Selecting the Best Image Pairs to Measure Slope Deformation

Yang, Wentao

doi:10.3390/s20174721

Cited by 7 publications

(3 citation statements)

References 22 publications

(38 reference statements)

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“…This method uses moving windows to calculate the correlation between texture similarities in pairs of diachronic images and is the most popular and effective method to derive sub-pixel displacements, for example, using aerial photographs and QuickBird satellite images 105 , Pléiades satellites 106 or Landsat-8 acquisitions 107 on different types of landslide. Slope displacement time series using Sentinel-2 images 108 or the SPOT1 and SPOT5-Pléiades archive 109 have also been obtained. Other than landslide kinematics, successive retrogressive and advancing phases can be successfully reconstructed 110 as well, with short revisiting time satellites.…”

Section: Technical Reviewmentioning

confidence: 99%

Landslide detection, monitoring and prediction with remote-sensing techniques

Intrieri

Tofani

Gigli

et al. 2023

Nat Rev Earth Environ

137

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Landslides are widespread occurrences that can become catastrophic when they occur near settlements and infrastructure. Detection, monitoring and prediction are fundamental to managing landslide risks and often rely on remote-sensing techniques (RSTs) that include the observation of Earth from space, laser scanning and ground-based interferometry. In this Technical Review, we describe the use of RSTs in landslide analysis and management. Satellite RSTs are used to detect and measure landslide displacement, providing a synoptic view over various spatiotemporal scales. Ground-based sensors (including ground-based interferometric radar, Doppler radar and lidar) monitor smaller areas, but combine accuracy, high acquisition frequency and configuration flexibility, and are therefore increasingly used in real-time monitoring and early warning of landslides. Each RST has advantages and limitations that depend on the application (detection, monitoring or prediction), the size of the area of concern, the type of landslide, deformation pattern and risks posed by landslide. The integration of various technologies is, therefore, often best. More effective landslide risk management requires greater leveraging of big data, more strategic use of monitoring resources and better communication with residents of landslide-prone areas.Sections improvement in temporal observation frequency is provided by the adoption of virtual constellation of mid-resolution multispectral satellites 29 . Jointly, Landsat-8 and Landsat-9 (a repeat cycle of 8 days) and Sentinel-2a and Sentinel-2b (a repeat cycle of 5 days) provide a global median average revisit interval of 2.3 days 30 .The swath width (or footprint) also varies between sensors, ranging from several kilometres for VHR satellites (about 13 km for World-View-3 and WorldView-4) to 120 km for SPOT-5, 185 km for Landsat-7 and 290 km for Sentinel-2. Ground sampling distance varies depending Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author selfarchiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Section: Technical Reviewmentioning

confidence: 99%

Landslide detection, monitoring and prediction with remote-sensing techniques

Intrieri

Tofani

Gigli

et al. 2023

Nat Rev Earth Environ

137

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“…1). Both image pairs are from the same date of different years to minimize uncertainties by having similar solar zenith/azimuth angles (Yang et al, 2020, Yang et al, 2020a.…”

Section: Slope Displacements Derived From the Sentinel-2 Imagesmentioning

confidence: 99%

“…The influence of the 2018 floods on adjacent hillslopes could be larger than reported here. In this work, the method we use can reliably track slope deformation >2 m (Stumpf et al, 2017;Yang, 2020) and we only monitored the downstream riverbanks for less than 2 years. It is possible that there could be more slow-moving slopes with cumulative displacements <2 m but not detected by our method.…”

Section: Concurrent Landslides and Channel Expansion During The 2018 mentioning

confidence: 99%

Landslide-lake outburst floods accelerate downstream slope slippage

Yang¹,

Fang²,

Liu‐Zeng³

2021

Preprint

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Abstract. The Jinsha River, carving a 2–4 km deep gorge, is one of the largest SE Asian rivers. Two successive landslide-lake outburst floods (LLFs) occurred after the 2018 Baige landslides along the river. Using Sentinel-2 images, we examined the LLFs' impacts on downstream river channel and adjacent hillslopes over a 100 km distance. The floods increased the width of the active river channel by 54 %. Subsequently, major landslides persisted for 15 months in at least nine locations for displacements > 2 m. Among them, three moving hillslopes, ~80 km downstream from the Baige landslides, slumped more than 10 m one year after the floods. Extensive undercuts by the floods probably removed hillslope buttresses and triggered deformation response, suggesting a strong and dynamic channel-hillslope coupling. Our findings indicate that infrequent catastrophic outburst flooding plays an important role in landscape evolution. Persistent post-flood hillslope movement should be considered in disaster mitigation in high-relief mountainous regions.

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