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Advances in Remote Sensing of Flooding
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Advances in Remote Sensing of Flooding

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 November 2014) | Viewed by 120619

Special Issue Editor

Prof. Dr. Yong Wang

E-Mail Website
Guest Editor
Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC 27858, USA
Interests: investigation of scale and scale effect on synthetic aperture radar (SAR) to urban target delineation; evaluation of surface deformation using interferometric SAR (InSAR) techniques; removal of thin clouds in optical imagery; mapping of flooding using geospatial datasets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After Hurricane Sandy made landfall in the early morning of 30 October 2012 near New Jersey and New York areas, USA flooding caused by the storm has been documented by ground pictures, aerial photos, and satellite images. These remotely sensed data have been saturated TV and computer screens. In response to a flooding event, it is important quickly to predict and to determine the extent of flooding and landuse type under the floodwater. As observed from news media/TV, the mayor of New York City retracted his order from not a big deal event to an extremely dangerous one in less than 24 hours related Sandy. His ultimate order to evacuate has saved thousands of lives. Therefore, as humans are facing the ever-changing environments and advancing the science and technology especially remote sensing, we as scientists are equipped with advanced knowledge and sophistical instruments/tools, and we are obligated to predict and to capture the extent of flooding in an efficient and effective manner. This type of information is essential to our leaders and emergency responders, as well as concerned public. Access of such critical information can greatly assist comprehensive preparation, emergency response, and relief activity. This is the call for papers that study and document the advances in mapping a flood event using primarily remote sensing technique and datasets.

Prof. Dr. Yong Wang
Guest Editor

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Keywords

  • flood and wetland mapping, and damage assessment
  • flood and mitigation activities
  • integration of geo-spatial and remotely sensed datasets in flood mapping
  • remote sensing and GIScience applications in mapping a flood event
  • remote sensing and hydrological and hydraulic modeling for a flood event, and
  • visualization and analysis of geo-spatial and remote sensing datasets for a flood event

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Published Papers (8 papers)

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Research

19 pages, 11645 KiB  
Article
Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China
by Quanlong Feng, Jiantao Liu and Jianhua Gong
Water 2015, 7(4), 1437-1455; https://doi.org/10.3390/w7041437 - 31 Mar 2015
Cited by 267 | Viewed by 17767
Abstract
Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a [...] Read more.
Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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18 pages, 2470 KiB  
Article
Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling
by Johanna Jalonen, Juha Järvelä, Juho-Pekka Virtanen, Matti Vaaja, Matti Kurkela and Hannu Hyyppä
Water 2015, 7(2), 420-437; https://doi.org/10.3390/w7020420 - 29 Jan 2015
Cited by 51 | Viewed by 8962
Abstract
Detailed modeling of floodplain flows and associated processes requires data on mixed, heterogeneous vegetation at river reach scale, though the collection of vegetation data is typically limited in resolution or lack spatial information. This study investigates physically-based characterization of mixed floodplain vegetation by [...] Read more.
Detailed modeling of floodplain flows and associated processes requires data on mixed, heterogeneous vegetation at river reach scale, though the collection of vegetation data is typically limited in resolution or lack spatial information. This study investigates physically-based characterization of mixed floodplain vegetation by means of terrestrial laser scanning (TLS). The work aimed at developing an approach for deriving the characteristic reference areas of herbaceous and foliated woody vegetation, and estimating the vertical distribution of woody vegetation. Detailed experimental data on vegetation properties were gathered both in a floodplain site for herbaceous vegetation, and under laboratory conditions for 2–3 m tall trees. The total plant area (Atot) of woody vegetation correlated linearly with the TLS-based voxel count, whereas the Atot of herbaceous vegetation showed a linear correlation with TLS-based vegetation mean height. For woody vegetation, 1 cm voxel size was found suitable for estimating both the Atot and its vertical distribution. A new concept was proposed for deriving Atot for larger areas from the point cloud attributes of small sub-areas. The results indicated that the relationships between the TLS attributes and Atot of the sub-areas can be derived either by mm resolution TLS or by manual vegetation sampling. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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27 pages, 7268 KiB  
Article
Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data
by Tina Gerl, Mathias Bochow and Heidi Kreibich
Water 2014, 6(8), 2367-2393; https://doi.org/10.3390/w6082367 - 11 Aug 2014
Cited by 37 | Viewed by 10665
Abstract
The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral [...] Read more.
The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral Ikonos data and LiDAR (Light Detection And Ranging) data in order to provide spatially detailed information about the building stock of the case study area of Dresden, Germany. The multi-parameter damage models FLEMOps (Flood Loss Estimation Model for the private sector) and regression-tree models have been adapted to the information derived from remote sensing data and were applied on the basis of the urban structure map. To evaluate this approach, which is suitable for risk analyses, as well as for post-disaster event analyses, an estimation of the flood losses caused by the Elbe flood in 2002 was undertaken. The urban structure mapping approach delivered a map with a good accuracy of 74% and on this basis modeled flood losses for the Elbe flood in 2002 in Dresden were in the same order of magnitude as official damage data. It has been shown that single-family houses suffered significantly higher damages than other urban structure types. Consequently, information on their specific location might significantly improve damage modeling, which indicates a high potential of remote sensing methods to further improve risk assessments. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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Graphical abstract

