Influences of the Shadow Inventory on a Landslide Susceptibility Model
1
Global Earth Observation and Data Analysis Center, National Cheng-Kung University, Tainan 701, Taiwan
2
Department of Earth Sciences, National Cheng-Kung University, Tainan 701, Taiwan
3
Department of Geography, Northern Illinois University, DeKalb, IL 60115, USA
4
Debris Flow Disaster Prevention Center, Soil and Water Conservation Bureau, Council of Agriculture, Nantou 540, Taiwan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(9), 374; https://doi.org/10.3390/ijgi7090374
Received: 13 July 2018 / Revised: 1 September 2018 / Accepted: 4 September 2018 / Published: 9 September 2018
(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)
A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system.
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Keywords:
shadow inventory; landslide inventory; landslide susceptibility model; digital elevation model (DEM)
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MDPI and ACS Style
Liu, C.-C.; Luo, W.; Chung, H.-W.; Yin, H.-Y.; Yan, K.-W. Influences of the Shadow Inventory on a Landslide Susceptibility Model. ISPRS Int. J. Geo-Inf. 2018, 7, 374. https://doi.org/10.3390/ijgi7090374
AMA Style
Liu C-C, Luo W, Chung H-W, Yin H-Y, Yan K-W. Influences of the Shadow Inventory on a Landslide Susceptibility Model. ISPRS International Journal of Geo-Information. 2018; 7(9):374. https://doi.org/10.3390/ijgi7090374
Chicago/Turabian StyleLiu, Cheng-Chien, Wei Luo, Hsiao-Wei Chung, Hsiao-Yuan Yin, and Ke-Wei Yan. 2018. "Influences of the Shadow Inventory on a Landslide Susceptibility Model" ISPRS International Journal of Geo-Information 7, no. 9: 374. https://doi.org/10.3390/ijgi7090374
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