InSAR-derived digital elevation models for terrain change analysis of earthquake-triggered flow-like landslides based on ALOS/PALSAR imagery

Huang, Yu; Yu, Miao; Liu, Fangzhou; Sawada, Kazuhide; Moriguchi, Shuji; Yashima, Atsushi; Li, Chengwei; Xue, Long

doi:10.1007/s12665-014-3939-5

Cited by 26 publications

(18 citation statements)

References 29 publications

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“…When it comes to the comparison of this type of terrain information, error estimation of data obtained in various periods using various techniques becomes crucial. Almost every technology, including theodolites, GPS, photogrammetry, InSAR, airborne LiDAR (ALS), and ground LiDAR, has its application ranges and restrictions in terms of spatial and time scales when employed to obtain 3-D terrain data [6,7]. By comparing the information with various precision and resolution methods that are collected during different periods, these multi-method, multi-period data of digital terrain models (DTM) have been used in landslide mechanism-related research [8], observations of surficial activity [9][10][11], landslide volume calculations [12][13][14], and volume variation estimations [15], as well as disaster scale assessment and simulation [16].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the previous comparative studies on multi-period DTM data have adopted ground control points, e.g., the discussion of landslide and river terrain variation by comparing the DTM data from ground measurements and those from aerial photogrammetry and ALS measurements [10,[17][18][19], analysis of river terrain variation and slope earthflows by contrasting between two-period [20,21] or three-period [22] ALS data and ground measurement data, evaluation of earthflow terrain variation using airborne and ground LiDAR data, and error assessment of these two techniques [23], using InSAR-derived DTM to discuss terrain changes before and after the landslide event [7], and error assessment based on the two LiDAR datasets of Taiwan [24]. All of these studies mentioned above were conducted based on accessible ground measurement points.…”

Section: Introductionmentioning

confidence: 99%

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Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Hsieh

Chan

2016

Remote Sensing

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In this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error estimation provides critical information for DTM data users. This study conducted error estimation from the perspective of general users for mountain/forest areas with poor traffic accessibility using limited information, including error reports obtained from the data generation process and comparison errors of terrain elevations. Our results suggested that the precision of the DTM data generated in this work using different aircrafts and generation techniques is suitable for landslide analysis. Especially in mountainous and densely vegetated areas, data generated by ALS can be used as a benchmark to solve the problem of insufficient control points. Based on DEM differencing of multiple periods, this study suggests that sediment delivery rate decreased each year and was affected by heavy rainfall during each period for the Meiyuan Shan landslide area. Multi-period aerial photogrammetry and ALS can be effectively applied after the landslide disaster for monitoring the terrain changes of the downstream river channel and their potential impacts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Hsieh

Chan

2016

Remote Sensing

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“…Over the past few decades, an explosion in the quantity and quality of geodetic data has been increasingly applied to geological and anthropogenic hazards [1][2][3][4][5][6][7]. It includes ground-, air-and space-based observations that span a range of spatial and temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

Modeling 3D Free-geometry Volumetric Sources Associated to Geological and Anthropogenic Hazards from Space and Terrestrial Geodetic Data

Camacho

Torres

2019

Remote Sensing

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Recent decades have shown an explosion in the quantity and quality of geodetic data, mainly space-based geodetic data, that are being applied to geological and anthropogenic hazards. This has produced the need for new approaches for analyzing, modeling and interpreting these geodetic data. Typically, modeling of deformation and gravity changes follows an inverse approach using analytical or numerical solutions, where normally regular geometries (point sources, disks, prolate or oblate spheroids, etc.) are assumed at the initial stages and the inversion is carried out in a linear context. Here we review an original methodology for the simultaneous, nonlinear inversion of gravity changes and/or surface deformation (measured with different techniques) to determine 3D (three-dimensional) bodies, without any a priori assumption about their geometries, embedded into an elastic or poroelastic medium. Such a fully nonlinear inversion has led to interesting results in volcanic environments and in the study of water tables variation due to its exploitation. This methodology can be used to invert geodetic remote sensing data or terrestrial data alone, or in combination.

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“…The expansion of the population in coastal cities results in rapid urbanisation [15,16]. Increasing traffic congestion has led to a demand for the development of urban railway transportation systems in big cities such as Beijing, Shanghai, Guangzhou, and Chongqing, where there has been construction of underground tunnels, and deep excavation in urban areas [17][18][19]. Urbanisation also causes other environmental and geological problems, such as groundwater pollution, land subsidence, sink holes in urban areas, and an increased risk of flooding [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Analysis and GIS Mapping of Flooding Hazards on 10 May 2016, Guangzhou, China

Lyu

Wang

Shen

et al. 2016

Water

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Abstract:On 10 May 2016, Guangdong Province, China, suffered a heavy rainstorm. This rainstorm flooded the whole city of Guangzhou. More than 100,000 people were affected by the flooding, in which eight people lost their lives. Subway stations, cars, and buses were submerged. In order to analyse the influential factors of this flooding, topographical characteristics were mapped using Digital Elevation Model (DEM) by the Geographical Information System (GIS) and meteorological conditions were statistically summarised at both the whole city level and the district level. To analyse the relationship between flood risk and urbanization, GIS was also adopted to map the effect of the subway system using the Multiple Buffer operator over the flooding distribution area. Based on the analyses, one of the significant influential factors of flooding was identified as the urbanization degree, e.g., construction of a subway system, which forms along flood-prone areas. The total economic loss due to flooding in city centers with high urbanization has become very serious. Based on the analyses, the traditional standard of severity of flooding hazards (rainfall intensity grade) was modified. Rainfall intensity for severity flooding was decreased from 50 mm to 30 mm in urbanized city centers. In order to protect cities from flooding, a "Sponge City" planning approach is recommended to increase the temporary water storage capacity during heavy rainstorms. In addition, for future city management, the combined use of GIS and Building Information Modelling (BIM) is recommended to evaluate flooding hazards.

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InSAR-derived digital elevation models for terrain change analysis of earthquake-triggered flow-like landslides based on ALOS/PALSAR imagery

Cited by 26 publications

References 29 publications

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

Modeling 3D Free-geometry Volumetric Sources Associated to Geological and Anthropogenic Hazards from Space and Terrestrial Geodetic Data

Analysis and GIS Mapping of Flooding Hazards on 10 May 2016, Guangzhou, China

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