Search Results (30)

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions. Full article

Our study focuses on investigating the present-day influence of wildfires on the global atmosphere. To achieve this, we utilized four observational biomass burning (BB) emissions datasets for present-day simulations employing the TM5 Chemical Transport Model (CTM). To assess how different emissions estimates influence the model’s ability to simulate the atmosphere, we compared the following datasets over the period 2003–2015: GFED4s, GFASv1.2, FEERv1.0-G1.2 and QFEDv2.6r1. Our study aims to investigate the role of wildfires in affecting important trace gases and aerosols. Their impact on atmospheric composition and their interactions with solar radiation affect the radiative balance at the Earth’s surface and, consequently, temperature trends in the troposphere. Full article

In recent years, frequent open biomass burning (OBB) activities such as agricultural residue burning and forest fires have led to severe air pollution and carbon emissions across South and Southeast Asia (SSEA). We selected this area as our study area and divided it into two sub-regions based on climate characteristics and geographical location: the South Asian Subcontinent (SEAS), which includes India, Laos, Thailand, Cambodia, etc., and Equatorial Asia (EQAS), which includes Indonesia, Malaysia, etc. However, existing methods—primarily emission inventories relying on burned area, fuel load, and emission factors—often lack accuracy and temporal resolution for capturing fire dynamics. Therefore, in this study, we employed high-resolution fire point data from China’s Feng Yun-4A (FY-4A) geostationary satellite and the Fire Radiative Power (FRP) method to construct a daily OBB emission inventory at a 5 km resolution in this region for 2020–2022. The results show that the average annual emissions of carbon (C), carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), non-methane organic gases (NMOGs), hydrogen (H₂), nitrogen oxide (NO_X), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), total particulate matter (TPM), total particulate carbon (TPC), organic carbon (OC), black carbon (BC), ammonia (NH₃), nitric oxide (NO), nitrogen dioxide (NO₂), non-methane hydrocarbons (NMHCs), and particulate matter ≤ 10 μm (PM₁₀) are 178.39, 598.10, 33.11, 1.44, 4.77, 0.81, 1.02, 0.28, 3.47, 5.58, 2.29, 2.34, 0.24, 0.58, 0.43, 0.99, 1.87, and 3.84 Tg/a, respectively. Taking C emission as an example, 90% of SSEA’s emissions come from SEAS, especially concentrated in Laos and western Thailand. Due to the La Niña climate anomaly in 2021, emissions surged, while EQAS showed continuous annual growth at 16.7%. Forest and woodland fires were the dominant sources, accounting for over 85% of total emissions. Compared with datasets such as the Global Fire Emissions Database (GFED) and the Global Fire Assimilation System (GFAS), FY-4A showed stronger sensitivity and regional adaptability, especially in SEAS. This work provides a robust dataset for carbon source identification, air quality modeling, and regional pollution control strategies. Full article

Biomass burning (BB) results from complex interactions between ecosystems, humans, and climate, releasing large amounts of gases and particles. Accurate BB emission estimates are essential for air quality, climate studies, and impact assessments. Various existing bottom-up BB emission inventories show significant discrepancies, varying by factors of 2 to 4 due to uncertainties in burned areas (BAs), emission factors (EFs), and vegetation parameters such as biomass density (BD) and burning efficiency (BE). Here, we investigate the role of vegetation parameters in these discrepancies in Africa. Two BB emission inventories, AMMABB-like (African Monsoon Multidisciplinary Analysis Biomass Burning) and GFED-like (Global Fire Emission Database) were developed for Organic Carbon (OC) and Black Carbon (BC). Both inventories used identical fire products, vegetation maps, and EF but different BD and BE values. Results highlight substantial differences in BD and BE, with relative gaps ranging from 44% to 85.12%, explaining the observed differences between BB emission inventories. Key vegetation classes responsible for BB emissions were identified. Discrepancies of 2.4 to 3.9 times were observed between AMMABB-like and GFED4-like, with higher values in the Southern Hemisphere. Better BD and BE estimates with regional distinctions for both hemispheres would improve BB emission accuracy in Africa. Full article

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. Full article

Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson’s correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (<1%). For small fires (2~10%), GFDL-ESM4(j) and UKESM1-0-LL(k) outperformed the other models. For medium fires (10–50%), CESM2-WACCM-FV2(e) was closest to GFED4s. There were large biases for all models for large fires (>50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future. Full article

