Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math

Xianyu Yu (), Yang Xia, Jianguo Zhou and Weiwei Jiang
Additional contact information
Xianyu Yu: School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
Yang Xia: School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
Jianguo Zhou: School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
Weiwei Jiang: School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China

Sustainability, 2023, vol. 15, issue 3, 1-29

Abstract: Landslides pose a great threat to the safety of people’s lives and property within disaster areas. In this study, the Zigui to Badong section of the Three Gorges Reservoir is used as the study area, and the land use (LU), land use change (LUC) and band math (band) factors from 2016–2020 along with six selected commonly used factors are used to form a land use factor combination (LUFC), land use change factor combination (LUCFC) and band math factor combination (BMFC). An artificial neural network (ANN), a support vector machine (SVM) and a convolutional neural network (CNN) are chosen as the three models for landslide susceptibility mapping (LSM). The results show that the BMFC is generally better than the LUFC and the LUCFC. For the validation set, the highest simple ranking scores for the three models were obtained for the BMFC (37.2, 32.8 and 39.2), followed by the LUFC (28, 26.6 and 31.8) and the LUCFC (26.8, 28.6 and 20); that is, the band-based predictions are better than those based on the LU and LUC, and the CNN model provides the best prediction ability. According to the four groups of experimental results with ANNs, compared with LU and LUC, band is easier to access, yields higher predictive performance, and provides stronger stability. Thus, band can replace LU and LUC to a certain extent and provide support for automatic and real-time landslide monitoring.

Keywords: landslide susceptibility mapping (LSM); land use/land use change (LU/LUC) factors; band math (band) factor; artificial neural network (ANN); support vector machine (SVM); convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/3/2226/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/3/2226/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:3:p:2226-:d:1046447

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().