First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available fire datasets that identify wildfire and prescribed fire burned areas across the United States. However, these datasets are all limited in some way. Their time periods could cover only a couple of decades or they may have stopped collecting data many years ago. Their spatial footprints may be limited to a specific geographic area or agency. Their attribute data may be limited to nothing more than a polygon and a year. None of the existing datasets provides a comprehensive picture of fires that have burned throughout the last few centuries. Our dataset uses these existing layers and utilizes a series of both manual processes and ArcGIS Python (arcpy) scripts to merge these existing datasets into a single dataset that encompasses the known wildfires and prescribed fires within the United States and certain territories. Forty different fire layers were utilized in this dataset. First, these datasets were ranked by order of observed quality (Tiers). The datasets were given a common set of attribute fields and as many of these fields were populated as possible within each dataset. All fire layers were then merged together (the merged dataset) by their common attributes to created a merged dataset containing all fire polygons. Polygons were then processed in order of Tier (1-8) so that overlapping polygons in the same year and Tier were dissolved together. Overlapping polygons in subsequent Tiers were removed from the dataset. Attributes from the original datasets of all intersecting polygons in the same year across all Tiers were also merged so that all attributes from all Tiers were included, but only the polygons from the highest ranking Tier were dissolved to form the fire polygon. The resulting product (the combined dataset) has only one fire per year in a given area with one set of attributes. While it combines wildfire data from 40 wildfire layers and therefore has more complete information on wildfires than the datasets that went into it, this dataset has also has its own set of limitations. Please see the Data Quality attributes within the metadata record for additional information on this dataset's limitations. Overall, we believe this dataset is designed be to a comprehensive collection of fire boundaries within the United States and provides a more thorough and complete picture of fires across the United States when compared to the datasets that went into it.

Fire history metrics enable rapidly increasing amounts of burned area data to be collapsed into a handful of data layers that can be used efficiently by diverse stakeholders. In this effort, the U.S. Geological Survey’s Landsat Burned Area product was used to identify burned area across CONUS over a 39-year period (1984-2022). The Landsat BA product was consolidated into a suite of annual BA products, which in-turn were used to calculate a series of contemporary fire history metrics (30 m resolution). Fire history metrics included: (1) fire frequency (FRQ), (2) time since last burn (TSLB) and (3) year of last burn (YLB), (4) longest fire-free interval (LFFI), and (5) average fire interval length (FIL). All metrics were reported using years as the unit. The FRQ, TSLB and YLB metrics are useful across a wide range of fire regimes, and can be used to inform risk of wildfire, answer fire-management questions, or support fire model parameterization. The FIL and LFFI, alternatively, provide data on the distribution of fire events across the period of record and can help guide land management in regions with frequent fire, such as the Midwest and Southeast.

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is… See the full description on the dataset page: https://huggingface.co/datasets/PengxiangLi/FIRE.

The foundation of quantitative wildfire hazard or risk assessment is a current-condition fuelscape (i.e., fuel and terrain layers), ideally updated to account for recent disturbances and calibrated to reflect the fire behavior potential observed in recent historical wildfire events. This data publication provides the fuelscape generated for a wildfire risk assessment focused on the sagebrush biome of the western United States (US). The data depict ca. 2020 fuel conditions, after customization, to better reflect expected fire behavior in sagebrush ecosystems, including influences from exotic annual grass (e.g., cheatgrass) invasion and conifer (e.g., pinyon, juniper) encroachment. These data are presented as used for biome-wide geospatial fire modeling at a 270-meter resolution. The work was conducted using simulation units called “pyromes,” which represent areas of relatively homogenous contemporary fire regimes. The sagebrush biome is represented by 31 pyromes, covering about 450 million acres in total area. Fuelscapes for the 31 pyromes are included in this data product as separate multiband GeoTIFFs. The bands of each GeoTIFF store eight layers of data that describe terrain (aspect, elevation, slope), tree canopy (cover, height, base height, bulk density), and surface fuel (FBFM40). These data form the Landscape (LCP) file commonly used by US wildland fire behavior modeling systems (e.g., FlamMap, FSPro, FSim). Each fuelscape dataset includes a 30-kilometer buffer to avoid truncating the simulated fires at pyrome boundaries. A shapefile and geopackage containing the boundaries and size of each pyrome are also included.In the western United States, hundreds of thousands of acres of highly imperiled sagebrush ecosystems are lost or degraded each year as a result of altered wildfire regimes. In response to these wildfire threats, extensive fuel treatment investments have been proposed throughout the region. Regional-scale assessment of wildfire risk offers a consistent means of evaluating threats to valued resources and assets, thereby facilitating the most cost-effective investments in management activities that can mitigate those risks. We used a large-fire simulation system (FSim) to estimate the probabilistic components of wildfire risk across the sagebrush biome, which includes portions of 13 western states. This publication includes the customized fuelscape data used for that fire-modeling work.

