Todd J. Hawbaker Melanie K. Vanderhoof Gail L. Schmidt Yen-Ju G. Beal Joshua D. Takacs Jeff T. Falgout Joshua J. Picotte John L. Dwyer 20220317 Raster Digital Data Set Denver, Colorado U.S. Geological Survey https://doi.org/10.5066/P9QKHKTQ https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ Todd J. Hawbaker Melanie K. Vanderhoof Gail L. Schmidt Yen-Ju G. Beal Josh D. Takacs Jeff T. Falgout Joshua J. Picotte John L. Dwyer 2020 Publication (Journal) Remote Sensing of Environment Vol. 244 Amsterdam, Netherlands Elsevier https://doi.org/10.1016/j.rse.2020.111801 The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images, corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion were visually assessed as part of a quality assurance check. Scenes with obvious commission errors were removed. The annual products include the maximum burn probability (BP), burn classification count (BC) or the number of scenes a pixel was classified as burned, filtered burn classification (BF) with burned areas persistent from the previous year removed, and the burn date (BD) or the Julian date of the first Landsat scene a burned areas was observed in. Vectorized versions of the BF raster are also provided as shapefiles (BF_labeled) with attributes including summary statistics of BC, BD, BP, as well as the majority level 3 ecoregion (Omernik and Griffith, 2014) and count of pixels by each National Land Cover Database Category (Vogelmann et al., 2001; Yang et al., 2018) for each burned area polygon. These products were generated for the conterminous United States for 1984 through 2019 individually for Landsat TM (5), Landsat ETM+ (7), OLI/TIRS (8), and for all sensors combined. The products for each sensor combination and year are contained in a compressed tar file are available through the USGS Science Base Catalog (https://doi.org/10.5066/P9QKHKTQ) and also at https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ Additional details about the algorithm used to generate these products are described in Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y, Takacs, J.D., Falgout, J.T., Picotte, J.J., and Dwyer, J.L. 2020. The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sensing of Environment, Vol. 244, https://doi.org/10.1016/j.rse.2020.111801 The purpose of this information is to provide a publicly available spatially-referenced, national dataset of burned areas. Products were produced for 1984 through 2019. All products for a year are combined into a compressed tar (.tar.gz) file. For example the annual products combined across all sensors for 2018 are LBA_CU_2018_20190614_C01_V01_BC.tif, LBA_CU_2018_20190614_C01_V01_BD.tif, LBA_CU_2018_20190614_C01_V01_BF.tif, LBA_CU_2018_20190614_C01_V01_BF_labeled.shp, LBA_CU_2018_20190614_C01_V01_BP.tif, and are combined into the LBA_CU_2018_20190614_C01_V01.tar.gz file for download. 20160101 20191231 observed Complete As needed -128.596464 -63.792494 47.80675 21.805094 None Prescribed fire Wildfire Landsat Burned area Wildland fire USGS Metadata Identifier USGS:5ed33f1082ce2832f0478d3a None United States None. There are no restrictions on use of these data, except for reasonable and proper acknowledgment of information sources. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. U.S. Geological Survey Todd J Hawbaker Research Ecologist mailing address

Mail Stop 980, W 6th Ave Kipling St

