RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data. This web app was developed to satisfy the requirements of Senate Bill No. 1101: An act to amend Sections 10295 and 10340 of the Public Contract Code, and to add Section 4114.4 to the Public Resources Code, relating to fire prevention.Methods:To develop these datasets, a feature service for fire perimeters was created from the CAL FIRE Fire and Resource Assessment Program’s Historic Wildland Fire Perimeters database (firep23_1) for fires or fires that were a part of complexes >= 1,000 acres from 2015 to 2023. This feature service is viewable on the California Vegetation Burn Severity Viewer and used to discover the RdNBR and CBI vegetation burn severity datasets. The feature service is titled Burn Severity Fire Perimeters (firep23_1_2015_2023_Fires_Complex_1000ac). After this feature service was uploaded to Google Earth Engine (GEE) as an asset, the Parks et al. 2018 script was used to generate RdNBR values with offset (rdnbr_w_offset) data for each individual fire and the Parks et al. 2019 script was used to generate bias corrected Composite Burn Index values (cbi_bc) data for each individual fire using 30m resolution Landsat Collection 2 data. To specify the date range of Landsat satellite images to be queried to create the one-year pre-fire and one-year post-fire mean composite image scenes in both scripts, the variable 'startday' was set to 152 (June 1st) and the variable 'endday' was set to 258 (September 15th) for all fires, as specified in Parks et al. (2019). These variables were used to define the ranges of Landsat scenes that were queried to create the one-year-pre-fire and one-year-post-fire mean composite Landsat scenes. These values were used, as they were detailed as the leaf-on period for the State of California in Parks et al. 2019. Once the RdNBR raster data for each fire had been produced using Parks et al. 2018's GEE script and the CBI raster data for each fire had been produced using Parks et al. 2019's GEE script, a Python script (run in a Jupyter Notebook embedded in the ArcGIS Pro software) was used to clip each fire-specific, continuous feature class to the extent of its fire perimeter. Each CBI feature class was additionally clipped to the extent of Conifer Forest and Hardwood Forest classes (defined in FVEG15's WHR13 Lifeform class for fires from 2015 to 2021 and defined in FVEG22's WHR13 Lifeform class for fires from 2022 to 2023).Once each continuous feature class had been clipped, values were reclassified to create a discrete RdNBR and CBI feature classes. Classes for RdNBR were arbitrarily chosen and do not correspond to meaningful categories of burn severity. Higher RdNBR values do indicate greater loss of vegetation greenness and negative values indicate an increase in greenness, but there is not necessarily a direct or linear correlation between RdNBR values and impacts to vegetation or ecological effects. Remotely sensed fire severity indices are translated into CBI using regression equations developed between CBI field plot data and the remote indices. Very few CBI plots exist in California or elsewhere in the U.S. for vegetation types other than forest. We therefore chose to include only forest vegetation in our CBI dataset. Classes for RdNBR were as follows: Code | Lower Limit (RdNBR) | Upper Limit (RdNBR) 1           < -1,000                           -1,000 2           -1,000                               -800 3           -800                                  -600 4           -600                                  -400 5           -400                                  -200 6           -200                                   0 7            0                                        200 8            200                                   400 9            400                                   600 10          600                                   800 11          800                                   1,000 12          1,000                                1,200 13          1,200                                1,400 14          1,400                                1,600 15          1,600                            > 1,600 Classes for CBI were as follows: Code | Lower Limit (CBI) | Upper Limit (CBI) | Burn Severity 1           0.00                           0.10                           Unburned 2           0.10                           1.25                           Low Vegetation Burn Severity 3           1.25                           2.25                           Moderate Vegetation Burn Severity 4           2.25                           3.00                           High Vegetation Burn Severity The discrete raster feature classes were then converted to vector feature classes. Finally, all individual discrete vector feature classes for individual fires were merged into two vector datasets, RdNBR Burn Severity Data (BurnSeverityRdNBR1523_1) and CBI Burn Severity Data (BurnSeverityCBIForest1523_1). These feature services are viewable on this web app, the California Vegetation Burn Severity Viewer.