A landslide inventory serves as the basis for assessing landslide susceptibility, hazard,\nand risk. It is generally prepared from optical imagery acquired from space borne or airborne\nplatforms, in which shadows are inevitably found in mountainous areas. The influences of\nshadow inventory on a landslide susceptibility model (LSM), however, have not been investigated\nsystematically. This paper employs both the shadow and landslide inventories prepared from\neleven For mosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a\nsemiautomatic expert system. A standard LSM based on the geometric mean of multivariables was\nused to evaluate the possible errors incurred by neglecting the shadow inventory. The results show\nthat the LSM performance was significantly improved by 49.21% for the top 1% of the most highly\nsusceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly\nsusceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow\ninventory from the calculation of landslide susceptibility index reveals the real contribution of each\nfactor. They are crucial in optimizing the coefficients of a non deterministic geometric mean LSM,\nas well as in deriving the threshold of a landslide hazard early warning system.
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