Lakewood CO 80225 United States 303-236-1371 303-236-5349 tjhawbaker@usgs.gov Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.3.1 (Build 4959) Service Pack N/A (Build N/A) A formal accuracy assessment has been completed for the version 2.0 Landsat Burned Area Products. The U.S. Geological Survey can make no guarantee as to the accuracy or completeness of this information, and it is provided with the understanding that it is not guaranteed to be correct or complete. Conclusions drawn from this information are the responsibility of the user. All Landsat Burned Area Products were generated with the same algorithm and produced with the same methods. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Conclusions drawn from this information are the responsibility of the user. Map scale resolution is 30 meters for Landsat Burned Area Products. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Earth Resource Observation and Science (EROS) Center 2020 Raster Digital Data Set Sioux Falls, SD Earth Resource Observation and Science (EROS) Center https://www.usgs.gov/land-resources/nli/landsat/us-landsat-analysis-ready-data https://earthexplorer.usgs.gov 30 m Digital and/or Hardcopy Resources 20160101 20191231 observed Landsat Analysis Ready Data (ARD) Source information used in support of the development of the data set. The Landsat BA algorithm is an automated approach that uses a combination of a generalized boosted regression model (GBRM; Hastie et al. 2009) to estimate the probability that a pixel was burned followed by a thresholding and segmentation process to generate a binary burn classification for each Landsat image. Details about the algorithm, and datasets used to train it are described below. 20200415 1. Selection and preprocessing on Landsat images. To train and verify the performance of the Landsat BA algorithm, we selected 38 ARD tiles in the conterminous United States (CONUS). ARD tile locations were spatially distributed in order to capture major ecosystems and fire regimes, largely based on Omernik Level 2 and 3 Ecoregions (Omernik 1987). Using the USGS EarthExplorer (https://earthexplorer.usgs.gov), we selected all available Tier 1 Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) images with cloud cover less than or equal to 80% and georeferencing root mean square error ≤ 10 m. The surface reflectance, the top-of-atmosphere brightness temperature, the pixel quality information, and metadata were ordered for each image through EarthExplorer. 20200415 2. Generation of predictor variables for the GBRM. Three major types of predictor variables were generated for use in the GBRM: (1) land surface conditions in the scene or image of interest; (2) reference conditions that characterize the land surface 1-3 years prior to disturbances visible in the scene of interest; and (3) change metrics quantifying the magnitude of change between the reference conditions and the scene. Scene-level predictors included: sensor identifier (e.g. 4, 5, 7, or 8), ecoregion identifier, surface reflectance and thermal band values, and a number of spectral indices. Reference variables represent pre-fire, surface conditions and included the 3-year lagged mean and standard deviation of each spectral index. Change variables included the absolute difference between spectral indices for each image and their corresponding 3-year lagged mean. Pixels flagged as cloudy, water, snow/ice, or fill in the quality information generated using the function of mask algorithm (Fmask; Zhu and Woodcock 2014) were excluded from all calculations. 20200415 3. Selection of training point locations. To generate a sample of training and testing data points, we randomly selected 10,000 points within the polygon of each ARD tile. We specified a minimum distance of 30 m between points. Using the Monitoring Trends in Burn Severity (MTBS; Eidenshink et al. 2007) perimeters and rasters, each point that intersected the MTBS data, was assigned a date of burn and severity. All other points were assumed to have not burned. 