghighi A, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol 569(February 2018):142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002
Dash P, Sar J (2020) Identification and validation of potential flood hazard area using GIS-based multi-criteria analysis and satellite data-derived water index. Journal of Flood Risk Management (April):1–14. https://doi.org/10.1111/jfr3.12620
Dhote PR, Aggarwal SP, Thakur PK, Garg V (2019) Flood inundation prediction for extreme flood events: a case study of Tirthan River, north west Himalaya. Himal Geol 40(2):128–140
Elmahdy S, Ali T, Mohamed M (2020) Flash flood susceptibility modeling and magnitude index using machine learning and geohydrological models: a modified hybrid approach. Remote Sens 12(17). https://doi.org/10.3390/RS12172695
Froehlich DC (1989) Local scour at bridge abutments. In: Proceedings of the 1989 National Conference on Hydraulic Engineering, pp 13–18 http://pubs.er.usgs.gov/publication/70015379
Ganguly KK, Nahar N, Hossain BM (2019) A machine learning-based prediction and analysis of flood affected households: a case study of floods in Bangladesh. International Journal of Disaster Risk Reduction 34(December 2018):283–294. https://doi.org/10.1016/j.ijdrr.2018.12.002
Giustarini L, Hostache R, Matgen P, Schumann GJ, Bates PD, Mason DC (2013) A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 51(4):2417–2430. https://doi.org/10.1109/TGRS.2012.2210901
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202(2016):18–27. https://doi.org/10.1016/j.rse.2017.06.031
Grimaldi S, Xu J, Li Y, Pauwels VRN, Walker JP (2020) Flood mapping under vegetation using single SAR acquisitions. Remote Sens Environ 237(May 2019):111582. https://doi.org/10.1016/j.rse.2019.111582
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). https://doi.org/10.3390/rs12132073
Jain SK, Singh RD, Jain MK, Lohani AK (2005) Delineation of flood-prone areas using remote sensing techniques. Water Resour Manag 19(4):333–347. https://doi.org/10.1007/s11269-005-3281-5
Joshi V, Kumar K (2006) Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region. J Mt Sci 3(3):228–236. https://doi.org/10.1007/s11629-006-0228-0
Kuldeep, Garg PK, Garg RD (2016) Geospatial techniques for flood inundation mapping. In: International Geoscience and Remote Sensing Symposium (IGARSS), 2016-Novem, pp 4387–4390. https://doi.org/10.1109/IGARSS.2016.7730143
Kumar A, Gupta AK, Bhambri R, Verma A, Tiwari SK, Asthana AKL (2018) Assessment and review of hydrometeorological aspects for cloudburst and flash flood events in the third pole region (Indian Himalaya). Polar Science 18:5–20. https://doi.org/10.1016/j.polar.2018.08.004
Kundu S, Aggarwal SP, Kingma N, Mondal A, Khare D (2015) Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India. Nat Hazards 76(1):123–138. https://doi.org/10.1007/s11069-014-1478-8
Li J, Wang J, Ye H (2021) Rapid flood mapping based on remote sensing cloud computing and Sentinel-1. J Phys Conf Ser 1952(2):022051. https://doi.org/10.1088/1742-6596/1952/2/022051
Lim J, Lee KS (2018) Flood mapping using multi-source remotely sensed data and logistic regression in the heterogeneous mountainous regions in North Korea. Remote Sens 10(7):10–14. https://doi.org/10.3390/rs10071036
Liu X, Sahli H, Meng Y, Huang Q, Lin L (2017) Flood inundation mapping from optical satellite images using spatiotemporal context learning and modest AdaBoost. Remote Sens 9(6). https://doi.org/10.3390/rs9060617
Liu X, Hu G, Chen Y, Li X, Xu X, Li S, Pei F, Wang S (2018) High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment:209. https://doi.org/10.1016/j.rse.2018.02.055
Long S, Fatoyinbo TE, Policelli F (2014) Flood extent mapping for Namibia using change detection and thresholding with SAR. Environ Res Lett 9(3). https://doi.org/10.1088/1748-9326/9/3/035002
Luo F, Yang W, Wu Q, Yan W (2012) A clustering approach for change detection in SAR images. In: Proceedings of the European conference on synthetic aperture radar, EUSAR, 2012-April(2), pp 388–391
