Landslides detection and volume estimation in Jinbu area of Korea

Abstract

Shallow landslides triggered by heavy rainfall are common phenomena in mountainous areas of temperate monsoon regions. On July 2006 intensive shallow landslides occurred in Jinbu area, Korea triggered by heavy rainfall. An inventory of 1412 shallow landslides was constructed from intensive field works and interpretation of web-based aerial photographs, and all landslides detected were mapped across the study area. The measurements were geometrical properties (landslide length, landslide width, landslide depth) of individual landslides to establish the relationship linking landslide area to landslide volume. The relationship linking landslide area to landslide volume was obtained from the inventory of 930 landslides and is a power law function with a scaling exponent g = 1.02, covers four orders of magnitude of landslide area and landslide volume, and is in reasonable agreement with existing relationships obtained from small scale shallow landslide events. The relationship can be used to estimate the volume of individual landslides with shallow soil depth when the area of landslide is known. However, geological and geomorphological setting should be considered to calculate accurate landslide volume with respect to disaster prevention.

Landslide Scaling: A Review

“…The γ value for landslides has been found to vary depending upon the failure material: soil landslides tend to be shallower and are described by lower scaling exponents (−1.09 to −1.40); mixed soil and bedrock slides are described by intermediate scaling exponents (−1.36 to −1.45); while bedrock landslides tend to have a deeper scar area and larger volume and are described by steeper scaling exponents (−1.34 to −1.92; Larsen et al, ; values from Table S1 In the supporting information). Cha et al () apply Larsen et al 's () scaling analysis methods and report a slightly less steep scaling exponent (−1.02) for analysis of 930 rainfall‐triggered landslides in Jinbu, Korea that were predominantly shallow (<1 m) soil slides.…”

Earthquake-triggered landslides and mudflows: Was this the wave that engulfed Ancient Helike?

“…We applied two independent methods to estimate their volumes. The first method uses published scalar relationships (Table 1) extracted from worldwide data of landslide areas and their average depth (Cha et al, 2018;Larsen and Torres Sanchez, 1998;Martin et al, 2002), following the equation proposed by Larsen et al (2010), which models the empirical relationship between V (volume) and A (area) of a landslide as an equation of the form:…”

MAST: An Earthquake-Triggered Landslides Extraction Method Combining Morphological Analysis Edge Recognition With Swin-Transformer Deep Learning Model

“…In addition to SVM and RF, various enhanced machine learning approaches have found application in automated ETLs extraction. For instance, neural-networkbased methodologies, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can adeptly discern spatial and temporal features of landslides from seismic data, significantly enhancing the precision of landslide extraction [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. For instance, Ramdhoni et al [14] employed the Smorph method to perform landslide extraction.…”