Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery....

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Autores principales:	Max J. van Gerrevink, Sander Veraverbeke
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fire severity differenced normalized burn ratio composite burn index spectral index optimality hyperspectral imagery Airborne Visible/Infrared Imaging Spectrometer Science Q
Acceso en línea:	https://doaj.org/article/68312b40f83a4b53abfa01abff4300b3
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spelling	oai:doaj.org-article:68312b40f83a4b53abfa01abff4300b32021-11-25T18:54:46ZEvaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity10.3390/rs132246112072-4292https://doaj.org/article/68312b40f83a4b53abfa01abff4300b32021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4611https://doaj.org/toc/2072-4292Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R<sup>2</sup> = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.Max J. van GerrevinkSander VeraverbekeMDPI AGarticlefire severitydifferenced normalized burn ratiocomposite burn indexspectral index optimalityhyperspectral imageryAirborne Visible/Infrared Imaging SpectrometerScienceQENRemote Sensing, Vol 13, Iss 4611, p 4611 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fire severity differenced normalized burn ratio composite burn index spectral index optimality hyperspectral imagery Airborne Visible/Infrared Imaging Spectrometer Science Q
spellingShingle	fire severity differenced normalized burn ratio composite burn index spectral index optimality hyperspectral imagery Airborne Visible/Infrared Imaging Spectrometer Science Q Max J. van Gerrevink Sander Veraverbeke Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
description	Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R<sup>2</sup> = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.
format	article
author	Max J. van Gerrevink Sander Veraverbeke
author_facet	Max J. van Gerrevink Sander Veraverbeke
author_sort	Max J. van Gerrevink
title	Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
title_short	Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
title_full	Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
title_fullStr	Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
title_full_unstemmed	Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity
title_sort	evaluating the hyperspectral sensitivity of the differenced normalized burn ratio for assessing fire severity
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/68312b40f83a4b53abfa01abff4300b3
work_keys_str_mv	AT maxjvangerrevink evaluatingthehyperspectralsensitivityofthedifferencednormalizedburnratioforassessingfireseverity AT sanderveraverbeke evaluatingthehyperspectralsensitivityofthedifferencednormalizedburnratioforassessingfireseverity
_version_	1718410527781158912

Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

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