Application of radar data to modeling rainfall-induced landslides

Chiang, Shou Hao; Chang, Kai H.

doi:10.1016/j.geomorph.2008.06.012

Cited by 65 publications

(33 citation statements)

References 45 publications

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“…Visual interpretation of post-event orthophotos revealed that Typhoon Aere caused 703 landslides in the Shihmen Reservoir watershed (Chang et al 2008), whereof 421 landslides occurred in the Baichi catchment Chiang and Chang 2009). Most of the observed slope failures in the Baichi catchment are shallow landslides that occur on slopes with a soil depth less than 2 m ).…”

Section: Typhoon-induced Landslides and Debris Flows In The Baichi Camentioning

confidence: 99%

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Hölbling

Friedl

Eisank³

2015

Earth Sci Inform

100

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Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/ sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre-and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre-and postevent images and attributed to landslide classes.

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Section: Typhoon-induced Landslides and Debris Flows In The Baichi Camentioning

confidence: 99%

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Hölbling

Friedl

Eisank³

2015

Earth Sci Inform

100

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“…Most researchers are aware of the importance of the spatial variability of rainfall in landslide research. In applications, however, it is commonly not addressed due to the scarcity of available rain gauges (Chiang and Chang 2009). With respect to rainfall thresholds, common approaches to determining the actual rainfall for a single landslide site or a certain region are to utilize single rain gauges near a specific landslide site (e.g.…”

Section: Observation Of Rainfall and Its Spatial Representation In Lamentioning

confidence: 99%

“…Another seemingly very attractive approach to determining continuous rainfall fields is the utilization of radar technology. Radar technology is capable of offering spatially varying rainfall fields at temporal resolutions of 10 min (and less) and spatial resolutions of around 1 km, which is highly desirable for landslide studies (Chiang and Chang 2009). In addition, there is also vertically differentiated information on precipitation values available.…”

Section: Observation Of Rainfall and Its Spatial Representation In Lamentioning

confidence: 99%

Spatially distributed rainfall information and its potential for regional landslide early warning systems

Canli

Loigge²,

Glade

2017

Nat Hazards

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Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.

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“…Traditional ways of monitoring precipitation using only a few rain gauges over an entire catchment is far from optimal because rain gauges have a very limited spatial representativeness. Weather radar provides precipitation estimates that are more representative over large areas and can be used to better predict the distribution of RMMs within a given area (Crosta and Frattini, 2003;Chiang and Chang, 2009;Jorgensen et al, 2011). Unfortunately, radar data are often too coarse to be used directly in EWSs.…”

Section: From Simple Thresholds To Patterns: the Example Of Precipitamentioning

confidence: 99%

Monitoring and prediction in early warning systems for rapid mass movements

Stähli

Sättele²,

Huggel

et al. 2015

Nat. Hazards Earth Syst. Sci.

125

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Abstract. Rapid mass movements (RMM) pose a substantial risk to people and infrastructure. Reliable and cost-efficient measures have to be taken to reduce this risk. One of these measures includes establishing and advancing the state of practice in the application of early warning systems (EWSs). EWSs have been developed during the past decades and are rapidly increasing. In this paper, we focus on the technical part of EWSs, i.e., the prediction and timely recognition of imminent hazards, as well as on monitoring slopes at risk and released mass movements. Recent innovations in assessing spatial precipitation, monitoring and precursors of the triggering and deformation of RMM offer new opportunities for next-generation EWSs. However, technical advancement can only be transferred into more reliable, operational EWSs with an adequate well-instructed dedicated staff. To this end, an intense dialog between scientists, engineers and those in charge of warning, as well as further experience with new comprehensive prototype systems jointly operated by scientists and practitioners, will be essential.

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Application of radar data to modeling rainfall-induced landslides

Cited by 65 publications

References 45 publications

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Spatially distributed rainfall information and its potential for regional landslide early warning systems

Monitoring and prediction in early warning systems for rapid mass movements

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