• To-Uyen Thi Doan Ho Chi Minh City University of Natural Resources and Environment
  • Ariyo Kanno Yamaguchi University
  • Koichi Yamamoto Yamaguchi University
  • Tsuyoshi Imai Yamaguchi University
  • Takaya Higuchi Yamaguchi University
  • Masahiko Sekine Yamaguchi University




qualitative evaluation, physical vulnerability, residential areas, multispectral classification, textural features, flood mapping, multi-temporal radar.


Mapping flood physical vulnerability is spatially limited because it requires input data such as building structures and materials, which are unavailable on large spatial scales. In this study, we propose a new method for qualitatively evaluating the flood vulnerability of residential areas in the context of the exposure and resilience to flood hazard on large spatial scales. This method utilizes the possible correlations between the structural physical vulnerability and residential types obtained from the statistical classifications of multispectral satellite images. Because multispectral classification is well-established as an inexpensive technique for automatically classifying land cover types over wide areas, our method is feasible and efficient for mapping the physical vulnerability of residential areas. As a case study, we present an application of the proposed approach to the Thach Ha district, Ha Tinh province, Vietnam, using the Japanese type 2 Advanced Visible and Near Infrared Radiometer (AVNIR-2) images and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images captured by the Advanced Earth Observing Satellite (ADEOS).


Download data is not yet available.

Author Biographies

To-Uyen Thi Doan, Ho Chi Minh City University of Natural Resources and Environment

Faculty of Information System and Remote Sensing

Ariyo Kanno, Yamaguchi University

Graduate School of Sciences and Technology for Innovation

Koichi Yamamoto, Yamaguchi University

Graduate School of Sciences and Technology for Innovation

Tsuyoshi Imai, Yamaguchi University

Graduate School of Sciences and Technology for Innovation

Takaya Higuchi, Yamaguchi University

Graduate School of Sciences and Technology for Innovation

Masahiko Sekine, Yamaguchi University

Graduate School of Sciences and Technology for Innovation


ADRC. 2002. 20th Century [1901–2000] Asian Natural Disasters Data Book.

Congalton, R. G. 1991. “A review of assessing the accuracy of classifications of remotely sensed data.” Remote Sensing of Environment 37: 35–46.

De León, V., and J. Carlos. 2006. Vulnerability: a conceptional and methodological review. United Nations University Institute for Environment and Human Security.

Dewantoro, M. D. R., and N. M. Farda. 2012. “ALOS PALSAR Image for landcover classification using pulse coupled neural network (PCNN).” International Journal of Advanced Research in Computer and Communication Engineering 1(5).

Ebert, A., N. Kerle, and A. Stein. 2007. “Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and space borne imagery and GIS data.” Natural Hazards 48(2): 275–294.

Haase, D. 2013. “Participatory modelling of vulnerability and adaptive capacity in flood risk management.” Natural Hazards, 67: 77–97.

Haralick, R. M., and K. Shanmugam. 1973. “Textural features for image classification.” IEEE Transactions on Systems, Man, and Cybernetics 3(6): 610-621.

Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A k-means clustering algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1): 100-108.

Kang, J. L., M. D. Su, and L. F. Chang. 2005. “Loss functions and framework for regional flood damage estimation in residential area.” Journal of Marine Science and Technology, 13(3): 193-199.

Kettig, R.L., and D.A. Landgrebe. 1976. “Classification of multispectral image data by extraction and classification of homogeneous objects.” IEEE Transactions on Geoscience Electronics, 14(1): 19-26.

Müller, A., J. Reiter, and U. Weiland. 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): 2107-2023. Retrieved from https://search.proquest.com/docview/1027223059?accountid=41859.

Pelling, M. 1997. “What determines vulnerability to floods; a case study in Georgetown, Guyana.” Environment and Urbanization 9(1): 203-226.

Richards, J. A. 1999. Remote Sensing Digital Image Analysis, Springer-Verlag: Berlin.

Rimba, A. B., M. D. Setiawati, A. B. Sambah, and F. Miura. 2017. “Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture, Japan.” Urban Science 1(1): 7.

Rosenqvist, A. K. E., C. M. Finlayson, J. Lowry, and D. Taylor. 2007. “The potential of long‐wavelength satellite‐borne radar to support implementation of the Ramsar Wetlands Convention.” Aquatic Conservation: Marine and Freshwater Ecosystems 17(3): 229-244.

Sanyal, J., and X. X. Lu. 2005. “Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India.” Hydrological Processes 19: 3699– 3716.

UNESCO-IHE. 2017. “Flood Vulnerability Indices (FVI)”.


van Westen, C. J., D. Alkema, M. C. J. Damen, N. Kerle, and N. C. Kingma. 2011. “Multi-hazard risk assessment: Distance education course”. United Nations University–ITC School on Disaster Geo-information Management (UNU-ITC DGIM).

Vojinovic, Z., M. Hammond, D. Golub et al. 2016. “Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya, Thailand”. Natural Hazards, 81: 589-616.

Wisner, B. 2003. “Turning knowledge into timely and appropriate action: Reflections on IDB/IDEA program of disaster risk indicators”. BID/IDEA Programa de Indicadores para la Gestión de Riesgos, Universidad Nacional de Colombia, Manizales.