Landslide mapping with remote sensing: challenges and opportunities
Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previ...
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| Published in: | International journal of remote sensing Vol. 41; no. 4; pp. 1555 - 1581 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Taylor & Francis
16.02.2020
Taylor & Francis Ltd |
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| ISSN: | 0143-1161, 1366-5901, 1366-5901 |
| Online Access: | Get full text |
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| Abstract | Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide. |
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| AbstractList | Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide. |
| Author | Nuremanguli, Tuohuti Ma, Haijian Liu, Yue Zhong, Cheng Gao, Peng Li, Hui Chen, Wenlong Hou, Yong |
| Author_xml | – sequence: 1 givenname: Cheng orcidid: 0000-0002-3614-2380 surname: Zhong fullname: Zhong, Cheng organization: Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences – sequence: 2 givenname: Yue surname: Liu fullname: Liu, Yue organization: Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences – sequence: 3 givenname: Peng orcidid: 0000-0002-9599-855X surname: Gao fullname: Gao, Peng organization: Department of Geography, University of South Carolina – sequence: 4 givenname: Wenlong surname: Chen fullname: Chen, Wenlong organization: Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences – sequence: 5 givenname: Hui orcidid: 0000-0002-0646-8371 surname: Li fullname: Li, Hui email: rslihui@cug.edu.cn organization: School of Earth Science, China University of Geosciences – sequence: 6 givenname: Yong surname: Hou fullname: Hou, Yong organization: Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences – sequence: 7 givenname: Tuohuti surname: Nuremanguli fullname: Nuremanguli, Tuohuti organization: School of Earth Science, China University of Geosciences – sequence: 8 givenname: Haijian surname: Ma fullname: Ma, Haijian organization: Development Research Centre, China Earthquake Administration |
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| Title | Landslide mapping with remote sensing: challenges and opportunities |
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