Fire Counts Research Articles

-temporal variability in fire events due to crop residue burning and their impact on atmospheric variables using ground and remote sensingdata in Punjab

We attempted monitoring and mapping of the active fire events in Punjab, during the wheat and rice harvesting period of 2017-2021 from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m aboard. The analysis showed that the highest fire counts were observed in the central region, followed by the south-west and thelowest in the north-east region of Punjab during all the years. Moreover, during the wheat season, highest fire counts were observed in 2019, being 2535, 11062 and 6212 in north-east, central and south-west regions, respectively. However, during rice, highest fire counts were observed in 2020 in the north-east being 2857 and in 2021 the central and south-west regions being 40960 and 30351 respectively. In line with the number of fire counts, the highest concentration of gases and particulate matter obtained from the Central Pollution Control Board (CPCB) was also observed in the central zone. During the wheat harvesting season, central zone experienced the highest concentration of PM2.5 and PM10 in May 2018 and that of SO2 and O3 in May 2019. Similarly, during the rice harvesting season, central zone also experienced the highest concentration of PM2.5 and PM10 during November 2017 and that of SO2 in 2018. However, the highest concentration of NO2 was observed in October 2018 and that of O3 in October 2020 in the central region. Analysis of the concentration of NO2 and SO2 obtained from the Soumi-NPP satellite also had similar results to CPCB. Such a high concentration of gases and particulate matter might be attributed to crop residue burning a significant positive correlation was observed between fire counts and concentration of particulate matter (PM2.5 and PM10). In view of the alarming deterioration of air quality, there is a dire need to check this practice in the region by providing incentives and viable alternatives to the farmers.

Assessing the Impact of Straw Burning on PM2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China

Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM2.5 levels, with R2 values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution.

Vegetation Warming and Greenness Decline across Amazonia during the Extreme Drought of 2023

In 2023, most parts of the world experienced exceptional heat. In particular, anomalous warm temperatures and heatwave events were evidenced across South America during the second half of the year. The situation was particularly critical in the Amazon region in terms of not only hydrometeorological drought but also ecological and socioeconomic impacts. In this study, remote-sensing data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to observe the changes in temperature and vegetation across Amazonia during the exceptional drought of 2023. This analysis was based on anomalies in the land surface temperature (LST) and vegetation indices: the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The amplitude of the LST (AMP-LST), an indicator of the energy partitioning between the latent and sensible heat flux, and fire counts were also considered. The results show widespread and extreme warming across Amazonia during the austral spring in 2023, accompanied by a decline in vegetation greenness, water stress conditions across northern Amazonia, and an enhanced fire occurrence across central and northern Amazonia.

Biomass burning records of the Shulehe Glacier No. 4 from Qilian Mountains, Northeastern Tibetan Plateau

Biomass burning play a key role in the global carbon cycle by altering the atmospheric composition, and affect regional and global climate. Despite its importance, a very few high-resolution records are available worldwide, especially for recent climate change. This study analyzes levoglucosan, a specific tracer of biomass burning emissions, in a 38-year ice core retrieved from the Shulehe Glacier No. 4, northeastern Tibetan Plateau. The levoglucosan concentration in the Shulehe Glacier No. 4 ice core ranged from 0.1 to 55 ng mL−1, with an average concentration of 8 ± 8 ng mL−1. The concentrations showed a decreasing trend from 2002 to 2018. Meanwhile, regional wildfire activities in Central Asian also exhibited a declining trend during the same period, suggesting the potential correspondence between levoglucosan concentration of the Shulehe Glacier No. 4 ice core and the fire activity of Central Asia. Furthermore, a positive correlation also exists between the levoglucosan concentration of the Shulehe Glacier No. 4 ice core and the wildfire counts in Central Asia from 2002 to 2018. While backward air mass trajectory analysis and fire spots data showed a higher distribution of fire counts in South Asia compared to Central Asia, but the dominance of westerly circulation in the northern TP throughout the year. Therefore, the levoglucosan in the Shulehe Glacier No. 4 provides clear evidence of Central Asian wildfire influence on Tibetan Plateau glaciers through westerlies. This highlights a great importance of ice core data for wildfire history reconstruction in the Tibetan Plateau Glacier regions.

