Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China

Chen, Weitao; Li, Xianju; Wang, Yanxin; Chen, Gang; Sheng-wei, Liu

doi:10.1016/j.rse.2014.07.004

Cited by 171 publications

(162 citation statements)

References 48 publications

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“…Due to the great extent and the complexity of the involved areas, we prevalently operated by exploiting LiDAR data. This procedure is confirmed by several authors who analysed ground surface processes by means of high-resolution DTMs (Glenn et al 2006;Ardizzone et al 2007;Haneberg et al 2009;Tarolli 2014;Pirasteh and Li 2016), also in forested areas (Eeckhaut et al 2007;Razak et al 2013;Chen et al 2014) and for large territories (Eeckhaut et al 2007). We employed a visual analysis methodology combining the highresolution DTM and orthoimages.…”

Section: Landslides Identification and Validationsupporting

confidence: 61%

Relationship between man-made environment and slope stability: the case of 2014 rainfall events in the terraced landscape of the Liguria region (northwestern Italy)

Giordan

Cignetti

Baldo

2017

Geomatics, Natural Hazards and Risk

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In the autumn of 2014, a series of rainfall events affected several sectors of the Liguria region, triggering many shallow landslides and causing three casualties and severe structural damages. The most intensely unstable area covered 385 km 2 , in which more than 1600 landslides have been identified. After these events, an airborne Light Detection and Ranging survey was carried out. The survey yielded a high-resolution digital terrain model (DTM) and aerial images that provided a means of identifying and mapping all the occurred landslides. The distribution analysis of slope instabilities highlighted the link with various human activities. In fact, the majority of the detected landslides occurred in man-modified areas. Geospatial and statistical analyses provided the identification of three main anthropic factors: terraces, their level of maintenance and road network. Moreover, they quantified their role in landslide triggering. These factors were not analysed as separate elements, but as a continuous process, overlapping in time, in man-made influence on landscape. The identification of such factors is a key element for a correct behaviour characterization of this landscape towards extreme flash floods events.

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Section: Landslides Identification and Validationsupporting

confidence: 61%

Relationship between man-made environment and slope stability: the case of 2014 rainfall events in the terraced landscape of the Liguria region (northwestern Italy)

Giordan

Cignetti

Baldo

2017

Geomatics, Natural Hazards and Risk

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“…Compared with the filter method, the wrapper method typically finds better feature subsets to improve classification performance [68]. LiDAR data were used to automatically identify landslides in the Three Gorges reservoir area, and classification accuracy was improved by employing the wrapper method for feature selection [69,70].…”

Section: Feature Selection Methodsmentioning

confidence: 99%

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Chen

et al. 2017

Remote Sensing

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Abstract:Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

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“…They have important significance in slope stability evaluation, slope safety early warning, and slippery slope hazard control for timely grasping of the slope deformation evolution rules and accurate prediction of future evolution rules and trends of slope deformation [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Currently the main methods of landslide deformation prediction include Grey models, neural networks, support vector machine (SVM), least squares support vector machine (LS-SVM), and a variety of combinations of forecasting methods [5][6][7][8][9]. When the original data sequence fluctuation is large and the information is too dispersed, the prediction accuracy of Grey theory is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie

Liang

Zhong

et al. 2017

IJGI

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Abstract:Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

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Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China

Cited by 171 publications

References 48 publications

Relationship between man-made environment and slope stability: the case of 2014 rainfall events in the terraced landscape of the Liguria region (northwestern Italy)

Relationship between man-made environment and slope stability: the case of 2014 rainfall events in the terraced landscape of the Liguria region (northwestern Italy)

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

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