22 May 2013 Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan
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J. of Applied Remote Sensing, 7(1), 073561 (2013). doi:10.1117/1.JRS.7.073561
Abstract
To mitigate the damage caused by landslide disasters, different mathematical models have been applied to predict landslide spatial distribution characteristics. Although some researchers have achieved excellent results around the world, few studies take the spatial resolution of the database into account. Four types of digital elevation model (DEM) ranging from 2 to 20 m derived from light detection and ranging technology to analyze landslide susceptibility in Mizunami City, Gifu Prefecture, Japan, are presented. Fifteen landslide-causative factors are considered using a logistic-regression approach to create models for landslide potential analysis. Pre-existing landslide bodies are used to evaluate the performance of the four models. The results revealed that the 20-m model had the highest classification accuracy (71.9%), whereas the 2-m model had the lowest value (68.7%). In the 2-m model, 89.4% of the landslide bodies fit in the medium to very high categories. For the 20-m model, only 83.3% of the landslide bodies were concentrated in the medium to very high classes. When the cell size decreases from 20 to 2 m, the area under the relative operative characteristic increases from 0.68 to 0.77. Therefore, higher-resolution DEMs would provide better results for landslide-susceptibility mapping.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Liang-Jie Wang, Kazuhide Sawada, Shuji Moriguchi, "Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan," Journal of Applied Remote Sensing 7(1), 073561 (22 May 2013). http://dx.doi.org/10.1117/1.JRS.7.073561
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KEYWORDS
Landslide (networking)

Spatial resolution

Mathematical modeling

LIDAR

Statistical modeling

Geology

Analytical research

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