Envisat multi‐polarized ASAR data for flood mapping

Henry, Jean-Baptiste; Chastanet, P.; Fellah, K.; Desnos, Yves-Louis

doi:10.1080/01431160500486724

Cited by 247 publications

(146 citation statements)

References 8 publications

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“…For most of the cases in Table 3, VV based water maps seem to have slightly higher accuracy than VH based water maps. We notice that many studies [11,40] suggested that VH is better than VV in terms of water body detection, because VV's higher sensitivity to wind-induced water surface condition makes it difficult to distinguish rough water surfaces and land. Our results suggest that the different performances of VV and VH are more similar than expected.…”

Section: Optimal Thresholds and Their Performancementioning

confidence: 90%

“…Visual interpretation is always one reliable and simple approach [8] except that it can be time consuming and subjective. Other popular methods include active contour models [9], texture-based segmentations [10], and grey-level thresholding [11]. Among all these methods, grey-scale thresholding is still the most commonly used approach to detect water areas using SAR imagery [12], due to its efficiency and acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

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Abstract:The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.

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Section: Optimal Thresholds and Their Performancementioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

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“…According to this approach, all pixels of an intensity image whose backscattering coefficient (σ 0 ) is smaller than a given threshold value (e.g., Hess et al, 1995;Henry et al, 2006) are classified as flooded. Thresholding is computationally inexpensive and suitable for rapid mapping purposes (Martinis et al, 2009), but is valid only for open water surfaces that behave as specular reflectors, thus scattering a small amount of radiation back to the radar antenna.…”

Section: The Fuzzy Approachmentioning

confidence: 99%

“…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.

235

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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“…Floods lead to the hindrance of transportation, bring with them pollution and disease, lead to the destruction of infrastructure, and more people lose their lives due to floods than due to any other natural hazard [1][2][3]. It is widely accepted that synthetic aperture radar (SAR) data is a suitable choice for the mapping and monitoring of flooded areas [4][5][6][7][8] as it allows for observations even during heavy cloud cover (typical during rainy seasons). Leinenkugel et al.…”

mentioning

confidence: 99%

Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province

et al. 2013

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Earth Observation is a powerful tool for the detection of floods. Microwave sensors are typically favored as they deliver data enabling water detection independent of solar illumination or cloud cover conditions. However, scale issues play an important role in radar based flood mapping. Depending on the flood related phenomenon under investigation, some sensors might be more suitable than others. In this study, we elucidate flood mapping at different spatial scale investigating the capability of Envisat ASAR Wide Swath Mode data at 150 m spatial resolution, as well as TerraSAR-X Scansar and Stripmap data at 8.25 m and 2.5 m resolution to especially assess urban flooding. For this purpose, we evaluate the results of automated multi-temporal water extraction from data sources of different scale against other parameters, such as settlement density, also taking a highly accurate building layer digitized from Quickbird data into consideration. Results reveal that while Envisat ASAR WSM derived flood maps are suitable to support the understanding of general flood patterns in a larger region, high resolution data of sensors OPEN ACCESSRemote Sens. 2013, 5 5123 such as TerraSAR-X is needed to truly assess urban flooding. However, even radar data of high spatial resolution still shows limitations; mainly in regions with a dense accumulation of corner reflectors leading to effects of layover, foreshortening, and shadowing, and hence the "over radiation" of flood affected areas.

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Envisat multi‐polarized ASAR data for flood mapping

Cited by 247 publications

References 8 publications

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

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

Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province

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