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.
Highlights
IntroductionRiver in China is under serious threat caused by many active small and large landslides, which are major geological hazards in the region
The socio-economic stability of the area near the Three Gorges Reservoir (TGR) along the YangtzeRiver in China is under serious threat caused by many active small and large landslides, which are major geological hazards in the region
The objective of this paper is to examine the applicability of a feature selection (FS) method, random forests (RF) algorithm, and mathematical morphology (MM) method for object-oriented landslide mapping (OOLM) using ‘ZY-3’ high spatial resolution (HSR) satellite images in the TGR, China
Summary
River in China is under serious threat caused by many active small and large landslides, which are major geological hazards in the region. More than 3800 landslides have been reported [1], progressively causing injuries and casualties to individuals as well as significant economic and environmental consequences [2,3,4,5]. Though considerable attention has been paid to landslide mapping (LM) over the past few decades, mapping landslides reliably and accurately for practical engineering applications has proved to be difficult [6,7,8,9]. The availability of high spatial resolution (HSR) remote sensing satellite images have enabled more reliable mapping of landslides more rapidly than ever before [2,11,12]. There has been considerable research on LM based on HSR data, such as IKONOS [13], pan-sharpened
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