Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Miller, Jay D.; Thode, Andrea E.

doi:10.1016/j.rse.2006.12.006

Cited by 1,094 publications

(1,097 citation statements)

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“…In order to improve classification accuracy of the dNBR in mixed forest and shrubland in California, Miller and Thode [1] developed the Relative differenced Normalized Burn Ratio (RdNBR). The RdNBR normalizes the dNBR by the initial image reflectance to account for spatial variation in pre-fire leaf area index.…”

Section: Remotely Sensed Burn-severity Indicesmentioning

confidence: 99%

“…In theory, the dNBR offset = 0 when the phenologies of the pre-fire and post-fire image are perfectly matched. This processing step was described in Miller and Thode [1], but was not explicitly included in the original RdNBR equation. It is included in the RdNBR equation below, which is the RdNBR equation recorded in the metadata for all RdNBR images processed by the national Monitoring Trends in Burn Severity Program (described below) (for example of formula see [21]).…”

Section: Remotely Sensed Burn-severity Indicesmentioning

confidence: 99%

“…We evaluate the applicability of methodological refinements to field-based and remotely sensed indices of burn severity developed in other locations, to a new location, the northern Cascade Range of Washington, USA. We compare two remotely sensed indices of burn severity; the differenced Normalized Burn Ratio (dNBR) and the Relative differenced Normalized Burn Ratio (RdNBR), a refinement of the dNBR which had stronger correlations with field-based assessments of burn severity and higher classification accuracy when evaluated in other regions [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Regional empirical validation of burn severity indices is necessary because previous research has shown that the relationships between remotely sensed and field-based assessments vary among regions [6]. Regressions that predict remotely sensed burnseverity from field measurements can be used to classify the remotely sensed data into distinct severity classes [1,2]. Although some information is lost in the classification process, categorical images facilitate: (1) visual interpretation of images; (2) comparisons of multiple fires or fires from multiple regions; (3) analysis of the spatial context and spatial pattern of severity; (4) spatially explicit predictions of the impacts of the fire (i.e., soil erosion, water quality, succession, and carbon emissions); and (5) targeted management responses to those impacts.…”

Section: Introductionmentioning

confidence: 99%

“…This classification scheme should be applicable to ecologically similar areas in the Cascade Range, from northern Oregon to southern British Columbia. No empirical validation of the relationship of the two burn severity indices with field data has been conducted in the Cascade Range of Washington and Oregon, although they have been conducted for many different vegetation types across North America [1,2,[7][8][9][10][11][12][13][14]. In the northern Cascade Range, the benefits of using remote sensing to quantify the severity and spatial pattern of fires are particularly strong.…”

Section: Introductionmentioning

confidence: 99%

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How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

2012

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Abstract:Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity developed in one location did not show the same improvement when used in a new location. We evaluated three methods of assessing burn severity in the field: the Composite Burn Index (CBI)-a standardized method of assessing burn severity that combines ecologically significant variables related to burn severity into one numeric site index-and two modifications of the CBI that weight the plot CBI score by the percentage cover of each stratum. Unexpectedly, models using the CBI had higher R 2 and better classification accuracy than models using the weighted versions of the CBI. We suggest that the weighted versions of the CBI have lower accuracies because weighting by percentage cover decreases the influence of the dominant tree stratum, which should have the strongest relationship to optically sensed reflectance, and increases the influence of the substrates strata, which should have the weakest relationship with optically sensed reflectance in forested ecosystems. Using a large data set of CBI plots (n = 251) from four fires and CBI scores derived from additional field-based assessments of burn severity (n = 388), we predicted two metrics of image-based burn severity, the Relative differenced Normalized Burn Ratio (RdNBR) and the differenced Normalized Burn Ratio (dNBR). Predictive models for RdNBR showed slightly better OPEN ACCESSRemote Sens. 2012, 4 457 classification accuracy than for dNBR (overall accuracy = 62%, Kappa = 0.40, and overall accuracy = 59%, Kappa= 0.36, respectively), whereas dNBR had slightly better explanatory power, but strong differences were not apparent. RdNBR may provide little or no improvement over dNBR in systems where pre-fire reflectance is not highly variable, but may be more appropriate for comparing burn severity among regions.