Source apportionment of fine particulate matter in middle Indo-Gangetic Plain by coupled radiocarbon –molecular organic tracer method

Despite significant emissions of fine particulate matter (FPM) from the Indo-Gangetic Plain (IGP) that affect the climate and air quality in the region, the sources of these emissions are not adequately addressed. This research uses a combined radiocarbon-molecular organic tracer technique to investigate the degree of contamination, seasonal fluctuations, and contribution of FPM in the middle IGP (Patna), India. The findings indicated levoglucosan (L) as the single primary BB tracer chemical, ranging from 149 ng/m3 to 490 ng/m3 (median 282 ng/m3). Winter (median 462 ng/m3) showed a 2–3 times higher level of L than the monsoon season (median 180 ng/m3). A significant association of L with other organic tracers such as galactosan (G), mannosan (M), vallinic acid, syringic acid, p-hydroxybenzoic acid (pHA) and dehydroabietic acid (DHAA)(r = 0.53 to 0.89, p < 0.05), and moderate connection with Cl− (r = 0.21, p < 0.05), SO42− (r = 0.29, p < 0.05), and NO3- (r = 0.22, p < 0.05) indicated significant BB contribution. However, non-sea salt (nss-K+) was not related to L. Based on seven days of air mass back trajectories and MODIS active fire counts analysis, we conclude that OAs composition is not the local origin but is also impacted by long-range atmospheric transport from Pakistan/Afghanistan, followed by the Bay of Bengal and the Arabian Sea. Chemical analysis of organic tracers and positive matrix factorization (PMF) modeling study identified three unique sources, i.e., biomass burning, secondary aerosols formation, and mixed type (fossil fuels and construction dust) as the primary source of FPM in Patna, accounted for 46.1 %, 28.9 %, and 24.9 %, of total emissions, respectively. The radiocarbon (14 C) analysis of total carbon (TC) samples further supported this conclusion. The results of the 14 C study indicated that emissions from BB, such as wood and stubble, were responsible for 57% of the TC concentration.

Land Cover Disaggregated Fire Occurrence and Particulate Matter2.5 Relationship in the Mekong Region: A Comprehensive Study

Air pollution has become an increasing concern in the Mekong region due to seasonal vegetative burning triggered by related anthropogenic activities and climate change. While the assumption of a correlation between agriculture burning and air pollution is a common postulation, little evidence exists on the association between fire incidents and air pollution concentrations. The current study explores the relationship between satellite-derived fire occurrence, land surface characteristics, and particulate matter 2.5 (PM2.5) concentrations for the five Lower Mekong countries, namely Cambodia, Laos, Myanmar, Thailand, and Vietnam, in an effort to gain new insights into fire distributions related to air quality. Publicly available daily active fire hotspots from the VIIRS satellite instrument, annual land cover products from the MODIS satellite, and mean monthly ground-level PM2.5 estimates from the V5.GL.04 database were analyzed in two relational assessments; first, the distribution of VIIRS active fire counts and fire radiative power (FRP) temporally and spatially and secondly, the correlations between the monthly VIIRS active fire counts, cumulative monthly FRP and mean monthly PM2.5 estimates per country and land cover type. The results suggest a statistically significant positive correlation between monthly fire counts, cumulative FRP, and PM2.5 estimates for each country, which differ based on land cover. The strongest correlation between monthly fire incidences and PM2.5 estimates was found in the case of Myanmar. For all countries combined, fires detected in forests displayed the highest correlation with monthly PM2.5 estimates. This study demonstrates the use of the VIIRS active fire product and provides important insights into temporal and spatial fire distributions as baseline information for fire prevention and mitigation strategies in the Mekong region.

Multiparameter Detection of Summer Open Fire Emissions: The Case Study of GAW Regional Observatory of Lamezia Terme (Southern Italy)

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.

