This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.

Use this app to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.For more information about the wildfire response efforts, visit the CAL FIRE incident page.

🇺🇸 미국

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."

🇺🇸 미국 English Use this app to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.For more information about the wildfire response efforts, visit the CAL FIRE incident page.

Version InformationThe data is updated annually with fire perimeters from the previous calendar year.Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. A duplicate 2020 Erbes fire was removed. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. There were 2,132 perimeters that received updated attribution, the bulk of which had IRWIN IDs added. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020). YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres._Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria:~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage 2002: 10 acres timber, 50 acres brush, 300 acres grass, damages or destroys three or more structures, or does $300,000 worth of damage~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Large and Damaging Redbook Fire data criteria:1979: Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981: 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992: 1981 criteria plus 1500 acres agricultural products, or destroys three residence or one commercial structure or does $300,000 damage1993: 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2006: 300 acres or larger and burned at least: 30 acres of timber, or 300 acres of brush, or 1,500 acres of woodland, or 1,500 acres of grass, or 1,500 acres of agricultural products, or 3 or more structures destroyed, or $300,000 or more dollar damage loss.2008: 300 acres and largerYear# of Missing Large and Damaging Redbook Fires197922198013198115198261983319842019855219861219875619882319898199091991219921619931719942219959199615199791998101999720004200152002162003520042200512006112007320084320093201022011020124201322014720151020162201711201862019220203202102022020230Total488Enumeration of fires in the Redbook that are missing from Fire Perimeter data. Three letter unit code follows fire name.1979-Sylvandale (HUU), Kiefer (AEU), Taylor(TUU), Parker#2(TCU), PGE#10, Crocker(SLU), Silver Spur (SLU), Parkhill (SLU), Tar Springs #2 (SLU), Langdon (SCU), Truelson (RRU), Bautista (RRU), Crocker (SLU), Spanish Ranch (SLU), Parkhill (SLU), Oak Springs(BDU), Ruddell (BDF), Santa Ana (BDU), Asst. #61 (MVU), Bernardo (MVU), Otay #20 1980– Lightning series (SKU), Lavida (RRU), Mission Creek (RRU), Horse (RRU), Providence (RRU), Almond (BDU), Dam (BDU), Jones (BDU), Sycamore (BDU), Lightning (MVU), Assist 73, 85, 138 (MVU)1981– Basalt (LNU), Lightning #25(LMU), Likely (MNF), USFS#5 (SNF), Round Valley (TUU), St. Elmo (KRN), Buchanan (TCU), Murietta (RRU), Goetz (RRU), Morongo #29 (RRU), Rancho (RRU), Euclid (BDU), Oat Mt. (LAC & VNC), Outside Origin #1 (MVU), Moreno (MVU)1982- Duzen (SRF), Rave (LMU), Sheep’s trail (KRN), Jury (KRN), Village (RRU), Yuma (BDF)1983- Lightning #4 (FKU), Kern Co. #13, #18 (KRN)1984-Bidwell (BTU), BLM D 284,337, PNF #115, Mill Creek (TGU), China hat (MMU), fey ranch, Kern Co #10, 25,26,27, Woodrow (KRN), Salt springs, Quartz (TCU), Bonanza (BEU), Pasquel (SBC), Orco asst. (ORC), Canel (local), Rattlesnake (BDF)1985- Hidden Valley, Magic (LNU), Bald Mt. (LNU), Iron Peak (MEU), Murrer (LMU), Rock Creek (BTU), USFS #29, 33, Bluenose, Amador, 8 mile (AEU), Backbone, Panoche, Los Gatos series, Panoche (FKU), Stan #7, Falls #2 (MMU), USFS #5 (TUU), Grizzley, Gann (TCU), Bumb, Piney Creek, HUNTER LIGGETT ASST#2, Pine, Lowes, Seco, Gorda-rat, Cherry (BEU), Las pilitas, Hwy 58 #2 (SLO), Lexington, Finley (SCU), Onions, Owens (BDU), Cabazon, Gavalin, Orco, Skinner, Shell, Pala (RRU), South Mt., Wheeler, Black Mt., Ferndale, (VNC), Archibald, Parsons, Pioneer (BDU), Decker, Gleason(LAC), Gopher, Roblar, Assist #38 (MVU)1986– Knopki (SRF), USFS #10 (NEU), Galvin (RRU), Powerline (RRU), Scout, Inscription (BDU), Intake (BDF), Assist #42 (MVU), Lightning series (FKU), Yosemite #1 (YNP), USFS Asst. (BEU), Dutch Kern #30 (KRN)1987- Peach (RRU), Ave 32 (TUU), Conover (RRU), Eagle #1 (LNU), State 767 aka Bull (RRU), Denny (TUU), Dog Bar (NEU), Crank (LMU), White Deer (FKU), Briceburg (LMU), Post (RRU), Antelope (RRU), Cougar-I (SKU), Pilitas (SLU) Freaner (SHU), Fouts Complex (LNU), Slides (TGU), French (BTU), Clark (PNF), Fay/Top (SQF), Under, Flume, Bear Wallow, Gulch, Bear-1, Trinity, Jessie, friendly, Cold, Tule, Strause, China/Chance, Bear, Backbone, Doe, (SHF) Travis Complex, Blake, Longwood (SRF), River-II, Jarrell, Stanislaus Complex 14k (STF), Big, Palmer, Indian (TNF) Branham (BLM), Paul, Snag (NPS), Sycamore, Trail, Stallion Spring, Middle (KRN), SLU-864 1988- Hwy 175 (LNU), Rumsey (LNU), Shell Creek (MEU), PG&E #19 (LNU), Fields (BTU), BLM 4516, 417 (LMU), Campbell (LNF), Burney (SHF), USFS #41 (SHF), Trinity (USFS #32), State #837 (RRU), State (RRU), State (350 acres), RRU), State #1807, Orange Co. Asst (RRU), State #1825 (RRU), State #2025, Spoor (BDU), State (MVU), Tonzi (AEU), Kern co #7,9 (KRN), Stent (TCU), 1989– Rock (Plumas), Feather (LMU), Olivas (BDU), State 1116 (RRU), Concorida (RRU), Prado (RRU), Black Mt. (MVU), Vail (CNF)1990– Shipman (HUU), Lightning 379 (LMU), Mud, Dye (TGU), State 914 (RRU), Shultz (Yorba) (BDU), Bingo Rincon #3 (MVU), Dehesa #2 (MVU), SLU 1626 (SLU)1991- Church (HUU), Kutras (SHF)1992– Lincoln, Fawn (NEU), Clover, fountain (SHU), state, state 891, state, state (RRU), Aberdeen (BDU), Wildcat, Rincon (MVU), Cleveland (AEU), Dry Creek (MMU), Arroyo Seco, Slick Rock (BEU), STF #135 (TCU)1993– Hoisington (HUU), PG&E #27 (with an undetermined cause, lol), Hall (TGU), state, assist, local (RRU), Stoddard, Opal Mt., Mill Creek (BDU), Otay #18, Assist/ Old coach (MVU), Eagle (CNF), Chevron USA, Sycamore (FKU), Guerrero, Duck1994– Schindel Escape (SHU), blank (PNF), lightning #58 (LMU), Bridge (NEU), Barkley (BTU), Lightning #66 (LMU), Local (RRU), Assist #22 & #79 (SLU), Branch (SLO), Piute (BDU), Assist/ Opal#2 (BDU), Local, State, State (RRU), Gilman fire 7/24 (RRU), Highway #74 (RRU), San Felipe, Assist #42, Scissors #2 (MVU), Assist/ Opal#2 (BDU), Complex (BDF), Spanish (SBC)1995-State 1983 acres, Lost Lake, State # 1030, State (1335 acres), State (5000 acres), Jenny, City (BDU), Marron #4, Asist #51 (SLO/VNC)1996- Modoc NF 707 (Ambrose), Borrego (MVU), Assist #16 (SLU), Deep Creek (BDU), Weber (BDU), State (Wesley) 500 acres (RRU), Weaver (MMU), Wasioja (SBC/LPF), Gale (FKU), FKU 15832 (FKU), State (Wesley) 500 acres, Cabazon (RRU), State Assist (aka Bee) (RRU), Borrego, Otay #269 (MVU), Slaughter house (MVU), Oak Flat (TUU)1997- Lightning #70 (LMU), Jackrabbit (RRU), Fernandez (TUU), Assist 84 (Military AFV) (SLU), Metz #4 (BEU), Copperhead (BEU), Millstream, Correia (MMU), Fernandez (TUU)1998- Worden, Swift, PG&E 39 (MMU), Chariot, Featherstone, Wildcat, Emery, Deluz (MVU), Cajalco Santiago (RRU)1999- Musty #2,3 (BTU), Border # 95 (MVU), Andrews,

To document and structure damaged or destroyed by the Oak wildland fire open the associated Field Map app.NOTE - this feature service is configured to not allow record deletion. If a record needs to be deleted contact the program manager below.This is the schema developed and used by the CAL FIRE Office of State Fire Marshal to assess and record structure damage on wildland fire incidents. The schema is designed to be configured in the Esri Collector/Field Maps app for data collection during or after an incident.

This data publication provides plot-based measurements of: stem diameter for each stem > 0.4 centimeters on every sampled shrub, shrub status at time of sampling (live/dead/charred), and estimated shrub height obtained from 5 plots sampled in 2012-2013 on each of three sites (15 total plots) in the Cleveland National Forest. The sites are located near Kitchen Creek Road on southern Laguna Mountain in San Diego County, CA and were characterized by time since last burn -- 7 years, 28 years, or 68 years prior to the study.

These data also include stem diameter, biomass, and representative percent moisture values from shrubs harvested outside of the study plots at 18 locations across the three sites. Locations were outside, but near to, the study plots to avoid destructive sampling within the study plots. These data were collected between 2011 and 2013.Data were collected to document differences in biomass accumulation in three nearby sites with varying burn histories in southern California chaparral. Sites had burned approximately 7, 28, and 68 years ago at the time of sampling. The destructively sampled measurements were used to calculate age and species-specific regression equations relating stem diameter to dry biomass. The stem diameter measurements from the 15 study plots were used as input into these equations to estimate plot-level biomass.

This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia.

This dataset is a high-resolution (60 cm) grass biomass map derived from drone/UAS (unmanned aerial system)-based multispectral remote sensing, calibrated to in situ field data. It was developed for research on prescribed fire behavior responses to vegetation conditions, and vegetation community regrowth-responses post-fire. It addressed a key need for nondestructive grassland biomass measurements, in a use case where directly measuring grass biomass by destructive harvest would have disturbed the intact fuel beds needed for burning in the prescribed fire experiment.

This data set provides landcover maps of (1) peatland type (bog, fen, marsh, swamp) with levels of biomass (open, forested) and (2) Burn Severity Index (BSI) (Dyrness and Norum, 1983) for four wildfire areas in northern Alberta, Canada. The four wildfire sites include the Utikuma fire site of 2011, Kidney Lake fire site of 2011, Fort McMurray west fire site of 2009, and Fort McMurray east fire site of 2009. The peatland classification at 12.5-m resolution (fen vs. bog including treed vs. open vs. shrubby) at each wildfire site was based on a pre-burn 2007 multi-date, multi-sensor fusion (Optical-IR, C-band and L-band SAR) approach. Over 350 field locations were sampled in central Alberta to train and validate the peatland type maps. The additional site, Wabasca, was an unburned site. Burn severity was measured in the field using the Burn Severity Index (BSI) (Dyrness and Norum 1987), a qualitative assessment of burnt moss that uses a 1-5 scale, with 1 being unburnt and 5 being severely burnt. The field data of ground consumption were correlated with Landsat pre- and post-burn imagery, specific to peatlands, to develop multivariate models for calculating burn severity and %-not-sphagnum-moss. These models were used to generate the Burn Severity Maps at 30-m resolution (percent unburned moss, and the burn severity index (BSI)). All sites were visited in 2013 for field measurements and the Utikuma site was also visited in 2012 for field measurements. Additional biophysical data for the various peatlands (aboveground biomass – tree and shrub, plant heights, density, etc. were collected and will be provided in another data set.

