Remote Sensing, GIS, and AHP for Assessing Physical Vulnerability to Tsunami Hazard
Remote sensing image processing, spatial data analysis through GIS approach, and analytical hierarchy process were introduced in this study for assessing the vulnerability area and inundation area due to tsunami hazard in the area of Rikuzentakata, Iwate Prefecture, Japan. Appropriate input parameters were derived from GSI DEM data, ALOS AVNIR-2, and field data. We used the parameters of elevation, slope, shoreline distance, and vegetation density. Five classes of vulnerability were defined and weighted via pairwise comparison matrix. The assessment results described that 14.35km2 of the study area was under tsunami vulnerability zone. Inundation areas are those of high and slightly high vulnerability. The farthest area reached by a tsunami was about 7.50km from the shoreline and shows that rivers act as flooding strips that transport tsunami waves into the hinterland. This study can be used for determining a priority for land-use planning in the scope of tsunami hazard risk management.
[1] M. Pelling, The vulnerability of Cities: Natural Disasters and Social
Resilience, Earthscan Publication, London, 2003.
[2] C. Bollin, C. Cardenas, H. Hahn, and K. S. Vasta, Disaster Risk
Management by Communities and Local Governments, Inter-American
Development Bank, Washinton DC, 2003.
[3] Karen E. Joyce, Kim C. Wright, Sergey V. Samsonov and Vincent G.
Ambrosia, Remote Sensing and The Disaster Management Cycle,
Advances in Geoscience and Remote Sensing, Gary Jedlovec (Ed.),
ISBN:978-953-307-005-6, InTech Published, 2009.
[4] F. Yamazaki, Y. Yano, and M. Matsuoka, “Visual damage interpretation
of buildings in Bam city using Quickbird images following the 2003
Bam, Iran earthquake,” Earthquake Spectra, vol. 21, no. S1, pp.
S329−S336, 2005.
[5] F. Yamazaki and M. Matsuoka, “Remote sensing technology in postdisaster
damage assessment,” Journal of Earthquakes and Tsunamis,
World Scientific Publishing Company, vol. 1, no. 3, 2007, pp. 193−210.
[6] S. J. Carver, “Integrating multi-criteria evaluation with geographical
information systems,” International Journal of Geographical
Information Systems, vol. 5, no. 3, pp. 321−339, 1991.
[7] P. Jankowski, “Integrating geographic information systems and multiple
criteria decision making methods,” International Journal of Geographic
Information System, vol. 9, no. 3, pp. 251−273, 1995.
[8] S. Eckert, R. Jelinek, G. Zeug, and E. Krausmann, “Remote sensingbased
assessment of tsunami vulnerability and risk in Alexandria,
Egypt,” Applied Geography, vol. 32, no. 2, pp. 714−723, 2012.
[9] R. S. Mahendra, P. C. Mohanty, H. Bisoyi, T. S. Kumar, and S. Nayak,
“Assessment and management of coastal multi-hazard vulnerability
along the Cuddalore Villupuram, east coast of India using geospatial
techniques,” Ocean & Coastal Management, vol. 54, no. 4, pp.
302−311, 2011.
[10] T. P. Sinaga, N. Adhi, Yang-Won Lee, and S. Yongcheol, “GIS mapping
of tsunami vulnerability: case study of the Jembrana Regency in Bali,
Indonesia,” KSCE Journal of Civil Engineering, vol. 15, no. 3, pp.
537−543, 2011.
[11] F. Usman and K. Murakami, “Preliminary evaluation for strategy on
coastal vegetation belt againt tsunami hazard in Pacitan, Indonesia,”
European Journal of Scientific Research, vol. 63, no. 4, pp. 530−542,
2011.
[12] G. Strunz, J. Post, K. Zosseder, S. Wegscheider, M. Muck, T.
Riedlinger, H. Mehl, S. Dech, J. Birkmann, N. Gebert, H. Harjono, H. Z.
Anwar, Sumaryono, R. M. Khomarudin, and A. Muhari, “Tsunami risk
assessment in Indonesia,” Natural Hazards and Earth System Science,
vol. 11, pp. 67−82, 2011.
