International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Figure 8. LiDAR point clouds (left) and volume analysis in
study area (right)
Data fusion using LiDAR point clouds and ortho-imagery of
damage area allows us to get more detailed three-dimensional
damage information such as a damage extent analysis, a profile
analysis using data fusion between pre- and post-disaster
LiDAR data and aerial photos, and a run-off debris volume
analysis of landslides region (Table 3).
Table 3. Damage analysis results
Damaged area(m') Damaged volume( m)
Fill 1,499 4,096
Cut 6,380 52,477
S. CONCLUSION
To respond a variety of natural disasters in time occurred
around the world and decrease its damage, effective
countermeasure has been prepared quickly at the national level.
This paper suggests a multi-sensor data fusion approach for
timely disaster and evaluates the accurate and quantitative
analysis results for rainfall induced landslides damages in
Korea.
Analysis results revealed that the accuracy of an ortho-image
for study was 1.45m and the damage extents caused by
landslide using change detection algorithm was estimated about
8.6 ha. The damaged area and the 3D volume of run-off debris
calculated with LiDAR data acquired during pre and post
disaster period was 6,380 m' and 52,477 m’ respectively.
The multi-source data fusion approach using ortho-image, point
clouds as well as field investigation data is expected to enhance
the capability for more accurate damage analysis and the faster
hazard mapping.
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AKNOWLEDGEMENTS
Authors would like to specially thank all volunteers for
participating in EMT activities for flood and landslide disaster
Last July 2011 around Seoul, Korea.
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