In 2014 In 2014, approximately 105 acres of Gambel Oak (Quercus gambelii) dominated montane shrublands were identified by Pitkin County for mechanical mastication to improve ecological conditions. Mechanical mastication uses equipment to clear vegetation and simulate natural disturbance regimes such as fire. Fire is a natural part of the ecosystem in oak shrublands; the suppression of wildfires has led to “overly mature” or old stands. These stands are not as beneficial to wildlife as younger, more diverse stands. Additionally, overly mature stands can accumulate forest debris, which increases the chance of intense wildfires. The goal of the mechanical mastication was to create a multi-aged mosaic of shrublands in order to improve wildlife habitat and to reduce fuel loads to lessen the chance of catastrophic wildfires. On the south side of Brush Creek Road, approximately 58 acres were successfully treated. The areas treated in 2014 show remarkable regeneration, improved structural diversity and increased presence of wildlife. Regeneration in the treated areas was swift, preventing any erosion or impacts to views. The photo series captured from 2014 to 2019 show the increased diversity and multi-aged mosaic that were the goal of the restoration project. The graphic below shows the restoration process over time from pre-treatment, through mechanical mastication, to today. The photographs of the current oak stands display new growth, a diversity of canopy heights and a variety of vegetation.

This dataset is derived from Russian forest fire imagery from the National Forest Fire Center of Russia archive that was collected by the Center of Remote Sensing, Institute of Solar Terrestrial Physics, Irkutsk, Russia for the 1998 and 1999 fire seasons. The data are vector (point) maps of forest fire locations (1998 and 1999) in ArcView shapefile format.

This data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations. The two algorithms are NASA's operational Earth Observing System MODIS fire detection product and Brazil's National Institute for Space Research (INPE) algorithm. These data are the ASCII files used in the logistic regression and error matrices presented in the paper.

This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites.

Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2023 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. “Estimating Wildfire Impacts on the Biomass of Southern California’s Chaparral Shrublands.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. * Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 * Please cite this dataset as: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 * This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple), acer saccharinum (silver maple), acer saccharum (sugar maple), acer spicatum (mountain maple), acer spp. (unidentified maple), alnus rubra (red alder), alnus spp. (unidentified alder), arbutus menziesii (arbutus), betula alleghaniensis (yellow birch), betula papyrifera (white birch), betula populifolia (gray birch), betula spp. (unidentified birch), carpinus caroliniana (blue-beech), carya cordiformis (bitternut hickory), chamaecyparis nootkatensis (yellow-cedar), fagus grandifolia (american beech), fraxinus americana (white ash), fraxinus nigra (black ash), fraxinus pennsylvanica (red ash), juglans cinerea (butternut), juglans nigra (black walnut), juniperus virginiana (eastern redcedar), larix laricina (tamarack), larix lyallii (subalpine larch), larix occidentalis (western larch), larix spp. (unidentified larch), malus spp. (unidentified apple), ostrya virginiana (ironwood, hop-hornbeam), picea abies (norway spruce), picea engelmannii (engelmann spruce), picea glauca (white spruce), picea mariana (black spruce), picea rubens (red spruce), picea sitchensis (sitka spruce), picea spp. (unidentified spruce), pinus albicaulis (whitebark pine), pinus banksiana (jack pine), pinus contorta (lodgepole pine), pinus monticola (western white pine), pinus ponderosa (ponderosa pine), pinus resinosa (red pine), pinus spp. (unidentified pine), pinus strobus (eastern white pine), pinus sylvestris (scots pine), populus balsamifera (balsam poplar), populus grandidentata (largetooth aspen), populus spp. (unidentified poplar), populus tremuloides (trembling aspen), populus trichocarpa (black cottonwood), prunus pensylvanica (pin cherry), prunus serotina (black cherry), pseudotsuga menziesii (douglas-fir), quercus alba (white oak), quercus macrocarpa (bur oak), quercus rubra (red oak), quercus spp. (unidentified oak), salix spp. (unidentified willow), sorbus americana (american mountain-ash), thuja occidentalis (eastern white-cedar), thuja plicata (western redcedar), tilia americana (basswood), tsuga canadensis (eastern hemlock), tsuga heterophylla (western hemlock), tsuga mertensiana (mountain hemlock), tsuga spp. (unidentified hemlock), ulmus americana (white elm), unidentified needleaf, unidentified broadleaf, broadleaf species, needleaf species, unknown species

