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An Object-Based Image Analysis of WorldView-3 Image for Urban Flood Vulnerability Assessment and Dissemination Through ESRI Story Maps

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Abstract

The utilization of remote sensing and GIS is increasing in importance across the world for disaster preparedness, assessments, mitigation, and governance. A major focus is on extracting and disseminating useful information from satellite and airborne data sources for effective planning and decision making during and after disasters. This study presents a case study from Ko-Rian, a sub-district in Ayutthaya, Thailand which is frequently hit by monsoon flooding. First, an approach is demonstrated to extract buildings and roads from very-high-resolution satellite images. The results showed that use of selected multiple indices with object-based image analysis produced excellent results in mixed urban environment. Secondly, a procedure and framework are proposed for geodatabase modeling of urban flood vulnerability and disseminating information to a wider community through ESRI story maps. The proposed approach using virtual reality technologies such as Google Street View for assessing physical vulnerability and ESRI story maps to share the information can significantly reduce the time and costs of the traditional survey. The techniques and approach presented in this study can be useful to develop detailed vulnerability profile for effective decision making and dynamic planning for a flood resilient city.

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The output is presented as a story map here: https://arcg.is/1X8mnv.

References

  • Alemy, A., Hudzik, S., & Matthews, C. N. (2017). Creating a user-friendly interactive interpretive resource with esri’s arcgis story map program. Historical Archaeology, 51(2), 288–297. https://doi.org/10.1007/s41636-017-0013-7

    Article  Google Scholar 

  • Antoniou, V., Ragia, L., Nomikou, P., Bardouli, P., Lampridou, D., Ioannou, T., Kalisperakis, I., & Stentoumis, C. (2018). Creating a story map using geographic information systems to explore geomorphology and history of methana peninsula. ISPRS International Journal of Geo-Information, 7(12), 484. https://doi.org/10.3390/ijgi7120484

    Article  Google Scholar 

  • Avagyan, A., Manandyan, H., Arakelyan, A., & Piloyan, A. (2018). Toward a disaster risk assessment and mapping in the virtual geographic environment of Armenia. Natural Hazards, 92(1), 283–309. https://doi.org/10.1007/s11069-018-3208-0

    Article  Google Scholar 

  • Balasbaneh, A. T., Bin Marsono, A. K., & Gohari, A. (2019). Sustainable materials selection based on flood damage assessment for a building using LCA and LCC. Journal of Cleaner Production, 222, 844–855. https://doi.org/10.1016/j.jclepro.2019.03.005

    Article  Google Scholar 

  • Baltsavias, E. P. (2004). Object extraction and revision by image analysis using existing geodata and knowledge: Current status and steps towards operational systems. ISPRS Journal of Photogrammetry & Remote Sensing, 58, 129–151. https://doi.org/10.1016/j.isprsjprs.2003.09.002

    Article  Google Scholar 

  • Barazzetti, L., Roncoroni, F., Brumana, R., & Previtali, M. (2016). Georeferencing accuracy analysis of a single WorldView-3 image collected over milan. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives, XL1-B1, 429–434.

    Article  Google Scholar 

  • Benarchid, O., & Raissouni, N. (2013). Building extraction using object-based classification and shadow information in very high resolution multispectral images, a case study: Tetuan, Morocco. Canadian Journal on Image Processing and Computer Vision, 4(1), 1–8.

    Google Scholar 

  • Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11–28. https://doi.org/10.1016/j.isprsjprs.2016.03.014

    Article  Google Scholar 

  • Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205(2018), 338–351. https://doi.org/10.1016/j.rse.2017.11.024

    Article  Google Scholar 

  • Custer, R., & Nishijima, K. (2015). Flood vulnerability assessment of residential buildings by explicit damage process modelling. Natural Hazards, 78, 461–496. https://doi.org/10.1007/s11069-015-1725-7

