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Special Issue "Advances in Remote Sensing of Flooding"

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A special issue of Water (ISSN 2073-4441).

Deadline for manuscript submissions: closed (30 November 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Yong Wang (Website)

1 School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu, Sichuan 611731, China; E-Mail: wangy2012@uestc.edu.cn
2 Department of Geography, East Carolina University, Greenville, NC 27858, USA
Phone: 2523281043
Interests: coastal shoreline and shoreline change; flood and wetland mapping; remote sensing and GIScience applications; and simulation and assessment of large-scale reservoirs and environmental systems

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

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs).

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

Published Papers (8 papers)

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Research

Open AccessArticle Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China
Water 2015, 7(4), 1437-1455; doi:10.3390/w7041437
Received: 30 November 2014 / Revised: 13 March 2015 / Accepted: 25 March 2015 / Published: 31 March 2015
Cited by 6 | PDF Full-text (11645 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling
Water 2015, 7(2), 420-437; doi:10.3390/w7020420
Received: 26 November 2014 / Revised: 13 January 2015 / Accepted: 21 January 2015 / Published: 29 January 2015
Cited by 6 | PDF Full-text (2470 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data
Water 2014, 6(8), 2367-2393; doi:10.3390/w6082367
Received: 1 April 2014 / Revised: 28 July 2014 / Accepted: 28 July 2014 / Published: 11 August 2014
Cited by 6 | PDF Full-text (7268 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment
Water 2014, 6(6), 1515-1545; doi:10.3390/w6061515
Received: 8 February 2014 / Revised: 14 May 2014 / Accepted: 20 May 2014 / Published: 30 May 2014
Cited by 17 | PDF Full-text (4886 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products
Water 2014, 6(5), 1221-1245; doi:10.3390/w6051221
Received: 19 February 2014 / Revised: 13 April 2014 / Accepted: 25 April 2014 / Published: 7 May 2014
Cited by 5 | PDF Full-text (3701 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge
Water 2013, 5(4), 1598-1621; doi:10.3390/w5041598
Received: 31 July 2013 / Revised: 23 September 2013 / Accepted: 24 September 2013 / Published: 7 October 2013
Cited by 4 | PDF Full-text (2858 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data
Water 2013, 5(4), 1533-1560; doi:10.3390/w5041533
Received: 29 July 2013 / Revised: 11 September 2013 / Accepted: 12 September 2013 / Published: 30 September 2013
Cited by 7 | PDF Full-text (15054 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery
Water 2013, 5(3), 1036-1051; doi:10.3390/w5031036
Received: 25 April 2013 / Revised: 3 June 2013 / Accepted: 1 July 2013 / Published: 11 July 2013
Cited by 17 | PDF Full-text (9880 KB) | HTML Full-text | XML Full-text
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 [...] 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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