Martinis S, Rieke C (2015) Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at river Saale, Germany. Remote Sens 7(6):7732–7752. https://doi.org/10.3390/rs70607732
Mason DC, Horritt MS, Dall’Amico JT, Scott TR, Bates PD (2007) Improving river flood extent delineation from synthetic aperture radar using airborne laser altimetry. IEEE Trans Geosci Remote Sens 45(12):3932–3943. https://doi.org/10.1109/TGRS.2007.901032
Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007) Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int J Appl Earth Obs Geoinf 9(3):247–263. https://doi.org/10.1016/j.jag.2006.03.003
McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432. https://doi.org/10.1080/01431169608948714
Nico G, Pappalepore M, Pasquariello G, Refice A, Samarelli S (2000) Comparison of SAR amplitude vs. coherence flood detection methods - a GIS application. Int J Remote Sens 21(8):1619–1631. https://doi.org/10.1080/014311600209931
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, C (1):62–66
Pandey P, Chauhan P, Praveen CMB, Suresh KT (2021) Cause and Process Mechanism of Rockslide Triggered Flood Event in Rishiganga and Dhauliganga River Valleys , Chamoli , Uttarakhand , India Using Satellite Remote Sensing and in situ Observations. Journal of the Indian Society of Remote Sensing 3. https://doi.org/10.1007/s12524-021-01360-3
Patela NN, Angiuli E, Gamba P, Gaughan A, Lisini G, Stevens FR, Tatem AJ, Trianni G (2015) Multitemporal settlement and population mapping from landsatusing google earth engine. International Journal of Applied Earth Observation and Geoinformation 35(PB):199–208. https://doi.org/10.1016/j.jag.2014.09.005
Pulvirenti L, Pierdicca N, Chini M, Guerriero L (2011) An algorithm for operational flood mapping from synthetic aperture radar (SAR) data using fuzzy logic. Natural Hazards and Earth System Science 11(2):529–540. https://doi.org/10.5194/nhess-11-529-2011
Rahman MR, Thakur PK (2017) Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: a case study from the Kendrapara District of Orissa state of India. Egypt J Remote Sens Space Sci 21:37–41. https://doi.org/10.1016/j.ejrs.2017.10.002
Rosser JF, Leibovici DG, Jackson MJ (2017) Rapid flood inundation mapping using social media, remote sensing and topographic data. Nat Hazards 87(1):103–120. https://doi.org/10.1007/s11069-017-2755-0
Roy DNC, Roy DNG (2019) Risk Management in Small Hydro power projects of Uttarakhand: An Innovative Approach. In: IIMB Management Review. https://doi.org/10.1016/j.iimb.2019.10.012
Sadler JM, Goodall JL, Morsy MM, Spencer K (2018) Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and random Forest. J Hydrol 559:43–55. https://doi.org/10.1016/j.jhydrol.2018.01.044
Shrestha, A. B., Steiner, J., Nepal, S., Maharjan, S. B., Jackson, M., Rasul, G., & Bajracharya, B. (2021). Understanding the Chamoli Cause , process , impacts , context of rapid infrastructure development. 1–15. https://www.icimod.org/article/understanding-the-chamoli-flood-cause-process-impacts-and-context-of-rapid-infrastructure-development/
Sidhu N, Pebesma E, Câmara G (2018) Using Google earth engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing 51(1):486–500. https://doi.org/10.1080/22797254.2018.1451782
Singh S, Dhasmana MK, Shrivastava V, Sharma V, Pokhriyal N, Thakur PK, Aggarwal SP, Nikam BR, Garg V, Chouksey A, Dhote PR (2018) Estimation of revised capacity in Gobind Sagar reservoir using Google earth engine and GIS. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42(5):20–23
Singh S, Dhote PR, Thakur PK, Chouksey A, Aggarwal SP (2020) Identification of flash - floods - prone river reaches in Beas river basin using GIS - based multi - criteria technique : validation using field and satellite observations. Natural Hazards:0123456789. https://doi.org/10.1007/s11069-020-04406-w
Skakun S (2010) A neural network approach to flood mapping using satellite imagery. Computing and Informatics 29(6):1013–1024
