References

NHESS

Natural Hazards and Earth System Sciences

NHESS

Nat. Hazards Earth Syst. Sci.

1684-9981

Copernicus Publications

Göttingen, Germany

10.5194/nhess-10-305-2010

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

Petropoulos

G. P.

¹ Knorr

¹ Scholze

¹ Boschetti

² Karantounias

University of Bristol, Department of Earth Sciences, Wills Memorial Building, Queens Road, BS8 1RJ, Bristol, UK

Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USA

Agricultural University of Athens, Department of Natural Resources Development and Agricultural Engineering, 75, Iera Odos St., 118 55, Athens, Greece

17 02 2010

10 2 305 317

2010

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://nhess.copernicus.org/articles/10/305/2010/nhess-10-305-2010.html

The full text article is available as a PDF file from https://nhess.copernicus.org/articles/10/305/2010/nhess-10-305-2010.pdf

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.

References 1

Abrams, M. and Hook, S.: ASTER User Handbook, Jet Propulsion Laboratory & EROS data centre, 135 pp., available at: <a href="http://asterweb.jpl.nasa.gov/content/03_data/04_Documents/aster_user_guide_v2.pdf">http://asterweb.jpl.nasa.gov/content/03_data/04_Documents/aster_user_guide_v2.pdf</a>(last access: 14 April 2009), 1999.

Barbosa, P., Kucera, J., Strobi, P., Vogt, P., Camia A., and San-Miguel, J.: European Forest Fire Information System (EFFIS) – rapid damage assessment: appraisal of burnt area maps in southern Europe using MODIS data (2003–2005), Forest Ecol. Manag., 232, Supp. 1, p. S218, 2006.

Boschetti, L., Flasse, S., and Brivio, P. A.: Analysis of the conflict between omission and commission in low spatial resolution thematic products: the Pareto Boundary, Remote Sens. Environ., 91(3–4), 280–292, 2003.

Boschetti, L., Roy, D., Barbosa, P., Boca, R., and Justice, C.: A MODIS assessment of the summer 2007 extent burnt in Greece, Int. J. Remote Sens., 29(8), 2433–2436, 2008.

Brown, M., Gunn, S. R., and Lewis, H. G.: Support vector machines for optimal classification and spectral unmixing, Ecol. Model., 120, 167–179, 1999.

Burges, C.: A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Disc., 2(2), 121–167, 1998.

Carrao, H., Concalves, P., and Caetano, M..: Contribution of multispectral and multitemporal information from MODIS images to land cover classification, Remote Sens. Environ., 112, 986–997, 2008.

Chen, Y., Jing, L., Bo, Y., Shi, P. and Zhang, S.: Detection of coal fire location and change based on multi-temporal thermal remotely sensed data and field measurements, Int. J. Remote Sens., 28(15), 3173–3179, 2007.

Chuvieco, E. and Congalton, R. G.: Application of remote sensing and geographic information systems to forest fire hazard mapping, Remote Sensing of the Environment, 29, 147–159, 1989.

Chuvieco, E., Salas, J., and Vega, C.: Remote sensing and GIS for long-term fire risk mapping, in: A review of remote sensing methods for the study of large wildland fires, edited by: Chuvieco, E., Mega fires Project ENV-CT96-0256, Alcala de Henares, Spain, 91–108, 1997.

Congalton, R. G. and Green, K.: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis, New York, 1999.

Cuomo, V., Lasaponara, R., and Tramutoli, V.: Evaluation of a new satellite-based method for forest fire detection, Int. J. Remote Sens., 22(9), 1799–1826, 2001.

Dixon, B. and Candade, N.: Multispectral land use classification using neural networks and support vector machines: one or the other, or both?, Int. J. Remote Sens., 29(4), 1185–1206, 2008.

EC: Forest Fires in Europe, European Commission, Joint Research Center, Institute for Environment and Sustainability, Report No 6, EUR 22312 EN, Italy, 2006.

Epting, J., Verbyla, D., and Sorbel, B.: Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+, Remote Sens. Environ., 96, 328–339, 2005.

Escuin, S., Navaro, R., and Fernandez, P.: Fire severity assessment by using NBR (Normalised Burn ratio) and NDVI (Normalised Difference Vegetation Index) derived from LANDSAT Landsat TM/ETM images, Int. J. Remote Sens., 29(4), 1053–1073, 2008.

Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., and Smith, A. M. S.: Characterising and mapping forest fire fuels using ASTER imagery and gradient modeling, Forest Ecol. Manag., 217, 129–146, 2005.

FAO: Global forest fire assessment 1990–2000, Forest Resources Assessment Programme, Working Paper No. 55, available at: <a href="http://www.fao.org/forestry/fo/fra/docs/Wp55_eng.pdf">http://www.fao.org/forestry/fo/fra/docs/Wp55_eng.pdf</a>(last access: 14 April 2009), 2001.

Foody, G. M.: Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185–201, 2002.

Foody, G. M. and Mather, A.: A relative evaluation of multiclass image classification by support vector machines, IEEE T. Geosci. Remote Sens., 42, 1335–1343, 2004.

Giglio, L., Csizar I., Restas, A., Morisette, J. T., Schroede, W., Morton, D., and Justice, C. O.: Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER), Remote Sens. Environ., 112(6), 3055–3063, 2008.

Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An enchased contextual fire detection algorithm for MODIS, Remote Sens. Environ., 87, 273–282, 2003.

Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., and Kasibhatla, P.: Global estimation of burned area using MODIS active fire observations, Atmos. Chem. Phys., 6, 957–974, 2006.

Gitas, I. Z., Polychronaki, A., Katagis, T., and Malinis, G.: Contribution of remote sensing to isaster management activities: A case study of the large fires in the Peloponnese, Greece, Int. J. Remote Sens., 29(6), 1847–1853, 2008.

Guang-Xiong, P., Jing, L. Yun-hao, C., and Abdul-Patah, N.: A forest fire risk assessment using ASTER images in Peninsular Malaysia, Journal of China University of Mining and Technology, 17(2), 232–237, 2007.

Huang, C., Davis, L. S., and Townshend, J. R. G.: An assessment of support vector machines for land cover classification, Int. J. Remote Sens., 23, 725–749, 2002.

Huang, C., Song, K., Kim, S., Townshend, J. R. G., Davis, P., Masek, J. G., and Goward, S. N.: Use of dark object concept and support vector machines to automate forest cover change analysis, Remote Sens. Environ., 112, 970–985, 2008.

Jensen, J. R.: Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall: Saddle River, NJ, 2000.

JRC-EEA: CORINE land cover updating for the year 2000: image 2000 and CLC2000, in: Products and Methods. Report EUR 21757 EN, edited by: Lima, V., JRC-Ispra, CORINE data are available at: <a href="http://reports.eea.europa.eu/COR0-landcover/en">http://reports.eea.europa.eu/COR0-landcover/en</a>(last access: 28 November 2009), 2005.

Justice, C. O., Townshen, D., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., and Morisette, J. T.: An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83, 3–15, 2002.

Karimi, Y., Orasher, S. O., Patel, R. M., and Kim, S. H.: Application of support vector machine technology for weed and nitrogen stress detection in corn, Comput. Electron. Agr., 51, 99–109, 2006.

Kasischke, E. S., French, N. F., Bourgeau-Chavez, L. L., and Christensen, N. L.: Estimating release of carbon from 1990 and 1991 forest fires in Alaska, J. Geophys. Res., 100, 2941–2951, 1995.

Kaufman, Y. J., Tucker, C. J., and Fung, I.: Remote sensing of biomass burning in the tropics, J. Geophys. Res., 95, 9927–9939, 1990.

Kavzoglou, T. and Colkesen, I.: A kernel functions analysis for support vector machines for land cover classification, Int. J. Appl. Earth Obs., 11, 352–359, 2009.

Kavzoglu, T. and Colkesen I.: A kernel functions analysis for support vector machines for land cover classification, Int. J. Appl. Earth Obs., 11, 352–359, 2009.

Keramitsoglou, I., Kontoes, C., Sykioti, O., Sifakis, N., and Xofis, P.: Reliable, accurate and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery, Forest Ecol. Manag., 255, 3556–3562, 2008.

Keuchel, J., Naumann, S., Heiler, M., and Siegmund, A.: Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data, Remote Sens. Environ., 86, 53–541, 2003.

