Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multi-band ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

[1]  Der-Chiang Li,et al.  A class possibility based kernel to increase classification accuracy for small data sets using support vector machines , 2010, Expert Syst. Appl..

[2]  Charalambos Kontoes,et al.  A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Lihong Su,et al.  Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis , 2009 .

[5]  W. Keeton,et al.  Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007 , 2009 .

[6]  Patrick Hostert,et al.  Land cover mapping of large areas using chain classification of neighboring Landsat satellite images , 2009 .

[7]  D. Roy,et al.  The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product , 2008 .

[8]  A. Polychronaki,et al.  Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece , 2008 .

[9]  Benjamin Koetz,et al.  Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data , 2008 .

[10]  C. Justice,et al.  Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER) , 2008 .

[11]  I. Keramitsoglou,et al.  Reliable, accurate and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery , 2008 .

[12]  P. Barbosa,et al.  A MODIS assessment of the summer 2007 extent burned in Greece , 2008 .

[13]  Hugo Carrão,et al.  Contribution of multispectral and multitemporal information from MODIS images to land cover classification , 2008 .

[14]  Chengquan Huang,et al.  Use of a dark object concept and support vector machines to automate forest cover change analysis , 2008 .

[15]  Ioannis Z. Gitas,et al.  Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece , 2008 .

[16]  Ming-Hseng Tseng,et al.  A genetic algorithm rule-based approach for land-cover classification , 2008 .

[17]  S. Escuin,et al.  Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images , 2008 .

[18]  Barnali M. Dixon,et al.  Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? , 2008 .

[19]  A. Yüksel,et al.  Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project , 2008, Sensors.

[20]  J. Grégoire,et al.  A new, global, multi‐annual (2000–2007) burnt area product at 1 km resolution , 2008 .

[21]  Jon Atli Benediktsson,et al.  Fusion of Support Vector Machines for Classification of Multisensor Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[22]  A. Nonomura,et al.  Wildfire damage evaluation by merging remote sensing with a fire area simulation model in Naoshima, Kagawa, Japan , 2007, Landscape and Ecological Engineering.

[23]  Rosa Lasaponara,et al.  Remotely sensed characterization of forest fuel types by using satellite ASTER data , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Li Jing,et al.  Detection of coal fire location and change based on multi‐temporal thermal remotely sensed data and field measurements , 2007 .

[25]  Giles M. Foody,et al.  Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats , 2007, Ecol. Informatics.

[26]  Li Jing,et al.  A Forest Fire Risk Assessment Using ASTER Images in Peninsular Malaysia , 2007 .

[27]  A. Smith,et al.  Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS , 2007 .

[28]  Raymond F. Kokaly,et al.  Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing , 2007 .

[29]  Patrick Hostert,et al.  Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines , 2007 .

[30]  Peter Vogt,et al.  European Forest Fire Information System (EFFIS)—rapid damage assessment: Appraisal of burnt area maps in southern Europe using MODIS data (2003–2005) , 2006 .

[31]  Josiane Zerubia,et al.  AbstractBurnt area mapping using Support Vector Machines , 2006 .

[32]  Paul M. Mather,et al.  Some issues in the classification of DAIS hyperspectral data , 2006 .

[33]  S. Prasher,et al.  Application of support vector machine technology for weed and nitrogen stress detection in corn , 2006 .

[34]  Y. Shimabukuro,et al.  Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective , 2006 .

[35]  J. Randerson,et al.  Global estimation of burned area using MODIS active fire observations , 2005 .

[36]  Paul E. Gessler,et al.  Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling , 2005 .

[37]  C. Justice,et al.  Validation of the MODIS active fire product over Southern Africa with ASTER data , 2005 .

[38]  J. Morisette.  Validation of MODIS Active Fire Detection Products Derived from Two Algorithms , 2005 .

[39]  D. Verbyla,et al.  Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM , 2005 .

[40]  D. Roya,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[41]  Iphigenia Keramitsoglou,et al.  Combining AVHRR imagery with CORINE Land Cover data to observe forest fires and to assess their consequences , 2004 .

[42]  J. W. Wagtendonk,et al.  Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity , 2004 .

[43]  S. Plummer,et al.  Burnt area detection at global scale using ATSR‐2: The GLOBSCAR products and their qualification , 2004 .

[44]  S. Flasse,et al.  Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary , 2004 .

[45]  Yoram J. Kaufman,et al.  A technique for detecting burn scars using MODIS data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .

[48]  Alexander Siegmund,et al.  Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data , 2003 .

[49]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[50]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

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

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

[53]  Coskun Ozkan,et al.  Forest fire analysis with remote sensing data , 2001 .

[54]  R. Lasaponara,et al.  Evaluation of a new satellite-based method for forest fire detection , 2001 .

[55]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[56]  Martin Brown,et al.  Support vector machines for optimal classification and spectral unmixing , 1999 .

[57]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[58]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[59]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[60]  E. Kasischke,et al.  Estimating release of carbon from 1990 and 1991 forest fires in Alaska , 1995 .

[61]  Yoram J. Kaufman,et al.  Remote sensing of biomass burning in the tropics , 1990 .

[62]  E. Chuvieco,et al.  Application of remote sensing and geographic information systems to forest fire hazard mapping. , 1989 .

[63]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .