Abstract
Landfall of the Amphan (very severe cyclonic storm) occurred at 1730 hrs Indian Standard Time (IST) on May 20, 2020, near the West Bengal (W.B.) coast of India. High wind speed, storm surge, and torrential rainfall-induced flooding caused devastation in W.B. The present study aims to analyse the impacts of Amphan cyclone on land use/land cover (LULC) such as built-up area, cropland, brick-kiln industries and vegetation cover of nine districts of W.B. namely, Barddhaman, Nadia, North 24 Parganas, South 24 Parganas, Purba Medinipur, Paschim Medinipur, Haora, and Kolkata. Flood extent has been mapped using Sentinel-1A and B interferometric wide swath (IW) ground range detected (GRD) VV polarisation images dated May 22, 2020. The total actual flooded area covers 488 km2 of the study area. For the pre-cyclone period, LULC classification and normalised difference vegetation index (NDVI) have been done using Sentinel-2B multispectral instrument (MSI) images dated May 14, 2020. Post-cyclone NDVI has been computed using Sentinel-2B MSI images dated June 3, 2020. Flood-affected cropland covers a large chunk (88.2%) of the total actual flooded area. Mean NDVI values of non-flooded and flooded cropland and vegetation cover have been reduced between May 14, 2020, and June 3, 2020. District, block and pixel-wise changes in pre- and post-cyclone NDVI values have also been analysed. This study helps planners and policy makers to understand the district-wise flooding behavior, severity of damage to cropland and vegetation cover and to plan restriction on high-value land use in flooded low-lying areas.
Similar content being viewed by others
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Change history
05 June 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10661-021-09124-9
References
Agnihotri, A. K., Ohri, A., Gaur, S., Shivam, Das, N., & Mishra, S. (2019). Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin. Environmental Monitoring and Assessment, 191, 760. https://doi.org/10.1007/s10661-019-7903-4
Akram, S. K. W., Mondal, I., & Bandyopadhyay, J. (2019). Crop suitability analysis in water resource management of Paschim Medinipur District, India: a remote sensing approach. Sustainable Water Resources Management, 5, 797–815. https://doi.org/10.1007/s40899-018-0262-4
Alam, E., & Dominey-Howes, D. (2015). A new catalogue of tropical cyclones of the northern Bay of Bengal and the distribution and effects of selected landfalling events in Bangladesh. International Journal of Climatology, 35(6), 801–835. https://doi.org/10.1002/joc.4035
Ban, H. J., Kwon, Y. J., Shin, H., Ryu, H. S., & Hong S. (2017). Flood monitoring using satellite-based RGB composite imagery and refractive index retrieval in visible and near-infrared bands. Remote Sensing, 9(4), 313.https://doi.org/10.3390/rs9040313
Bhatt, C. M., Rao, G. S., & Jangam, S. (2020). Detection of urban flood inundation using RISAT-1 SAR images: A case study of Srinagar, Jammu and Kashmir (North India) floods of September 2014. Modeling Earth Systems and Environment, 6(1), 429–438. https://doi.org/10.1007/s40808-019-00690-z
Bhatt, C. M., Rao, G. S., Farooq, M., Manjusree, Shukla, A. P., Sharma, S. V. S. P., Kulkarni, S. S., Begum, A., Bhanumurthy V., Diwakar, P. G., & Dadhwal, V. K. (2016). Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India. Geomatics, Natural Hazards and Risk, 8(2), 309-327. https://doi.org/10.1080/19475705.2016.1218943
Bhowmik, A. K., & Cabral, P. (2013). Cyclone Sidr impacts on the Sundarbans floristic diversity. Earth Science Research, 2(2), 62–79. https://doi.org/10.5539/esr.v2n2p62
Clement, M. A., Kilsby, C. G., & Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152–168. https://doi.org/10.1111/jfr3.12303
Census of India. (2011a). Administrative atlas-West Bengal. Retrieved June 30, 2020, from https://censusindia.gov.in/2011census/maps/atlas/West%20Bengal.html
Census of India. (2011b). District cesus handbook-West Bengal. Retrieved March 26, 2021, from https://censusindia.gov.in/2011census/dchb/WB.html
Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., & Mosavi, A. (2020). Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of the Total Environment, 705, 135983. https://doi.org/10.1016/j.scitotenv.2019.135983
ESA. (2020a). Sentinel-2 mission details. Retrieved July 16, 2020, from https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/sentinel-2
ESA. (2020b). Multispectral instrument (MSI) overview. Retrieved July 16, 2020, from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument
ESA. (2020c). Sentinel-1 mission details. Retrieved July 16, 2020, from https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/sentinel-1
GKMS, & IMD. (2020). Special agro-met advispry service bulletin for the cyclone “Amphan”. Retrieved May 19, 2020, from http://imdkolkata.gov.in/acwc/fdpdata/view
Greenwood, F., Nelson, E. L., & Greenough, P. G. (2020). Flying into the hurricane: A case study of UAV use in damage assessment during the 2017 hurricanes in Texas and Florida. PLoS ONE, 15(2), e0227808. https://doi.org/10.1371/journal.pone.0227808
GSI, & NRSC. (2012). Geomorphology and Lineament NGLM, Landform, 50K’, West Bengal. Retrieved July 16, 2020, from http://bhuvan.nrsc.gov.in/gis/thematic/index.php
Hoque, M. A. A., Phinn, S., Roelfsema, C., & Childs, I. (2016). Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: A case study in Bangladesh. International Journal of Remote Sensing, 37(22), 5320–5343. https://doi.org/10.1080/01431161.2016.1239286
Hu, T., & Smith, R. B. (2018). The impact of Hurricane Maria on the vegetation of Dominica and Puerto Rico using multispectral remote sensing. Remote Sensing, 10(6), 827. https://doi.org/10.3390/rs10060827
IMD. (2020).All India weather summary. Retrieved May 21, 2020, from www.imdpune.gov.in
Kumar, R. (2016). Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu & Kashmir): An application of geoinformatics. Remote Sensing Applications: Society and Environment, 4, 188–203. https://doi.org/10.1016/j.rsase.2016.10.002
Lin, T. C., Hogan, J. A., & Chang, C. T. (2020). Tropical cyclone ecology: A scale-link perspective. Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2020.02.012
Manavalan, R. (2017). SAR image analysis techniques for flood area mapping - literature survey. Earth Science Informatics, 10, 1–14. https://doi.org/10.1007/s12145-016-0274-2
Mandal, M. S. H., & Hosaka, T. (2020). Assessing cyclone disturbances (1988–2016) in the Sundarbans mangrove forests using Landsat and Google Earth Engine. Natural Hazards, 102(1), 133–150. https://doi.org/10.1007/s11069-020-03914-z
Manjusree, P., Bhatt, C. M., Begum, A., Rao, G. S., & Bhanumurthy, V. (2015). A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India). Singapore Journal of Tropical Geography, 36(3), 308–323. https://doi.org/10.1111/sjtg.12126
Mason, D. C., Schumann, G. J. P., Neal, J. C., Garcia-Pintado, J., & Bates, P. D. (2012). Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study. Remote Sensing of Environment, 124, 705–716. https://doi.org/10.1016/j.rse.2012.06.017
Mishra, A. (2014). Temperature rise and trend of cyclones over the eastern coastal region of India. Journal of Earth Science & Climatic Change, 05(9), 227. https://doi.org/10.4172/2157-7617.1000227
Mohapatra, M., Mandal, G. S., Bandyopadhyay, B. K., Tyagi, A., & Mohanty, U. C. (2012). Classification of cyclone hazard prone districts of India. Natural Hazards, 63, 1601–1620. https://doi.org/10.1007/s11069-011-9891-8
NCRMP. (2020).Cyclones and their impact in India. Retrieved June 30, 2020, from https://ncrmp.gov.in/cyclones-their-impact-in-india/
Negrón-Juárez, R., Baker, D. B., Chambers, J. Q., Hurtt, G. C., & Goosem, S. (2014). Multi-scale sensitivity of Landsat and MODIS to forest disturbance associated with tropical cyclones. Remote Sensing of Environment, 140, 679–689. https://doi.org/10.1016/j.rse.2013.09.028
Neumann, C. J. (1993). Global overview. In Global Guide to Tropical Cyclone Forecasting (pp. 1.1–1.56). World Meteorological Organisation, Geneva.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., & Mukhopadhyay, B. (2014). Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65(1), 1–18.
Pulvirenti, L., Chini, M., Pierdicca, N., & Boni, G. (2016). Use of SAR data for detecting floodwater in urban and agricultural areas: The role of the interferometric coherence. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1532–1544. https://doi.org/10.1109/TGRS.2015.2482001
Ramsey, E. W., Hodgson, M. E., Sapkota, S. K., & Nelson, G. A. (2001). Forest impact estimated with NOAA AVHRR and landsat TM data related to an empirical hurricane wind-field distribution. Remote Sensing of Environment, 77(3), 279–292. https://doi.org/10.1016/S0034-4257(01)00217-6
RSMC. (2020). Super cyclonic storm “APMPHAN” over the southeast Bay of Bengal (16th - 21st May 2020): summary. Retrieved July 10, 2020, from http://www.rsmcnewdelhi.imd.gov.in/images/pdf/publications/preliminary-report/amphan.pdf
Samanta, A. (1997). Cyclone hazards and community response in coastal West Bengal: An anthropo-historical perspective. Economic and Political Weekly, 32(38), 2424–2428.