Sobek (2017) Mike 1D Reference Manual. https://manuals.mikepoweredbydhi.help/2017/Water_Resources/MIKE_1D_reference.pdf
Sun X, Li L, Zhang B, Chen D, Gao L (2015) Soft urban water cover extraction using mixed training samples and support vector machines. Int J Remote Sens 36(13):3331–3344. https://doi.org/10.1080/01431161.2015.1042594
Syifa M, Park SJ, Achmad AR, Lee CW, Eom J, Eom J (2019) Flood mapping using remote sensing imagery and artificial intelligence techniques: a case study in Brumadinho, Brazil. Journal of Coastal Research 90(sp1):197–204. https://doi.org/10.2112/SI90-024.1
Thakur PK, Ranjan R, Singh S, Dhote PR, Sharma V, Srivastav V, Dhasmana M, Aggarwal SP, Chauhan P, Nikam BR, Garg V, Chouksey A (2020) Synergistic use of remote sensing, gis and hydrological models for study of august 2018 Kerala floods. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 43(B3):1263–1270. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1263-2020
Thayyen RJ, Mishra PK, Jain SK, Wani JM, Singh H (2021) Hanging glacier avalanche ( Raunthigad - Rishiganga ) and debris flow disaster of 7 th February 2021. In: Uttarakhand , India , A Preliminary assessment, pp 1–37
Tsyganskaya V, Martinis S, Twele A, Cao W, Schmitt A, Marzahn P, Ludwig R (2016) A fuzzy logic-based approach for the detection of flooded vegetation by means of synthetic aperture radar data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41(July):371–378. https://doi.org/10.5194/isprsarchives-XLI-B7-371-2016
Tullos D, Byron E, Galloway G, Obeysekera J, Prakash O, Sun YH (2016) Review of challenges of and practices for sustainable management of mountain flood hazards. Nat Hazards 83(3):1763–1797. https://doi.org/10.1007/s11069-016-2400-3
Türkeş M, Turp M, An T, Ozturk N, Kurnaz ML, Müller H, Rufin P, Griffiths P, Barros Siqueira AJ, Hostert P, Yavaşli DD, Tucker CJ, Melocik KA, Chen H, Ito Y, Sawamukai M, Tokunaga T, Costa MH, Botta A et al (2015) Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens Environ 21(1):997–1027. https://doi.org/10.1080/014311600210191
Vanama VSK, Mandal D, Rao YS (2020) GEE4FLOOD: rapid mapping of FLOOD areas using temporal Sentinel-1 SAR images with Google earth engine cloud platform. J Appl Remote Sens 14(03):1. https://doi.org/10.1117/1.jrs.14.034505
Yulianto F, Sofan P, Zubaidah A, Sukowati KAD, Pasaribu JM, Khomarudin MR (2015) Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia. Nat Hazards 77(2):959–985. https://doi.org/10.1007/s11069-015-1633-x
Yunus AP, Masago Y, Hijioka Y, Singh A, Ranjan AK, Patra AK, Gorai AK, Usali N, Ismail MH, Adam E, Katlane R, Nechad B, Ruddick K, Zargouni F, Garg V, Aggarwal SP, Chauhan P, Zhang Y, Pulliainen J et al (2019) Mapping_of_Coastal_Water_Quality. Remote Sens Environ 11(1):139012. https://doi.org/10.3390/s17040777
Zeltner N (2016) Using the Google Earth Engine for Global Glacier Change Assessment (Issue October). University of Zurich
Zhao G, Pang B, Xu Z, Yue J, Tu T (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037
Acknowledgements
The authors acknowledge the Indian Institute of Technology, Roorkee for providing all the required infrastructure facilities and support for completion of this work. The efforts of scientists associated with Environmental Systems Research Institute (ESRI), Google Earth Engine, Google Earth and for providing topographic data, and high-resolution base layers are also acknowledged. The critical review of the editor and the anonymous reviewers which contributed to make the manuscript clearer are thankfully acknowledge.
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Singh, S., Kansal, M.L. Chamoli flash-flood mapping and evaluation with a supervised classifier and NDWI thresholding using Sentinel-2 optical data in Google earth engine. Earth Sci Inform 15, 1073–1086 (2022). https://doi.org/10.1007/s12145-022-00786-8
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DOI: https://doi.org/10.1007/s12145-022-00786-8