Temporal stacking of sub-pixel offset tracking for monitoring slow-moving landslides in vegetated terrain
Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir...
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description | Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain. |
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The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. 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The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain.</description><subject>Agriculture</subject><subject>Ambient noise</subject><subject>Azimuth</subject><subject>Background noise</subject><subject>Canyons</subject><subject>Civil Engineering</subject><subject>Deformation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geography</subject><subject>Geology</subject><subject>Hydrogeology</subject><subject>Image resolution</subject><subject>Interferometry</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Monitoring</subject><subject>Monitoring methods</subject><subject>Natural Hazards</subject><subject>Original Paper</subject><subject>Pixels</subject><subject>Radar data</subject><subject>Reservoirs</subject><subject>SAR (radar)</subject><subject>Superhigh frequencies</subject><subject>Synthetic aperture radar</subject><subject>Synthetic aperture radar interferometry</subject><subject>Terrain</subject><subject>Tracking</subject><subject>Vegetation</subject><subject>Velocity</subject><subject>Velocity distribution</subject><issn>1612-510X</issn><issn>1612-5118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LAzEQDaJgrf4BTwHP0Xzt11GKX1DwUsFbyGYnJXV3syZp1X_vri168zDMPOa9N8xD6JLRa0ZpcRMZFTInlMuxOC9IcYRmLGecZIyVx78zfT1FZzFuKOUVFdUMuRV0gw-6xTFp8-b6NfYWx21NBvcJ7QhshIRTOCytD7jzvUs-TDC2_oN0fjfNre6b2LoGInY93sEakk7Q4AQhaNefoxOr2wgXhz5HL_d3q8UjWT4_PC1ul0QLyROpjGYCGgO84bYUeaVrU_ESZAl1o3mdW2kZZUZnmeGFyWoJmhaVoI0sq7rmYo6u9r5D8O9biElt_Db040klaCaELHJBRxbfs0zwMQawagiu0-FLMaqmSNU-UjVGqn4iVcUoEntRHKbvIfxZ_6P6BntQe_8</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Chang, Fengnian</creator><creator>Dong, Shaochun</creator><creator>Yin, Hongwei</creator><creator>Ye, Xiao</creator><creator>Zhang, Wei</creator><creator>Zhu, Honghu</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope></search><sort><creationdate>20240601</creationdate><title>Temporal stacking of sub-pixel offset tracking for monitoring slow-moving landslides in vegetated terrain</title><author>Chang, Fengnian ; 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The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. 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subjects | Agriculture Ambient noise Azimuth Background noise Canyons Civil Engineering Deformation Earth and Environmental Science Earth Sciences Geography Geology Hydrogeology Image resolution Interferometry Landslides Landslides & mudslides Monitoring Monitoring methods Natural Hazards Original Paper Pixels Radar data Reservoirs SAR (radar) Superhigh frequencies Synthetic aperture radar Synthetic aperture radar interferometry Terrain Tracking Vegetation Velocity Velocity distribution |
title | Temporal stacking of sub-pixel offset tracking for monitoring slow-moving landslides in vegetated terrain |
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