Schaefer, M., Teeuw, R., Day, S., Zekkos, D., Weber, P., Meredith, T., & Westen, C. J. V. (2020). Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis. Natural Hazards, 101, 755–784. https://doi.org/10.1007/s11069-020-03893-1
Sebastian, M., & Behera, M. R. (2015). Impact of SST on tropical cyclones in North Indian Ocean. Procedia Engineering, 116, 1072–1077. https://doi.org/10.1016/j.proeng.2015.08.346
Singh, L. P., Parkash, B., & Singhvi, A. K. (1998). Evolution of the loewr gangetic plain landforms and soils in West Bengal India. Catena, 33(2), 75–104. https://doi.org/10.1016/S0341-8162(98)00066-6
Singh, O. P., Ali Khan, T. M., & Rahman, M. S. (2000). Changes in the frequency of tropical cyclones over the North Indian Ocean. Meteorology and Atmospheric Physics, 75(1–2), 11–20. https://doi.org/10.1007/s007030070011
Staben, G. W., & Evans, K. G. (2008). Estimates of tree canopy loss as a result of Cyclone Monica, in the Magela Creek catchment northern Australia. Austral Ecology, 33(4), 562–569. https://doi.org/10.1111/j.1442-9993.2008.01911.x
State Inter Agency Group. (2020). Joint rapid need assessment report on cyclone Amphan. Retrieved March 25, 2021 from https://nidm.gov.in/covid19/PDF/covid19/state/West%20Bengal/223.pdf
Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS based support vector machine model with different kernel types. CATENA, 125, 91–101. https://doi.org/10.1016/j.catena.2014.10.017
Tiwari, V., Kumar, V., Matin, M. A., Thapa, A., Ellenburg, W. L., Gupta, N., & Thapa, S. (2020). Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS One, 15(8), e0237324. https://doi.org/10.1371/journal.pone.0237324
Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304
UNICEF India. (2020). West Bengal Cyclone Apmphan 2020, Situation Report 3. Retrieved June 30, 2020, from https://reliefweb.int/sites/reliefweb.int/files/resources/UNICEF%20India%2C%20West%20Bengal%20Humanitarian%20Situation%20Report%20No.%203%20%28Cyclone%20Amphan%29%20-%20for%2021%20May%202020.pdf
UN-SPIDER Knowledge Portal. (2021). Recommended Practice: Radar-based Flood Mapping. Retrieved March 25, 2021, from https://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-flood-mapping
Walcker, R., Laplanche, C., Herteman, M., Lambs, L., & Fromard, F. (2019). Damages caused by hurricane Irma in the human-degraded mangroves of Saint Martin (Caribbean). Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-55393-3
WBDM-CDD. (2021). West Bengal Hazard Mapping : Cyclone. Retrieved March 26, 2021, from http://wbdmd.gov.in/Pages/cyclonemapping.aspx
WHO (World Health Organisation). (2020). Tropical cyclones. Retrieved May 06, 2020 from https://www.who.int/health-topics/tropical-cyclones#tab=tab_1
WMO (World Meteorological Organisation). (2020). Tropical cyclones. Retrieved May 06, 2020 from https://public.wmo.int/en/our-mandate/focus-areas/natural-hazards-and-disaster-risk-reduction/tropical-cyclones
World Bank. (2003). India financing rapid onset natural disaster losses in India: a risk management approach. Report no. 26844-IN. Retrieved May 06, 2020 from http://documents.worldbank.org/curated/en/382141468759569928/pdf/268440IN.pdf
Zhang, X., Wang, Y., Jiang, H., & Wang, X. (2013). Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale. International Journal of Remote Sensing, 34(21), 7874–7886. https://doi.org/10.1080/01431161.2013.827344
Acknowledgements
Authors are thankful to the European Space Agency (ESA) and United States Geological Survey (USGS) for providing satellite images at no cost. Authors acknowledge Ms. Ananya Pati, research scholar at Department of Oceanographic Studies, Jadavpur University, Kolkata (India) for providing field photographs. Authors also acknowledge anonymous reviewers for their constructive comments that immensely improved the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, R., Rani, S. & Maharana, P. Assessing the impacts of Amphan cyclone over West Bengal, India: a multi-sensor approach. Environ Monit Assess 193, 283 (2021). https://doi.org/10.1007/s10661-021-09071-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-021-09071-5