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Remote Sens. 2017, 9(4), 333; doi:10.3390/rs9040333

Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China

1
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2
Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Zhong Lu, Chaoying Zhao and Prasad S. Thenkabail
Received: 9 January 2017 / Revised: 27 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Abstract

Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has suffered widespread and significant landslide events in recent years. In our study, an object-oriented landslide mapping (OOLM) framework was proposed for reliable and accurate LM from ‘ZY-3’ high spatial resolution (HSR) satellite images. The framework was based on random forests (RF) and mathematical morphology (MM). RF was first applied as an object feature information reduction tool to identify the significant features for describing landslides, and it was then combined with MM to map the landslides. Three object-feature domains were extracted from the ‘ZY-3’ HSR data: layer information, texture, and geometric features. A total group of 124 features and 24 landslides were used as inputs to determine the landslide boundaries and evaluate the landslide classification accuracy. The results showed that: (1) the feature selection (FS) method had a positive influence on effective landslide mapping; (2) by dividing the data into two sets, training sets which consisted of 20% of the landslide objects (OLS) and non-landslide objects (ONLS), and test sets which consisted of the remaining 80% of the OLS and ONLS, the selected feature subsets were combined for training to obtain an overall classification accuracy of 93.3% ± 0.12% of the test sets; (3) four MM operations based on closing and opening were used to improve the performance of the RF classification. Seven accuracy evaluation indices were used to compare the accuracies of these landslide mapping methods. Finally, the landslide inventory maps were obtained. Based on its efficiency and accuracy, the proposed approach can be employed for rapid response to natural hazards in the Three Gorges area. View Full-Text
Keywords: Landslide mapping (LM); Random forest (RF); ZY-3; The Three Gorges; Feature selection (FS) Landslide mapping (LM); Random forest (RF); ZY-3; The Three Gorges; Feature selection (FS)
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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).

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Chen, T.; Trinder, J.C.; Niu, R. Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China. Remote Sens. 2017, 9, 333.

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