Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination

Chuvieco, Emilio; Martín, M. Pilar; Palacios, A.

doi:10.1080/01431160210153129

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%

“…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%

“…Although its application to burn scar detection was mainly done with MODIS and SPOT sensors [58][59][60], we decided to include it in our Landsat TM study and evaluate its potential. BAI was initially designed for NOAA-AVHRR images in Mediterranean environments but has also been tested with Landsat TM [18]. It focuses on the specific spectral signal of charcoal in areas affected by fire and is computed from the spectral distance from each pixel to a reference spectral point, where recently burned areas tend to converge [18].…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

“…BAI was initially designed for NOAA-AVHRR images in Mediterranean environments but has also been tested with Landsat TM [18]. It focuses on the specific spectral signal of charcoal in areas affected by fire and is computed from the spectral distance from each pixel to a reference spectral point, where recently burned areas tend to converge [18]. Reference reflectance values of the convergence point, set to 0.1 for red and 0.06 for NIR, were defined based on literature and several sets of satellite sensor images analyzed by the authors [9].…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

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Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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Abstract:Remote mountainous regions are among the Earth's last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

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Section: Summary Of Results Which Si Should We Choose?supporting

confidence: 79%

Section: Summary Of Results Which Si Should We Choose?mentioning

confidence: 99%

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

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Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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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%