This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra.

Overview Communities is part of the Highly Valued Resource or Asset (HVRA) data used in the National Wildfire Risk Assessment for Forest Service Lands (NaWRA, Dillon 2000). Community density classes can be used as Sub-HVRAs within the Map Values, Exposure Analysis and Quantitative Wildfire Risk Assessment functionality within the Interagency Fuel Treatment Decision Support System (IFTDSS, https://iftdss.firenet.gov). Data Details Communities represents population density classes based on Residentially Developed Populated Areas (RDPA, Haas et al. 2013). RDPA was developed using the LandScan USATM population data (Oak Ridge National Laboratory, 2008) with some additional smoothing to conservatively identify pixels that were most likely to have people and residential structures located within them (Haas et al. 2013).
RDPA was summarized into three population density classes for use in IFTDSS. Classification used similar population density ranges to the Federal Register Wildland Urban Interface categorization:

Low density: >0-28 people per square mile

Medium density: >28-250 people per square mile

High density: > 250 people per square mile

Citations Dillon, Gregory K. 2020. Results and application of the National Wildfire Risk Assessment. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; and Steffens, Ron, eds. Proceedings of the Fire Continuum—Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Pgs 252-257. Haas, Jessica R.; Calkin, David E.; Thompson, Matthew P. 2013. A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States. Landscape and Urban Planning. 119: 44-53. Oak Ridge National Laboratory (2008). LandScan 2008 High Resolution Population Distribution Model. L. UT-Battelle, editor., Oak Ridge, TN

Ramat Hanadiv 1980’s Fire Outbreak: Post-Fire Vegetation Dynamics and Vegetal Potentialities. Jean-Marc Dufour-Dror, Ecology and Environment, vol. 6, nos. 3-4 (March 2001): pp. 223-238 [original Hebrew article] This layer is based on a research project concerning the spatial extent of the May 1980's fire; determining the vegetation dynamics within the burnt area, and defining the potential of vegetation in the burnt area. The spatial extension of the fire has been determined according to the actual distribution of the in situ remains of burnt branch and trunks of Phillyrea latifolia specimens. These limits were drawn on a recent aerial picture of Ramat Hanadiv, then digitized into a computerized spatial data layer. This GIS map enabled us to calculate the size of the area burnt in May 1980: 25.3% of the park area. It was also determined that the fire outbreak has expanded in 6 different sites. A medium close matorral dominated by Phillyrea latifolia and Calycotome villosa has developed within the study area since 1980. The analysis of the matorral structure and floristic composition shows that this vegetation formation is gradually changing from a progressive dynamic stage mainly resulting from the resprouting process - to a stage of auto-succession. The matorral current dynamics does not imply that it is able to develop spontaneously toward a forest structure. The analysis of the potential of vegetation within the study area suggests that the present matorral should be considered as the current potential of vegetation, while special vegetation surveys carried into the burnt area and its surroundings suggest that the abiotic conditions may not prevent the development of a Quercus calliprinos oak forest structure. This hypothesis implicates a necessary distinction between the ""current potential of vegetation"" resulting from an anthropogenic matorralization process, apparently irreversible and the ""environment potential of vegetation"" which may be a sclerophyll oak forest. This interpretation is presented as alternative hypothesis compared to a former one suggesting that the under-development of Quercus calliprinos at Ramat Hanadiv may be related to the characteristic water regime due to local geological-hydrological conditions.