Group-occurring landslides and debris flows caused by the continuous heavy rainfall in June 2019 in Mibei Village, Longchuan County, Guangdong Province, China

Bai, Huilin; Feng, Wenkai; Yi, Xiaoyu; Fang, Hongyu; Wu, Yiying; Deng, Pengcheng; Dai, Hongchuan; Hu, Rui

doi:10.1007/s11069-021-04819-1

Cited by 23 publications

(9 citation statements)

References 23 publications

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Section: Longchuan Chinamentioning

confidence: 99%

“…The study area is located in the vicinity of annual precipitation ranges from 1300 to 2500 mm (Bai et al, 2021). The area is composed of Paleozoic completely weathered granite and Quaternary granite residual soil (Bai et al, 2021). Between 10 and 13 June 2019, an intense rainfall event, which was characterized by cumulative rainfall of 270 mm, triggered 327 shallow landslides between 300 and 400 m of altitudes and slopes ranging from 35 to 45° (Feng et al, 2022).…”

Section: Longchuan Chinamentioning

confidence: 99%

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HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

Meena

Nava²,

Bhuyan³

et al. 2022

Preprint

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Abstract. Multiple landslide events occur often across the world which has the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the HR-GLDD, a high resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions. HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, two recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results for testing the HR-GLDD in individual test sites thereby indicating the robustness of the dataset for such purposes. The HR-GLDD can be accessed open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

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Section: Longchuan Chinamentioning

confidence: 99%

Section: Longchuan Chinamentioning

confidence: 99%

HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

Meena

Nava²,

Bhuyan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This study comprises two sets of sub-experiments that differ in the selection of environmental impact variables (Table S1). In the experiment evaluating landslide susceptibility, climate, terrain, geology, and land cover were recognized as the principal variables based on prior studies of landslide occurrences in the Guangdong Province [33,34]. Climate variables were selected based on the prevalence of rainfall-triggered landslides in the area, particularly during the flood season spanning from March to September [35].…”

Section: Selection Of Environmental Variablesmentioning

confidence: 99%

Delineating the Area for Sustainable Cultivation of Morinda officinalis Based on the MaxEnt Model

Liang,

Tang,

Qin

2024

Agronomy

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Morinda officinalis, a traditional medicinal plant in southern China, has a well-established cultivation history in Zhaoqing and Yunfu City of the Guangdong Province, China, contributing significantly to the local economy. Inadequate cultivation practices of Morinda officinalis may heighten the risk of landslide occurrences due to its specific growth and harvesting characteristics. This issue presents a challenge to the sustainable advancement of agriculture and forestry in the area, underscoring the necessity for scholarly intervention to address and devise effective remedies. This research utilized the MaxEnt model to assess landslide susceptibility and habitat suitability for Morinda officinalis, aiming to delineate appropriate cultivation zones amidst changing climatic conditions. The findings indicate that the model demonstrated a high level of accuracy, achieving combined AUC values of 0.802 for landslide susceptibility and 0.861 for habitat suitability evaluations, meeting the criteria for classification as “highly accurate”. Regions such as the Yun’an District, Luoding City, and the Xinxing District in Yunfu City were identified as having a low landslide risk and being highly conducive to Morinda officinalis cultivation under current climate conditions. Future projections indicate an anticipated expansion of the species’ distribution area between 2021 and 2040 under different climate scenarios, with subsequent variations. Spatial analysis unveiled a notable trend in the research area, indicating greater suitability for cultivation in the southern region compared to the northern region. This suggests that Yunfu City holds promise for facilitating the cultivation of Morinda officinalis more effectively than Zhaoqing City. This research offers a practical approach for promoting the sustainable growth of the regional Morinda officinalis industry, while also serving as a valuable resource for other economic crops encountering comparable developmental obstacles.

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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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Group-occurring landslides and debris flows caused by the continuous heavy rainfall in June 2019 in Mibei Village, Longchuan County, Guangdong Province, China

Cited by 23 publications

References 23 publications

HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery

Delineating the Area for Sustainable Cultivation of Morinda officinalis Based on the MaxEnt Model

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

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