Near real-time indicators of burn severity in the western U.S. from active fire tracking

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

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.

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite was designed to provide data continuity to the Moderate Resolution Imaging Spectroradiometer (MODIS), which has been providing global active fire observations for over a decade. Fire radiative power (FRP) retrievals obtained from active fire satellite observations can be used for the estimation of biomass burning emissions. However, FRP estimates obtained from MODIS and VIIRS are not directly comparable due to the different spectral response of the bands used for the retrieval (figure 1), which may be further aggravated by differences in spatial sampling (e.g., VIIRS pixel aggregation scheme) and satellite orbits. In this paper we compared FRP retrievals from VIIRS and MODIS active fire products before and after atmospheric correction of the data showing an improved agreement of the FRP retrievals between both sensors after the application of the atmospheric correction.

A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records

The Visible-Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite began acquiring Earth observations in November 2011. VIIRS data from all spectral bands became available three months after launch when all infrared-band detectors were cooled down to operational temperature. Before that, VIIRS sensor data record (SDR) products were successfully generated for the visible and near infrared (VNIR) bands. Although VIIRS calibration has been significantly improved through the four years of the SNPP mission, SDR reprocessing for this early mission phase has yet to be performed. Despite a rapid decrease in the telescope throughput that occurred during the first few months on orbit, calibration coefficients for the VNIR bands were recently successfully generated using an automated procedure that is currently deployed in the operational SDR production system. The reanalyzed coefficients were derived from measurements collected during solar calibration events that occur on every SNPP orbit since the beginning of the mission. The new coefficients can be further used to reprocess the VIIRS SDR products. In this study, they are applied to reprocess VIIRS data acquired over pseudo-invariant calibration sites Libya 4 and Sudan 1 in Sahara between November 2011 and February 2012. Comparison of the reprocessed SDR products with the original ones demonstrates improvements in the VIIRS calibration provided by the reprocessing. Since SNPP is the first satellite in a series that will form the Joint Polar Satellite System (JPSS), calibration methods developed for the SNPP VIIRS will also apply to the future JPSS measurements.

Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite incorporates fire‐sensitive channels, including a dual‐gain high‐saturation temperature 4 µm channel, enabling active fire detection and characterization. The active fire product, based on the 750 m moderate resolution “M” bands of VIIRS, is one of the standard operational products generated by the Interface Data Processing Segment of the S‐NPP ground system. The product builds on an earlier “Collection 4” version of the algorithm used for processing Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following postlaunch quality assessments and corrections in the input VIIRS Sensor Data Record data processing, an initial low detection bias was removed and the product achieved Beta quality in April 2012. Daily spurious detections along‐scan lines were also significantly reduced as a result of further processing improvements in October 2012. Direct product comparison with MODIS over 4 months of data in 2013 has shown that VIIRS produces approximately 26% more detections than MODIS within the central 3 pixel VIIRS aggregation zone of approximately ±31° scan angle range and 70% more detections outside of that zone, mainly as a result of the superior VIIRS scanning and sampling characteristics. Further development is in progress to ensure high‐quality VIIRS fire products that continue the MODIS data record and better serve the user community by delivering a full image classification product and fire radiative power retrievals. Research is also underway to take advantage of the radiometric signal from the 375 m VIIRS imager “I” bands.

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The "MODIS-like" VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the "dark-target" algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012 - 31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04-0.06 and the correlation of 0.83-0.86, with the largest MB (0.10-0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13-0.14 and a higher correlation (0.93-0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02-0.04 for fine mode, 0.06-0.12 for coarse mode and 0.03-0.05 for bi-mode at 440nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g. surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models.

Efficacy of resource objective wildfires for restoration of ponderosa pine (Pinus ponderosa) forests in northern Arizona

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

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.

Nightfire is a new fire product created by the National Oceanic and Atmospheric Administration (NOAA). The Nightfire algorithm detects and characterizes sub-pixel heat sources using multispectral data collected globally each night by the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The Nightfire algorithm is applied to two types of data streams. The global data stream has a 7–10 h latency that renders the data of low value to the first responder community. The second type of data stream comes from direct readout ground stations, where fire detection data can be available in less than 1 h. The Nightfire algorithm currently detects fires with high accuracy, however there are two areas where research and development (R&D) is needed to improve the value of VIIRS satellite fire detections to the first responder community. This includes refinement of the file format and file content, and improvements in the data delivery mechanisms. It should be possible to develop services that deliver graphics and text results to smartphones and other mobile devices used by the first responder community.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Forested fire refugia (trees that survive fires) are important disturbance legacies that provide seed sources for post-fire regeneration. Conifer regeneration has been limited following some recent western fires, particularly in ponderosa pine (Pinus ponderosa) forests. However, the extent, characteristics, and predictability of ponderosa pine fire refugia are largely unknown. Within 23 fires in ponderosa pine-dominated forests of the Colorado Front Range (1996–2013), we evaluated the spatial characteristics and predictability of refugia: first using Monitoring Trends in Burn Severity (MTBS) burn severity metrics, then using landscape variables (topography, weather, anthropogenic factors, and pre-fire forest cover). Using 1-m resolution aerial imagery, we created a binary variable of post-fire conifer presence (‘Conifer Refugia’) and absence (‘Conifer Absence’) within 30-m grid cells. We found that maximum patch size of Conifer Absence was positively correlated with fire size, and 38% of the burned area was ≥ 50m from a conifer seed source, revealing a management challenge as fire sizes increase with warming further limiting conifer recovery. In predicting Conifer Refugia with two MTBS-produced databases, thematic burn severity classes (TBSC) and continuous Relative differenced Normalized Burn Ratio (RdNBR) values, Conifer Absence was high in previously forested areas of Low and Moderate burn severity classes in TBSC. RdNBR more accurately identified post-fire conifer survivorship. In predicting Conifer Refugia with landscape variables, Conifer Refugia were less likely during burn days with high maximum temperatures: while Conifer Refugia were more likely on moister soils and closer to higher order streams, homes, and roads; and on less rugged, valley topography. Importantly, pre-fire forest canopy cover was not strongly associated with Conifer Refugia. This study further informs forest management by mapping post-fire patches lacking conifer seed sources, validating the use of RdNBR for fire refugia, and detecting abiotic and topographic variables that may promote conifer refugia.