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
Main Authors: Zhong, Cheng, Liu, Yue, Gao, Peng, Chen, Wenlong, Li, Hui, Hou, Yong, Nuremanguli, Tuohuti, Ma, Haijian
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
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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.
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
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  organization: Development Research Centre, China Earthquake Administration
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Snippet Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide...
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SubjectTerms Accuracy
automation
Detection
Geomorphology
hydrologic factors
Hydrology
Landslides
Machine learning
Mapping
Performance enhancement
Remote sensing
Weather
Weather conditions
Title Landslide mapping with remote sensing: challenges and opportunities
URI https://www.tandfonline.com/doi/abs/10.1080/01431161.2019.1672904
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Volume 41
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