31 pages, 4886 KiB  
Article
Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment
by Yashon O. Ouma and Ryutaro Tateishi
Water 2014, 6(6), 1515-1545; https://doi.org/10.3390/w6061515 - 30 May 2014
Cited by 510 | Viewed by 43268
Abstract
This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are [...] Read more.
This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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25 pages, 3701 KiB  
Article
Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products
by Singaiah Chintalapudi, Hatim O. Sharif and Hongjie Xie
Water 2014, 6(5), 1221-1245; https://doi.org/10.3390/w6051221 - 7 May 2014
Cited by 31 | Viewed by 7998
Abstract
In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of [...] Read more.
In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km2 watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze the precipitation products. Comparison with rain gauge observations reveals that there were significant biases in the satellite rainfall products and large variations in the estimated amounts. The radar basin average precipitation compared very well with the rain gauge product while the gauge-adjusted TRMM 3B42V7 precipitation compared best with observed rainfall among all satellite precipitation products. The NEXRAD MPE simulated streamflows matched the observed ones the best yielding the highest Nash-Sutcliffe Efficiency correlation coefficient values for both the July and August 2007 events. Simulations driven by TRMM 3B42V7 matched the observed streamflow better than other satellite products for both events. The PERSIANN coarse resolution product yielded better runoff results than the higher resolution product. The study reveals that satellite rainfall products are viable alternatives when rain gauge or ground radar observations are sparse or non-existent. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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24 pages, 2858 KiB  
Article
An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge
by Younghun Jung, Venkatesh Merwade, Kyudong Yeo, Yongchul Shin and Seung Oh Lee
Water 2013, 5(4), 1598-1621; https://doi.org/10.3390/w5041598 - 7 Oct 2013
Cited by 15 | Viewed by 7479
Abstract
Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling [...] Read more.
Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE) methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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28 pages, 15054 KiB  
Article
Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data
by Ashleigh B. Turner, Jeffrey D. Colby, Ryan M. Csontos and Michael Batten
Water 2013, 5(4), 1533-1560; https://doi.org/10.3390/w5041533 - 30 Sep 2013
Cited by 45 | Viewed by 13682
Abstract
This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for [...] Read more.
This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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16 pages, 9880 KiB  
Article
Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery
by Andreas Schmitt and Brian Brisco
Water 2013, 5(3), 1036-1051; https://doi.org/10.3390/w5031036 - 11 Jul 2013
Cited by 89 | Viewed by 9432
Abstract
One fundamental task in wetland monitoring is the regular mapping of (temporarily) flooded areas especially beneath vegetation. Due to the independence of weather and illumination conditions, Synthetic Aperture Radar (SAR) sensors could provide a suitable data base. Using polarimetric modes enables the identification [...] Read more.
One fundamental task in wetland monitoring is the regular mapping of (temporarily) flooded areas especially beneath vegetation. Due to the independence of weather and illumination conditions, Synthetic Aperture Radar (SAR) sensors could provide a suitable data base. Using polarimetric modes enables the identification of flooded vegetation by means of the typical double-bounce scattering. In this paper three decomposition techniques—Cloude-Pottier, Freeman-Durden, and Normalized Kennaugh elements—are compared to each other in terms of identifying the flooding extent as well as its temporal change. The image comparison along the time series is performed with the help of the Curvelet-based Change Detection Method. The results indicate that the decomposition algorithm has a strong impact on the robustness and reliability of the change detection. The Normalized Kennaugh elements turn out to be the optimal representation for Curvelet-based change detection processing. Furthermore, the co-polarized channels (same transmit and receive polarization in horizontal (HH) and vertical (VV) direction respectively) appear to be sufficient for wetland monitoring so that dual-co-polarized imaging modes could be an alternative to conventional quad-polarized acquisitions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Flooding)
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