Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation

Pulvirenti, Luca; Chini, Marco; Pierdicca, Nazzareno; Guerriero, L.; Ferrazzoli, P.

doi:10.1016/j.rse.2010.12.002

Cited by 222 publications

(133 citation statements)

References 36 publications

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“…To run the models, the flood conditions have been simulated by assuming that soil has the same electromagnetic characteristics (i.e., dielectric permittivity) as water and a negligible roughness (Pulvirenti et al, 2011) as well. To predict the radar return under dry conditions, fairly standard values of soil roughness (i.e., height standard deviation in the order of 1 cm) and volumetric moisture (in the range 0.2-0.3 m 3 m −3 ) have been assumed.…”

Section: The Model-based Fuzzy Thresholdsmentioning

confidence: 99%

“…For agricultural areas, the simulations have shown that the increase of the double bounce backscattering due to an inundation can become quite large (at least 2-3 dB) when the crops have reached at least an intermediate stage of growth (i.e., in spring or in summer). This effect is particularly evident for wheat, and for the C and X bands (Pulvirenti et al, 2011). The degree of membership to the class of agricultural flooded areas involved in the enhancement of backscattering is defined by the standard S-function ( Fig.…”

Section: The Model-based Fuzzy Thresholdsmentioning

confidence: 99%

“…Since in operative applications such availability cannot be supposed, in the present work we follow a different method. The theoretical knowledge on the scattering of the electromagnetic radiation from various types of surfaces, that in Pulvirenti et al (2011) proved to be very useful for flood mapping using SAR, is exploited. In particular, three electromagnetic models predicting the radar return from agricultural, forested and urban areas, have been extended to simulate the backscattering under flooded conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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.

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234

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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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Section: The Model-based Fuzzy Thresholdsmentioning

confidence: 99%

Section: The Model-based Fuzzy Thresholdsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

Self Cite

234

140

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show abstract

“…Another used method is the multi-temporal segmentation approach [18]; the procedure is based on the image segmentation technique, which allows identifying homogeneous regions in order to extract a reliable backscattering value, and on the use of an electromagnetic model to interpret the radar return from the image segments. The image segmentation techniques includes: point, line, and edge detection, thresholding segmentation region, segmentation using morphological image processing.…”

Section: B Radar Sar Satellite Imagerymentioning

confidence: 99%

Development of a downstream emergency response service for flood and related risks in Romania based on satellite data

2016

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Abstract. Recently, the National Meteorological Administration initiated and started to implement user-driven services, based on satellite remote sensing and geo-information capacities. The paper presents this downstream emergency response service whose aim is to provide updated and accurate cartographic information in river flood prevention and post-crisis phase. The service is targeted to develop an interoperable framework for the management of the available geo-information using cutting-edge techniques and satellite data in order to provide high quality and accurate spatial products. An appropriate methodology was developed and tested, in order to process the optical or radar satellite imagery, with medium and high range spatial resolution, to rapid mapping the flood extent, to integrate the information in a GIS environment and finally to obtain standardized, cartographic products. The service is able to provide customized flood geospatial products (updated reference maps for the area affected, near real-time flood delineation maps, maximum flood extent maps, flooded area classification, flood evolution maps, damage assessment maps and reports) tailored to specific users and featuring near-real time delivery. A dedicated geo-portal, was developed to display, query, analyse and retrieve the spatial products. The end-users are able to access the system using a simple web browser to view and query the flood related product archive or download the selected products.

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“…Numerous authors assess flood situations based on a single radar data set, simply depicting the uni-temporal flood water extent and discuss flood water impact on other land cover classes, such as urban areas or infrastructure. Advanced studies rely on multitemporal data to analyze flood progression [6,7,[11][12][13][14][15][16][17][18][19].…”

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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Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation

Cited by 222 publications

References 36 publications

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

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

Development of a downstream emergency response service for flood and related risks in Romania based on satellite data

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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