Burn Area Index

The Burn Area Index (BAI) was developed by Chuvieco et al. (2002) to identify burned areas using the red and NIR spectral bands. BAI emphasizes the charcoal signal in post-fire images by considering the spectral distance from each pixel to a reference spectral point where recently burned areas tend to converge. Burn severity indices quantify post-fire damage by measuring the spectral changes that occur when vegetation is consumed and the underlying soil and char are exposed. BAI is particularly suited for burned area detection, fire severity assessment, post-fire monitoring. The general formula is 1 / ((0.1 - RED)^2 + (0.06 - NIR)^2), which requires RED and NIR spectral bands.

BAI is supported by 23 satellite sensors in our database, including BJ3A, BJ3N, Dragonette-1, Dragonette-2/3, Gaofen-1 and 18 more. Each sensor uses different band designations — for example, BJ3A uses the formula 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2), while BJ3N uses 1 / ((0.1 - Red)^2 + (0.06 - NIR)^2). Select a sensor above to see its specific band mapping.

BAI requires RED (640-760), NIR (780-1400). These wavelength regions target the specific spectral features that this index is designed to measure.

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.