The Western United States MTBS-Interagency database of large wildfires, 1984–2024 (WUMI2024a)

Although fire is a common disturbance in shrub–steppe, few studies have specifically tested burned area mapping accuracy in these semiarid to arid environments. We conducted a preliminary assessment of the accuracy of the Monitoring Trends in Burn Severity (MTBS) burned area product on four shrub–steppe fires that exhibited varying degrees of within-fire patch heterogeneity. Independent burned area perimeters were derived through visual interpretation and were used to cross-compare the MTBS burned area perimeters with classifications produced using set thresholds on the Relativised differenced Normalised Burn Index (RdNBR), Mid-infrared Burn Index (MIRBI) and Char Soil Index (CSI). Overall, CSI provided the most consistent accuracies (96.3–98.6%), with only small commission errors (1.5–4.4%). MIRBI also had relatively high accuracies (92.2–97.9%) and small commission errors (2.1–10.8%). The MTBS burned area product had higher commission errors (4.3–15.5%), primarily due to inclusion of unburned islands and fingers within the fire perimeter. The RdNBR burned area maps exhibited lower accuracies (92.9–96.0%). However, the different indices when constrained by the MTBS perimeter provided variable results, with CSI providing the highest and least variable accuracies (97.4–99.1%). Studies seeking to use MTBS perimeters to analyse trends in burned area should apply spectral indices to constrain the final burned area maps. The present paper replaces a former paper of the same title (http://dx.doi.org/10.1071/WF13206), which was withdrawn owing to errors discovered in data analysis after the paper was accepted for publication.

BackgroundThe Monitoring Trends in Burn Severity (MTBS) program has been providing the fire science community with large fire perimeter and burn severity data for the past 14 years. As of October 2019, 22 969 fires have been mapped by the MTBS program and are available on the MTBS website (https://www.mtbs.gov). These data have been widely used by researchers to examine a variety of fire and climate science topics. However, MTBS has undergone significant changes to its fire mapping methodology, the remotely sensed imagery used to map fires, and the subsequent fire occurrence, burned boundary, and severity databases. To gather a better understanding of these changes and the potential impacts that they may have on the user community, we examined the changes to the MTBS burn mapping protocols and whether remapped burned area boundary and severity products differ significantly from the original MTBS products.ResultsAs MTBS data have been used over the course of many years and for many disparate applications, users should be aware that the MTBS burned area and severity products have been actively reviewed and revised to benefit from more robust satellite image availability and to address any observed quality issues. In a sample of 123 remapped fires, we found no significant change in the burned area boundary products when compared to the original mapped fires; however, significant changes did exist in the distribution of unburned, low, and moderate burn severity pixels within the thematic product.ConclusionsAnalysis of these remapped fires provides a look into how the MTBS fire mapping methods have evolved over time. In the future, additional changes to the MTBS data record may impact data users’ downstream applications. The MTBS program has an established continuous improvement approach to the MTBS methodology and products, and subsequently encourages users to confirm that they are using the most recent data.

Maps of burned area derived from Land Remote-Sensing Satellite (LANDSAT) system imagery may be more reliable than fire perimeter records based on ground observation or manual cartographic delineation. This study evaluates accuracy of LANDSAT-derived burned area maps associated with 19 fires (65 to 86,776 ha) within shrublands of southern California in the period 1996–2018. High spatial resolution aerial images collected soon after these 19 fires were used to verify burned fractions within LANDSAT ground resolution elements. Validated burned fractions were used to optimize classification thresholds applied to burn severity metrics based on LANDSAT Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Normalized Difference Vegetation Index (NDVI). The burn severity metrics included post-fire spectral index data (e.g., NBR), pre-fire to post-fire differences (e.g., dNBR), and relative differences (e.g., RdNBR). Optimized classifications of dNBR had the greatest overall accuracy (94%) compared to dNBR2 (93%) and dNDVI (92%). Classifications based on standard difference metrics yielded burn area maps that were 1% more accurate than those based on relative-difference metrics for each spectral index. Classification products based solely on post-fire imagery were 85% to 90% accurate, depending on the spectral index utilized. In comparison, perimeter data from the California Fire and Resource Assessment Programme (FRAP) and Monitoring Trends in Burn Severity (MTBS) substantially overestimated burned extent (by about 44%). The MTBS severity maps excluding the ‘unburned/low severity’ class slightly overestimated burned extent (11%). Commission error in the FRAP data set was attributed to low cartographic detail and inclusion of internal unburned patches. Site-specific differences in unburned soil and vegetation fractions (within partly-burned areas) correlated strongly with overestimation of burned area in the optimized LANDSAT-derived maps; areal overestimates of 10%–15% resulted from data grain size rather than map commission error. The main innovation presented in this study is an empirical method to predict dNBR thresholds based on local (pre-fire) NDVI mean and variance, which produced burned area maps of 90% median accuracy. This LANDSAT-based method could support an efficient reconstruction of fire history in recent decades, which would be more comprehensive and accurate than available fire records for southern California.

