Flood Inundation Modelling Using LiDAR and SAR Data

Bates, Paul; Horritt,; Cobby, David M.; Mason, David C.

doi:10.1002/0470094001.ch5

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…하지만 광 학 센서 기반의 영상촬영은 기상조건에 따른 정보수집 제한 이 있다. 이와 비교하여, 날씨에 영향을 받지 않는 Synthetic Aperture Radar (SAR) 영상의 환경모니터링 활용은 호소 의 변화를 시계열적으로 분석하여 폐기물 발생 전후 지속해 서 모니터링할 수 있다 (Bates et al, 2005;Chapman et al, 2015;Mason et al, 2009;Rahman and Thakur, 2018;Sass et al, 2012).…”

Section: 서론unclassified

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses

Yoon,

Yu,

Lee

2023

GEO DATA

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This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.

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Section: 서론unclassified

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses

Yoon,

Yu,

Lee

2023

GEO DATA

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“…Nowadays floodplain morphology can be depicted in great detail as digital terrain models (DTMs) and combined with hydraulic models to predict inundation from surface water flow (Bates et al . ). The Environment Agency (EA) provides such indicative inundation probability maps online, and they provide warning and planning advice services, so there need be little doubt as to the many who may at some time be inundated, even without environmental change.…”

Section: Introductionmentioning

confidence: 97%

The English floodplain

Lewin

2014

Geographical Journal

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English floodplains are platforms for flood risk vulnerability; they are varied in form and in the timing of their development, both naturally and in terms of human occupation. An understanding of floodplain generation is one of the diverse research areas that can contribute towards effective flood mitigation efforts. The performance of remedial measures like catchment sediment management, flood embanking and channel dredging need to be evaluated with regard to active fluvial process systems so that they may be applied appropriately, particularly given uncertain future environmental changes.Peer reviewe

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“…The use of SAR images has proven to be helpful in assessing the temporal variations of lake behaviours and vulnerabilities (Bates et al 2005;Mason et al 2010). This SAR technology has a high capability to routinely capture and store images through clouds and hazy atmospheric conditions like fog, smog, light rain, mist (Chapman et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Freshwater lake inundation monitoring using Sentinel-1 SAR imagery in Eastern Uganda

Barasa

Wanyama

2020

Annals of GIS

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In flood hazard assessment, little attention has been given to the seasonal observation of freshwater inundation using Sentinel-1 Synthetic-Aperture Radar (SAR) images in the tropics. To assess these spatial variations, this study examined the inundation magnitudes and reflectance of riparian flora in the raining seasons (February-April and September-November) and dry-periods (May-August and December-January). The inundation areas were determined using an object-oriented classification algorithm, whilst the merits concerning the lake, riparian vegetation and shoreline were well-defined using backscatter-coefficient values. Findings indicated that the SAR images are practical to monitor inundation coverages and discern lake basin specific features such as highland areas, shoreline, water and riparian-vegetation. Seasonally, inundated sizes were comparatively higher in February (902 sq. km) and October (700 sq.km). Backscatter values of inundated parts varied from −16 dB to −19 dB, whereas those of riparian vegetation were higher in the months of May and August. This study hence displays a clear-cut correlation between riparian-vegetation, hydrology and climate variables.

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Flood Inundation Modelling Using LiDAR and SAR Data

Cited by 5 publications

References 38 publications

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses

The English floodplain

Freshwater lake inundation monitoring using Sentinel-1 SAR imagery in Eastern Uganda

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