Detection and Volume Estimation of Large-Scale Landslides Based on Elevation-Change Analysis Using DEMs Extracted From High-Resolution Satellite Stereo Imagery

Tsutsui, K.; Rokugawa, Shuichi; Nakagawa, Hideaki; Miyazaki, Shuichi; Cheng, Chung-Chih; Shiraishi, Takeshi; Yang, Shiun-Der

doi:10.1109/tgrs.2007.895209

Cited by 98 publications

(56 citation statements)

References 16 publications

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“…The difficulties caused by the limited ground control and missing camera calibration protocol can be solved by the application of self-calibrating bundle adjustment methods [17]. It was demonstrated by [18] that DEM differencing using DEMs derived from VHR satellite data can be effectively used to investigate mass displacement of large landslides. This technique is also effective for a delineation of large landslides [18].…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment

et al. 2015

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Frequently occurring landslides in Ethiopia endanger rapidly expanding settlements and infrastructure. We investigated a large landslide on the western escarpment of the Main Ethiopian Rift close to Debre Sina. To understand the extent and amplitude of the movements, we derived vectors of horizontal displacements by feature matching of very high resolution satellite images (VHR). The major movements occurred in two phases, after the rainy seasons in 2005 and 2006 reaching magnitudes of 48˘10.1 m and 114˘7.2 m, respectively. The results for the first phase were supported by amplitude tracking using two Envisat/ASAR scenes from the 31 July 2004 and the 29 October 2005. Surface changes in vertical direction were analyzed by subtraction of a pre-event digital elevation model (DEM) from aerial photographs and post-event DEM from ALOS/PRISM triplet data. Furthermore, we derived elevation changes using satellite laser altimetry measurement acquired by the ICESat satellite. These analyses allowed us to delineate the main landslide, which covers an area of 6.5 km 2 , shallow landslides surrounding the main landslide body that increased the area to 8.5 km 2 , and the stable area in the lower part of the slope. We assume that the main triggering factor for such a large landslide was precipitation cumulated over several months and we suspect that the slope failure will progress towards the foot of the slope.

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Section: Introductionmentioning

confidence: 99%

“…It was demonstrated by [18] that DEM differencing using DEMs derived from VHR satellite data can be effectively used to investigate mass displacement of large landslides. This technique is also effective for a delineation of large landslides [18].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment

et al. 2015

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“…Recent innovations in remote sensing have led to the deployment of a new generation of aerospace sensors with high-spatial-resolution and agile imaging capabilities. Data from these recently available sensors, such as high-resolution satellite imagery (Nichol et al, 2006;Tsutsui et al, 2007), radar imagery (Colesanti and Wasowski, 2006), or lidar point clouds (Van Den Eeckhaut et al, 2007), are quite suitable for landslide identification and classification.…”

Section: Introductionmentioning

confidence: 99%

Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides

Yang

Chen

2010

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tLandslides are a major type of geohazards claiming thousands of casualties and billions of dollars in property damages every year. Catastrophic landslide activities are often triggered by some extreme events such as earthquakes, excessive precipitations, or volcanic eruptions. Quickly identifying the spatial distribution of landslides induced by these extreme events is crucial for coordinating rescue efforts and planning in situ investigations. In this study, we propose an automated method for detecting the spatial distribution of earthquake-triggered landslides by examining after-event vegetation changes. Central to this method is the use of pre-and post-event remote sensor images covering the same area. Geometric correction and radiometric normalization are performed before deriving a vegetation index from each image. Then, an image differencing procedure is applied to the two derived indices. With the resultant difference image, an initial landslide distribution map is generated by highlighting the pixels with a threshold percentage decrease in the brightness values as a direct result of the image subtraction. The threshold percentage value is interactively determined by using a visual interpretation method. The final landslide distribution map is produced after using a modal filter to suppress boundary errors in the initial map. This method has been implemented in a test site, approximately 30 km from the epicenter of the Sichuan earthquake (7.9 Ms) that struck on 12 May 2008. A pre-event Thematic Mapper image and a post-event Advanced Spaceborne Thermal Emission and Reflection Radiometer scene are used. The thematic accuracy assessment indicates that 90% of the landslides have correctly been mapped. Given the relatively simple procedures and the good mapping accuracy, the image processing and change detection method identified in this study seems to be promising from an operational perspective.

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“…Owing to recent progress in photogrammetry and surveying technologies (e.g., LiDAR), it is now possible to produce centimeter-level ultra-high resolution digital elevation models (DEMs) that enable more accurate observations of geomorphologic changes. Many researchers have investigated methods for detecting large-scale landslides and debris flow areas with these technologies for the purpose of monitoring geomorphic changes, calculating the extent of changes and assessing debris flow behavior (McKean and Roering, 2004;Du and Teng, 2007;Tsutsui et al, 2007;Schelidl et al, 2008;Bull et al, 2010;Kim et al, 2014). However, because these studies examined LiDAR DEMs with relatively long intervals between acquisitions (≥5 years), some natural topographic changes were missed.…”

Section: Figure 1 Examples Of Debris Flow Damage In South Koreamentioning

confidence: 99%

Analysis of Debris Flow Behavior Using Airborne Lidar and Image Data

Kim¹,

Yune²,

Paik³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The frequency of debris flow events caused by severe rainstorms has increased in Korea. LiDAR provides high-resolution topographical data that can represent the land surface more effectively than other methods. This study describes the analysis of geomorphologic changes using digital surface models derived from airborne LiDAR and aerial image data acquired before and after a debris flow event in the southern part of Seoul, South Korea in July 2011. During this event, 30 houses were buried, 116 houses were damaged, and 22 human casualties were reported. Longitudinal and cross-sectional profiles of the debris flow path reconstructed from digital surface models were used to analyze debris flow behaviors such as landslide initiation, transport, erosion, and deposition. LiDAR technology integrated with GIS is a very useful tool for understanding debris flow behavior.

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Detection and Volume Estimation of Large-Scale Landslides Based on Elevation-Change Analysis Using DEMs Extracted From High-Resolution Satellite Stereo Imagery

Cited by 98 publications

References 16 publications

Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment

Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment

Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides

Analysis of Debris Flow Behavior Using Airborne Lidar and Image Data

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