2002
DOI: 10.1080/01431160210153129
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Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination

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Cited by 329 publications
(239 citation statements)
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“…Among them, MIRBI, BAI, and Greenness are the best when averaging all vegetation types, while NDVI and NBR get better scores in isolated vegetation types. Other studies obtained similar results, e.g., better discrimination capabilities of BAI [18,22,28], Greenness [24,62], or MIRBI in shrub-savannah ecosystems [25,27,52]. Although NBR is often considered the best SI for burned area mapping short time after fire and therefore widely used for burn severity assessments [7,19,21,23,[27][28][29], our results demonstrated that in certain vegetation types other SI would offer a better option.…”
Section: Summary Of Results Which Si Should We Choose?supporting
confidence: 79%
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“…Among them, MIRBI, BAI, and Greenness are the best when averaging all vegetation types, while NDVI and NBR get better scores in isolated vegetation types. Other studies obtained similar results, e.g., better discrimination capabilities of BAI [18,22,28], Greenness [24,62], or MIRBI in shrub-savannah ecosystems [25,27,52]. Although NBR is often considered the best SI for burned area mapping short time after fire and therefore widely used for burn severity assessments [7,19,21,23,[27][28][29], our results demonstrated that in certain vegetation types other SI would offer a better option.…”
Section: Summary Of Results Which Si Should We Choose?supporting
confidence: 79%
“…The other SI analyzed in this study performed consistently worse and proved to be less suitable for long term monitoring of fire disturbance. The spectral response of BAI strongly depends on the persistence of charcoal deposits [18]. Both of its modified versions, which were initially designed for the MODIS sensor, couldn't reach the same performance as the best SI, as also found by [27], and therefore represent a second choice for long term monitoring of post-fire vegetation recovery.…”
Section: Summary Of Results Which Si Should We Choose?mentioning
confidence: 99%
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“…Wildland fires, a hot topic of research since the early days of satellite remote sensing, have been used extensively for developing and testing new image analysis techniques and methods and Geographical Information System (GIS) models (Fernández et al 1997, Chuvieco et al 2002. The European Association of Remote Sensing Laboratories Special Interest Group on Forest Fires (EARSeL FFSIG) actively promotes the integration of remote sensing and GIS into the day-to-day activities of forest managers at all scales, supporting researchers, local governments and global organizations.…”
Section: Introductionmentioning
confidence: 99%