Understanding the evolution of wildfire regimes throughout the United States (US) is crucial in the preparation, mitigation, and planning for national wildfires. Recent wildfire trajectories demonstrating an increase in both frequency and size across the US have made documenting the changes in wildfire regimes a topic of growing importance. While previous studies have examined wildfire regimes using ecoregions, this study analyzes wildfire regimes through the Geographic Area Coordination Center (GACC) regions across the Contiguous US over 34 years, 1984–2017. GACCs are geopolitical boundaries designed by wildfire agencies to promote an efficient way to distribute resources during emergencies such as wildfires. Wildfire observations originate from the Monitoring Trends in Burn Severity (MTBS) database which records large fire events that are 1000(500) acres or greater in the Western (Eastern) US. Using GACCs and MTBS data, this study examines wildfire regimes across the Contiguous US through the following three parameters: total burned area, frequency, and average burned area. This study characterizes the trend direction of the wildfire parameters and which are statistically significant. Results demonstrate that most GACC regions display statistically significant trends, including wildfire regimes that are beyond the Western US (e.g., Southern GACC). The Northwest and Southwest GACCs demonstrate statistically significant positive trends in every parameter observed. The California and Great Basin GACCs demonstrate statistically significant positive trends in the average burned area. The Eastern GACC is the only region to not display any significant trends. Determining significant wildfire regimes and their trend direction can help wildfire agencies to minimize the negative impacts on the environment, society, and economy.

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.

Elected officials and leaders of environmental agencies need information about the effects of large wildfires in order to set policy and make management decisions. Recently, the Wildland Fire Leadership Council (WFLC), which implements and coordinates the National Fire Plan (NFP) and Federal Wildland Fire Management Policies (National Fire Plan 2004), adopted a strategy to monitor the effectiveness of the National Fire Plan and the Healthy Forests Restoration Act (HFRA). One component of this strategy is to assess the environmental impacts of large wildland fires and identify the trends of burn severity on all lands across the United States. To that end, WFLC has sponsored a six-year project, Monitoring Trends in Burn Severity (MTBS), which requires the U.S. Department of Agriculture Forest Service (USDA-FS) and the U.S. Geological Survey (USGS) to map and assess the burn severity for all large current and historical fires. Using Landsat data and the differenced Normalized Burn Ratio (dNBR) algorithm, the USGS Center for Earth Resources Observation and Science (EROS) and USDA-FS Remote Sensing Applications Center will map burn severity of all fires since 1984 greater than 202 ha (500 ac) in the east, and 404 ha (1,000 ac) in the west. The number of historical fires from this period combined with current fires occurring during the course of the project will exceed 9,000. The MTBS project will generate burn severity data, maps, and reports, which will be available for use at local, state, and national levels to evaluate trends in burn severity and help develop and assess the effectiveness of land management decisions. Additionally, the information developed will provide a baseline from which to monitor the recovery and health of fire-affected landscapes over time. Spatial and tabular data quantifying burn severity will augment existing information used to estimate risk associated with a range of current and future resource threats. The annual report of 2004 fires has been completed. All data and results will be distributed to the public on a Web site.

Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.

