High-Resolution 3-D Flood Information From Radar Imagery for Flood Hazard Management

Schumann, Guy; Hostache, Renaud; Puech, Christian; Hoffmann, Lucien; Matgen, Patrick; Pappenberger, Florian; Pfister, Laurent

doi:10.1109/tgrs.2006.888103

Cited by 169 publications

(125 citation statements)

References 17 publications

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“…(iii) flood inundation maps (Brivio et al, 2002;Cruz et al, 2010;Frazier and Page, 2000;Jarihani et al, 2014;Schumann et al, 2007;Sheng et al, 2001);…”

Section: Introductionmentioning

confidence: 99%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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“…(iii) flood inundation maps (Brivio et al, 2002;Cruz et al, 2010;Frazier and Page, 2000;Jarihani et al, 2014;Schumann et al, 2007;Sheng et al, 2001);…”

Section: Introductionmentioning

confidence: 99%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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“…Examples are the Mississippi flood of 1993 (Brakenridge et al, 1994), the 1996 and 1997 inundations in the Red River Valley (Barber et al, 1996;Wilson and Rashid, 2005), the August 2002 Elbe river flood (Henry et al, 2006), the overflow of the River Thames in 1992 (Horritt et al, 2001), the 2006 event of the River Dee in Wales (Schumann et al, 2009a), the River Mosel flood of 1997 and the 2003 event on the River Alzette floodplain (Schumann et al, 2007). Reviews of the state of the art in flood remote sensing were provided by Smith (1997), Sanyal and Lu (2004) and by Schumann et al (2009b).…”

Section: Introductionmentioning

confidence: 99%

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Pulvirenti

Pierdicca

Chini

et al. 2011

Nat. Hazards Earth Syst. Sci.

234

140

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Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data.

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“…The most important thing that worth mention is the easy but novel way of RS data sharing -thesis-based RS data subscription and data push through virtual data catalog mounting as local . 10 GeoSOT: Geographical Coordinate Subdividing Grid with One Dimension Integer Coding Tree The runtime implementing of RS data management and sharing is demonstrated as figure 2. Firstly, pipsCloud interprets the data requests, and check the user authentication.…”

Section: Rs Data Management and Sharingmentioning

confidence: 99%

“…Particularly, many timecritical RS applications even demand real-time or near realtime processing capacities ( [7][8]). Some relevant examples are large debris flow investigation ( [9], flood hazard management ( [10]) and large ocean oil spills surveillance ( [11] [12]). Generally, these large-scale data processing problems in RS applications ([4][13] [14]) with high QoS requirement are typically regarded as both compute-intensive and data-intensive.…”

Section: Introductionmentioning

confidence: 99%

pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

173

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With the increasing requirement of accurate and up-to-date resource & environmental information for regional and global monitoring, large-region covered multi-temporal, multi-spectral massive remote sensing (RS) datasets are exploited for processing. The remote sensing data processing generally follows a complex multi-stage processing chain, which consists of several independent processing steps subject to types of RS applications. In general the RS data processing for regional environmental and disaster monitoring are recognized as typical both compute-intensive and data-intensive applications.To solve the aforementioned issues efficiently, we propose pipsCloud which combine recent Cloud computing and HPC techniques to enable large-scale RS data processing system as on-demand real-time services. Benefiting from the ubiquity, elasticity and high-level of transparency of Cloud computing model, the massive RS data managing and data processing for dynamic environmental monitoring are all encapsulate as Cloud with Web interfaces. Where, a Hilbert-R + based data indexing mechanism is employed for optimal query and access of RS imageries, RS data products as well as interim data. In the core platform beneath the Cloud services, we provide a parallel file system for massive high-dimensional RS data and offers interfaces for intensive irregular RS data accessing so as to provide improved data locality and optimized I/O performance. Moreover, we adopt an adaptive RS data analysis workflow manage system for on-demand workflow construction and collaborative execution of distributed complex chain of RS data processing, such as forest fire detection, mineral resources and coastline monitoring. Through the experimental analysis we have show the efficiency of the pipsCloud platform.

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High-Resolution 3-D Flood Information From Radar Imagery for Flood Hazard Management

Cited by 169 publications

References 17 publications

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

pipsCloud: High performance cloud computing for remote sensing big data management and processing

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