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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 : Volume 10, Issue 2 (17/02/2010)

By Petropoulos, G. P.

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Book Id: WPLBN0003990663
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: 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 : Volume 10, Issue 2 (17/02/2010)  
Author: Petropoulos, G. P.
Volume: Vol. 10, Issue 2
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Scholze, M., Boschetti, L., Knorr, W., Petropoulos, G. P., & Karantounias, G. (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 : Volume 10, Issue 2 (17/02/2010). Retrieved from http://ebook.worldlibrary.net/


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

Summary
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

Excerpt
Abrams, M. and Hook, S.: ASTER User Handbook, Jet Propulsion Laboratory & EROS data centre, 135 pp., available at: http://asterweb.jpl.nasa.gov/content/03_data/04_Documents/aster_user_guide_v2.pdf(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: http://www.fao.org/forestry/fo/fra/docs/Wp55_eng.pdf(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 classif

 

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