Next Article in Journal
Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests
Next Article in Special Issue
Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan
Previous Article in Journal
Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling
Previous Article in Special Issue
Erosion Associated with Seismically-Induced Landslides in the Middle Longmen Shan Region, Eastern Tibetan Plateau, China
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(9), 938; https://doi.org/10.3390/rs9090938

Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China

1
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2
China Institute of Geo-Environment Monitoring, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 23 July 2017 / Revised: 30 August 2017 / Accepted: 6 September 2017 / Published: 11 September 2017
(This article belongs to the Special Issue Remote Sensing of Landslides)
View Full-Text   |   Download PDF [37050 KB, uploaded 11 September 2017]   |  

Abstract

In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannon’s entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors for each cluster is determined based on the CF values of each factor. Furthermore, the logistic regression model is used as an example of statistical models in each cluster using the selected causative factors for landslide prediction. Finally, a global landslide susceptibility map is obtained by combining the regional maps. Experimental results based on both qualitative and quantitative analysis indicated that the proposed framework can achieve more accurate landslide susceptibility maps when compared to some existing methods, e.g., the proposed framework can achieve an overall prediction accuracy of 91.76%, which is 7.63–11.5% higher than those existing methods. Therefore, the local scale LSM technique is very promising for further improvement of landslide prediction. View Full-Text
Keywords: landslide susceptibility; logistic regression; causative factors; K-means cluster; Three Gorges area landslide susceptibility; logistic regression; causative factors; K-means cluster; Three Gorges area
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, Q.; Wang, Y.; Niu, R.; Peng, L. Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China. Remote Sens. 2017, 9, 938.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top