Mapping Burn Severity After Wildfires: NBR and Satellite-Based Assessment
Quick Answer: Burn severity — the degree to which fire has altered the ecosystem — is mapped from satellites using the Normalized Burn Ratio (NBR), which exploits the contrast between NIR (decreases when vegetation burns) and SWIR (increases when vegetation burns and soil is exposed). The differenced NBR (dNBR = pre-fire NBR − post-fire NBR) classifies severity into categories from unburned to high severity. Landsat and Sentinel-2 are the primary sensors at 20-30m resolution. The USGS Monitoring Trends in Burn Severity (MTBS) program has mapped every large US fire since 1984 using Landsat. Timing matters: immediate post-fire imagery captures fire effects, but delayed assessment (1 year later) captures ecosystem response.
After the 2020 wildfire season in California — the largest in the state's recorded history — resource managers faced an urgent question: where exactly did the fires burn, and how severely? The answer determines everything from erosion risk assessment to reforestation planning to insurance claims processing. Ground survey of the 1.7 million hectares that burned would have taken years. Satellite-based burn severity mapping provided the answer within weeks.
What Is Burn Severity?
Burn severity describes the degree of ecological change caused by fire:
Unburned/Very Low: Fire didn't reach this area, or surface fire passed through without significantly affecting vegetation or soil.
Low Severity: Surface fire consumed ground litter and some understory vegetation. Overstory trees survived. Soil is largely intact.
Moderate Severity: Understory consumed, some overstory mortality. Mix of surviving and killed trees. Some soil organic matter consumed.
High Severity: Complete canopy mortality. All surface organic matter consumed. Mineral soil exposed. Root systems killed.
The distinction matters because high-severity burns fundamentally change the ecosystem for decades — destroying seed banks, eliminating soil biota, increasing erosion risk dramatically, and often converting forest to shrubland.
The Normalized Burn Ratio (NBR)
NBR exploits two spectral responses that change dramatically with fire:
NIR (Band 8A in Sentinel-2, ~865 nm): Healthy vegetation reflects strongly in NIR. Burned vegetation absorbs NIR. Dead trees and charred surfaces have very low NIR reflectance.
SWIR (Band 12 in Sentinel-2, ~2190 nm): Healthy vegetation absorbs SWIR moderately. Burned areas, exposed soil, and charred surfaces reflect SWIR more strongly.
NBR = (NIR − SWIR) / (NIR + SWIR)
- Healthy forest: High NBR (0.4-0.8) — high NIR, moderate SWIR
- Burned area: Low or negative NBR (−0.5 to 0.1) — low NIR, high SWIR
Differenced NBR (dNBR)
The change in NBR between pre-fire and post-fire images:
dNBR = NBR_pre − NBR_post
| dNBR Range | Severity Class |
|---|---|
| < 0.1 | Unburned |
| 0.1 - 0.27 | Low severity |
| 0.27 - 0.44 | Moderate-low |
| 0.44 - 0.66 | Moderate-high |
| > 0.66 | High severity |
These thresholds are approximate — they vary by vegetation type, climate, and pre-fire condition. Local calibration against field plots improves accuracy.
Relativized dNBR (RdNBR)
Standard dNBR is biased by pre-fire vegetation condition: a high-biomass forest shows larger dNBR for the same severity than a sparse woodland simply because it had more to burn. RdNBR normalizes by pre-fire condition:
RdNBR = dNBR / √(|NBR_pre|)
This produces more comparable severity estimates across different vegetation types and pre-fire conditions.
Timing Considerations
Immediate Assessment (Days to Weeks)
Using imagery acquired as soon as clouds and smoke clear after the fire:
- Captures the direct fire effect (charring, canopy consumption, soil exposure)
- May include actively smoldering areas and smoke residuals
- Best for: emergency response, erosion risk assessment, BAER (Burned Area Emergency Response)
Extended Assessment (1 Growing Season Later)
Using imagery from the first growing season after the fire:
- Captures vegetation response (what's regrowing vs. what's dead)
- More accurately reflects long-term ecological impact
- A tree that was scorched but survived will show green regrowth; one that was killed won't
- Best for: reforestation planning, ecological impact assessment, long-term monitoring
The USGS MTBS program produces both initial and extended assessment products for every large US fire.
Sentinel-2 vs. Landsat for Burn Severity
Both sensors are widely used:
| Feature | Sentinel-2 | Landsat 8/9 |
|---|---|---|
| Resolution | 20m (SWIR bands) | 30m |
| Revisit | 5 days | 16 days |
| Archive | 2015-present | 1984-present |
| SWIR bands | B11 (1610nm), B12 (2190nm) | B6 (1610nm), B7 (2190nm) |
Sentinel-2 advantage: Higher resolution and more frequent imaging increase the chance of a cloud-free post-fire observation.
Landsat advantage: 40-year archive enables historical burn severity assessment back to 1984. The MTBS program uses this for comprehensive fire history analysis.
Applications
Burned Area Emergency Response (BAER)
Within days of fire containment, burn severity maps drive emergency stabilization:
- High-severity areas → highest erosion and debris flow risk
- Prioritize erosion control treatments (mulching, log erosion barriers, channel protection)
- Identify infrastructure at risk from post-fire debris flows
- Determine watershed impacts on water supply
Reforestation Planning
Burn severity maps guide planting decisions:
- High severity: Active reforestation often needed (no surviving seed source)
- Moderate severity: Natural regeneration may be sufficient if seed trees survived
- Low severity: Monitoring only; forest will recover without intervention
Fire Effects Monitoring
Long-term monitoring of burn severity patterns reveals:
- Are fires getting more severe over time? (Yes, in many western US forests)
- What proportion of each fire burns at high severity?
- How does severity relate to pre-fire fuel conditions, weather, and topography?
Insurance and Damage Assessment
Satellite burn severity maps provide objective documentation of fire impact for:
- Property damage claims
- Agricultural loss assessment
- Timber loss quantification
- Infrastructure damage documentation
Beyond NBR: Additional Indicators
NDVI change: Simpler than NBR but less sensitive to burn severity; more sensitive to vegetation response timing.
Burn Area Index (BAI): Designed to emphasize charcoal signal in the red-NIR spectral space.
SAR-based detection: Sentinel-1 coherence loss and backscatter change detect burned areas through smoke and clouds — useful when optical imagery is unavailable immediately post-fire.
Thermal anomalies: Active fire detection (MODIS, VIIRS) maps fire progression in near-real-time, complementing post-fire severity assessment.
Accuracy
Validation against field-measured Composite Burn Index (CBI) plots shows:
- Binary burned/unburned: 90-95% accuracy
- 4-class severity: 70-80% overall accuracy
- Continuous severity (correlation with CBI): R² = 0.60-0.80
Errors are highest at class boundaries (where moderate and high severity overlap spectrally) and in heterogeneous landscapes where small-scale severity variation occurs within single pixels.
Burn severity mapping from satellites has become so routine and reliable that it's embedded in standard post-fire management workflows worldwide. The combination of well-understood physics (NBR), free satellite data (Landsat, Sentinel-2), and established operational programs (MTBS, Copernicus EMS) means that every significant wildfire on Earth can be mapped to a consistent standard — providing the spatial intelligence that post-fire landscape management requires.