[13] M. Papathoma and D. Dominey-Howes, “Tsunami vulnerability
assessment and its implications for coastal hazard analysis and disaster
management planning, Gulf of Corinth, Greece,” Natural Hazards and
Earth System Sciences, vol. 3, no. 6, pp. 733−747, 2003.
[14] F. Yamazaki, K. Kouchi, and M. Matsuoka, “Tsunami damage detection
using moderate-resolutuion satellite imagery,” Proceeding of the 8th
U.S. National Conference on Earthquake Engineering, California, US,
2006.
[15] M. Papathoma, D. Dominey-Howes, Y. Zong, and D. Smith, “Assessing
tsunami vulnerability, an example from Herakleio, Crete,” Nat. Hazards
Earth Syst. Sci., vol. 3, pp. 377−389, 2013.
[16] F. Dall'Osso, M. Gonella, G. Gabbianelli, G. Withycombe, and D.
Dominey-Howes, “A revised (PTVA) model for assessing the
vulnerability of buildings to tsunami damage,” Nat. Hazards Earth Syst.
Sci., vol. 9, pp. 1557−1565, doi:10.5194/nhess-9-1557-2009, 2009.
[17] F. Dall’Osso, L. Bovio, A. Cavalletti, F. Immordino, M. Gonella, and G.
Gabbianelli, “A novel approach (the CRATER method) for assessing
tsunamivulnerability at the regional scale using ASTER imagery,”
Italian Journal of Remote Sensing, vol. 42, no. 2, pp. 55−74, 2010.
[18] B. Horn, “Hill shading and the reflectance map,” Proceedings of IEEE,
vol. 69, no. 1, 1981, pp. 14−47. [19] K. Iida, “Magnitude, energy and generation mechanisms of tsunamis and
a catalogue of earthquakes associated with tsunamis,” Proceeding of
Tsunami Meeting at the 10th Pacific Science Congress, 1963, pp. 7−18.
[20] R. A. Van Zuidam, Guide to Geomorphologic−Aerial Photographic
Interpretation and Mapping, International Institute for Geo-Information
Science and Earth Observation, Enschede, The Netherlands, 1983.
[21] A. R. Huete, “A Soil-Adjusted Vegetation Index (SAVI),” Remote
Sensing of Environment, vol. 25, no.3, pp. 295−309, 1988.
[22] L. S. Araujo, Joao Roberto dos Santos, Yosio Edemir Shimabukuro,
“Relationship between SAVI and biomass data of forest and savanna
contact zone and the Brazilian Amazonia,” International Archives of
Photogrammetry and Remote Sensing, vol. XXXIII, part B7,
Amsterdam, 2000.
[23] M. Bouvet, G. Chander, P. Goryl P, R. Santer, Saunier, “Preliminary
radiometric calibration assessment of ALOS AVNIR-2,” Geoscience
and Remote Sensing Symposium, IEEE International, pp. 2673−2676,
23−28 July 2007.
[24] A. K. Sah, B. P. Sah, K. Honji, N. Kubo, S. Senthil, “Semi-automated
cloud/shadow removal and land cover change detection using satellite
imagery,” International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, vol. XXXIX-B7, XXII ISPRS
Congress, Melbourne, Australia, 25 August–01 September 2012, pp.
335−340.
[25] M. C. Hanse, R. S. Defries, J. R. G. Townshend, R. Sohlberg, “Global
land cover classification at 1km spatial resolution using a classification
tree approach,” International Journal of Remote Sensing, vol. 21, pp.
1331−1364, 2000.
[26] P. Gong P, R. Pu, G. S. Biging, M. R. Larrieu, “Estimation of forest
Leaf area index using vegetation indices derived from hyperion
Hyperspectral data,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 41, no. 6, 2003.
[27] A. R. Huete, “A Soil-Adjusted Vegetation Index (SAVI),” Remote Sens.
Environ., vol. 25, 295−309, 1988.
[28] J. R. Jensen, Remote Sensing of the Environmental Earth Resources
Perspective, Prentice Hall, New Jersey-USA, 2000.