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Despite the significant impacts of biomass burning (BB) on global climate change and regional air pollution, there is a relative lack of research on the temporal trends and geographic patterns of BB in Northeast China (NEC). This study investigates the spatial–temporal distribution of BB and its impact on the atmospheric environment in the NEC region during 2004 to 2023 based on remote sensing satellite data and reanalyzed data, using the Siegel’s Repeated Median Estimator and Mann–Kendall test for trend analysis, HDBSCAN to identify significant BB change regions, and Moran’s Index to examine the spatial autocorrelation of BB. The obtained results indicate a fluctuating yet overall increasing BB trend, characterized by annual increases of 759 for fire point counts (FPC) and 12,000 MW for fire radiated power (FRP). BB predominantly occurs in the Songnen Plain (SNP), Sanjiang Plain (SJP), Liaohe Plain (LHP), and the transitional area between SNP and the adjacent Greater Khingan Mountains (GKM) and Lesser Khingan Mountains (LKM). Cropland and urban areas exhibit the highest growth in BB trends, each surpassing 60% (p &lt; 0.05), with the most significant growth cluster spanning 68,634.9 km2. Seasonal analysis shows that BB peaks in spring and autumn, with spring experiencing the highest severity. The most critical periods for BB are March–April and October–November, during which FPC and FRP contribute to over 80% of the annual total. This trend correlates with spring planting and autumn harvesting, where cropland FPC constitutes 71% of all land-cover types involved in BB. Comparative analysis of the aerosol extinction coefficient (AEC) between areas with increasing and decreasing BB indicates higher AEC in BB increasing regions, especially in spring, with the vertical transport of BB reaching up to 1.5 km. County-level spatial autocorrelation analysis indicates high–high clustering in the SNP and SJP, with a notable resurgence of autocorrelation in the SNP, suggesting the need for coordinated provincial prevention and control efforts. Finally, our analysis of the impact of BB on atmospheric pollutants shows that there is a correlation between FRP and pollutants, with correlations for PM2.5, PM10, and CO of 0.4, 0.4, and 0.5, respectively. In addition, the impacts of BB vary by region and season, with the most significant impacts occurring in the spring, especially in the SNP, which requires more attention. In summary, considering the escalating BB trend in NEC and its significant effect on air quality, this study highlights the urgent necessity for improved monitoring and strategic interventions.

Unearthing the legacy of wildfires: post fire pyrogenic carbon and soil carbon persistence across complex Pacific Northwest watersheds

Wildfires have the potential to dramatically alter the carbon (C) storage potential, ecological function, and the fundamental mechanisms that control the C balance of Pacific Northwest (PNW) forested ecosystems. In this study, we explored how wildfire influences processes that control soil C stabilization and the consequent soil C persistence, and the role of previous fire history in determining soil C fire response dynamics. We collected mineral soils at four depth increments from burned (low, moderate, and high soil burn severity classes) and unburned areas and surveyed coarse woody debris (CWD) in sites within the footprint of the 2020 Holiday Farm Fire and in surrounding Willamette National Forest and the H.J. Andrews Experimental Forest. We found few changes in overall soil C pools as a function of fire severity; we instead found that unburned sites contained high levels of pyrogenic C (PyC) that were commensurate with PyC concentrations in the high severity burn sites—pointing to the high background rate of fire in these ecosystems. An analysis of historical fire events lends additional support, where increasing fire count is loosely correlated with increasing PyC concentration. An unexpected finding was that PyC concentration was lower in low soil burn severity sites than in control sites, which we attribute to fundamental ecological differences in regions that repeatedly burn at high severity compared with those that burn at low severity. Our CWD analysis showed that high mean fire return interval (decades between fire events) was strongly correlated with low annual CWD accumulation rate; whereas areas that burn frequently had a high annual CWD accumulation rate. Within the first year postfire, trends in soil density fractions demonstrated no significant response to fire for the mineral-associated organic matter pool but slight increases in the particulate pool with increasing soil burn severity—likely a function of increased charcoal additions. Overall, our results suggest that these PNW forest soils display complex responses to wildfire with feedbacks between CWD pools that provide varying fuel loads and a mosaic fire regime across the landscape. Microclimate and historic fire events are likely important determinants of soil C persistence in these systems.

