The Palisades Fire struck a coastal chaparral wildland–urban interface with steep terrain, damaging vegetation and infrastructure across Los Angeles County.
Fast, reliable maps of where vegetation was lost and how severe the burn was support burn-severity assessment, recovery monitoring, and future fire-risk planning.
Index-based change detection turns two satellite images into a clear, quantitative map of vegetation damage.
Commercial VNIR hyperspectral imagery from the Wyvern Dragonette-001 satellite, chosen for its narrow red-edge bands that are highly sensitive to vegetation stress.
- Resolution: 5.3 m, 23 bands (500–800 nm), 12-bit
- Pre-fire: 04 Nov 2024 · Post-fire: 23 Jan 2025
- Bands used: red-edge (b20, ~750 nm) and NIR (b23, ~799 nm)
- Visual co-registration check and multipoint geometric correction (total RMS 0.0002).
- Select red-edge (b20) and NIR (b23) bands from the hyperspectral cube.
- Convert radiance to TOA reflectance using image metadata.
- Stack bands and re-project WGS84 → WGS 1984 UTM Zone 11N.
- Compute CIred-edge and NDVIred-edge for both dates.
- Difference (post − pre) to build change maps; negative Δ = vegetation loss.
Vegetation loss concentrated in forested hilltops with high burn severity, while urban and built-up areas showed little change. Both indices agreed spatially: CIred-edge captured chlorophyll loss and NDVIred-edge captured structural canopy loss, confirming the pattern.
CIRED-EDGE · PRE VS POST
NDVIRED-EDGE · PRE VS POST
- Calibrate severity thresholds against field or dNBR reference data.
- Add atmospheric correction (surface reflectance) for cross-sensor robustness.
- Validate against an independent burn-perimeter dataset.
See the full write-up
Complete README, all maps, and methodology on GitHub.