Flood mapping using LIDAR DEM. Limitations of the 1-D modeling highlighted by the 2-D approach

Costabile, Pierfranco; Macchione, Francesco; Natale, L.; Petaccia, Gabriella

doi:10.1007/s11069-015-1606-0

Cited by 79 publications

(48 citation statements)

References 55 publications

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“…NDVI values for full vegetation cover and water were required to solve Equation (7). These values are estimates of the maximum and the minimum values of NDVI.…”

Section: Automatic Selection Of Endmembersmentioning

confidence: 99%

“…Floodplain mapping and flood risk assessment are frequently assessed using one-dimensional (1-D) and two-dimensional (2-D) hydraulic models [1,4,5]. A key element for the reliability of such model-based analyses is the accurate setup of the river model, which is primarily related to the representation of the topography and of the land surface hydraulic properties [6][7][8]. Earth observation by satellites has been shown to be helpful in this respect.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

et al. 2017

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The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water (γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

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“…NDVI values for full vegetation cover and water were required to solve Equation (7). These values are estimates of the maximum and the minimum values of NDVI.…”

Section: Automatic Selection Of Endmembersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

et al. 2017

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“…Instead, 2D hydraulic modelling is a much more consistent approach because of its ability to represent overbank flow [48] and high topography complexity, even in urban areas where flow can be represented on the scale of individual buildings [49].…”

Section: D Hydrodynamic Modellingmentioning

confidence: 99%

Flood Damage Analysis: First Floor Elevation Uncertainty Resulting from LiDAR-Derived Digital Surface Models

et al. 2016

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Abstract:The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis has improved significantly, owing to the increased accuracy of hydrodynamic models. In addition, considerable error reduction has been achieved in the estimation of first floor elevation, which is a critical parameter for determining structural and content damages in buildings. However, as with any discrete measurement technique, LiDAR data contain object space ambiguities, especially in urban areas where the presence of buildings and the floodplain gives rise to a highly complex landscape that is largely corrected by using ancillary information based on the addition of breaklines to a triangulated irregular network (TIN). The present study provides a methodological approach for assessing uncertainty regarding first floor elevation. This is based on: (i) generation an urban TIN from LiDAR data with a density of 0.5 points¨m´2, complemented with the river bathymetry obtained from a field survey with a density of 0.3 points¨m´2. The TIN was subsequently improved by adding breaklines and was finally transformed to a raster with a spatial resolution of 2 m; (ii) implementation of a two-dimensional (2D) hydrodynamic model based on the 500-year flood return period. The high resolution DSM obtained in the previous step, facilitated addressing the modelling, since it represented suitable urban features influencing hydraulics (e.g., streets and buildings); and (iii) determination of first floor elevation uncertainty within the 500-year flood zone by performing Monte Carlo simulations based on geostatistics and 1997 control elevation points in order to assess error. Deviations in first floor elevation (average: 0.56 m and standard deviation: 0.33 m) show that this parameter has to be neatly characterized in order to obtain reliable assessments of flood damage assessments and implement realistic risk management.

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“…The 3-D models derived from SfM are currently used for geomorphological applications (Westoby et al, 2012) and flood mapping. This second application is often assisted using precise DTM derived from lidar (Smith et al, 2014;Meesuk et al, 2015, Costabile et al, 2015. Particular applications of SfM can be used to make 3-D models of facades and acquire a useful dataset for the identification of marks left by water.…”

Section: Introductionmentioning

confidence: 99%

Low cost, multiscale and multi-sensor application for flooded area mapping

Giordan

Notti

Villa³

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed.In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multisensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy) flood. We first mapped the flooded areas at the basin scale using free satellite data from low-to mediumhigh-resolution from both the SAR (Sentinel-1, COSMOSkymed) and multispectral sensors (MODIS, Sentinel-2). Using very-and ultra-high-resolution images from the lowcost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and nonurbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very-and ultra-high-resolution images were processed with structure from motion (SfM) for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.

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Flood mapping using LIDAR DEM. Limitations of the 1-D modeling highlighted by the 2-D approach

Cited by 79 publications

References 55 publications

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

Flood Damage Analysis: First Floor Elevation Uncertainty Resulting from LiDAR-Derived Digital Surface Models

Low cost, multiscale and multi-sensor application for flooded area mapping

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