Water and methanol soluble aerosol brown carbon over Bhopal, central India – Changes in optical properties and radiative effects during the COVID-19 lockdowns

Water-soluble organic carbon (WSOC) and methanol-soluble organic carbon (MeS-OC) in ambient PM2.5 over Bhopal, central India, were optically characterized during the COVID-19 lockdowns year (2020) to assess the influence of source reductions on these species and the induced changes in their radiative effects. During the lockdowns (23 March – May 31, 2020), the light absorption coefficients (babs-405) of WSOC and MeS-OC had higher values (75 %–105 %) compared to the corresponding period in 2019. MeS-OC light absorption at 365 nm and 550 nm were about thrice and twice, respectively, compared to WSOC absorption at these wavelengths. Increased fire counts and higher MAC405 (Mass Absorption Cross-section) of MeS-OC and WSOC (8 %–58 %) during the lockdowns compared to the corresponding period in 2019, indicated frequent biomass burning events because vehicular and industrial sources were completely shutdown during this period. Fractional solar radiation absorption (f300–400 nm) by MeS-OC and WSOC normalized by EC absorption was higher (15 % ± 9 % and 9 % ± 4 %, respectively) during the lockdowns compared to the corresponding period in 2019. The SFE300–400nm (Simple Forcing Efficiency) values for MeS-OCthisstudy→literature and WSOCthis study→literature were 8 % ± 5 % and 13 % ± 5 % higher, respectively, during lockdowns compared to the same period in 2019. Overall, the results suggested that biomass burning emissions during COVID-19 lockdowns offset (and overtook) any source emissions reduction induced aerosol radiative heating gains.

Source contribution of black carbon aerosol during 2020–2022 at an urban site in Indo-Gangetic Plain

The extensive emissions of black carbon (BC) from the Indo-Gangetic Plain (IGP) region of India have been well recognized. Particularly, biomass emissions from month-specific crop-residue burning (April, May, October, November) and heating activities (December–February) are considered substantial contributors to BC emissions in the IGP. However, their precise contribution to ambient BC aerosol has not been quantified yet and remains an issue of debate. Therefore, this study aims to fill this gap by quantifying the contribution of these month-specific biomass emissions to ambient BC at an urban site in IGP. This study presents the analysis of BC mass concentrations (MBC) measured for 3 years (2020−2022) in Delhi using an optical photometer i.e., continuous soot monitoring system (COSMOS). A statistical analysis of monthly mean MBC and factors affecting the MBC (ventilation coefficients, air mass back trajectories, fire counts) is performed to derive month-wise contribution due to background concentration, conventional emission, regional transport, crop-residue burning, and heating activities. The yearly mean MBC (5.3 ± 4.7, 5.6 ± 5.0, and 5.3 ± 3.5 μg m−3 during 2020, 2021, and 2022, respectively) remained relatively consistent with repetitive monthly patterns in each year. The peak concentrations were observed from November to January and low concentrations from June to September. Anthropogenic activities contributed significantly to MBC over Delhi with background concentration contributing only 30 % of observed MBC. The percentage contribution of emissions from crop-residue burning varied from 15 % (May) to 37 % (November), while the contribution from heating activities ranged from 25 % (December) to 39 % (January). This source quantification study highlights the significant impact of month-specific biomass emissions in the IGP and can play a vital role in better management and control of these emissions in the region.

Increased summertime wildfire as a major driver of the clear-sky dimming in the Siberian Arctic from 2000 to 2020

