Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 51-57, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 51-57, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

THE EXTENT OF AGRICULTURAL LAND DAMAGE IN VARIOUS TSUNAMI WAVE HEIGHT SCENARIOS: DISASTER MANAGEMENT AND MITIGATION

S. Antoni1, R. A. Bantan1, H. M. Taki2, W. Anurogo3, M. Z. Lubis3, T. A. Al Dubai1, and A. G. Al-Zubieri1 S. Antoni et al.
  • 1Marine Geology Department, Faculty of Marine Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • 2Department of Urban and Regional Planning, Faculty of Environmental Design, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • 3Geomatics Engineering, Batam State Polytechnic, Batam 29461, Indonesia

Keywords: Tsunami, Run-up Modelling, Remote Sensing, Risk Management and Mitigation

Abstract. The southern coastal areas of Java are highly vulnerable areas of earthquake hazard because they located 200 km from the southern Java subduction zone. This zone is an active seismicity area, resulting in many tectonic earthquakes caused by collisions and shift between the plates. This shift when it occurs under the sea surface with a large power intensity can lead to a tsunami. This research conducted to identify the extent of agricultural land (AL) damaged by the tsunami for disaster risk management and mitigation. Numerical modelling was performed to determine the run-up height of the tsunami through numerical data. This model was designed using the worst-case scenario. The tsunami inundation model analysed from the coming wave (run-up) with a height of 30 m. This model used scenarios of tsunami run-up height in a coastline, coarse coefficient and slope. The data extracted using remote sensing (RS) data was the slope obtained from the ASTER image GDEM data, the agricultural land productivity data obtained using NDVI vegetation index transformation and field data on productivity, and tsunami hazard analysis with various altitude scenarios using run-up model impact on existing AL conditions. The elevation-data was obtained from the 15 m ASTER image data (GDEM) that was reclassified into a slope class map. The risk of destruction of AL based on wave height extracted by using RS data generated rice risk loss index of AL of 190.5071 tons for a height of 1 m, 1851.522 tons for a height of 5 m, 7402.71 tons for a height of 10 m, 10776.47 tons to a height of 15 m, 11823.9 tons for height 20 m, and 11824.27 tons to a height of 30 m.