20200415 4. Training and applying the GBRM to generate scene-level burn probability (BP images. At each sample point location, values of the response and predictor variables were collected. These were then used to fit a GBRM using 1000 trees with a learning rate of 0.1 and 3 splits per tree. The resulting GBRM was applied to each Landsat image in the ARD time series to generate the burn probability images, indicating the likelihood that a pixel was burned in a fire. 20200415 5. Generating scene-level burn classification (BC) images. The burn probability images were classified using a thresholding process consisting of three steps: (1) identifying initial burn patches using a 98% burn probability threshold, (2) removing burn patches less than 45 pixels (4.05 ha or 10 acres), and (3) adding adjacent pixels with 95% or higher burn probability to the burn patches. 20200415 6. Visual quality assessment. Prior to generating the annual composites, individual scenes with > 0.010 burn proportion were visually assessed and flagged for exclusion from the annual composites if there were obvious large commission errors caused by sensor artifacts, poor cloud/shadow masking, or the GBRM. 20200415 7. Generating annual composites for maximum burn probability (BP), burn classification count (BC), filtered burn classification (BF), and burn date (BD) composites. Annual maximum burn probability (BP) composites were generated by calculating the pixel-level maximum of the scene-level burn probability images for a given year. Annual Burn classification count (BC) composites are calculated as the number of times a pixel was classified as burned in the scene-level burn classification products for a given year. Filtered burn classification (BF) images are generated from the annual burn classification count images. In these images, burned patches with more than 33% overlap with burned patches from the previous year are removed and added to the BF layer for the previous year. The burn date (BD) images indicate the Julian day of the first Landsat scene a burned pixel was observed in. The BF composites were vectorized into the BF_Labeled shapefiles using an 8-neighbor rule. Each burn patch in these shapefiles is attributed with the minimum, maximum, mean, and standard deviation of the BC, BD, and BP rasters, as well as the majority level 3 ecoregion (Omernik and Griffith, 2014) and count of pixels by each National Land Cover Database Category (Vogelmann et al., 2001; Yang et al., 2018). The NLCD version used depended on the year. For example, burned patches from 2003 were combined with 2001 NLCD data. Because the 1992 NLCD data differed from the other NLCD versions, NLCD categories were reclassified generated using a simplified NLCD classification scheme and a second set of pixel counts based on the simplified scheme were calculated. Finally, the majority of the simplified NLCD categories within the burn patch was identified. The annual products were generated for the conterminous United States for 1984 through 2019 using the scene-level products from each sensor individually for Landsat TM (5), Landsat ETM+ (7), OLI/TIRS (8), and for all sensors combined. 20200415 References: Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.L., Quayle, B., & Howard, S. 2007. A project for monitoring trends in burn severity. Fire Ecology, 3, 3-21 Hastie, T., Tibshirani, R., & Friedman, J. 2009. The Elements of Statistical Learning; Data Mining, Inference, and Prediction. (2nd ed.). New York, NY, USA: Springer Omernik, J.M., Griffith, G.E., 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental Management 54, 1249–1266. https://doi.org/10.1007/s00267-014-0364-1 Vogelmann, J.E., Howard, S.M., Yang, L.Y., Larson, C.R., Wylie, B.K., Van Driel, N., 2001. Completion of the 1990s National Land Cover Data Set for the Conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources. Photogrammetric Engineering and Remote Sensing 65, 650–662. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G., Rigge, M., Xian, G. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 108–123. https://www.sciencedirect.com/science/article/pii/S092427161830251X Zhu, Z., & Woodcock, C.E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152, 217-234 20200415 Raster Pixel 100000 160000 1 WGS_1984_Albers WGS_1984_Albers: 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 30.0 30.0 Meter D_WGS_1984 WGS_1984 6378140.0 298.257222101 LBA_CU_2019_20200415_C01_V01_BC_L7.tif Landsat Burned Area Products Annual Burn Classification Count (LBA_CU_2019_20200415_C01_V01_BC_L7.tif) Pixel values are 1 to 248, representing the number of times a pixel was classified as burned in the scene-level Landsat Burned Area Products for this year. Producer defined Value Burn classification count Producer defined 1 to 248 Burn classification count Producer defined 249 and 250 Extreme values mask Producer defined 251 Water Producer defined 252 Snow/ice Producer defined 253 Clouds Producer defined 254 Clouds shadows Producer defined 255 Fill Producer defined LBA_CU_2019_20200415_C01_V01_BD_L7.tif Landsat Burned Area Products Annual Burn Date (LBA_CU_2019_20200415_C01_V01_BD_L7.tif) Pixel values represent the Julian date of the Landsat scene that a burned area was first observed. Producer defined Value Burn date Producer defined 1 to 366 Burn date (Julian day) Producer defined 999 Fill Producer defined LBA_CU_2019_20200415_C01_V01_BF_L7.tif Landsat Burned Area Products Annual Filtered Burn Classification (LBA_CU_2019_20200415_C01_V01_BF_L7.tif) Generated from the annual burn classification count products for this year and the preceeding year. In these images, burned patches with more than 33% overlap with burned patches from the previous year are removed and added to the BF layer for the previous year. Producer defined Value Filtered Burn Classification Producer defined 1 Burned Producer defined 255 Fill Producer defined LBA_CU_2019_20200415_C01_V01_BP_L7.tif Landsat Burned Area Products Annual Maximum Burn Probability (LBA_CU_2019_20200415_C01_V01_BP_L7.tif) Burn probability images for each Landsat image were summarized by year to generate annual maximum burn probability composites. Producer defined Value Maximum Burn Probability Producer defined 0 to 100 Burn probability (%) Producer defined 249 and 250 Extreme values mask Producer defined 251 Water Producer defined 252 Snow/ice Producer defined 253 Clouds Producer defined 254 Clouds shadows Producer defined 255 Fill Producer defined LBA_CU_2019_20200415_C01_V01_BF_labeled_L7.shp Landsat Burned Area Products Filtered Burn Classification Polygons (LBA_CU_2019_20200415_C01_V01_BF_labeled_L7.shp) vectorized from the Annual Filtered Burn Classification product (LBA_CU_2019_20200415_C01_V01_BF_labeled_L7.shp). Producer defined ecol3 The majority level 3 ecoregion value for all ecoregion values within the polygon Omernik and Griffith, 2014 50102 Lake Nipigon and Lac Seul Upland (5.1.2) Omernik and Griffith 2014. 50103 Central Laurentians and Mecatina Plateau (5.1.3) Omernik and Griffith 2014. 50106 Abitibi Plains and Riviere Rupert Plateau (5.1.6) Omernik and Griffith 2014. 50201 Northern Lakes and Forests (5.2.1) Omernik and Griffith 2014. 50202 Northern Minnesota Wetlands (5.2.2) Omernik and Griffith 2014. 50203 Algonquin/Southern Laurentians (5.2.3) Omernik and Griffith 2014. 50301 Northern Appalachian and Atlantic Maritime Highlands (5.3.1) Omernik and Griffith 2014. 50303 North Central Appalachians (5.3.3) Omernik and Griffith 2014. 50401 Mid-Boreal Uplands and Peace-Wabaska Lowlands (5.4.1) Omernik and Griffith 2014. 50402 Clear Hills and Western Alberta Upland (5.4.2) Omernik and Griffith 2014. 50403 Mid-Boreal Lowland and Interlake Plain (5.4.3) Omernik and Griffith 2014. 60203 Columbia Mountains/Northern Rockies (6.2.3) Omernik and Griffith 2014. 60204 Canadian Rockies (6.2.4) Omernik and Griffith 2014. 60205 North Cascades (6.2.5) Omernik and Griffith 2014. 