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      A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

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          Abstract

          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.

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          Status of land cover classification accuracy assessment

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            Red tides in the Gulf of Mexico: Where, when, and why?

            [1] Independent data from the Gulf of Mexico are used to develop and test the hypothesis that the same sequence of physical and ecological events each year allows the toxic dinoflagellate Karenia brevis to become dominant. A phosphorus-rich nutrient supply initiates phytoplankton succession, once deposition events of Saharan iron-rich dust allow Trichodesmium blooms to utilize ubiquitous dissolved nitrogen gas within otherwise nitrogen-poor sea water. They and the co-occurring K. brevis are positioned within the bottom Ekman layers, as a consequence of their similar diel vertical migration patterns on the middle shelf. Upon onshore upwelling of these near-bottom seed populations to CDOM-rich surface waters of coastal regions, light-inhibition of the small red tide of ~1 ug chl l(-1) of ichthytoxic K. brevis is alleviated. Thence, dead fish serve as a supplementary nutrient source, yielding large, self-shaded red tides of ~10 ug chl l(-1). The source of phosphorus is mainly of fossil origin off west Florida, where past nutrient additions from the eutrophied Lake Okeechobee had minimal impact. In contrast, the P-sources are of mainly anthropogenic origin off Texas, since both the nutrient loadings of Mississippi River and the spatial extent of the downstream red tides have increased over the last 100 years. During the past century and particularly within the last decade, previously cryptic Karenia spp. have caused toxic red tides in similar coastal habitats of other western boundary currents off Japan, China, New Zealand, Australia, and South Africa, downstream of the Gobi, Simpson, Great Western, and Kalahari Deserts, in a global response to both desertification and eutrophication.
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              Locating and estimating the areal extent of wildfires in alaskan boreal forests using multiple-season AVHRR NDVI composite data

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                2010
                11 March 2010
                : 10
                : 3
                : 1967-1985
                Affiliations
                [1 ] Department of Earth Sciences, University of Bristol, Queens Road, BS8 1RJ, Bristol, UK; E-Mail: marko.scholze@ 123456bristol.ac.uk
                [2 ] InfoCosmos, Pindou 71, 13341, Athens, Greece, http://www.infocosmos.eu/
                [3 ] Agroecosystem Management Program, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 44691, USA; E-Mail: vadrevu.2@ 123456osu.edu
                [4 ] National Agricultural Research Foundation, Institute of Mediterranean Forest Ecosystems and Forest Products Technology, Terma Alkmanos, Ilisia, 11528 Athens, Greece; E-Mail: gxnrtc@ 123456fria.gr
                [5 ] Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece; E-Mail: gkarant@ 123456aua.gr
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: george.petropoulos@ 123456bristol.ac.uk or petropoulos.george@ 123456gmail.com ; Tel.: +30-210-2486905; Fax: +30-210-2486905.
                Article
                sensors-10-01967
                10.3390/s100301967
                3264462
                22294909
                8c8d619d-4ec1-4b00-8a9f-8e7de7ca25cb
                © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 18 December 2009
                : 20 January 2010
                : 4 February 2010
                Categories
                Article

                Biomedical engineering
                greek forest fires 2007,artificial neural networks,spectral angle mapper,landsat tm,burnt area mapping

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