Quantum Computing QA Dataset

Question-Answer dataset for los-angeles-fires-2025

  Usage

from datasets import load_dataset

dataset = load_dataset("darkB/los-angeles-fires-2025-qa-dataset")

  Example Data

Sample data point:

{ "text": "[INST] How often did hurricanes strike on the main transmission lines since they were built according to McCullough? [/INST] McCullough argues that despite having lasted for over eight decades,", "article_title": "January… See the full description on the dataset page: https://huggingface.co/datasets/darkB/los-angeles-fires-2025-qa-dataset.

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). This dataset includes all of the attribute information for each point, including if the location of the point intersects a treatment type or combination of treatments as well as a KML file showing the location of each point.

Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions, and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011-2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, 3, and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery, resprouting vigorously within one year, while four years post-fire, areas previously dominated by conifers were divided approximately equally between being classified as dominated by quaking aspen saplings with herbaceous species in the understory or minimally recovered. Relative to using a pixel-based Normalized Difference Vegetation Index (NDVI), our object-based approach showed higher rates of revegetation. High-resolution imagery can provide an effective means to monitor post-fire site conditions and complement more prevalent efforts with moderate- and coarse-resolution sensors.

About this dataset

Context

The dataset tabulates the Angel Fire population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Angel Fire. The dataset can be utilized to understand the population distribution of Angel Fire by age. For example, using this dataset, we can identify the largest age group in Angel Fire.

Key observations

The largest age group in Angel Fire, NM was for the group of age 55 to 59 years years with a population of 174 (15.98%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Angel Fire, NM was the 35 to 39 years years with a population of 10 (0.92%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Angel Fire is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Angel Fire total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Angel Fire Population by Age. You can refer the same here

The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityFactsCommonAttributes_01/MapServer/0 Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Angel Fire. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Angel Fire. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Angel Fire, the median household income stands at $105,833 for householders within the 45 to 64 years age group, followed by $67,813 for the 65 years and over age group. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $30,972.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Angel Fire median household income by age. You can refer the same here

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

The southeastern United States experiences frequent wild and prescribed fire activity. Mapped burn severity products in the southeastern U.S. face challenges accurately characterizing fire effects due to rapid post-fire recovery limiting observation windows, limited availability of cloud-free imagery, spectral confusion within wetland areas, and operational constraints. As mapped burn severity datasets are generally focused on large wildfires, the many small and prescribed fires of the Southeastern U.S. are not well-represented in existing burn severity products. Accurate and detailed characterization of burn severity across the region is significant to the estimation of fire-related emissions, measurement of fuel loads and aboveground carbon storage, and guiding land management activities. The U.S. Geological Survey (USGS) developed an algorithm to improve the prediction of post-fire burn severity within the southeastern United States. A burn severity model was developed utilizing over 5000 Composite Burn Inventory (CBI) plots, where post-fire impacts were characterized in the field for 232 unique fire events across the continental US. For each CBI plot location, predictor variables were generated from ARD Landsat scenes capturing first and second-order fire effects, climate norms, and fire seasonality. A gradient-boosted decision tree model was developed to predict post-fire burn severity as a CBI value (0-3), aligning field and satellite observations of fire effects. The model was applied to the extent of burned area identified by the Landsat Burned Area Product to generate annual (2000-2022) burn severity mosaics of predicted CBI burn severity for 78 ARD Landsat tiles encompassing the southeastern United States. These data provide an improved characterization of burn severity in the southeastern United States, with support for small and prescribed fire activity.

