{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:48:08Z","timestamp":1774154888103,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2010,3,11]],"date-time":"2010-03-11T00:00:00Z","timestamp":1268265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.<\/jats:p>","DOI":"10.3390\/s100301967","type":"journal-article","created":{"date-parts":[[2010,3,14]],"date-time":"2010-03-14T13:26:04Z","timestamp":1268573164000},"page":"1967-1985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping"],"prefix":"10.3390","volume":"10","author":[{"given":"George P.","family":"Petropoulos","sequence":"first","affiliation":[{"name":"Department of Earth Sciences, University of Bristol, Queens Road, BS8 1RJ, Bristol, UK"},{"name":"InfoCosmos, Pindou 71, 13341, Athens, Greece"}]},{"given":"Krishna Prasad","family":"Vadrevu","sequence":"additional","affiliation":[{"name":"Agroecosystem Management Program, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 44691, USA"}]},{"given":"Gavriil","family":"Xanthopoulos","sequence":"additional","affiliation":[{"name":"National Agricultural Research Foundation, Institute of Mediterranean Forest Ecosystems and Forest Products Technology, Terma Alkmanos, Ilisia, 11528 Athens, Greece"}]},{"given":"George","family":"Karantounias","sequence":"additional","affiliation":[{"name":"Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece"}]},{"given":"Marko","family":"Scholze","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, University of Bristol, Queens Road, BS8 1RJ, Bristol, UK"}]}],"member":"1968","published-online":{"date-parts":[[2010,3,11]]},"reference":[{"key":"ref_1","unstructured":"Crutzen, P.J., and Goldammer, J.G. (1993). Fire in the Environment: the Ecological, Atmospheric and Climatic Importance\u2019s of Vegetation Fires, John Wiley & Sons."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"G02016","DOI":"10.1029\/2005JG000142","article-title":"Global distribution and seasonality of active fires as observed with the Terra and Aqua MODIS sensors","volume":"111","author":"Giglio","year":"2006","journal-title":"J. Geophys. Res.\u2014Biogeosci"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0034-4257(93)00074-J","article-title":"Locating and estimating the aerial extent of wildfires in Alaskan boreal forests using multiple-season AVHRR NDVI composite data","volume":"51","author":"Kasischke","year":"1995","journal-title":"Remote Sens. Environ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TGRS.2004.826801","article-title":"A technique for detecting burn scars using MODIS data","volume":"42","author":"Li","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI141.1","article-title":"Validation of MODIS active fire detection products derived from two algorithms","volume":"9","author":"Morisette","year":"2005","journal-title":"Earth Interact"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005JG000143","article-title":"Use of a radiative transfermodel to simulate the post fire spectral response to burn severity","volume":"111","author":"Chuvieco","year":"2006","journal-title":"J. Geophys. Res"},{"key":"ref_7","unstructured":"Chuvieco, E. (1997). A Review of Remote Sensing Methods for the Study of Large Wildland Fires."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.rse.2007.01.017","article-title":"Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS","volume":"109","author":"Lobola","year":"2007","journal-title":"Rem. Sens. Environm"},{"key":"ref_9","unstructured":"Koutsias, N., Karteris, M., Fernandez-Palacios, A., Navarro, C., Jurado, J., Navarro, R., and Lobo, A. (1999). Remote Sensing of Large Wildfires in the European Mediterranean Basin, Springer-Verlag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3553","DOI":"10.1080\/014311600750037570","article-title":"Bias in land cover change estimates due to misregistration","volume":"21","author":"Verbyla","year":"2000","journal-title":"Intern. J. Rem. Sens"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3832\/ifor0305-0010006","article-title":"Remote sensing support for post fire forest management","volume":"1","author":"Corona","year":"2008","journal-title":"iForest"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalised Burn ratio) and NDVI (Normalised Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escquin","year":"2008","journal-title":"Intern. J. Rem. Sens"},{"key":"ref_13","unstructured":"Deering, D.J., Rouse, J.W., Haas, R.H., and Schell, J.A. (1975, January August). Measuring production of grazing units from Landsat MSS data. Ann Arbor, MI, USA."},{"key":"ref_14","unstructured":"Key, C.H., and Benson, N.C. (2000). Measuring and Remote Sensing of Burn Severity, US Geological Survey Wildland Fire Workshop. USGS Open-File Report 02-1131;."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2008.06.004","article-title":"A forward\/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces","volume":"64","author":"Koutsias","year":"2009","journal-title":"ISPRS J. Photogram. Rem. Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1046\/j.1365-2699.2000.00441.x","article-title":"Fires and land-cover change in the tropics: a remote sensing analysis at the landscape scale","volume":"27","author":"Eva","year":"2000","journal-title":"J. Biogeog"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1080\/01431160500212195","article-title":"Mapping burnt areas in Mediterranean countries using spectral mixture analysis from a uni-temporal perspective","volume":"27","author":"Quintano","year":"2006","journal-title":"Int. J. Rem. Sens"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1080\/01431160600954704","article-title":"Production of Landsat ETM+ reference imagery of burnt areas within Southern African savannahs: comparison of methods and application to MODIS","volume":"28","author":"Smith","year":"2007","journal-title":"Int. J. Rem. Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1071\/WF05097","article-title":"Remote sensing techniques to assess active fire characteristics and post-fire effects","volume":"15","author":"Lentile","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(03)00184-6","article-title":"An enchased contextual fire detection algorithm for MODIS","volume":"87","author":"Giglio","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"S218","DOI":"10.1016\/j.foreco.2006.08.245","article-title":"European Forest Fire Information System (EFFIS)\u2014rapid damage assessment: appraisal of burnt area maps in southern Europe using MODIS data (2003\u20132005)","volume":"232","author":"Barbosa","year":"2006","journal-title":"For. Ecol. Manag"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1016\/j.rse.2008.05.013","article-title":"The collection 5 MODIS burnt area product\u2014Global evaluation by comparison with the MODIS active fire product","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_24","first-page":"8405","article-title":"Reliability of biomass burning estimates from savanna fires: biomass burning in northern Australia during the 1999 biomass burning and lightning experiment B field campaign","volume":"108","author":"Edwards","year":"2003","journal-title":"J. Geophys. Res"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.1080\/01431160500239008","article-title":"Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data","volume":"26","author":"Holden","year":"2005","journal-title":"Intern. J. Rem. Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/01431160701294661","article-title":"Multispectral land use classification using neural networks and support vector machines: one or the other, or both?","volume":"29","author":"Dixon","year":"2008","journal-title":"Intern. J. Rem. Sens"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4235","DOI":"10.1080\/01431160110107707","article-title":"Agreement assessment of NOAA\/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas","volume":"23","author":"Domenikiotis","year":"2002","journal-title":"Int. J. Rem. Sens"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1080\/01431160118510","article-title":"Forest fire analysis with remote sensing data","volume":"22","author":"Sunar","year":"2001","journal-title":"Int. J. Rem. Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/01431160500182992","article-title":"The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images","volume":"27","author":"Gitas","year":"2006","journal-title":"Intern. J. Rem. Sens"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1080\/01431160701373739","article-title":"Classification of Landsat TM imagery for land cover using neural networks","volume":"29","author":"Aitkenhead","year":"2008","journal-title":"Int. J. Rem. Sens"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmeber spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.isprsjprs.2006.11.003","article-title":"Mapping East African tropical forests and woodlands\u2014a comparison of classifiers","volume":"61","author":"Nangendo","year":"2007","journal-title":"ISPRS J. Photogram. Rem. Sens"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"419","DOI":"10.3844\/jcssp.2007.419.423","article-title":"The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral imagery analysis","volume":"3","author":"Zulhaidi","year":"2007","journal-title":"J. Comput. Sci"},{"key":"ref_34","first-page":"1","article-title":"Endmembers discrimination in MODIS using spectral angle mapper and maximum likelihood algorithms","volume":"1","author":"Kumar","year":"2008","journal-title":"Int. J. Appl. Rem. Sens"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Rem. Sens"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The Spectral Image Processing System (SIPS)\u2014interactive visualization and analysis of imaging spectrometer Data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/S0034-4257(02)00127-X","article-title":"Lithologic mapping in the mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data","volume":"84","author":"Rowan","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_38","first-page":"10","article-title":"Olympic flames","volume":"16","author":"Xanthopoulos","year":"2007","journal-title":"Wildfire"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18471853","DOI":"10.1080\/01431160701874553","article-title":"Contribution of remote sensing to disaster management activities: a case study of the large fires in the Peloponnese, Greece","volume":"29","author":"Gitas","year":"2008","journal-title":"Int. J. Rem. Sens"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2433","DOI":"10.1080\/01431160701874561","article-title":"A MODIS assessment of the summer 2007 extent burntburnt in Greece","volume":"29","author":"Bochetti","year":"2008","journal-title":"Int. J. Rem. Sens"},{"key":"ref_41","first-page":"299","article-title":"A comparative analysis of fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping","volume":"11","author":"Kontoes","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.1080\/01431160110075596","article-title":"An assessment of two classification methods for mapping Thames estuary intertidal habitats using CASI data","volume":"23","author":"Hunter","year":"2002","journal-title":"Int. J. Rem. Sens"},{"key":"ref_43","unstructured":"Richards, I.A., and Xi, J. (1996). Remote Sensing Digital Image Analysis\u2014An Introduction, Springer. [3rd ed]."},{"key":"ref_44","unstructured":"ENVI User\u2019s Guide (2008). ENVI on-line software user\u2019s manual, ITT Visual Information Solutions."},{"key":"ref_45","unstructured":"Haykin, S. (1994). Neural Networks, Macmillan College Publishing Company."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction: neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Rem. Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1080\/014311697218719","article-title":"Strategies and best practice for neural network image classification","volume":"18","author":"Kanellopoulos","year":"1997","journal-title":"Int. J. Rem. Sens"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Congalton, R., and Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC\/Lewis Press.","DOI":"10.1201\/9781420048568"},{"key":"ref_49","first-page":"419","article-title":"Accuracy assessment of satellite derived land-cover data: a review","volume":"60","author":"Janssen","year":"1994","journal-title":"Photogram. Eng. Rem. Sens"},{"key":"ref_50","unstructured":"WWF Hellas, Mt. Parnitha National park burnt area mapping using IKONOS imagery, 2007. Available online: http:\/\/www.wwf.gr\/index.php?option=com_content&task=view&id=539&itemid=294 (accessed on August 14, 2009)."},{"key":"ref_51","first-page":"25","article-title":"Comparison of classifiers of Remote-sensing data for land-use\/land-cover mapping","volume":"86","author":"Dwivedi","year":"2004","journal-title":"Cur. Sci"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1016\/j.cageo.2008.07.004","article-title":"Boosting a fast neural network for supervised land cover classification","volume":"35","author":"Candy","year":"2009","journal-title":"Comput. Geosci"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0924-2716(03)00020-0","article-title":"Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data","volume":"58","author":"Segl","year":"2003","journal-title":"ISPRS J. Photogramm. Rem. Sens"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/3\/1967\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:01:43Z","timestamp":1760220103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/3\/1967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,3,11]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2010,3]]}},"alternative-id":["s100301967"],"URL":"https:\/\/doi.org\/10.3390\/s100301967","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,3,11]]}}}