[29] J. Qi, A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian, “A
Modified Soil Adjusted Vegetation Index,” Remote Sens. Environ., vol.
48, pp. 119−126, 1994.
[30] T. L. Saaty, “A scaling method for priorities in hierarchical structures,”
Journal of Mathematical Psychology, vol. 15, pp. 234−281, 1977.
[31] T. L. Saaty, The analytic hierarchy process, planning, priority setting,
resource allocation, McGraw-Hill, New York, 1980, pp. 287.
[32] T. L. Saaty, “Decision making with the AHP; why is the principle
eigenvector necessary,” Eur J Oper Res, vol. 145, 85–91, 2003.
[33] A. M. Youssef, B. Pradhan, E. Tarabees, “Integrated evaluation of urban
development suitability based on remote sensing and GIS techniques:
contribution from the analytic hierarchy process,” Arab J Geosci, vol. 4,
pp. 463–473, 2010.
[34] T.L Saaty, “Decision making for leaders: The analytical hierarchy
process for decisions in a complex world,” The Analytical Hierarchy
Process Series, vol. 2, pp. 71−74, 1996.
[35] E. H. Forman, M. A. Selly, Decision by Objectives, World Scientific
Publishing Company, 2001.
[36] J. R. Eastman, W. Jin, P. A. K. Kyem, J. Toledano, “Raster procedures
for multi-criteria/multi-objective decisions,” Photogrammetric
Engineering & Remote Sensing, American Society for Photogrammetry
and Remote Sensing, vol. 61, no. 5, pp. 539–547, 1995.
[37] Geospatial Authority of Japan (GSI), Map of inundation area due to the
2011 Great East Japan Earthquake, Map number 61, 62, 65, 66, 68, 69,
70, 71, available at www.gsi.go.jp/kikaku/kikaku40016.html., Accessed
on 25 June 2013.
[1] M. Pelling, The vulnerability of Cities: Natural Disasters and Social
Resilience, Earthscan Publication, London, 2003.
[2] C. Bollin, C. Cardenas, H. Hahn, and K. S. Vasta, Disaster Risk
Management by Communities and Local Governments, Inter-American
Development Bank, Washinton DC, 2003.
[3] Karen E. Joyce, Kim C. Wright, Sergey V. Samsonov and Vincent G.
Ambrosia, Remote Sensing and The Disaster Management Cycle,
Advances in Geoscience and Remote Sensing, Gary Jedlovec (Ed.),
ISBN:978-953-307-005-6, InTech Published, 2009.
[4] F. Yamazaki, Y. Yano, and M. Matsuoka, “Visual damage interpretation
of buildings in Bam city using Quickbird images following the 2003
Bam, Iran earthquake,” Earthquake Spectra, vol. 21, no. S1, pp.
S329−S336, 2005.
[5] F. Yamazaki and M. Matsuoka, “Remote sensing technology in postdisaster
damage assessment,” Journal of Earthquakes and Tsunamis,
World Scientific Publishing Company, vol. 1, no. 3, 2007, pp. 193−210.
[6] S. J. Carver, “Integrating multi-criteria evaluation with geographical
information systems,” International Journal of Geographical
Information Systems, vol. 5, no. 3, pp. 321−339, 1991.
[7] P. Jankowski, “Integrating geographic information systems and multiple
criteria decision making methods,” International Journal of Geographic
Information System, vol. 9, no. 3, pp. 251−273, 1995.
[8] S. Eckert, R. Jelinek, G. Zeug, and E. Krausmann, “Remote sensingbased
assessment of tsunami vulnerability and risk in Alexandria,
Egypt,” Applied Geography, vol. 32, no. 2, pp. 714−723, 2012.
[9] R. S. Mahendra, P. C. Mohanty, H. Bisoyi, T. S. Kumar, and S. Nayak,
“Assessment and management of coastal multi-hazard vulnerability
along the Cuddalore Villupuram, east coast of India using geospatial
techniques,” Ocean & Coastal Management, vol. 54, no. 4, pp.
302−311, 2011.