60206 Cypress Upland (6.2.6) Omernik and Griffith 2014. 60207 Cascades (6.2.7) Omernik and Griffith 2014. 60208 Eastern Cascades Slopes and Foothills (6.2.8) Omernik and Griffith 2014. 60209 Blue Mountains (6.2.9) Omernik and Griffith 2014. 60210 Middle Rockies (6.2.10) Omernik and Griffith 2014. 60211 Klamath Mountains (6.2.11) Omernik and Griffith 2014. 60212 Sierra Nevada (6.2.12) Omernik and Griffith 2014. 60213 Wasatch and Uinta Mountains (6.2.13) Omernik and Griffith 2014. 60214 Southern Rockies (6.2.14) Omernik and Griffith 2014. 60215 Idaho Batholith (6.2.15) Omernik and Griffith 2014. 70105 Coastal Western Hemlock-Sitka Spruce Forests (7.1.5) Omernik and Griffith 2014. 70106 Pacific and Nass Ranges (7.1.6) Omernik and Griffith 2014. 70107 Strait of Georgia/Puget Lowland (7.1.7) Omernik and Griffith 2014. 70108 Coast Range (7.1.8) Omernik and Griffith 2014. 70109 Willamette Valley (7.1.9) Omernik and Griffith 2014. 80101 Eastern Great Lakes Lowlands (8.1.1) Omernik and Griffith 2014. 80102 Lake Erie Lowland (8.1.2) Omernik and Griffith 2014. 80103 Northern Allegheny Plateau (8.1.3) Omernik and Griffith 2014. 80104 North Central Hardwood Forests (8.1.4) Omernik and Griffith 2014. 80105 Driftless Area (8.1.5) Omernik and Griffith 2014. 80106 Southern Michigan/Northern Indiana Drift Plains (8.1.6) Omernik and Griffith 2014. 80107 Northeastern Coastal Zone (8.1.7) Omernik and Griffith 2014. 80108 Acadian Plains and Hills (8.1.8) Omernik and Griffith 2014. 80109 Maritime Lowlands (8.1.9) Omernik and Griffith 2014. 80110 Erie Drift Plain (8.1.10) Omernik and Griffith 2014. 80201 Southeastern Wisconsin Till Plains (8.2.1) Omernik and Griffith 2014. 80202 Huron/Erie Lake Plains (8.2.2) Omernik and Griffith 2014. 80203 Central Corn Belt Plains (8.2.3) Omernik and Griffith 2014. 80204 Eastern Corn Belt Plains (8.2.4) Omernik and Griffith 2014. 80301 Northern Piedmont (8.3.1) Omernik and Griffith 2014. 80302 Interior River Valleys and Hills (8.3.2) Omernik and Griffith 2014. 80303 Interior Plateau (8.3.3) Omernik and Griffith 2014. 80304 Piedmont (8.3.4) Omernik and Griffith 2014. 80305 Southeastern Plains (8.3.5) Omernik and Griffith 2014. 80306 Mississippi Valley Loess Plains (8.3.6) Omernik and Griffith 2014. 80307 South Central Plains (8.3.7) Omernik and Griffith 2014. 80308 East Central Texas Plains (8.3.8) Omernik and Griffith 2014. 80401 Ridge and Valley (8.4.1) Omernik and Griffith 2014. 80402 Central Appalachians (8.4.2) Omernik and Griffith 2014. 80403 Western Allegheny Plateau (8.4.3) Omernik and Griffith 2014. 80404 Blue Ridge (8.4.4) Omernik and Griffith 2014. 80405 Ozark Highlands (8.4.5) Omernik and Griffith 2014. 80406 Boston Mountains (8.4.6) Omernik and Griffith 2014. 80407 Arkansas Valley (8.4.7) Omernik and Griffith 2014. 80408 Ouachita Mountains (8.4.8) Omernik and Griffith 2014. 80409 Southwestern Appalachians (8.4.9) Omernik and Griffith 2014. 80501 Middle Atlantic Coastal Plain (8.5.1) Omernik and Griffith 2014. 80502 Mississippi Alluvial Plain (8.5.2) Omernik and Griffith 2014. 80503 Southern Coastal Plain (8.5.3) Omernik and Griffith 2014. 80504 Atlantic Coastal Pine Barrens (8.5.4) Omernik and Griffith 2014. 90201 Aspen Parkland/Northern Glaciated Plains (9.2.1) Omernik and Griffith 2014. 90202 Lake Manitoba and Lake Agassiz Plain (9.2.2) Omernik and Griffith 2014. 90203 Western Corn Belt Plains (9.2.3) Omernik and Griffith 2014. 90204 Central Irregular Plains (9.2.4) Omernik and Griffith 2014. 90301 Northwestern Glaciated Plains (9.3.1) Omernik and Griffith 2014. 90303 Northwestern Great Plains (9.3.3) Omernik and Griffith 2014. 90304 Nebraska Sand Hills (9.3.4) Omernik and Griffith 2014. 90401 High Plains (9.4.1) Omernik and Griffith 2014. 90402 Central Great Plains (9.4.2) Omernik and Griffith 2014. 90403 Southwestern Tablelands (9.4.3) Omernik and Griffith 2014. 