Rapid flood and damage mapping using synthetic aperture radar in response to Typhoon Hagibis, Japan

Tay, Cheryl; Yun, Sang‐Ho; Chin, Shi Tong; Bhardwaj, Alok; Jung, Jungkyo; Hill, Emma M.

doi:10.1038/s41597-020-0443-5

Cited by 64 publications

(42 citation statements)

References 10 publications

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“…Combining ascending + descending data into a single stack helps reduce bias introduced from the acquisition geometry (e.g., radar shadows, foreshortening, layover) (Adriano et al, 2020). The combined effect of stacking hundreds of images with both geometries is a major improvement from previous SAR-based amplitude change studies that have focused on individual acquisition geometries and a relatively small number of SAR images (Adriano et al, 2020;Jung and Yun, 2020;Mondini et al, 2019). Our findings indicate that future catastrophic events will benefit from a large number of pre-event images (S1 data has been collected since 2014).…”

Section: Discussionmentioning

confidence: 68%

“…2 and 5). In many cases, these amplitude changes occur in farmlands where the ground surface is commonly worked and there can be large changes in water content due to flooding, which influences the SAR reflectivity (Adriano et al, 2020;DeVries et al, 2020;Jung and Yun, 2020). These false positives are particularly noticeable in the rapid response imagery (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…We identified potential landslides (and other ground surface change) by examining the change in amplitude, Aratio, defined as the pre-event stack minus post-event stack, Apre -Apost. Because GEE provides the amplitude data in dB, Apre -Apost is equivalent to the standard amplitude ratio approach Aratio = log10(Apre/Apost), which is the most commonly used amplitudebased change detection method (e.g., Jung and Yun, 2020;Mondini et al, 2019). Aratio can be either positive or negative, with positive values corresponding to a decrease in SAR amplitude after a landslide event.…”

Section: Sar Amplitude-based Change Detection On Google Earth Enginementioning

confidence: 99%

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Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine

Handwerger

Jones

Huang

et al. 2020

Preprint

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Abstract. The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and improving our understanding of where landslides occur. Satellite-based synthetic aperture radar (SAR) can be used to identify landslides, often within days after triggering events, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Although there are many landslide detection methods using SAR, most require downloading a large volume of data to a local system and specialized processing software and training. Here we present a SAR-based amplitude change detection approach designed for those without SAR expertise that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to identify landslides on Google Earth Engine (GEE). We provide strategies that can aid in rapid response and event inventory mapping. We test our GEE-based approach in a ~ 277 km2 area in Hiroshima Prefecture, Japan where ~ 3,800 landslides were triggered by rainfall in July 2018. Our ability to detect landslides improves with the total number of SAR images acquired before and after the landslide event, by combining both ascending and descending acquisition geometry data, and by using topographic data to mask out flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize these state-of-the-art remote sensing data.

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Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Sar Amplitude-based Change Detection On Google Earth Enginementioning

confidence: 99%

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Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine

Handwerger

Jones

Huang

et al. 2020

Preprint

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“…Smooth water acts primarily as a forward specular target, producing little scattering of energy back to the sensor. Because of this unique scattering mechanism, SARs have been used extensively to detect surface water (Agnihotri et al., 2019; Ouled Sghaier et al., 2018; Tay et al., 2020). Vegetation canopies, in contrast, produce significant multiple scattering within the canopy and between the canopy of underlying surface.…”

Section: Introductionmentioning

confidence: 99%

Rice Inundation Assessment Using Polarimetric UAVSAR Data

Huang

Runkle

Isbell³

et al. 2021

Earth and Space Science

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Rice cultivation practices in the United States typically include managing irrigation water for inundated conditions. In the Arkansas Delta region, for example, rice fields can require 5-20 cm of inundation for seeding, with intermittent or continuous flooding during growth phases until drainage near harvest, and between rotations for wildfowl habitat or land maintenance (Reba et al., 2020). Irrigation practices, access to water, regulatory policies, and infrastructure vary across the Midsouth USA resulting in many unique combinations of landforms, irrigation regimes, and water sources. When using flood irrigation, producers have challenges managing and measuring water quantities at the field or paddy level. One well or well outlet can irrigate multiple fields, water is gravity-driven from field to field, underground pipe and plumbing infrastructure can use a combination of ground and surface water, and surface poly-pipe can move water from field to field. In Arkansas, total water use throughout the growing season can range from 382 mm on zero grade (fields without of slope) to 1,034 mm for contour levees (Smith et al., 2007). Regardless of the approach to irrigation, the ground water supply is depleting faster than recharge (Kresse et al., 2014), and public private partnerships are investigating tradeoffs across irrigation regimes, greenhouse gas emissions, water quantity, and management practices (Nalley et al., 2015; Reba et al., 2013; Reba & Massey, 2020). To scale these field experiments the science community and public and private sectors can benefit from synoptic, large area metrics on rice field inundation conditions. Traditionally, optical satellite remote sensing tools have focused on identifying surface or open water conditions. These sensors and techniques struggle when postharvest residues are present or once crop emergence

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“…Furthermore, a majority of current climate models seem to underestimate the “extremeness” of impacts, namely in the agricultural sector, terrestrial ecosystems, or heat-related human mortality (Schewe et al 2019 ). Given that without draconian mitigation strategies, in the authors’ opinions, both the 1.5 °C and 2 °C targets defined in the Paris Agreement are likely be missed, society will be exposed to increasing losses and disasters from more frequent and intense catastrophic weather extreme events, for example, events similar or even more powerful than the 2019 Hurricane Dorian and Typhoon Hagibis (Tay et al 2020 ).…”

mentioning

confidence: 99%