Burn Severity (dNBR)
Burn Severity Assessment using Normalized Burn Ratio · Also known as: dNBR, Delta NBR, burn severity index
Burn severity is a quantitative measure of fire-induced changes in vegetation and soil, assessed using satellite-based spectral indices. The Normalized Burn Ratio (NBR) and its delta (dNBR) compare pre-fire and post-fire spectral reflectance in the near-infrared and shortwave-infrared bands to detect fire-caused vegetation damage and soil exposure. Developed by Key and Benson in 2006, dNBR has become the standard remote-sensing tool for rapid post-fire assessment and is used for emergency response, recovery planning, and ecological analysis.
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When to use it
Use dNBR for rapid post-fire assessment in the hours to weeks following fire containment, before extensive field surveys are possible. Particularly valuable for large fires where field access is limited or dangerous. Integrate dNBR with ground-based surveys (field plots, drones) for validation and to assess factors (species, topography, fire behavior) not captured by spectral data alone. Applicable globally wherever satellite coverage is available.
Strengths & limitations
- Rapid assessment capability: dNBR maps available within days of fire containment, enabling quick response decisions
- Large-area coverage: satellite imagery spans thousands of square kilometers in a single scene, practical for large fires
- Objective, quantitative metrics: spectral indices are less subjective than visual fire damage assessment
- Freely available satellite data (Landsat, Sentinel-2) and open-source processing software reduce costs
- Long historical archives enable retrospective burn severity analysis for fire ecology research
- Validated against field-measured burn damage across diverse forest types and fire conditions
- dNBR is a spectral measure, not a direct measure of ecological impact; high dNBR indicates vegetation damage but does not measure ecosystem function or recovery potential
- Topography and vegetation heterogeneity complicate interpretation: shadows and dense understory can bias severity classification
- Timing of post-fire imagery matters: delay between fire and image acquisition allows recovery signal to blur burn severity estimates
- Cloud cover and atmospheric conditions can obscure imagery quality; seasonal or regional cloud patterns limit timeliness
- Pixel size (30 m for Landsat) averages severity within pixels, missing fine-scale variation in burn patterns
Frequently asked
What is the relationship between dNBR and fire intensity?
dNBR measures vegetation/soil damage (burn severity), not the rate of heat release (fire intensity). A fast-moving, high-intensity fire may cause variable dNBR depending on fuel moisture and residence time; a slow, cooler fire may cause high dNBR if it persists long enough to consume deep litter and kill trees. Both intensity and residence time affect dNBR.
What satellite data should I use for dNBR?
Landsat 8–9 (30 m resolution, free, 16-day revisit) are the most widely used and have long archives. Sentinel-2 (10–20 m resolution, free, 5-day revisit) is better for fine detail but has a shorter archive. For very high resolution (< 1 m), use commercial satellites (Worldview, Planet) or drones. Choose based on your fire size, desired detail, and budget.
How soon after the fire should I obtain post-fire imagery?
Ideally within 7–14 days: early imaging captures maximum dNBR while vegetation scorch is fresh and before decay and regrowth complicate the signal. Delays > 30 days allow rapid recovery of sensitive vegetation, potentially underestimating severity. For immediate tactical response, dNBR can be computed from imagery acquired within hours if available.
Why does dNBR differ from field-assessed burn severity?
dNBR is an optical measure sensitive to overstory canopy damage. Field crews assess multiple burn indicators (scorch height, tree kill, soil exposure, litter consumption). Discrepancies arise when low dNBR but high understory/litter damage occurs (low overstory damage but intense surface fire), or vice versa. Integration of dNBR with field data is essential.
Sources
- Key, C. H., & Benson, N. C. (2006). Landscape Assessment (LA): Sampling and Analysis Methods. General Technical Report RMRS-GTR-164-CD, USDA Forest Service Rocky Mountain Research Station. link ↗
- Parks, S. A., Holsinger, L. M., Miller, C., & Parisien, M. A. (2019). Wildland-urban interface in the western U.S.: Spatial patterns and demographic transitions over time. Journal of Geophysical Research: Biogeosciences, 124(3), 558–573. link ↗
How to cite this page
ScholarGate. (2026, June 3). Burn Severity Assessment using Normalized Burn Ratio. ScholarGate. https://scholargate.app/en/forestry/burn-severity
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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