DOI: 10.5281/zenodo.17238635
title: The California Interagency Wildfire Severity (CaliWiSe) Database

description: OVERVIEW

This dataset contains multi-band raster images representing burn severity and spectral response across wildfire perimeters in the United States. Each image corresponds to a unique fire event and includes several bands including burn severity, moisture, and vegetation indices.

The data were generated using Google Earth Engine, primarily from Landsat 4–8 Surface Reflectance Tier 1 collections data. All images are scaled and exported as float-encoded multi-band GeoTIFFs.

AUTHORSMitchell Hung & A. Park Williams

For questions or issues, please contact:Mitchell Hung (mjhung@stanford.edu)

SOURCE WILDFIRE POLYGONSMTBS and CALFIRE FRAP perimeters, 1985–2023.

FILE STRUCTURE / DIRECTORY GUIDE

Each year’s data are organized into compressed archives corresponding to the source fire perimeter database:

├── calfire_YYYY.zip → CAL FIRE FRAP wildfire perimeters for year YYYY├── mtbs_YYYY.zip → MTBS wildfire perimeters for year YYYY├── band_info.csv → Band names and scale factors for all exported layers├── README.md → Dataset documentation

Each .zip archive contains one GeoTIFF per fire event within that year’s source dataset:

DATASET CONTENTSEach exported .tif file contains the following bands:

CBI - Composite Burn Index (bias-corrected model output) [scale: 100]dnbr - Delta Normalized Burn Ratio [scale: 1000]rbr - Relativized Burn Ratio [scale: 1000]rdnbr - Relativized Delta Normalized Burn Ratio [scale: 1000]dndvi - Delta Normalized Difference Vegetation Index [scale: 1000]devi - Delta Enhanced Vegetation Index [scale: 1000]dndmi - Delta Normalized Difference Moisture Index [scale: 1000]dmirbi - Delta Mid-Infrared Bi-Spectral Index [scale: 1000]post_nbr - Post-fire Normalized Burn Ratio [scale: 1]post_mirbi - Post-fire Mid-Infrared Bi-Spectral Index [scale: 1000]

Note: To retrieve original float values, divide by the appropriate scale factor.

DATA DESCRIPTION

Composite Burn Index (CBI)Estimated using a Random Forest regression model (Parks et al. 2019).

Pixel MaskingThe Landsat QA_PIXEL band and Hansen Global Forest Change water mask were used to remove pixels obscured by:- Cloud- Cloud shadow- Water- Snow

Spectral IndicesCollections: Landsat 4, 5, 7, and 8 (Surface Reflectance Tier 1)

Formulas:NDVI = (NIR - RED) / (NIR + RED)NBR = (NIR - SWIR2) / (NIR + SWIR2)RBR = (SWIR1 - RED) / (SWIR1 + RED)NDMI = (NIR - SWIR1) / (NIR + SWIR1)EVI = 2.5 * ((NIR - RED) / (NIR + 6RED - 7.5BLUE + 1))MIRBI = (SWIR1 - SWIR2) / (SWIR1 + SWIR2)

Temporal definitions:Pre-fire = Mean fire season value for the year prior to the fire.If flagged (cloud/shadow/water), the second year prior was used.Post-fire = Mean fire season value for the year following the fire.If flagged, the second year following was used.Delta = Post-fire minus Pre-fire.

EXPORT DETAILS

Projection : EPSG:4326Pixel resolution : 30 metersRegion : Fire perimeter bounding boxesFormat : GeoTIFFData type : Float32Date range : 1985–2023

REFERENCESParks, S.A.; Holsinger, L.M.; Koontz, M.J.; Collins, L.; Whitman, E.; Parisien, M.-A.; Loehman, R.A.; Barnes, J.L.;Bourdon, J.-F.; Boucher, J.; et al. (2019)."Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests."Remote Sens. 11, 1735. https://doi.org/10.3390/rs11141735