Knorn, J., Rabe, A., Radeloff, V. C., Kuemmerle, T., Kozak, J., and Horstert, P.: Land cover mapping of large areas using chain classification of neighboring Landsat satellite images, Remote Sens. Environ., 113, 957–964, 2009.

Koetz, B., Morsdoff, F., Linden, S., Curt, T., and Allogower, B.: Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data, Forest Ecol. Manag., 256(3), 263–271, 2008.

Kokaly, R. F., Rockwell, B. W., Haire, S. L., and King, T. V.: Characterisation of post-fire surface cover, soils and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sesing, Remote Sens. Environ., 106, 305–325, 2007.

Kontoes, C. C., Poilve, H., Florsch, G., Keramitsoglou, I., and Paralikidis, S.: A comparative analysis of fixed thresholding vs a classification tree approach for operational burn scar detection and mapping, Int. J. Appl. Earth Obs., 11(5), 299–316, available at: <a href="http://www.risk-eos.com/actus/pge/index.php?arbo=0">http://www.risk-eos.com/actus/pge/index.php?arbo=0</a>, last access: 28 November 2009.

Kuemmerle, T., Chaskovskyy, O., Knorn, J., Radeloff, V. C., Kruhlov, I., Keeton, W. S., and Hostert, P.: Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sens. Environ., 113, 1194–1207, 2009.

Lasaponara, R. and Lanorte, A.: Remotely sensed characterisation of forest fuel types by using satellite ASTER data, Int. J. Appl. Earth Obs., 9, 225–234, 2007.

Li, D.-C. and Liu, C.-W.: A class possibility based kernel to increase classification accuracy for small data sets using support vector machines, Expert Syst. Appl., 37, 3104–3110, 2010.

Li, R. R. Kaufman, Y. J, Hao, W. M., Salmon, J. M., and Gao, B.-C.: A technique for detecting burn scars using MODIS data., IEEE T. Geosci. Remote Se., 42(6), 1300–1308, 2004.

Lobola, T., Neal, K. J., and Csisar, I.: Regionally adaptable dNBR-based algorithm for bunred area mapping from MODIS, Remote Sens. Environ., 109, 429–442, 2007.

LPDAAC: Land Processes Distributed Active Archive centre: Geo-Referencing ASTER Level-1B Data: General Overview and Examples of Particular COTS Packages and Public Domain Software, available at: <a href="http://edcdaac.usgs.gov/aster/ASTER_GeoRef_FINAL.pdf">http://edcdaac.usgs.gov/aster/ASTER_GeoRef_FINAL.pdf</a>(last access: 4 April 2009), 2006.

Morisette, J. T., Giglio, L., Csiszar, I., and Justice, C. O.: Validaiton of the MODIS active fire product over Southern Africa with ASTER data, Int. J. Remote Sens., 26(19), 4239–4264, 2005a.

Morisette, J. T., Giglio, L., Csiszar, I., Setzer, A., Schroeder, W., Morton, D., and Justice, C. O.: Validiaiton of MODIS active fire detection products derived from two algorithms, Earth Interact., 9, 1–25, 2005b.

Nonomoura, A., Masuda, T., and Moriya, H.: Wildfire damage evaluation by merging remote sensing with a fire area simulation model in Naoshima, Kagawa, Japan, Landsc. Ecol. Eng., 3, 109–117, 2007

Pal, M., and Mather, P. M.: Some issues in the classification of DAIS hyperspectral data, Int. J. Remote Sens., 27, 2895–2916, 2006

Quintano, C., Fernandez-Manso, A., Fernandez-Manso, O., and Shimabukuro, Y. E.: Mapping burnt areas in Mediterranean countries using spectral mixture analysis from a uni-temporal perspective, Int. J. Remote Sens., 27(4), 645–662, 2006

Roy, D. P., Boschetti, L., Justice, C. O., and Ju, J.: The collection 5 MODIS burnt area product – Global evaluation by comparison with the MODIS active fire product, Remote Sens. Environ., 112(9), 3690–3707, available at: <a href="http://modis-fire.umd.edu/products.asp">http://modis-fire.umd.edu/products.asp</a>(last access: 28 November 2009), 2008.