Background Timely information on wildfire burn severity is critical to assess and mitigate potential post-fire impacts on soils, vegetation, and hillslope stability. Tracking individual fire spread and intensity using satellite active fire data provides a pathway to near real-time (NRT) information. Here, we generated a large database (n = 2177) of wildfire events in the western United States (U.S.) between 2012 and 2021 using active fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Fire Events Data Suite (FEDS) algorithm to track large fire growth every 12 h. We integrated fire tracking data with final fire perimeters and burn severity data from the Monitoring Trends in Burn Severity (MTBS) program to evaluate the relationship between burn severity and fire behavior metrics derived from the fire tracking approach, including the rate of fire spread and average fire radiative power (FRP) of fire detections for each 12-h growth increment. Results When stratified by vegetation type, FRP and rate of spread metrics were positively correlated with classified burn severity for each 12-h growth increment, highlighting the potential to rapidly identify areas of high and low severity burning. In forests, integrated measures of FRP over the fire lifetime captured persistent flaming and smoldering that compensated for initial differences between AM (01:30) and PM (13:30) fire detections. Predictive modeling of these relationships based on multiple fire behavior indicators and vegetation type from the LANDFIRE program yielded an accuracy of 78% for the separation of unburned/low and moderate/high burn severity classes. Conclusions These results demonstrate the ability to capture within-fire differences in burn severity using NRT indicators from fire tracking to assist with emergency management and disaster preparedness for post-fire hazards, such as landslides, debris flows, or changes in stream flow and water quality. As VIIRS data are available within minutes of each satellite overpass in the U.S., rapid estimates of burn severity based on fire tracking can be made days or weeks before a large wildfire is fully contained.

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGISTM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool’s structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

Abstract. In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 "unknown" or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.

A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States

<p>The study of complex interactions between fire and atmospheric dynamics of the earth system is drawing increasing attention in recent years, especially when fire seasons are extended due to global warming, where the historical daily burnt area data played a pivotal role in analyzing wildfire regimes change. Existing products could not fully meet the temporal requirements: daily burnt area data in global fire emissions database (GFED4) starts from mid-2000 using MODIS while ESA Fire Climate Change Initiative (FireCCILT10) Dataset from 1982 to 2017 is provided on a monthly grid.</p><p>Advanced Very High Resolution Radiometer (AVHRR) series of sensors are widely used to develop pre‐MODIS daily historical records. However, compared to MODIS, the AVHRR sensor has a lower radiometric and geometric quality and is missing Short Wave Infrared (SWIR) band. To address the data quality problem, this research study presents a time-series mapping method for daily burned area using AVHRR composite. Daily fire-sensitive indices are calculated to develop a time-series data composite which is masked by the burnable surface of GLASS_GLC land cover product. Then, Continuous Change Detection and Classification (CCDC) time-series model, which originally implemented on Landsat data monitoring land cover change, is revised to detect an abrupt change in the time-series data composite and remove noise, ensuring temporal consistency. The image of a time-series breakpoint is further classified using a spatial contextual method to distinguish biomass burning from other forest degradation change like a landslide and is used to generate burned area probability map.</p><p>The methodology is verified in California, US, where fuel aridity increased during 1984–2015 driven by anthropogenic climate change. The samples are collected based on the National Monitoring Trends in Burn Severity（MTBS）Burned Areas Boundaries Dataset from 1984 – 2018 and California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters from since 1950. Primary results show that the proposed method can effectively detect burned area on daily basis with CCDC algorithm reducing the complexity of change detection.</p>

Remote sensing products provide a vital understanding of wildfire effects across a landscape, but detection and delineation of low- and mixed-severity fire remain difficult. Although data provided by the Monitoring Trends in Burn Severity (MTBS) project are frequently used to assess severity in the United States, alternative indices can offer improvement in the measurement of low-severity fire effects and would be beneficial for future product development and adoption. This research note evaluated one such alternative, the Mid-Infrared Bi-Spectral Index (MIRBI), which was developed in savannah ecosystems to isolate spectral changes caused by burning and reduce noise from other factors. MIRBI, differenced MIRBI (dMIRBI) and burn severity indices used by MTBS were assessed for spectral optimality at distinguishing severity and the ability to differentiate between unburned and burned canopy in a conifer forest. The MIRBI indices were better at isolating changes caused by burning and demonstrated higher spectral separability, particularly at low severity. These findings suggest that MIRBI indices can provide an enhanced alternative or complement to current MTBS products in high-canopy-cover forests for applications such as discernment of fire perimeters and unburned islands, as well as identification of low-severity fire effects.

Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States

MODIS–Landsat fusion for large area 30 m burned area mapping