The California Interagency Wildfire Severity (CaliWiSe) Database

OVERVIEW This dataset contains multi-band raster images representing burn severity and spectral response across wildfire perimeters in California. 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 J. Hung & A. Park Williams For questions or issues, please contact:Mitchell J. Hung ([e-mail]) 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 → California 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## 数据集概览 本数据集包含多波段栅格影像，用以表征美国加利福尼亚州野火边界范围内的火烧烈度与光谱响应特征。每幅影像对应唯一的火灾事件，涵盖火烧烈度、水分及植被指数等多个波段。 本数据集通过谷歌地球引擎（Google Earth Engine）生成，主要源自Landsat 4–8地表反射率一级（Surface Reflectance Tier 1）数据集。所有影像均经过标准化缩放处理，并以浮点编码的多波段地理标记图像文件格式（GeoTIFF）导出。 ## 作者 米切尔·J·洪（Mitchell J. Hung）与A.帕克·威廉姆斯（A. Park Williams） 如有疑问或技术问题，请联系：米切尔·J·洪（[e-mail]） ## 野火边界数据源 1985–2023年的MTBS与CALFIRE FRAP火场边界数据。 ## 文件结构与目录指南 每年的数据按照对应火场边界数据源整理为压缩归档文件： ├── calfire_YYYY.zip → 对应YYYY年加利福尼亚消防局FRAP野火边界数据 ├── mtbs_YYYY.zip → 对应YYYY年加利福尼亚州MTBS野火边界数据 ├── band_info.csv → 包含所有导出图层的波段名称与缩放因子 ├── README.md → 数据集官方说明文档 每个.zip归档文件包含对应年度源数据中每一场火灾事件的一幅GeoTIFF影像。 ## 数据集内容 每个导出的.tif文件包含以下波段： - CBI - 综合火烧指数（Composite Burn Index，经偏差校正的模型输出）[缩放因子：100] - dnbr - 归一化燃烧比差值（Delta Normalized Burn Ratio）[缩放因子：1000] - rbr - 相对燃烧比（Relativized Burn Ratio）[缩放因子：1000] - rdnbr - 相对归一化燃烧比差值（Relativized Delta Normalized Burn Ratio）[缩放因子：1000] - dndvi - 归一化差分植被指数差值（Delta Normalized Difference Vegetation Index）[缩放因子：1000] - devi - 增强植被指数差值（Delta Enhanced Vegetation Index）[缩放因子：1000] - dndmi - 归一化差分水分指数差值（Delta Normalized Difference Moisture Index）[缩放因子：1000] - dmirbi - 中红外双谱指数差值（Delta Mid-Infrared Bi-Spectral Index）[缩放因子：1000] - post_nbr - 灾后归一化燃烧比（Post-fire Normalized Burn Ratio）[缩放因子：1] - post_mirbi - 灾后中红外双谱指数（Post-fire Mid-Infrared Bi-Spectral Index）[缩放因子：1000] 注：如需还原原始浮点数值，请将对应波段的数值除以其指定的缩放因子。 ## 数据说明 ### 综合火烧指数（CBI） 通过随机森林回归模型估算（Parks等，2019）。 ### 像素掩膜处理 使用Landsat QA_PIXEL波段与Hansen全球森林变化水体掩膜，移除受云、云影、水体及积雪干扰的像素。 ### 光谱指数 数据源包括Landsat 4、5、7、8（地表反射率一级产品），各指数计算公式如下： 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 + 6*RED - 7.5*BLUE + 1)) MIRBI = (SWIR1 - SWIR2) / (SWIR1 + SWIR2) ### 时间界定规则 - 灾前时段：火灾发生前一年的火季平均值。若该时段存在云、云影、水体标记，则改用火灾发生前两年的火季平均值。 - 灾后时段：火灾发生后一年的火季平均值。若该时段存在标记，则改用火灾发生后两年的火季平均值。 - 差值计算：灾后时段均值减去灾前时段均值。 ## 导出参数 - 投影坐标系：EPSG:4326 - 像素分辨率：30米 - 覆盖范围：对应火场的边界框 - 输出格式：GeoTIFF - 数据类型：Float32 - 时间范围：1985–2023年 ## 参考文献 Parks, 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.; 等. (2019). 赋予北美森林卫星反演火灾烈度指标生态学意义. *Remote Sens.* 11, 1735. https://doi.org/10.3390/rs11141735