90404 Flint Hills (9.4.4) Omernik and Griffith 2014. 90405 Cross Timbers (9.4.5) Omernik and Griffith 2014. 90406 Edwards Plateau (9.4.6) Omernik and Griffith 2014. 90407 Texas Blackland Prairies (9.4.7) Omernik and Griffith 2014. 90501 Western Gulf Coastal Plain (9.5.1) Omernik and Griffith 2014. 90601 Southern Texas Plains/Interior Plains and Hills with Xerophytic Shrub and Oak Forest (9.6.1) Omernik and Griffith 2014. 100101 Thompson-Okanogan Plateau (10.1.1) Omernik and Griffith 2014. 100102 Columbia Plateau (10.1.2) Omernik and Griffith 2014. 100103 Northern Basin and Range (10.1.3) Omernik and Griffith 2014. 100104 Wyoming Basin (10.1.4) Omernik and Griffith 2014. 100105 Central Basin and Range (10.1.5) Omernik and Griffith 2014. 100106 Colorado Plateaus (10.1.6) Omernik and Griffith 2014. 100107 Arizona/New Mexico Plateau (10.1.7) Omernik and Griffith 2014. 100108 Snake River Plain (10.1.8) Omernik and Griffith 2014. 100201 Mojave Basin and Range (10.2.1) Omernik and Griffith 2014. 100202 Sonoran Desert (10.2.2) Omernik and Griffith 2014. 100204 Chihuahuan Desert (10.2.4) Omernik and Griffith 2014. 110101 California Coastal Sage, Chaparral, and Oak Woodlands (11.1.1) Omernik and Griffith 2014. 110102 Central California Valley (11.1.2) Omernik and Griffith 2014. 110103 Southern and Baja California Pine-Oak Mountains (11.1.3) Omernik and Griffith 2014. 120101 Madrean Archipelago (12.1.1) Omernik and Griffith 2014. 120102 Piedmonts and Plains with Grasslands, Xeric Shrub, and Oak and Conifer Forests (12.1.2) Omernik and Griffith 2014. 130101 Arizona/New Mexico Mountains (13.1.1) Omernik and Griffith 2014. 150401 Southern Florida Coastal Plain (15.4.1) Omernik and Griffith 2014. h ARD tile horizontal coordinate Producer defined 1 32 v ARD tile vertical coordinate Producer defined 1 20 hv ARD tile label, combining horizontal and vertical coordinates Producer defined Text labels combining the ARD horizontal and vertical coordinates year Year of Landsat images were used to generate the Annual Filtered Burn Classification product (LBA_CU_2019_20200415_C01_V01_BF_L7.tif). LBA_CU_2019_20200415_C01_V01_BF_L7.tif 1984 2019 count_sum Count of pixels within the burned patch in the Annual Filtered Burn Classification product (LBA_CU_2019_20200415_C01_V01_BF_L7.tif). LBA_CU_2019_20200415_C01_V01_BF_L7.tif 0 2401992 bc_min Minimum burn classification count within the burned patch from the Annual Burn Count Classification product (LBA_CU_2019_20200415_C01_V01_BC_L7.tif). LBA_CU_2019_20200415_C01_V01_BC_L7.tif 0 6 bc_mean Mean burn classification count within the burned patch from the Annual Burn Count Classification product (LBA_CU_2019_20200415_C01_V01_BC_L7.tif). LBA_CU_2019_20200415_C01_V01_BC_L7.tif 0 42.6 bc_max Maximum burn classification count within the burned patch from the Annual Burn Count Classification product (LBA_CU_2019_20200415_C01_V01_BC_L7.tif). LBA_CU_2019_20200415_C01_V01_BC_L7.tif 0 66 bc_std Standard deviation of burn classification count within the burned patch from the Annual Burn Count Classification product (LBA_CU_2019_20200415_C01_V01_BC_L7.tif). LBA_CU_2019_20200415_C01_V01_BC_L7.tif 0 23.0 bd_min Minimum burn date within the burned patch from the Annual Burn Date product (LBA_CU_2019_20200415_C01_V01_BD_L7.tif). LBA_CU_2019_20200415_C01_V01_BD_L7.tif 0 366 bd_max Maximum burn date within the burned patch from the Annual Burn Date product (LBA_CU_2019_20200415_C01_V01_BD_L7.tif). LBA_CU_2019_20200415_C01_V01_BD_L7.tif 1 366 bd_mean Mean burn date within the burned patch from the Annual Burn Date product (LBA_CU_2019_20200415_C01_V01_BD_L7.tif). LBA_CU_2019_20200415_C01_V01_BD_L7.tif 1 366 bd_std Standard deviation of burn date within the burned patch from the Annual Burn Date product (LBA_CU_2019_20200415_C01_V01_BD_L7.tif). LBA_CU_2019_20200415_C01_V01_BD_L7.tif 0 180.4 