A warming Arctic is expected to exacerbate wildfires in Siberia, potentially creating a critical feedback to the Arctic climate change. However, our understanding of these fire-climate interactions remains limited. This study investigated changes in East Siberian wildfires and their influence on fire emissions, aerosol optical depth (AOD), and the surface clear-sky insolation across the Siberian Arctic from 2000 to 2020 using satellite observations. Our analysis reveals a substantial increase in wildfires, with fire counts doubling (a 114% increase) and fire radiative power surging by 8.4 × 106 MW compared to the early 21st century. Over 93% of this increase occurred during the boreal summer months. These intensified wildfires led to a significant rise in aerosol emission (organic carbon, PM2.5, and black carbon) exceeding 75% in East Siberia. Consequently, fire-season AOD in the Siberian Arctic increased by 33% (6.0 × 10−2), with 85% (5.1 × 10−2) attributable to wildfire changes. The wildfire-associated increase in AOD resulted in enhanced clear-sky dimming of 4.1 ± 3.2 W m−2 across the Siberian Arctic from 2000 to 2020. These findings suggest a critical feedback mechanism: a warming Arctic drives increased wildfires in Siberia, which in turn significantly impact the Arctic surface radiative budget through enhanced clear-sky dimming. Future simulations and projections for the Arctic should prioritize incorporating the feedback effects of intensifying wildfires.

A zero‐inflated Poisson spatial model with misreporting for wildfire occurrences in southern Italian municipalities

AbstractWe propose a Poisson model for zero‐inflated spatial counts contaminated by measurement error: we accommodate the excess of zeroes in the counts, consider the possible under/over reporting of the response and account for the neighboring structure of spatial areal units. Bayesian inferences are provided by MCMC implementation through the R package NIMBLE. To evaluate the model performance, a simulation study is carried out under configurations that allow for structured and unstructured spatial random effects. The proposed model is applied to investigate the distribution of the counts of wildfire occurrences in the municipal areas of two neighboring Italian regions for the summer season 2021. Fire counts are obtained by processing MODIS satellite data, while several socio‐economic and environmental‐driven potential risk factors are also considered in the model formulation. Data from multiple sources with different spatial support are processed in order to comply with the municipal units. Results suggest the appropriateness of the approach and provide some insights on the features of wildfire occurrences.

Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.

Emergency policies are not enough to resolve Amazonia’s fire crises

The fire crises in the Amazon continues to increase the risk of large-scale forest dieback, threatening regional biodiversity and global climate. This issue gained international attention in 2019 when fires in the Brazilian Amazon led to a fire ban imposition. Despite the uncertainty of its impact, the fire ban was reenacted in subsequent years. Here we assess the effectiveness of each fire ban by comparing observed fire counts with climate-driven predictions of fire for 2019–2021. While the 2019 ban likely reduced the number of fires to expected levels, it was largely ineffective in the years that followed. Four years later in 2023 under a different political dynamic, the Brazilian Amazon faced another fire crisis. Resolving this recurrent issue requires interventions that target the underlying causes of fire and extend beyond emergency measures, including long-term strategies focused on landscape management, public awareness and education, and engagement with local communities and stakeholders.

A comprehensive investigation on the effect of UHI in Baripada city using geo‐spatial technique and MLTHP model

AbstractAccording to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report.

National-scale spatiotemporal patterns of vegetation fire occurrences using MODIS satellite data.

As the risk of climate change increases, robust fire monitoring methods become critical for fire management purposes. National-scale spatiotemporal patterns of the fires and how they relate to vegetation and environmental conditions are not well understood in Zimbabwe. This paper presents a spatially explicit method combining satellite data and spatial statistics in detecting spatiotemporal patterns of fires in Zimbabwe. The Emerging Hot Spot Analysis method was utilized to detect statistically significant spatiotemporal patterns of fire occurrence between the years 2002 and 2021. Statistical analysis was done to determine the association between the spatiotemporal patterns and some environmental variables such as topography, land cover, land use, ecoregions and precipitation. The highest number of fires occurred in September, coinciding with Zimbabwe's observed fire season. The number of fires significantly varied among seasons, with the hot and dry season (August to October) recording the highest fire counts. Additionally, although June, July and November are not part of the official fire season in Zimbabwe, the fire counts recorded for these months were relatively high. This new information has therefore shown the need for revision of the fire season in Zimbabwe. The northern regions were characterized by persistent, oscillating, diminishing and historical spatiotemporal fire hotspots. Agroecological regions IIa and IIb and the Southern Miombo bushveld ecoregion were the most fire-prone areas. The research findings also revealed new critical information about the spatiotemporal fire patterns in various terrestrial ecoregions, land cover, land use, precipitation and topography and highlighted potential areas for effective fire management strategies.

