Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas

Yan, Liangxuan; Gong, Quanbing; Wang, Fei; Chen, Lixia; Li, Deying; Yin, Kunlong

doi:10.3390/rs15061518

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…Text classification is an automated process that aims to categorize textual data into predefined classes or labels. It encompasses several steps, including preprocessing the raw text, feature extraction, and training or predicting using machine learning or deep learning models 26 . The workflow for text classification is visualized in Fig.…”

Section: Research Theory and Improved Random Forest Modelmentioning

confidence: 99%

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

Wang

2024

Sci Rep

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In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

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Section: Research Theory and Improved Random Forest Modelmentioning

confidence: 99%

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

Wang

2024

Sci Rep

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“…Based on difference between landslide points and surroundings in spectra, texture, shape, and indirect interpretation keys, we visually interpreted landslide images of Beichuan. Landslide sites have different reflexivity in comparison to surrounding vegetation and are always colored bright white; landslide sites are usually dustpan-, strap-, or ellipse-shaped and can also be irregular; and compared to slide zones, the sediment of landslides is rougher in texture [19,20]. The interpretation result is shown in Figure 2.…”

Section: Data Collectionmentioning

confidence: 99%

The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning

Wang,

et al. 2024

Remote Sensing

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Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model.

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“…Rainfall landslides are characterized by geomorphological features such as a wide range of distribution, small scale and multiple groups. In the landslide development area, the surface vegetation and soil are damaged by rainfall, resulting in exposed ground surface and reduced vegetation cover [46,47]. These features make the landslides exhibit distinct spectral, morphological and textural characteristics in remote sensing images and clearly distinguish them from surrounding features [48].…”

Section: Remote Sensing Characterization Of Landslidesmentioning

confidence: 99%

Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China

Wu,

Li,

Yuan

et al. 2023

Remote Sensing

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Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.

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Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas

Cited by 11 publications

References 34 publications

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning

Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China

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