A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio

Parks, Sean A.; Dillon, Gregory K.; Miller, Carol

doi:10.3390/rs6031827

Cited by 305 publications

(331 citation statements)

References 30 publications

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“…Next, we computed dNBR, Relative dNBR (RdNBR) (Miler and Thode, 2007) and Relativized Burn Ratio (RBR) (Parks et al, 2014) and denoted them respectively by dNBR_L, RdNBR_L, and RBR_L…”

Section: Methodsmentioning

confidence: 99%

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

Quintano

Fernández–Manso

Fernández‐Manso

2018

International Journal of Applied Earth Observation and Geoinfor

127

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Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6-11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managers.

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“…Next, we computed dNBR, Relative dNBR (RdNBR) (Miler and Thode, 2007) and Relativized Burn Ratio (RBR) (Parks et al, 2014) and denoted them respectively by dNBR_L, RdNBR_L, and RBR_L…”

Section: Methodsmentioning

confidence: 99%

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

Quintano

Fernández–Manso

Fernández‐Manso

2018

International Journal of Applied Earth Observation and Geoinfor

127

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“…Habitualmente se considera que los intervalos de RdNBR son más consistentes para su extrapolación a otros incendios distintos a donde fueron calibrados (Miller et al, 2009;Cansler and McKenzie, 2012;Parks et al 2014), especialmente para el umbral que separa las clases de moderada y alta severidad, y se aduce como argumento la falta de relación entre RdNBR y NBR pre. En nuestro caso, esa ausencia de relación se dio entre dNBR y NBR pre y entre RdNBR y NBR pre, ya que estos últi-mos presentaron una débil correlación (R 2 = 0,205).…”

Section: Discusión Y Conclusionesunclassified

Validación de los índices de teledetección dNBR y RdNBR para determinar la severidad del fuego en el incendio forestal de Oia-O Rosal (Pontevedra) en 2013

Arellano

Vega²,

Silva³

et al. 2017

Rev. Teledetec.

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<p>Fire severity evaluation and mapping following wildfire is an essential task for post-fire rehabilitation activities and forest management planning. For that purpose, some spectral indexes are used to quantify the changes caused by fire, being Landsat satellite one of the most frequently used. Even though Galicia is the Spanish region with the highest number of fires in the country, the information on fire severity estimation through satellite imagery is scarce. In the present study, the capacity of dNBR (differenced Normalized Burn Ratio) and RdNBR (Relative difference Normalized Burn Ratio), through Landsat 8 imagery processing, are compared for the first time in Galicia to test both indexes with field data following the methodology from CBI (Composite Burn Index) in Oia-O Rosal (Pontevedra) wildfire occurred in the summer of 2013. The results indicate that the models for dNBR and RdNBR estimation according to CBI were similar, explaining a 69 and 73% of variability, respectively. These models allow to obtain a new fire severity thresholds for dNBR and RdNBR for the burned area. Although, both indexes showed a similar and quite high overall accuracy in the classification of the different fire severity classes (75% y 83% for dNBR and RdNBR, respectively), RdNBR was slightly more accurate than dNBR. Additionally, the dNBR-based fire severity map significantly underestimated the high fire severity area, compared with RdNBR. Those preliminary results can be useful to evaluate fire severity spatial distribution, in wildfires in Galicia although new data will be necessary before an operational tool to be available.</p>

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“…In addition, permafrost thawing has been known to cause erosion, disappearance of lakes, landslides, and ground subsidence (e.g., [6,9,12]). In a study using airborne LiDAR, Jones et al [29] assessed fire-induced thermokarst development and concluded that in regions with ice-rich permafrost in the Arctic, the impact of tundra fires for initiating widespread thermokarst development is greater than what has been estimated to so far.…”

Section: Introductionmentioning

confidence: 98%

Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations

et al. 2018

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Abstract:The discontinuous permafrost zone is one of the world's most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.

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A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio

Cited by 305 publications

References 30 publications

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

Validación de los índices de teledetección dNBR y RdNBR para determinar la severidad del fuego en el incendio forestal de Oia-O Rosal (Pontevedra) en 2013

Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations

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