Advances in Remote Sensing of Flooding

With the publication of eight original research articles, four types of advances in the remote sensing of floods are achieved. The uncertainty of modeled outputs using precipitation datasets derived from in situ observations and remote sensors is further understood. With the terrestrial laser scanner and airborne light detection and ranging (LiDAR) coupled with high resolution optical and radar imagery, researchers improve accuracy levels in estimating the surface water height, extent, and flow of floods. The unmanned aircraft system (UAS) can be the game changer in the acquisition and application of remote sensing data. The UAS may fly everywhere and every time when a flood event occurs. With the development of urban structure maps, the flood risk and possible damage is well assessed. The flood mitigation plans and response activities become effective and efficient using geographic information system (GIS)-based urban flood vulnerability and risk maps.

[1]  H. Kreibich,et al.  Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data , 2014 .

[2]  Hongjie Xie,et al.  Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products , 2014 .

[3]  Eric W. Constance,et al.  The 3D Elevation Program initiative: a call for action , 2014 .

[4]  David Harding,et al.  The Need for a National Lidar Dataset , 2008 .

[5]  M. Curtarelli,et al.  The Use of Optical Remote Sensing For Mapping Flooded Areas , 2013 .

[6]  Yong Wang,et al.  An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data , 2002 .

[7]  Y. Ouma,et al.  Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment , 2014 .

[8]  Michael A. Batten,et al.  Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data , 2013 .

[9]  Giorgos Mallinis,et al.  An object-based approach for flood area delineation in a transboundary area using ENVISAT ASAR and LANDSAT TM data , 2011 .

[10]  Hannu Hyyppä,et al.  Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling , 2015 .

[11]  Andreas Schmitt,et al.  Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery , 2013 .

[12]  Yong Wang,et al.  Mapping Extent of Floods: What We Have Learned and How We Can Do Better , 2002 .

[13]  Jianhua Gong,et al.  Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China , 2015 .

[14]  Seung Oh Lee,et al.  An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge , 2013 .

[15]  Yong Wang,et al.  Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain , 2004 .

[16]  Marco Gianinetto,et al.  Postflood damage evaluation using Landsat TM and ETM+ data integrated with DEM , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  V. Klemas,et al.  Remote Sensing of Floods and Flood-Prone Areas: An Overview , 2015 .