[10] T. P. Sinaga, N. Adhi, Yang-Won Lee, and S. Yongcheol, “GIS mapping
of tsunami vulnerability: case study of the Jembrana Regency in Bali,
Indonesia,” KSCE Journal of Civil Engineering, vol. 15, no. 3, pp.
537−543, 2011.
[11] F. Usman and K. Murakami, “Preliminary evaluation for strategy on
coastal vegetation belt againt tsunami hazard in Pacitan, Indonesia,”
European Journal of Scientific Research, vol. 63, no. 4, pp. 530−542,
2011.
[12] G. Strunz, J. Post, K. Zosseder, S. Wegscheider, M. Muck, T.
Riedlinger, H. Mehl, S. Dech, J. Birkmann, N. Gebert, H. Harjono, H. Z.
Anwar, Sumaryono, R. M. Khomarudin, and A. Muhari, “Tsunami risk
assessment in Indonesia,” Natural Hazards and Earth System Science,
vol. 11, pp. 67−82, 2011.
[13] M. Papathoma and D. Dominey-Howes, “Tsunami vulnerability
assessment and its implications for coastal hazard analysis and disaster
management planning, Gulf of Corinth, Greece,” Natural Hazards and
Earth System Sciences, vol. 3, no. 6, pp. 733−747, 2003.
[14] F. Yamazaki, K. Kouchi, and M. Matsuoka, “Tsunami damage detection
using moderate-resolutuion satellite imagery,” Proceeding of the 8th
U.S. National Conference on Earthquake Engineering, California, US,
2006.
[15] M. Papathoma, D. Dominey-Howes, Y. Zong, and D. Smith, “Assessing
tsunami vulnerability, an example from Herakleio, Crete,” Nat. Hazards
Earth Syst. Sci., vol. 3, pp. 377−389, 2013.
[16] F. Dall'Osso, M. Gonella, G. Gabbianelli, G. Withycombe, and D.
Dominey-Howes, “A revised (PTVA) model for assessing the
vulnerability of buildings to tsunami damage,” Nat. Hazards Earth Syst.
Sci., vol. 9, pp. 1557−1565, doi:10.5194/nhess-9-1557-2009, 2009.
[17] F. Dall’Osso, L. Bovio, A. Cavalletti, F. Immordino, M. Gonella, and G.
Gabbianelli, “A novel approach (the CRATER method) for assessing
tsunamivulnerability at the regional scale using ASTER imagery,”
Italian Journal of Remote Sensing, vol. 42, no. 2, pp. 55−74, 2010.
[18] B. Horn, “Hill shading and the reflectance map,” Proceedings of IEEE,
vol. 69, no. 1, 1981, pp. 14−47. [19] K. Iida, “Magnitude, energy and generation mechanisms of tsunamis and
a catalogue of earthquakes associated with tsunamis,” Proceeding of
Tsunami Meeting at the 10th Pacific Science Congress, 1963, pp. 7−18.
[20] R. A. Van Zuidam, Guide to Geomorphologic−Aerial Photographic
Interpretation and Mapping, International Institute for Geo-Information
Science and Earth Observation, Enschede, The Netherlands, 1983.
[21] A. R. Huete, “A Soil-Adjusted Vegetation Index (SAVI),” Remote
Sensing of Environment, vol. 25, no.3, pp. 295−309, 1988.
[22] L. S. Araujo, Joao Roberto dos Santos, Yosio Edemir Shimabukuro,
“Relationship between SAVI and biomass data of forest and savanna
contact zone and the Brazilian Amazonia,” International Archives of
Photogrammetry and Remote Sensing, vol. XXXIII, part B7,
Amsterdam, 2000.
[23] M. Bouvet, G. Chander, P. Goryl P, R. Santer, Saunier, “Preliminary
radiometric calibration assessment of ALOS AVNIR-2,” Geoscience
and Remote Sensing Symposium, IEEE International, pp. 2673−2676,
23−28 July 2007.