    Article  Google Scholar 

  • Diakakis, M., Deligiannakis, G., Pallikarakis, A., & Skordoulis, M. (2017). Identifying elements that affect the probability of buildings to suffer flooding in urban areas using google street view. A case study from Athens metropolitan area in Greece. International Journal of Disaster Risk Reduction, 22, 1–9. https://doi.org/10.1016/j.ijdrr.2017.02.002

    Article  Google Scholar 

  • Egiebor, E. E., & Foster, E. J. (2019). Students’ perceptions of their engagement using GIS-story maps. Journal of Geography, 118(2), 51–65. https://doi.org/10.1080/00221341.2018.1515975

    Article  Google Scholar 

  • Füssel, H. M., & Klein, R. J. T. (2006). Climate change vulnerability assessments: An evolution of conceptual thinking. Climatic Change, 75(3), 301–329. https://doi.org/10.1007/s10584-006-0329-3

    Article  Google Scholar 

  • Klecka, W. R. (1980). Discriminant analysis. Vol. 19 of Quantitative applications in the social sciences. Sage University Paper, Beverly Hills and London.

  • Kumar, M., & Roy, P. S. (2013). Utilizing the potential of world view -2 for discriminating urban and vegetation features using object based classification techniques. Journal of the Indian Society of Remote Sensing, 41(3), 711–717. https://doi.org/10.1007/s12524-012-0257-9

    Article  Google Scholar 

  • Laben, C. A., & Brower, B. V. (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. 6,011,875. U.S.A.: United States Patents. https://doi.org/10.1016/j.(73)

  • Le Cozannet, G., Kervyn, M., Russo, S., Ifejika Speranza, C., Ferrier, P., Foumelis, M., Lopez, T., & Modaressi, H. (2020). Space—based earth observations for disaster risk management. Surveys in Geophysics, 41(6), 1209–1235. https://doi.org/10.1007/s10712-020-09586-5

    Article  Google Scholar 

  • Li, M., Stein, A., Bijker, W., & Zhan, Q. (2016). Urban land use extraction from very high resolution remote sensing imagery using a Bayesian network. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 192–205. https://doi.org/10.1016/j.isprsjprs.2016.10.007

    Article  Google Scholar 

  • Martín, T. V. S., Rosado, G. R., Vargas, P. A., & Gutierrez, L. (2018). Population and building vulnerability assessment by possible worst-case tsunami scenarios in Salinas Ecuador. Natural Hazards, 93(1), 275–297. https://doi.org/10.1007/s11069-018-3300-5

    Article  Google Scholar 

  • Müller, A., Reiter, J., & Weiland, U. (2011). Assessment of urban vulnerability towards floods using an indicator-based approach-a case study for Santiago de Chile. Natural Hazards and Earth System Sciences, 11(8), 2107–2123. https://doi.org/10.5194/nhess-11-2107-2011

    Article  Google Scholar 

  • Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel versus object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161. https://doi.org/10.1016/j.rse.2010.12.017

    Article  Google Scholar 

  • Nouri, H., Beecham, S., Anderson, S., & Nagler, P. (2013). High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors. Remote Sensing, 6(1), 580–602. https://doi.org/10.3390/rs6010580

    Article  Google Scholar 

  • Panagiota, M., Chanussot, J., & Pathier, E. (2011). State of the art on remote sensing for vulnerability and damage assessment on urban context. URBASIS Consort.

    Google Scholar 

  • Pricope, N. G., Halls, J. N., Rosul, L. M., & Hidalgo, C. (2019). Residential flood vulnerability along the developed North Carolina, USA coast: High resolution social and physical data for decision support. Data in Brief, 24, 103975. https://doi.org/10.1016/j.dib.2019.103975

    Article  Google Scholar 

  • Salehi, B., Zhang, Y., Zhong, M., & Dey, V. (2012). Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sensing, 4(8), 2256–2276. https://doi.org/10.3390/rs4082256

    Article  Google Scholar 

  • Seejata, K., Yodying, A., Chatsudarat, S., Chidburee, P., Mahavik, N., Kongmuang, C., & Tantanee, S. (2019). Assessment of flood hazard using geospatial data and frequency ratio model in Sukhothai province, Thailand. In 40th Asian conference on remote sensing, ACRS 2019 (pp. 1–4). Daejeon Convention Center (DCC), Daejeon, Korea.

