Remote sensing analysis for flood risk management in urban sprawl contexts

Franci, Francesca; Mandanici, Emanuele; Bitelli, Gabriele

doi:10.1080/19475705.2014.913695

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…Multi-temporal remote sensing analysis allowed to assess flooded areas by Franci et al (2014). The authors used it to compare the estimated results with real data from extreme events in an urban context.…”

Section: Remote Sensingmentioning

confidence: 99%

“…Practically, most modern technologies of geomatic surveying are touched upon in this special issue, i.e. photogrammetry (Borgogno Mondino 2014;Scaioni et al 2014), structure from motion (Forlani et al 2013), optical remote sensing (Franci et al 2014), laser scanning (Barbarella et al 2013), global navigation satellite systems (GNSS) (Cina & Piras 2014), synthetic aperture radar (SAR) (Frangioni et al 2014;Nascetti et al 2014;Vittuari et al 2014) and geographic information systems (GISs) as decision support using advanced models (Cencetti et al 2014;Federici et al 2014). A digital terrain model (DTM) is fundamental to successive analysis; Casella and Franzini (2014) have taken the vertical information and investigated, respectively, on high-resolution DTM accuracy and the dynamic aspect (movement) of the vertical component of the vertical component of the DTMs.…”

Section: Introductionmentioning

confidence: 99%

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Preface to the special issue: the role of geomatics in hydrogeological risk

Pirotti¹,

Guarnieri²,

Masiero³

et al. 2015

Geomatics, Natural Hazards and Risk

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In accordance with recent studies, most of the observed natural hazards throughout the globe are related to the dynamics of hydrological variability. This determines the fundamental importance of studies related to hydrogeological risk assessment, in terms of both prevention and mitigation of damages; science shall provide the modelling and forecasting tools in order to support the management of natural phenomena. Geomatic technologies have a leading role in this context, as they model the physical elements in the earth's surface, their dynamics in time and space, and the causes of their modifications. The main aim of this special issue is to provide a review of the state-of-the-art of geomatic technologies applied to landslides and flooding and to give certain insights on new ideas and future perspectives on these themes. The contributions presented in this issue were presented at the workshop “The Role of Geomatics in Hydrogeological Risk” held in Padova, Italy, where 82% of the municipalities are subject to a degree of hydrogeological risk and where several natural disasters occurred in the past years, which made the workshop location particularly well suited and makes this special issue significant

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Section: Remote Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preface to the special issue: the role of geomatics in hydrogeological risk

Pirotti¹,

Guarnieri²,

Masiero³

et al. 2015

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

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“…Moreover, the use of remote sensing techniques appears to be fundamental even for flood risk management (Franci et al 2014;Amarnath & Rajah 2015). Bryant and Gilvear (1999) used remote sensing to quantify geomorphic changes in the Tay River (Scotland), coupling the identification of land-use changes in riparian areas and the identification of bathymetric changes (derived from wavelength range -bandwidth and reflectance) for the riverbed.…”

Section: Introductionmentioning

confidence: 99%

Use of aerial multispectral images for spatial analysis of flooded riverbed-alluvial plain systems: the case study of the Paglia River (central Italy)

Brigante

Cencetti

Rosa

et al. 2017

Geomatics, Natural Hazards and Risk

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Image processing and classification techniques are widely used for land use definition. They can also provide interesting applications in fluvial geomorphology, for outlining morpho-sedimentary features (bars, channels, banks and floodplain) at various temporal stages, in order to monitor the evolution of river systems. Frequent monitoring is especially important for streams, in terms of flood risk in urban areas. This study shows how techniques of supervised analysis can be applied to river systems, also under particular conditions, like after flood events (when large portions of riverbed and alluvial plain are covered with mud). The procedure starts from the classical photogrammetric techniques, based on multispectral classification, and goes on with post processing operations of pixel aggregation and shadow treatment. The classification also uses the elevation information provided by Digital Surface Model produced by photogrammetry. This paper introduces a new technique of remote sensing in fluvial areas that allows for both the identification and classification of the fluvial features in a post flooding condition. Application of the procedure over time permits the evolution of the fluvial dynamics to be monitored in an accurate and inexpensive way, particularly for flood event conditions which lead to major changes in the dynamics of riverbeds.

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“…To differentiate between temporarily flooded areas and water bodies of normal water extent (permanent water) in a single image proved to be unreliable even when performed manually by expert analysts. Hence, it is common to compare a co-flood image with a pre-flood image or an independent reference water mask (Franci et al, 2015;Twele et al, 2016). These methods are generally fast to compute but depend on the quality and date of the pre-flood dataset, which may strongly bias the flood water delineation.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Water Bodies in Multi-Spectral Satellite Images for Situational Awareness in Emergency Response

Wieland

Martinis

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Satellite-based crisis information is frequently requested in the context of flood disasters to gain rapidly situational awareness and to prioritize response actions under often limited resources during emergency response. To assure that information products have the highest possible spatial, temporal and thematic resolutions, it is critical to be able to simultaneously analyse data from a large variety of satellite sensors. In this contribution, we present a solution to rapidly extract water bodies from Landsat TM, ETM+, OLI and Sentinel-2 for up-to-date situational awareness during emergency response. A convolutional neural network is used to segment water extent in these images, while clouds, cloud shadows and snow / ice are specifically handled by the network to remove potential bias from any downstream analysis. Atmospheric correction, post-processing and ancillary data are not required. To distinguish flood from permanent water we present a reference water mask that is derived by means of time-series analysis of archive imagery. Compared to widely-used mono-temporal reference water masks, it can be adapted to any area and time of interest. This study builds up on previous work of the authors and presents new results from recent flood disasters in Germany, Peru, China, India and Mozambique, as well as a flood monitoring application centred on the Indian state of Kerala. The processing chain produces very high overall accuracy and Kappa coefficient (>0.87) and shows consistent performance throughout a monitoring period of 12 months that covers 143 Landsat OLI and Sentinel-2 images.

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Remote sensing analysis for flood risk management in urban sprawl contexts

Cited by 26 publications

References 20 publications

Preface to the special issue: the role of geomatics in hydrogeological risk

Preface to the special issue: the role of geomatics in hydrogeological risk

Use of aerial multispectral images for spatial analysis of flooded riverbed-alluvial plain systems: the case study of the Paglia River (central Italy)

Semantic Segmentation of Water Bodies in Multi-Spectral Satellite Images for Situational Awareness in Emergency Response

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