Normalized Burn Ratio

Burn severity index for detecting and monitoring fire damage in vegetation. Higher values indicate healthy vegetation, lower values indicate burned areas. Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. NBR is particularly suited for fire damage assessment, burn severity mapping, post-fire recovery monitoring. The general formula is (NIR - SWIR) / (NIR + SWIR), which requires NIR and SWIR spectral bands.

NBR is supported by 4 satellite sensors in our database, including Landsat 8/9, Sentinel-2, SuperView-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula (B5 - B7) / (B5 + B7), while Sentinel-2 uses (B8 - B11) / (B8 + B11). Select a sensor above to see its specific band mapping.

NBR requires NIR (780-1400 nm), SWIR (1400-3000 nm). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

While NDVI is the most common vegetation index, NBR uses shortwave infrared bands to capture vegetation water content information that NDVI misses. The choice of index depends on your application, sensor availability, and atmospheric conditions.