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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Veröffentlicht in:International journal of remote sensing Jg. 41; H. 4; S. 1555 - 1581
Hauptverfasser: Zhong, Cheng, Liu, Yue, Gao, Peng, Chen, Wenlong, Li, Hui, Hou, Yong, Nuremanguli, Tuohuti, Ma, Haijian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 16.02.2020
Taylor & Francis Ltd
Schlagworte:
Accuracy
automation
Detection
Geomorphology
hydrologic factors
Hydrology
Landslides
Machine learning
Mapping
Performance enhancement
Remote sensing
Weather
Weather conditions
ISSN:0143-1161, 1366-5901, 1366-5901
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Zusammenfassung: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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ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2019.1672904