Global expansion of wildland-urban interface (WUI) and WUI fires: insights from a multiyear worldwide unified database (WUWUI)

Fires in the wildland-urban interface (WUI) are an important issue globally. To understand the change of WUI, we develop a 9 km worldwide unified wildland-urban interface database for 2001–2020 with Random Forest models and satellite data. We find that WUI has been increasing in all populated continents from 2001 to 2020 and the global relative increase is 24%, with the largest relative increase (∼59%) over Africa. Global total fire counts decrease by 10% from 2005 to 2020, whereas the WUI fraction of fire counts increases by 23%. The global total burned area decreases by 22% from 2005 to 2020, whereas the WUI fraction of burned area increases by 35%. These are mainly due to the expansion of WUI area. On all the populated continents, the WUI fractions of fire counts are higher than the WUI fractions of burned area, implying that WUI fires tend to have smaller sizes than wildland fires. We also project future WUI changes for the years 2030 and 2040, together with the projection of future fire burned area under different shared socioeconomic pathways (SSP) scenarios in the Community Earth System Model version 2 (CESM2). The projected global WUI fraction (excluding Antarctica and the oceans) is 5.9% in 2040 compared to 4.8% in 2020. The global WUI fraction of burned area is projected to increase from now to 2040 under most scenarios analyzed in this study, unless the WUI area stays at the 2020 level together with the projected burned area under SSP4-4.5. This study is a first step to understanding the changes of WUI fires at the global scale and demonstrates a growing importance of WUI fires. The global multi-year WUI and WUI fire datasets developed in this study can facilitate future work quantifying the impacts of WUI fires on air quality and climate.

Observed links between heatwaves and wildfires across Northern high latitudes

Data on Arctic and Sub-Arctic summer heat events are limited due to the sparse network of surface observation stations. Here, we analyze large heat events within 60°–80°N using land surface temperature (LST) observations from the moderate resolution imaging spectroradiometer (MODIS) sensor aboard the Terra satellite. Our heatwave (HW) detection method uses exceedances of the climatological 90th percentile of LST across summer months, and a spatio-temporal density-based clustering algorithm to distinguish space-time coherent events across Northern Hemispheric high latitudes for the summers of 2000–2022. We find a close link between HW duration, spatial extent and amplitude across the study region (correlations ranging from 0.63 to 0.73). MODIS-derived burned area data show that wildfire seasons are significantly correlated to summer HW activity, particularly in Siberia (r = 0.87 at p < 0.05) and Alaska and NW Canada (r = 0.45 at p < 0.05), and are also spatially co-located. MODIS active fire data also show substantial increase during larger heat events. For the strongest HWs in Siberia, the peak in daily fire count (from the MODIS active fire archive) lags behind peak HW activity. We conclude that there is a close link between intense fire summers and extensive HWs over boreal and shrub tundra regions (Interior Alaska, the Canadian Prairies and Eastern Siberia).

Forecast Zoning of Forest Fire Occurrence: A Case Study in Southern China

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial analysis, including kernel density and standard deviation ellipse methods, to predict forest fire occurrences. Historical fire data analysis reveals noteworthy findings: (i) Decreasing Trend in Forest Fires: The annual forest fire count in the southern region exhibits a decreasing trend from 2001 to 2019, indicating a gradual reduction in fire incidence. Spatial autocorrelation in fire point distribution is notably observed. (ii) Excellent Performance of Prediction Model: The constructed forest fire prediction model demonstrates outstanding performance metrics, achieving high accuracy, precision, recall, F1-scores, and AUC on the testing dataset. (iii) Seasonal Variations in High-Risk Areas: The probability of high-risk areas for forest fires in the southern region shows seasonal variations across different months. Notably, March to May sees increased risk in Guangxi, Guangdong, Hunan, and Fujian. June to August concentrates risk in Hunan and Jiangxi. September to November and December to February have distinct risk zones. These findings offer detailed insights into the seasonal variations of fire risk, providing a scientific basis for the prevention and control of forest fires in the southern region of China.