Benchmarked RADARSAT-2, SENTINEL-1 and RADARSAT Constellation Mission Change-Detection Monitoring at North Slide, Thompson River Valley, British Columbia: ensuring a Landslide-Resilient National Railway Network

Huntley, David; Rotheram-Clarke, Drew; Pon, Andy; Tomaszewicz, Alex; Leighton, Jon; Cocking, Robert; Joseph, J

doi:10.1080/07038992.2021.1937968

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…These have pixel dimensions of 1 m by 3 m in the spotlight-imaging beam mode (FSL30 at ~45° incidence). Such SAR imaging data are uncompromised by clouds and microwave backscatter ( 70 ) and easily discern felled vegetation, as well as disturbances related to slopes or changes of meter-scale surface texture. In addition, satellite LIDAR measurements of tree heights on the southern flanks of Tofua from ISS/GEDI ( 69 ) illustrated canopy heights in potentially disturbed areas ranging from 5-m to over 45-m elevation above sea level, with a typical value of about 20 m, consistent with highly approximated tree size measurements from submeter scale WV images.…”

Section: Methodsmentioning

confidence: 99%

The 2022 Hunga-Tonga megatsunami: Near-field simulation of a once-in-a-century event

et al. 2023

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The Hunga Tonga–Hunga Ha’apai (HTHH) volcanic eruption in January 2022 generated catastrophic tsunami and contends for the largest natural explosion in more than a century. The main island, Tongatapu, suffered waves up to 17 m, and Tofua Island suffered waves up to 45 m, comfortably placing HTHH in the “megatsunami” league. We present a tsunami simulation of the Tongan Archipelago calibrated by field observations, drone, and satellite data. Our simulation emphasizes how the complex shallow bathymetry of the area acted as a low-velocity wave trap, capturing tsunami for more than 1 hour. Despite its size and long duration, few lives were lost. Simulation suggests that HTHH’s location relative to urban centers saved Tonga from a worse outcome. Whereas 2022 seems to have been a lucky escape, other oceanic volcanoes have the capacity to spawn future tsunami at HTHH scale. Our simulation amplifies the state of understanding of volcanic explosion tsunami and provides a framework for assessment of future hazards.

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Section: Methodsmentioning

confidence: 99%

The 2022 Hunga-Tonga megatsunami: Near-field simulation of a once-in-a-century event

et al. 2023

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“…Level III investigations are most suitable for slower moving landslides where boreholes can be drilled for monitoring and unstable slopes instrumented safely. In the Thompson valley transportation corridor, Level III InSAR, UAV and RTK-GNSS change-detection datasets conclusively show that geotechnical solutions to stabilize slopes based on current physical models are only partly successful (Journault et al 2016(Journault et al , 2018Huntley et al 2017bHuntley et al , 2021c. To better understand the impacts of climate, landslide behaviour is further investigated though time-series monitoring of slopes and infrastructure (e.g., fibre optic reflectometry), boreholes (e.g., piezometers, inclinometers, and acoustic emissions), and weather variables (e.g., rain gauges, snow sensors, thermistors, anemometers, soil moisture meters).…”

Section: Level III Investigation (Time-seriesmentioning

confidence: 99%

IPL Project 202: Landslide Monitoring Best Practices for Climate-Resilient Railway Transportation Corridors in Southwestern British Columbia, Canada

Huntley

Bobrowsky

MacLeod

et al. 2023

Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022

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The paper outlines landslide mapping and change-detection monitoring protocols based on the successes of ICL-IPL Project 202 in southwestern British Columbia, Canada. In this region, ice sheets, glaciers, permafrost, rivers and oceans, high relief, and biogeoclimatic characteristics contribute to produce distinctive landslide assemblages. Bedrock and drift-covered slopes along the transportation corridors are prone to mass-wasting when favourable conditions exist. In high-relief mountainous areas, rapidly moving landslides include rock and debris avalanches, rock and debris falls, debris flows and torrents, and lahars. In areas with moderate to low relief, rapid to slow mass movements include rockslides and slumps, debris or earth slides and slumps, and earth flows. Slow-moving landslides include rock glaciers, rock and soil creep, solifluction, and lateral spreads in bedrock and surficial deposits. Research in the Thompson River Valley aims to gain a better understanding of how geological conditions, extreme weather events and climate change influence landslide activity along the national railway corridor. Remote sensing datasets, consolidated in a geographic information system, capture the spatial relationships between landslide distribution and specific terrain features, at-risk infrastructure, and the environmental conditions expected to correlate with landslide incidence and magnitude. Reliable real-time monitoring solutions for critical railway infrastructure (e.g., ballast, tracks, retaining walls, tunnels and bridges) able to withstand the harsh environmental conditions of Canada are highlighted. The provision of fundamental geoscience and baseline geospatial monitoring allows stakeholders to develop robust risk tolerance, remediation, and mitigation strategies to maintain the resilience and accessibility of critical transportation infrastructure, while also protecting the natural environment, community stakeholders, and the Canadian economy. We conclude by proposing a best-practice solution involving three levels of investigation to describe the form and function of the wide range of rapid and slow-moving landslides occurring across Canada, which is also applicable elsewhere.

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“…Because RCM was launched only recently, most studies on RCM are based on simulation data using quad-polarized RADARSAT-2 images [2]. The applicability of different satellite platforms (including Radarsat-2, Sentinel-1 and RCM) are analyzed in [11,12] for landslide monitoring. The authors emphasized the RCM advantages of a shorter revisit time and higher spatial resolution for the detection of small-sized slope movements, compared with previous SAR satellites.…”

Section: Introductionmentioning

confidence: 99%

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

2022

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Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.

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Benchmarked RADARSAT-2, SENTINEL-1 and RADARSAT Constellation Mission Change-Detection Monitoring at North Slide, Thompson River Valley, British Columbia: ensuring a Landslide-Resilient National Railway Network

Cited by 10 publications

References 38 publications

The 2022 Hunga-Tonga megatsunami: Near-field simulation of a once-in-a-century event

The 2022 Hunga-Tonga megatsunami: Near-field simulation of a once-in-a-century event

IPL Project 202: Landslide Monitoring Best Practices for Climate-Resilient Railway Transportation Corridors in Southwestern British Columbia, Canada

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

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