This set of Composite Burn Index (CBI) data was collected from 2009 to 2011 and supports several products created during the FIRESEV project, which was funded by the Joint Fire Sciences Program. FIRESEV (FIRE SEVerity mapping tools) is a comprehensive set of tools and protocols to deliver, create, and evaluate fire severity maps for all phases of fire management. This CBI data describes fire effects for the western U.S. for five vegetation strata after burning in 2008 to 2010 (Key and Benson 1999). The strata include substrates (litter, duff, fuel, and soil); herbs, low shrubs, and small trees; tall shrubs and sapling trees; intermediate trees; and big trees. The field assessments were conducted in deciduous and coniferous forests, shrublands, and grasslands. The dataset includes information on the fires that burned each area, plot locations and sample protocols, topographic characteristics, canopy characteristics, substrate and ground covers, pre- and post-burn estimates of vegetation in each stratum, estimates of the percentage of plot altered by stratum, CBI values calculated for each of the five strata, and a composite CBI value for the entire plot. Field photos at each location are included for perspective on the field conditions related to the CBI assessments.These data were collected to support the following major FIRESEV products: (1) a Severe Fire Potential Map (SFPM), which quantified the potential for fires to burn with high severity, should they occur, for any 30 meter (m) x 30 m piece of ground across the western United States (not including Alaska or Hawaii); (2) a fire severity mapping algorithm in the Wildland Fire Assessment Tool (WFAT), which was used to map predicted fire severity explicitly from fire effects simulation models (e.g., the First Order Fire Effects Model, the CONSUME model, and others) for real-time and planning wildfire applications; and (3) a suite of research studies, synthesis papers, and popular articles, which improved the description, interpretation, and mapping of fire severity for wildland fire managers. Our primary purpose for this sampling effort was to collect field data that could be used to assess the accuracy of the maps produced to quantify the probability of severe fires for the western US. In addition, data were collected to analyze the degree to which various measures of burn severity interpreted from satellite imagery (NBR, dNBR, RdNBR) correlated with field indicators of burn severity collected one year post-fire. We sought to assess measures in the field and remotely that related to three different axes of burn severity used in this project. These included 1) soil heating, 2) surface fuel consumption, and 3) change in vegetation cover and mortality. The CBI values comprising this collection were used in each of these products, either directly or indirectly, to compare on-site changes in vegetation, canopy structure, and soil characteristics with fire severity interpretations and assessments derived from satellite imagery. All of the products were based either directly or indirectly on the CBI dataset in this archive.Original metadata date was 11/19/2013. Minor metadata updates on 12/15/2016.

Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment involving post-fire seeding of declining sagebrush shrubs across semiarid areas of the western USA over two decades, we quantify drivers and consequences of selection biases in restoration using remotely sensed data. From following more than 1,500 wildfires, we find treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate unmeasured drivers of treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering sources of bias, restoration positively affected sagebrush recovery. Treatment effects varied with climate, indicating prioritization criteria for interventions. Our findings revise the perspective that post-fire sagebrush seedings have been broadly unsuccessful and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and the development of restoration theory.

Data Use
License
Creative Commons Zero v1.0 Universal (CC0 v1.0)
Recommended Citation
Simler-Williamson A, Germino M. 2022. Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action [Dataset]. Dryad. https://doi.org/10.25338/B8W63R

Funding
US National Science Foundation and Idaho EPSCoR: OIA-1757324
US National Science Foundation: DBI-2010868
Southwest Climate Adaptation Center
Northwest and North Central Climate Adaptation Centers

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Angel Fire, NM, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 15,726, while the mean income for the highest quintile (20% of households with the highest income) is 228,159. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 301,824, which is 132.29% higher compared to the highest quintile, and 1919.27% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Angel Fire median household income. You can refer the same here

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire � also termed the fire severity � are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate "wall-to-wall" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.

LANDFIRE’s (LF) 2016 Remap Anderson Fire Behavior Fuel Model 13 (FBFM13) product represents distinct distributions of fuel loadings found among surface fuel components (live and dead), size classes, and fuel types (Anderson 1982). The fuel models are described by the most common fire carrying fuel type (grass, brush, timber, or slash), loading and surface area-to-volume ratio by size class and component, fuel bed depth, and moisture of extinction. FBFM13 can be used for fire spread related characteristic models. LF Remap used vegetation products and 10-years of disturbance data to create Fuel Vegetation Type (FVT), Fuel Vegetation Cover (FVC), and Fuel Vegetation Height (FVH) for disturbed areas to represent pre-disturbance scenarios in FBFM13. A combination of pre-disturbance and non-disturbance Existing Vegetation Type (EVT) are used to assign surface fuel models. FBFM13 was developed using the most recent 10 years of Annual Disturbance products and is a capable fuels product that calculates Time Since Disturbance (TSD) assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.