Influences of the Shadow Inventory on a Landslide Susceptibility Model
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 landsl...
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| Format: | Article |
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MDPI AG
2018-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/7/9/374 |
| _version_ | 1818042239897567232 |
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| author | Cheng-Chien Liu Wei Luo Hsiao-Wei Chung Hsiao-Yuan Yin Ke-Wei Yan |
| author_facet | Cheng-Chien Liu Wei Luo Hsiao-Wei Chung Hsiao-Yuan Yin Ke-Wei Yan |
| author_sort | Cheng-Chien Liu |
| collection | DOAJ |
| description | 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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| format | Article |
| id | doaj.art-cdcc0318abdd4a9c8514354fef0e89e2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-10T08:43:10Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-cdcc0318abdd4a9c8514354fef0e89e22022-12-22T01:55:47ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-09-017937410.3390/ijgi7090374ijgi7090374Influences of the Shadow Inventory on a Landslide Susceptibility ModelCheng-Chien Liu0Wei Luo1Hsiao-Wei Chung2Hsiao-Yuan Yin3Ke-Wei Yan4Global Earth Observation and Data Analysis Center, National Cheng-Kung University, Tainan 701, TaiwanDepartment of Geography, Northern Illinois University, DeKalb, IL 60115, USAGlobal Earth Observation and Data Analysis Center, National Cheng-Kung University, Tainan 701, TaiwanDebris Flow Disaster Prevention Center, Soil and Water Conservation Bureau, Council of Agriculture, Nantou 540, TaiwanDebris Flow Disaster Prevention Center, Soil and Water Conservation Bureau, Council of Agriculture, Nantou 540, TaiwanA 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.http://www.mdpi.com/2220-9964/7/9/374shadow inventorylandslide inventorylandslide susceptibility modeldigital elevation model (DEM) |
| spellingShingle | Cheng-Chien Liu Wei Luo Hsiao-Wei Chung Hsiao-Yuan Yin Ke-Wei Yan Influences of the Shadow Inventory on a Landslide Susceptibility Model ISPRS International Journal of Geo-Information shadow inventory landslide inventory landslide susceptibility model digital elevation model (DEM) |
| title | Influences of the Shadow Inventory on a Landslide Susceptibility Model |
| title_full | Influences of the Shadow Inventory on a Landslide Susceptibility Model |
| title_fullStr | Influences of the Shadow Inventory on a Landslide Susceptibility Model |
| title_full_unstemmed | Influences of the Shadow Inventory on a Landslide Susceptibility Model |
| title_short | Influences of the Shadow Inventory on a Landslide Susceptibility Model |
| title_sort | influences of the shadow inventory on a landslide susceptibility model |
| topic | shadow inventory landslide inventory landslide susceptibility model digital elevation model (DEM) |
| url | http://www.mdpi.com/2220-9964/7/9/374 |
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