Roy, D. P., Jin, Y., Lewis, P. E., and Justice, C. O.: Prototyping a global algorithm for systematic fire affected area mapping using MODIS time series data, Remote Sens. Environ., 97, 137–162, 2005.

Sanchez-Hernandez, C., Boyd, D. S., and Foody, G. M.: Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats, Ecol. Inform., 2, 83-88, 2007.

Sifakis, D. P. and Keramitsoglou, I.: Combining AVHRR imagery with CORINE land cover data to observe forest fires and to assess their consequences, Int. J. Appl. Earth Obs., 5 (4), 263–274, 2004.

Sifakis, N., Iosifidis, C., and Kontoes, C.: Fire detection and monitoring over Greece in 2007 summer period using MSG satellite data, Geophysical Research Abstracts, EGU 2008-A-03989, 2008.

Simon, M., Plummer, S., Fierens, F., Hoeltzemann, J. J., and Arino, O.: Burnt area detection at global scale using ATSR-2: the Globscar products and their qualification, J. Geophys. Res., 109, D14S02, https://doi.org/10.1029/2003JD003622, 2004.

Smith, A. M. S, Drake, N. A., Wooster, M. J., Hudak, A. T., Holden, Z. A., and Gibbons, C. J.: Production of Landsat ETM+ reference imagery of burnt areas within Southern African savannahs: comparison of methods and application to MODIS, Int. J. Remote Sens., 28(12), 2753–2775, 2007.

Story, M. and Congalton, R. G.: Accuracy assessment: a user's perspective, Photogrammm. Eng. Rem. S., 52, 397–399, 1986

Su, L.: Optimising support vector machine learning for semi-arid vegetation mapping by using clustering analysis, ISPRS J. Photogramm., 64, 407–413, 2009

Sunar, F. and Ozkan, C.: Forest fire analysis with remote sensing data, Int. J. Remote Sens., 22(12), 2265–2277, 2001

Tansey, K., Grégoire, J. M. C., Defourny, P., Leigh, R., Pekel, van Bogaert, E., Bartholomé, E., and Bontemps, S.: A new, global, multi-annual (2000–2007) burnt area product at 1 km resolution and daily intervals, Geophys. Res. Lett., 35, L01401, https://doi.org/10.1029/2007GL031567, 2008.

Thome, K.: Algorithm Thoretical Basis document for ASTER Surface radiance and ASTER Surface Reflectance products, available at: <a href="http://www.science.aster.ersdac.or.jp/en/documnts/pdf/2b0105.pdf">http://www.science.aster.ersdac.or.jp/en/documnts/pdf/2b0105.pdf</a> last access: 14 April 2009), 1999.

Tseng, M.-H., Chen, S.-J., Hwang, G.-H., and Shen, M.-Y.: A genetic algorithm rule-based approach for land cover classification, ISPRS J. Photogramm., 63, 202–21, 2008

van der Linden, S., Janz, A., Waske, B., Eiden, M., and Hostert, P.: Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines, J. Appl. Remote Sens., 1, 013543, https://doi.org/10.1117/1.2813466, 2007.

Van Wagtendonk, J. W., Root, R. R., and Key, C. C.: Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity, Remote Sens. Environ., 92, 397–408, 2004.

Vapnik, V.: Statistical Learning Theory, Wiley, New York, 1998.

Waske, B. and Benediktsson, J.A.: Fusion of support vector machines for classification of multisensor data, IEEE T. Geosci. Remote Se., 45(12), 3858–3866, 2007.

WWF Hellas: Mt Parnitha National park burnt area mapping using IKONOS imagery, available at: <a href="http://www.wwf.gr/index.php?option=com_content&task=view&id=539&itemid=294">http://www.wwf.gr/index.php?option=com_content&task=view&id=539&itemid=294</a> (last access:14 April 2009), 2007.

Yuksel, A., Akay, A. E., and Gundogan R.: Using ASTER imagery in Land use/Cover classification of eastern Mediterranean landscapes according to CORINE land cover project, Sensors, 8, 1237–1251, 2008.

Zammit, O. Descombes, X., and Zeruba, J.: Burnt area mapping using Support Vector machines, Forest Ecol. Manag., 234, S240–S249, 2006.

Zhu, G. and Blumberg, D. G.: Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel, Remote Sens. Environ., 80, 233–240, 2002.