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products. Full article

In Southern Mediterranean regions, the issue of summer fires related to agriculture practices is a periodic recurrence. It implies a significant increase in carbon dioxide (CO₂) emissions and other combustion-related gaseous and particles compounds emitted into the atmosphere with potential impacts on air quality and global climate. In this work, we performed an analysis of summer fire events that occurred on August 2021. Measurements were carried out at the permanent World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) station of Lamezia Terme (Code: LMT) in Calabria, Southern Italy. The observatory is equipped with greenhouse gases and black carbon analyzers, an atmospheric particulate impactor system, and a meteo-station for atmospheric parameters to characterize atmospheric mechanisms and transport for land and sea breezes occurrences. High mole fractions of carbon monoxide (CO) and carbon dioxide (CO₂) coming from quadrants of inland areas were correlated with fire counts detected via the MODIS satellite (GFED-Global Fire Emissions Database) at 1 km of spatial resolution. In comparison with the typical summer values, higher CO and CO₂ were observed in August 2021. Furthermore, the growth in CO concentration values in the tropospheric column was also highlighted by the analyses of the L2 products of the Copernicus SP5 satellite. Wind fields were reconstructed via a Weather Research and Forecasting (WRF) output, the latter suggesting a possible contribution from open fire events observed at the inland region near the observatory. So far, there have been no documented estimates of the effect of prescribed burning on carbon emissions in this region. This study suggested that data collected at the LMT station can be useful in recognizing and consequently quantifying emission sources related to open fires. Full article

Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different forests and carbon emissions from forest fires. This study developed a high-resolution (0.05° × 0.05°) daily global inventory of carbon emissions from biomass burning during 2016–2022. The inventory estimates of carbon emissions from biomass burning are based on the newly released FY-3 data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Forest fire carbon emissions were assessed using active fire data from FY-3 series satellites from 2016 to 2022, and it was linearly compared with GFED, FEER, and GFAS data on time and spatial scales with R² of 0.7, 0.73, and 0.69, respectively. The results show spatial patterns of forest cover and carbon emissions, with South America, Africa, South-East Asia, and northern Asia as high-emission zones. The analysis shows an overall upward trend in global forest fire carbon emissions over the study period. Different types of forests exhibited specific emission patterns and temporal variations. For example, most needleleaf forest fires occur in areas with low tree cover, while broadleaf forest fires tend to occur in areas with high tree cover. The study showed that there was a relationship between inter-annual trends in forest fire carbon emissions and land cover, with biomass burning occurring mainly in the range of 60–70% tree cover. However, there were also differences between evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest indicating the importance of considering differences in forest types when estimating emissions. This study identifies the main sources of carbon emissions from forest fires globally, which will help policymakers to take more targeted measures to reduce carbon emissions and provide a reliable basis for appropriate measures and directions in future carbon mitigation actions. Full article

Greenhouse gases such as CH₄ generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH₄ indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH₄ concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies. Full article

Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people’s lives and property. This study evaluated the performances of three publicly available BA satellite products (GFED4, MCD64CMQ, and FireCCI5.1) in detecting Chinese forest fire burning from 2001 to 2016 across different time scales (yearly, monthly, and seasonally) and spatial scales (regional and provincial). The reference data were derived from the monthly China Forestry Statistical Yearbook (CFSY), and they were mainly used to evaluate the detection ability of each of the three BA products in the three major forest fire areas of China consisting of the Northeast (NE), Southwest (SW), and Southeast (SE) regions. The main results are as follows: (1) A significant declining BA trend was demonstrated in the whole study area and in the NE and SE subregions. Specifically, the slopes for the whole area ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, the slopes for the NE region ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, and the slopes for the SE region ranged from −594.24 ha/year for GFED4 to −3162.1 ha/year for the CFSY. The BA in China was mainly dominated by forest fires in the NE region, especially in 2003 and 2006 when this region accounted for 90% and 87% of occurrences, respectively. (2) Compared with the CFSY, GFED4 had the best performance at the yearly scale with an RMSE of 23.9 × 10⁴ ha/year and CC of 0.83. Similarly, at the monthly scale, GFED4 also had the best performance for the three regions, with the lowest RMSE ranging from 0.33 × 10⁴ to 5.4 × 10⁴ ha/month—far lower than that of FireCC5.1 which ranged from 1.16 × 10⁴ to 8.56 × 10⁴ ha/month (except for the SE region where it was slightly worse than MCD64CMQ). At the seasonal scale, GFFD4 had the best performance in spring and winter. It was also noted that the fewer BAs in summer made the differences among the products insignificant. (3) Spatially, GFED4 had the best performance in RMSEs for all the provinces of the three regions, in CCs for the provinces of the SW and SE regions, and in MEs for the provinces of the SE region. (4) All three products had stronger detection abilities for severe and disaster fires than for common fires. Additionally, GFED4 had a more consistent number of months with the CFSY than the other products in the NE region. Moreover, the conclusion that GFED4 had the best performance in the China region was also proved using other validated BA datasets. These results will help us to understand the BA detection abilities of the satellite products in China and promote the further development of multi-source satellite fire data fusion. Full article

Fire radiative power (FRP) is a basic parameter for fire characterization since it represents the heat emission rate of fires. Moreover, its temporal integration (fire radiative energy, FRE) is used as a proxy for estimating biomass burning and emissions. From satellite, FRP is generally computed by comparing the Medium InfraRed (MIR) signal of the fire pixel with the background value on the event image. Such an approach is possibly affected by some issues due to fire extent, clouds and smoke over the event area. The enlargement of the background window is the commonly used gimmick to face these issues. However, it may include unrepresentative signals of the fire pixel because of very different land use/cover. In this paper, the alternative Background Radiance Estimator by a Multi-temporal Approach (BREMA), based on the Robust Satellite Technique (RST), is proposed to characterize background and compute FRP. The approach is presented using data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) platform. Moreover, BREMA is here combined with the RST-FIRES (RST for FIRES detection) technique for fire pixel identification and the σ-SEVIRI retrieval algorithm for transmittance evaluation. Results compared to the operational SEVIRI-based FRP-PIXEL product, although highly correlated in terms of background radiance (r² = 0.95) and FRP values (r² = 0.96), demonstrated a major capability of BREMA to estimate background radiances regardless of cloudiness or smoke presence during the event and independently on fire extent. The possible impact of the proposed approach on the estimates of CO₂ emissions was also evaluated for comparison with the Global Fire Emissions Database (GFED4s). Full article

Biomass burning is an important and changing component of global and hemispheric carbon cycles. Boreal forest fires in Russia and Canada are significant sources of the greenhouse gases carbon dioxide (CO₂) and methane (CH₄). The influence of carbon monoxide (CO) on the greenhouse effect is practically absent; its main absorption bands of 4.6 and 2.3 μm are far away from the climatically important spectral regions. Meanwhile, CO concentrations in fire plumes are closely related to CO₂ and CH₄ emissions from fires. On the other hand, satellite measurements of CO are much simpler than those of the aforementioned gases. The Atmospheric Infrared Sounder (AIRS) operating in the Thermal IR spectral region has provided a satellite-based CO data set since October 2002. This satellite data allow to estimate CO emissions from biomass burning north of 30° N using a simple two-box mass-balance model. These results correlate closely with independently estimated CO emissions from the GFED4c bottom-up database. In 2021, both estimate record high emissions throughout the preceding two decades, double the annual emissions compared to previous periods. There have been two years with extremely high emissions (2003 and 2021) but for the rest of the data, an upward trend with a rate of 3.6 ± 2.2 Tg CO yr⁻² (4.8 ± 2.7% yr⁻¹) was found. A similar rate of CO emissions can be seen in the GFED4c data. Full article

Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO₂, CO, CH₄, NMHCs, NOx, NH₃, SO₂, BC, OC, PM_2.5, and PM₁₀ were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO₂ as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO₂ emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO₂ emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO₂ emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion. Full article

Biomass burning (BB) plays an important role in the formation of heavy pollution events during harvest seasons in the Beijing–Tianjin–Hebei (BTH) region by releasing trace gases and particulate matter into the atmosphere. A better understanding of spatial-temporal variations of BB in BTH is required to assess its impacts on air quality, especially on heavy haze pollution. The fourth version of the Global Fire Emissions Database (GFED4)’s fire counts and carbon emissions data were used in this research, which shows the varying number of fire counts in China from 2003 to 2020 demonstrated a fluctuating but generally rising trend, with a peak in 2013. Most fire counts were concentrated in three key periods: March (11%), June–July (33%), and October (9.68%). The increase in fire counts will inevitably lead to the growth of carbon emissions. The four major vegetation types of the fires were agriculture (58.1%), followed by grassland (35.5%), and forest (4.1%), with the fewest in peat. In addition, a separate study for the year 2020 found that the fire counts and carbon emissions were different for this year, with the overall average trend in the study time. For example, the monthly peak fire counts changed from June to March. The cumulative emissions of carbon, CO, CO₂, CH₄, dry matter, and particulate matter from BB in BTH reached 201 Gg, 39 Gg, 670 Gg, 2 Gg, 417 Gg, and 3 Gg in 2020, respectively. Full article