| dc.contributor.author |
Petropoulos, GP |
en |
| dc.contributor.author |
Vadrevu, KP |
en |
| dc.contributor.author |
Xanthopoulos, G |
en |
| dc.contributor.author |
Karantounias, G |
en |
| dc.contributor.author |
Scholze, M |
en |
| dc.date.accessioned |
2014-06-06T06:49:58Z |
|
| dc.date.available |
2014-06-06T06:49:58Z |
|
| dc.date.issued |
2010 |
en |
| dc.identifier.issn |
14248220 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.3390/s100301967 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/4927 |
|
| dc.subject |
Artificial neural networks |
en |
| dc.subject |
Burnt area mapping |
en |
| dc.subject |
Greek forest fires 2007 |
en |
| dc.subject |
Landsat TM |
en |
| dc.subject |
Spectral angle mapper |
en |
| dc.subject.other |
Artificial neural network classifiers |
en |
| dc.subject.other |
Burnt areas |
en |
| dc.subject.other |
Classification accuracy |
en |
| dc.subject.other |
Forest fires |
en |
| dc.subject.other |
LANDSAT TM |
en |
| dc.subject.other |
Overall accuracies |
en |
| dc.subject.other |
Satellite remote sensing |
en |
| dc.subject.other |
Spectral angle mappers |
en |
| dc.subject.other |
Algorithms |
en |
| dc.subject.other |
Deforestation |
en |
| dc.subject.other |
Estimation |
en |
| dc.subject.other |
Fires |
en |
| dc.subject.other |
Maps |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Satellite imagery |
en |
| dc.subject.other |
Photomapping |
en |
| dc.subject.other |
algorithm |
en |
| dc.subject.other |
article |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
audiovisual equipment |
en |
| dc.subject.other |
comparative study |
en |
| dc.subject.other |
fire |
en |
| dc.subject.other |
geographic information system |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
image processing |
en |
| dc.subject.other |
methodology |
en |
| dc.subject.other |
remote sensing |
en |
| dc.subject.other |
telecommunication |
en |
| dc.subject.other |
tree |
en |
| dc.subject.other |
Algorithms |
en |
| dc.subject.other |
Fires |
en |
| dc.subject.other |
Geographic Information Systems |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
Image Processing, Computer-Assisted |
en |
| dc.subject.other |
Maps as Topic |
en |
| dc.subject.other |
Neural Networks (Computer) |
en |
| dc.subject.other |
Remote Sensing Technology |
en |
| dc.subject.other |
Satellite Communications |
en |
| dc.subject.other |
Trees |
en |
| dc.title |
A comparison of spectral angle mapper and artificial neural network classifiers combined with landsat TM imagery analysis for obtaining burnt area mapping |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.3390/s100301967 |
en |
| heal.publicationDate |
2010 |
en |
| heal.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. © 2010 by the authors. |
en |
| heal.journalName |
Sensors |
en |
| dc.identifier.issue |
3 |
en |
| dc.identifier.volume |
10 |
en |
| dc.identifier.doi |
10.3390/s100301967 |
en |
| dc.identifier.spage |
1967 |
en |
| dc.identifier.epage |
1985 |
en |