[24] A. K. Sah, B. P. Sah, K. Honji, N. Kubo, S. Senthil, “Semi-automated
cloud/shadow removal and land cover change detection using satellite
imagery,” International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, vol. XXXIX-B7, XXII ISPRS
Congress, Melbourne, Australia, 25 August–01 September 2012, pp.
335−340.
[25] M. C. Hanse, R. S. Defries, J. R. G. Townshend, R. Sohlberg, “Global
land cover classification at 1km spatial resolution using a classification
tree approach,” International Journal of Remote Sensing, vol. 21, pp.
1331−1364, 2000.
[26] P. Gong P, R. Pu, G. S. Biging, M. R. Larrieu, “Estimation of forest
Leaf area index using vegetation indices derived from hyperion
Hyperspectral data,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 41, no. 6, 2003.
[27] A. R. Huete, “A Soil-Adjusted Vegetation Index (SAVI),” Remote Sens.
Environ., vol. 25, 295−309, 1988.
[28] J. R. Jensen, Remote Sensing of the Environmental Earth Resources
Perspective, Prentice Hall, New Jersey-USA, 2000.
[29] J. Qi, A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian, “A
Modified Soil Adjusted Vegetation Index,” Remote Sens. Environ., vol.
48, pp. 119−126, 1994.
[30] T. L. Saaty, “A scaling method for priorities in hierarchical structures,”
Journal of Mathematical Psychology, vol. 15, pp. 234−281, 1977.
[31] T. L. Saaty, The analytic hierarchy process, planning, priority setting,
resource allocation, McGraw-Hill, New York, 1980, pp. 287.
[32] T. L. Saaty, “Decision making with the AHP; why is the principle
eigenvector necessary,” Eur J Oper Res, vol. 145, 85–91, 2003.
[33] A. M. Youssef, B. Pradhan, E. Tarabees, “Integrated evaluation of urban
development suitability based on remote sensing and GIS techniques:
contribution from the analytic hierarchy process,” Arab J Geosci, vol. 4,
pp. 463–473, 2010.
[34] T.L Saaty, “Decision making for leaders: The analytical hierarchy
process for decisions in a complex world,” The Analytical Hierarchy
Process Series, vol. 2, pp. 71−74, 1996.
[35] E. H. Forman, M. A. Selly, Decision by Objectives, World Scientific
Publishing Company, 2001.
[36] J. R. Eastman, W. Jin, P. A. K. Kyem, J. Toledano, “Raster procedures
for multi-criteria/multi-objective decisions,” Photogrammetric
Engineering & Remote Sensing, American Society for Photogrammetry
and Remote Sensing, vol. 61, no. 5, pp. 539–547, 1995.
[37] Geospatial Authority of Japan (GSI), Map of inundation area due to the
2011 Great East Japan Earthquake, Map number 61, 62, 65, 66, 68, 69,
70, 71, available at www.gsi.go.jp/kikaku/kikaku40016.html., Accessed
on 25 June 2013.
@article{"International Journal of Earth, Energy and Environmental Sciences:65350", author = "Abu Bakar Sambah and Fusanori Miura", title = "Remote Sensing, GIS, and AHP for Assessing Physical Vulnerability to Tsunami Hazard ", abstract = "Remote sensing image processing, spatial data analysis through GIS approach, and analytical hierarchy process were introduced in this study for assessing the vulnerability area and inundation area due to tsunami hazard in the area of Rikuzentakata, Iwate Prefecture, Japan. Appropriate input parameters were derived from GSI DEM data, ALOS AVNIR-2, and field data. We used the parameters of elevation, slope, shoreline distance, and vegetation density. Five classes of vulnerability were defined and weighted via pairwise comparison matrix. The assessment results described that 14.35km2 of the study area was under tsunami vulnerability zone. Inundation areas are those of high and slightly high vulnerability. The farthest area reached by a tsunami was about 7.50km from the shoreline and shows that rivers act as flooding strips that transport tsunami waves into the hinterland. This study can be used for determining a priority for land-use planning in the scope of tsunami hazard risk management.
", keywords = "AHP, GIS, remote sensing, tsunami vulnerability. ", volume = "7", number = "10", pages = "671-9", }
