TY - GEN
T1 - Analyzing the geomorphologic and geometric characteristics of rainfall-induced shallow landslides
AU - Shou-Hao, Chiang
PY - 2016
Y1 - 2016
N2 - Previous landslide prediction studies have focused on the assessment of location of landslides. Besides location, landslide geometric features (i.e., size and shape) are important factors that influence the distribution and dynamics of landslides. Statistical methods have been used to determine the frequency-size or frequency-volume relationships of landslides, through examining landslide inventories. However, the question of what sets their size and shape is unanswered. In this study, a landslide geometry generating algorithm (LsGA) is developed for quantifying landslide geometric features, including area, perimeter, upper length, lower length, average length and average width, with incorporating an existing landslide inventory and digital elevation model (DEM). The Kaoping watershed in Southern Taiwan is selected as the study area, and the landslide inventory prepared after Typhoon Morakot (August 2009) were applied for LsGA analysis. Landslide geometric features generated by LsGA were then used to correlate to geo-environmental factors, such as slope and contributing area (CA), in a logistic regression model. Preliminary findings are: (1) smaller landslides are generally longer than larger landslides, (2) the upper length of small landslides is relatively wider than large landslides, (3) small landslides are more likely to be observed over gentle slopes, and (4) small landslides are more likely to be observed over lower part of slopes (high CA value, near channels).
AB - Previous landslide prediction studies have focused on the assessment of location of landslides. Besides location, landslide geometric features (i.e., size and shape) are important factors that influence the distribution and dynamics of landslides. Statistical methods have been used to determine the frequency-size or frequency-volume relationships of landslides, through examining landslide inventories. However, the question of what sets their size and shape is unanswered. In this study, a landslide geometry generating algorithm (LsGA) is developed for quantifying landslide geometric features, including area, perimeter, upper length, lower length, average length and average width, with incorporating an existing landslide inventory and digital elevation model (DEM). The Kaoping watershed in Southern Taiwan is selected as the study area, and the landslide inventory prepared after Typhoon Morakot (August 2009) were applied for LsGA analysis. Landslide geometric features generated by LsGA were then used to correlate to geo-environmental factors, such as slope and contributing area (CA), in a logistic regression model. Preliminary findings are: (1) smaller landslides are generally longer than larger landslides, (2) the upper length of small landslides is relatively wider than large landslides, (3) small landslides are more likely to be observed over gentle slopes, and (4) small landslides are more likely to be observed over lower part of slopes (high CA value, near channels).
KW - Geometric feature
KW - Landslide geometry generating algorithm
KW - Logistic regression
KW - Shallow landslide
KW - Typhoon
UR - http://www.scopus.com/inward/record.url?scp=85018436209&partnerID=8YFLogxK
M3 - 會議論文篇章
AN - SCOPUS:85018436209
T3 - 37th Asian Conference on Remote Sensing, ACRS 2016
SP - 1378
EP - 1383
BT - 37th Asian Conference on Remote Sensing, ACRS 2016
PB - Asian Association on Remote Sensing
T2 - 37th Asian Conference on Remote Sensing, ACRS 2016
Y2 - 17 October 2016 through 21 October 2016
ER -