  • Shahi, K., Shafri, H. Z. M., Taherzadeh, E., Mansor, S., & Muniandy, R. (2015). A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery. Egyptian Journal of Remote Sensing and Space Science, 18(1), 27–33. https://doi.org/10.1016/j.ejrs.2014.12.003

    Article  Google Scholar 

  • Stephenson, V., & D’Ayala, D. (2014). A new approach to flood vulnerability assessment for historic buildings in England. Natural Hazards and Earth System Sciences, 14(5), 1035–1048. https://doi.org/10.5194/nhess-14-1035-2014

    Article  Google Scholar 

  • Thanvisitthpon, N., Shrestha, S., & Pal, I. (2018). Urban flooding and climate change: A case study of Bangkok, Thailand. Environment and Urbanization ASIA, SAGE Publications, 9(1), 1–15. https://doi.org/10.1177/0975425317748532

    Article  Google Scholar 

  • Thanvisitthpon, N., Shrestha, S., Pal, I., Ninsawat, S., & Chaowiwat, W. (2020). Assessment of flood adaptive capacity of urban areas in Thailand. Environmental Impact Assessment Review, 81(2020), 106363. https://doi.org/10.1016/j.eiar.2019.106363

    Article  Google Scholar 

  • Trimble. (2014). Trimble eCognition® Developer. Munich, Germany: Trimble Germany GmbH.

  • UNESCAP. (2017). Specific hazards: Handbook on geospatial decision support in ASEAN countries. Retrieved from 21–03–2020 https://www.unescap.org/sites/default/files/publications/Highres_GeospatialHanbook_ESCAPIDD_0.pdf

  • Unsalan, C., & Boyer, K. L. (2005). A system to detect houses and residential street networks in multispectral satellite images. Computer Vision and Image Understanding, 98, 423–461. https://doi.org/10.1016/j.cviu.2004.10.006

    Article  Google Scholar 

  • Vojinovic, Z., Hammond, M., Golub, Daria, Hirunsalee, S., Weesakul, S., Meesuk, V., Medina, N., Sanchez, A., Kumara, S., & Abbott, M. (2016). Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya Thailand. Natural Hazards, 81(1), 589–616. https://doi.org/10.1007/s11069-015-2098-7

    Article  Google Scholar 

  • Wu, H., Cheng, Z., Shi, W., Miao, Z., & Xu, C. (2014). An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery. Natural Hazards, 71(1), 151–174. https://doi.org/10.1007/s11069-013-0905-6

    Article  Google Scholar 

  • Zhang, S., Zhang, L., Li, X., & Xu, Q. (2018). Physical vulnerability models for assessing building damage by debris flows. Engineering Geology, 247(2018), 145–158. https://doi.org/10.1016/j.enggeo.2018.10.017

    Article  Google Scholar 

  • Zhang, X., Feng, X., Xiao, P., He, G., & Zhu, L. (2015). Segmentation quality evaluation using region-based precision and recall measures for remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 73–84. https://doi.org/10.1016/j.isprsjprs.2015.01.009

    Article  Google Scholar 

  • Zhou, X. T., Jancso, T., Chen, J. C., & Malgorzata, W. (2012). Urban land cover mapping based on object oriented classification using WorldView 2 satellite remote sensing Images. In International scientific conference on sustainable development & ecological footprint. Sopron, Hungary.

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Correspondence to Chitrini Mozumder or Nitin Tripathi.

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Aahlaad, M., Mozumder, C., Tripathi, N. et al. An Object-Based Image Analysis of WorldView-3 Image for Urban Flood Vulnerability Assessment and Dissemination Through ESRI Story Maps. J Indian Soc Remote Sens 49, 2639–2654 (2021). https://doi.org/10.1007/s12524-021-01416-4

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