bp_min Minimum burn probability within the burned patch from the Annual Maximum Burn Probability product (LBA_CU_2019_20200415_C01_V01_BP_L7.tif). LBA_CU_2019_20200415_C01_V01_BP_L7.tif 0 100 bp_max Maximum burn probability within the burned patch from the Annual Maximum Burn Probability product (LBA_CU_2019_20200415_C01_V01_BP_L7.tif). LBA_CU_2019_20200415_C01_V01_BP_L7.tif 0 100 bp_mean Mean burn probability within the burned patch from the Annual Maximum Burn Probability product (LBA_CU_2019_20200415_C01_V01_BP_L7.tif). LBA_CU_2019_20200415_C01_V01_BP_L7.tif 0 172.6 bp_std Standard deviation burn probability within the burned patch from the Annual Maximum Burn Probability product (LBA_CU_2019_20200415_C01_V01_BP_L7.tif). LBA_CU_2019_20200415_C01_V01_BP_L7.tif 0 83.0 nlcd_0 Count of NLCD pixels with value equal to 0 (nodata) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 2276203 nlcd_11 Count of NLCD pixels with value equal to 11 (Open water) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 9376 nlcd_12 Count of NLCD pixels with value equal to 12 (Perennial Ice/Snow) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 680 nlcd_21 Count of NLCD pixels with value equal to 21 (Developed, Open Space) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 84484 nlcd_22 Count of NLCD pixels with value equal to 22 (Developed, Low Intensity) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 46788 nlcd_23 Count of NLCD pixels with value equal to 23 (Developed, Medium Intensity) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 64258 nlcd_24 Count of NLCD pixels with value equal to 24 (Developed, High Intensity) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 16738 nlcd_31 Count of NLCD pixels with value equal to 31 (Barren) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 88337 nlcd_32 Count of NLCD pixels with value equal to 32 (Quarries/Strip Mines/Gravel Pits) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 12268 nlcd_33 Count of NLCD pixels with value equal to 33 (Transitional) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1331124 nlcd_41 Count of NLCD pixels with value equal to 41 (Deciduous Forest) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 113571 nlcd_42 Count of NLCD pixels with value equal to 42 (Evergreen Forest) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1897685 nlcd_43 Count of NLCD pixels with value equal to 43 (Mixed Forest) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 201449 nlcd_51 Count of NLCD pixels with value equal to 51 (Shrubland) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 783527 nlcd_52 Count of NLCD pixels with value equal to 52 (Shrub/Scrub) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1878469 nlcd_61 Count of NLCD pixels with value equal to 61 (Orchards/Vineyards/Other) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 9182 nlcd_71 Count of NLCD pixels with value equal to 71 (Grassland/Herbaceous) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 2056069 nlcd_81 Count of NLCD pixels with value equal to 81 (Pasture/Hay) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 166683 nlcd_82 Count of NLCD pixels with value equal to 82 (Cultivated Crops) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 281828 nlcd_83 Count of NLCD pixels with value equal to 83 (Small Grains) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 20374 nlcd_84 Count of NLCD pixels with value equal to 84 (Fallow) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 12385 nlcd_85 Count of NLCD pixels with value equal to 85 (Urban/Recrational Grasses) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 4684 nlcd_90 Count of NLCD pixels with value equal to 90 (Woody Wetlands) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1095169 nlcd_91 Count of NLCD pixels with value equal to 91 (Woody