Rainfall-induced catastrophic landslide in Quang Tri Province: the deadliest single landslide event in Vietnam in 2020

Tien, Pham Van; Luong, Le Hong; Duc, Do Minh; Trinh, Phan Trong; Quynh, Dinh Thi; Lan, Nguyen Chau; Thuy, Dang Thi Thanh; Phi, Nguyen Quoc; Cường, Trần Quốc; Dang, Khang; Loi, Doan Huy

doi:10.1007/s10346-021-01664-y

Cited by 19 publications

(11 citation statements)

References 2 publications

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“…There were several major landslide events in Vietnam in October 2020 that were related to a particularly wet period between 6-28 October as the country was hit by six tropical cyclones (Van Tien et al, 2021). We examined landslide events from Huong Phung Commune on 18 October 2020 and Quang Nam Province on 28 October 2020 (Van Tien et al, 2021). We selected a 1080 km 2 AOI in Huong Phung and a 416 km 2 AOI in Quang Nam (Fig.…”

Section: Rainfall-triggered Landslides 2020 Huong Phung and Quang Nam...mentioning

confidence: 99%

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Handwerger

Huang

Jones

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

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Section: Rainfall-triggered Landslides 2020 Huong Phung and Quang Nam...mentioning

confidence: 99%

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Handwerger

Huang

Jones

et al. 2022

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Rainfall is one of the factors that cannot be ignored that affects and controls landslides. Throughout the world, judging from the reasons for many landslides (Fan et al, 2020;Yang et al, 2020;Van Tien et al, 2021a;Van Tien et al, 2021), the LR bo model retains some important trigger factors which are not retained by the LR ao model. Although the relationship between landslides and faults cannot be ignored (Wang et al, 2021), most landslides are induced by rainfall in the study area, which does not have a strong relationship with faults.…”

Section: Discussionmentioning

confidence: 99%

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Sun

Wen

et al. 2021

Front. Earth Sci.

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This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

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“…There were several major landslide events in Vietnam in October 2020 that were related to a particularly wet period between 6-28 October as the country was hit by six tropical cyclones (Van Tien et al, 2021a). We examined landslide events from Huong Phung Commune on 18 October 2020 and Quang Nam Province on 28 October 2020 (Van Tien et al, 2021a). We selected a 1080 km 2 AOI in Huong Phung and a 416 km 2 AOI in Quang Nam (Fig.…”

Section: Rainfall-triggered Landslides Huong Phung and Quang Nam Vietnammentioning

confidence: 99%

Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

Handwerger

Jones

Amatya

et al. 2021

Preprint

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Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change detection approach that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to detect areas with high landslide density using the cloud-based Google Earth Engine (GEE). Importantly, our approach does not require downloading a large volume of data to a local system or specialized processing software. We provide strategies, including a landslide density heatmap approach, that can aid in rapid response and landslide detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries, and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

show abstract

Rainfall-induced catastrophic landslide in Quang Tri Province: the deadliest single landslide event in Vietnam in 2020

Cited by 19 publications

References 2 publications

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

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