An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery

Abstract Remote sensing techniques offer potential for effective flood detection with the advantages of low-cost, large-scale, and real-time surface observations. The easily accessible data sources of optical remote sensing imagery provide abundant spectral information for accurate surface water body extraction, and synthetic aperture radar (SAR) systems represent a powerful tool for flood monitoring because of their all-weather capability. This paper introduces a new approach for flood monitoring by the combined use of both Landsat 8 optical imagery and COSMO-SkyMed radar imagery. Specifically, the proposed method applies support vector machine and the active contour without edges model for water extent determination in the periods before and during the flood, respectively. A map difference method is used for the flood inundation analysis. The proposed approach is particularly suitable for large-scale flood monitoring, and it was tested on a serious flood that occurred in northeastern China in August 2013, which caused immense loss of human lives and properties. High overall accuracies of 97.46% for the optical imagery and 93.70% for the radar imagery are achieved by the use of the techniques presented in this study. The results show that about 12% of the whole study area was inundated, corresponding to 5466 km2 of land surface.

[1]  Lorenzo Bruzzone,et al.  An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Paul D. Bates,et al.  A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[4]  Z. Kundzewicz.  Flood risk and vulnerability in the changing climate , 2008 .

[5]  S. Lehner,et al.  Topography and morphodynamics in the German Bight using SAR and optical remote sensing data , 2005 .

[6]  Giles M. Foody,et al.  Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification , 2004 .

[7]  Matthew S. Horritt,et al.  A statistical active contour model for SAR image segmentation , 1999, Image Vis. Comput..

[8]  Yun Chen,et al.  Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization , 2015 .

[9]  L. Lymburner,et al.  Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia , 2016 .

[10]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[11]  R. Oberstadler,et al.  Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany , 1997 .

[12]  Francesca Romana Cinti,et al.  The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data , 2013, IEEE Geoscience and Remote Sensing Letters.

[13]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[14]  Thilo Wehrmann,et al.  Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data , 2012 .

[15]  Jin Chen,et al.  Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery , 2012 .

[16]  Bor-Chen Kuo,et al.  A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Paul D. Bates,et al.  Remote sensing and flood inundation modelling , 2004 .

[18]  X. Tong,et al.  Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction , 2016 .

[19]  Nazzareno Pierdicca,et al.  Integrating Physical and Topographic Information Into a Fuzzy Scheme to Map Flooded Area by SAR , 2008, Sensors.

[20]  Paul D. Bates,et al.  Flood Detection in Urban Areas Using TerraSAR-X , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[21]  David Gillieson,et al.  Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan , 2011 .

[22]  Nazzareno Pierdicca,et al.  Monitoring Flood Evolution in Vegetated Areas Using COSMO-SkyMed Data: The Tuscany 2009 Case Study , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  C. V. D. Sande,et al.  A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment , 2003 .

[24]  Hui Wang,et al.  An active contour model and its algorithms with local and global Gaussian distribution fitting energies , 2014, Inf. Sci..

[25]  Wolfgang Schwanghart,et al.  Local-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data , 2016 .

[26]  J. Lira.  Segmentation and morphology of open water bodies from multispectral images , 2006 .

[27]  Shahid Habib,et al.  Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[28]  ByoungChul Ko,et al.  Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers , 2015, Sensors.

[29]  D. Mason,et al.  Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model , 2001 .

[30]  Sandro Martinis,et al.  Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data , 2009 .

[31]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[32]  Dmitri Kavetski,et al.  Probabilistic Flood Mapping Using Synthetic Aperture Radar Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Florian Pappenberger,et al.  High-Resolution 3-D Flood Information From Radar Imagery for Flood Hazard Management , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Silvana G. Dellepiane,et al.  A New Method for Cross-Normalization and Multitemporal Visualization of SAR Images for the Detection of Flooded Areas , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Xinwu Li,et al.  Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses , 2013, Remote. Sens..

[36]  P. Bates,et al.  The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods , 2011 .

[37]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[38]  D. Moorhead,et al.  Increasing risk of great floods in a changing climate , 2002, Nature.

[39]  John Trinder,et al.  Extracting hurricane eye morphology from spaceborne SAR images using morphological analysis , 2016 .

[40]  Martin Brown,et al.  Linear spectral mixture models and support vector machines for remote sensing , 2000, IEEE Trans. Geosci. Remote. Sens..

[41]  Patrick Matgen,et al.  Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[42]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[43]  A. Coletta,et al.  COSMO-SkyMed an existing opportunity for observing the Earth , 2010 .

[44]  S. Haruyama,et al.  Geomorphologic land classification map of the Mekong Delta utilizing JERS‐1 SAR images , 2008 .

[45]  Philip A. Townsend,et al.  Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR , 2002 .

[46]  Patrick Matgen,et al.  Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies , 2011 .

[47]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[48]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[49]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[50]  Huan Xie,et al.  New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images , 2014 .

[51]  L. Smith.  Satellite remote sensing of river inundation area, stage, and discharge: a review , 1997 .

[52]  Nazzareno Pierdicca,et al.  Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation , 2011 .

[53]  Xixi Lu,et al.  Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review , 2004 .

[54]  F. Blasco,et al.  Estimating the extent of floods in Bangladesh using SPOT data , 1992 .

[55]  Jiaping Wu,et al.  Water body delineation using index composition and HIS transformation , 2012 .

[56]  Paul D Bates,et al.  Integrating remote sensing observations of flood hydrology and hydraulic modelling , 1997 .

[57]  Laurence C. Smith,et al.  Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic , 2008, IEEE Geoscience and Remote Sensing Letters.

[58]  Paul D. Bates,et al.  Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study , 2012 .

[59]  C. Verpoorter,et al.  Automated mapping of water bodies using Landsat multispectral data , 2012 .