Wetlands) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 283852 nlcd_92 Count of NLCD pixels with value equal to 92 (Emergent Herbaceous Wetlands) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 285207 nlcd_95 Count of NLCD pixels with value equal to 95 (Emergent Herbaceous Wetlands) within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 220437 nlcdr_11 Count of NLCD open water pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 9376 nlcdr_12 Count of NLCD perennial ice/snow pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 680 nlcdr_20 Count of NLCD developed pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 212268 nlcdr_31 Count of NLCD barren pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1331746 nlcdr_40 Count of NLCD forest (deciduous, coniferous, or mixed) pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1897685 nlcdr_52 Count of NLCD perenial shrub/scrub (shrubland or shrub/scrub) or pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1878469 nlcdr_71 Count of NLCD grassland/herbaceous pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 2056069 nlcdr_81 Count of NLCD pasture/hay (fallow or pasture/hay) pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 166683 nlcdr_82 Count of NLCD cultivated crops (cultivated crops or small grains) pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 281828 nlcdr_90 Count of NLCD woody wetland pixels within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 1095169 nlcdr_95 Count of NLCD emergent herbaceous wetlands within the burned patch. Vogelmann et. al., 2001 and Yang et. al., 2018 0 285207 nlcdr_domi The attribute label of the simplified NLCD category (e.g. nlcdr_X) that had the majority of burned pixels within the burn patch. Vogelmann et. al., 2001 and Yang et. al., 2018 11 NLCD water Vogelmann et. al., 2001 and Yang et. al., 2018 20 NLCD developed Vogelmann et. al., 2001 and Yang et. al., 2018 31 NLCD barren Vogelmann et. al., 2001 and Yang et. al., 2018 40 NLCD forest (deciduous, coniferous, or mixed) Vogelmann et. al., 2001 and Yang et. al., 2018 52 NLCD perenial shrub/scrub Vogelmann et. al., 2001 and Yang et. al., 2018 71 NLCD grassland/herbaceous Vogelmann et. al., 2001 and Yang et. al., 2018 81 NLCD pasture/hay Vogelmann et. al., 2001 and Yang et. al., 2018 82 NLCD cultivated crops Vogelmann et. al., 2001 and Yang et. al., 2018 90 NLCD woody wetlands Vogelmann et. al., 2001 and Yang et. al., 2018 95 NLCD emergent herbaceous wetlands Vogelmann et. al., 2001 and Yang et. al., 2018 None None None None None None None None None None U.S. Geological Survey Todd J. Hawbaker mailing address

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Lakewood CO 80225 USA 1-303-236-1371 tjhawbaker@usgs.gov Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Geological Survey. Although this information product, for the most part, is in the public domain, it also contains copyrighted materials as noted in the text. Permission to reproduce copyrighted items for other than personal use must be secured from the copyright owner. This database has been approved for release and publication by the Director of the USGS. Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, it is released on condition that neither the USGS nor the United States Government may be held liable for any damages resulting from its authorized or unauthorized use. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Raster Digital Data Set https://doi.org/10.5066/P9QKHKTQ https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ None. No fees are applicable for obtaining the data set. 20220317 U.S. Geological Survey Todd J Hawbaker Research Ecologist mailing address

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Lakewood CO 80225 United States 303-236-1371 303-236-5349 tjhawbaker@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time