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Section: Remotely Sensed Burn-severity Indicesmentioning

confidence: 99%

Section: Remotely Sensed Burn-severity Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

2012

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Using field data to assess model predictions of surface and ground fuel consumption by wildfire in coniferous forests of California

et al. 2014

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Inventories of greenhouse gas (GHG) emissions from wildfire provide essential information to the state of California, USA, and other governments that have enacted emission reductions. Wildfires can release a substantial amount of GHGs and other compounds to the atmosphere, so recent increases in fire activity may be increasing GHG emissions. Quantifying wildfire emissions however can be difficult due to inherent variability in fuel loads and consumption and a lack of field data of fuel consumption by wildfire. We compare a unique set of fuel data collected immediately before and after six wildfires in coniferous forests of California to fuel consumption predictions of the first-order fire effects model (FOFEM), based on two different available fuel characterizations. We found strong regional differences in the performance of different fuel characterizations, with FOFEM overestimating the fuel consumption to a greater extent in the Klamath Mountains than in the Sierra Nevada. Inaccurate fuel load inputs caused the largest differences between predicted and observed fuel consumption. Fuel classifications tended to overestimate duff load and underestimate litter load, leading to differences in predicted emissions for some pollutants. When considering total ground and surface fuels, modeled consumption was fairly accurate on average, although the range of error in estimates of plot level consumption was very large. These results highlight the importance of fuel load input to the accuracy of modeled fuel consumption and GHG emissions from wildfires in coniferous forests.

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Quantifying fire‐wide carbon emissions in interior Alaska using field measurements and Landsat imagery

Rogers

Veraverbeke

Azzari

et al. 2014

J. Geophys. Res. Biogeosci.

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Carbon emissions from boreal forest fires are projected to increase with continued warming and constitute a potentially significant positive feedback to climate change. The highest consistent combustion levels are reported in interior Alaska and can be highly variable depending on the consumption of soil organic matter. Here we present an approach for quantifying emissions within a fire perimeter using remote sensing of fire severity. Combustion from belowground and aboveground pools was quantified at 22 sites (17 black spruce and five white spruce-aspen) within the 2010 Gilles Creek burn in interior Alaska, constrained by data from eight unburned sites. We applied allometric equations and estimates of consumption to calculate carbon losses from aboveground vegetation. The position of adventitious spruce roots within the soil column, together with estimated prefire bulk density and carbon concentrations, was used to quantify belowground combustion. The differenced Normalized Burn Ratio (dNBR) exhibited a clear but nonlinear relationship with combustion that differed by forest type. We used a multiple regression model based on transformed dNBR and deciduous fraction to scale carbon emissions to the fire perimeter, and a Monte Carlo framework to assess uncertainty. Because of low-severity and unburned patches, mean combustion across the fire perimeter (1.98 ± 0.34 kg C m À2) was considerably less than within a defined core burn area (2.67 ± 0.40 kg C m À2) and the mean at field sites (2.88 ± 0.23 kg C m À2). These areas constitute a significant fraction of burn perimeters in Alaska but are generally not accounted for in regional-scale estimates. Although total combustion in black spruce was slightly lower than in white spruce-aspen forests, black spruce covered most of the fire perimeter (62%) and contributed the majority (67 ± 16%) of total emissions. Increases in spring albedo were found to be a viable alternative to dNBR for modeling emissions.

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Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Cited by 1,094 publications

References 21 publications

How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

Using field data to assess model predictions of surface and ground fuel consumption by wildfire in coniferous forests of California

Quantifying fire‐wide carbon emissions in interior Alaska using field measurements and Landsat imagery

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