Forest Fire Events Research Articles

Temporal Trend in Landslide Occurrences in Post-fire Areas over the Past Two Decades

Analyzing the effects of forest fires on sediment dynamics is essential to establish effective mitigation measures to reduce the risk of fire-related sediment disasters, such as landslides, debris flows, and hyperconcentrated flows. The objective of this study was to investigate the temporal trend in landslide occurrences after a forest fire based on the statistical data of forest fires and landslides recorded over the past two decades. The results suggested that the landslide occurrences in the post-fire area increased the most in 1 - 5 years after the fire. Typically, landslide occurrences in the post-fire area decreased as the burnt slopes were covered with grass and shrubs within five years after a fire. However, our data showed that the frequency of landslide occurrences revealed relatively higher value until 6 - 10 years after the fire. This result was considered owing to the decrease in relative resistance to landslides initiated by root decay in the burnt slopes. Overall, the risk of landslides after forest fires is presumed to be higher than the pre-fire condition for the period that the root system of burnt slopes is regrowth and recovery (10 - 20 years or more after the fire).

How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region

Forest fire is an ecosystem regulating factor and affects the stability, renewal, and succession of forest ecosystems. However, uncontrolled forest fires can be harmful to the forest ecosystem and to the public at large. Although Yunnan, China is regarded as a global hotspot for forest fires, a general lack of understanding prevails there regarding the mechanisms and interactions that cause forest fires. A logistic regression model based on fire points in Yunnan detected by satellite in 2005–2019 was used to estimate how environmental factors in local areas affect forest fire events. The results show that meteorology is the dominant cause of the frequent forest fires in the area. Other factors of secondary importance are the daily minimum relative humidity and the daily maximum temperature. When using the logistic regression model based on the data of fire points in Yunnan over the period 2005–2019, the key threshold for the daily minimum relative humidity is 28.07% ± 11.85% and the daily maximum temperature is 21.23 ± 11.15 °C for a forest fire probability of 50%. In annual and monthly dynamic trends, the daily minimum relative humidity also plays a dominant role in which combustible substance load remains relatively stable from January to March, and the impact on forest fire becomes greater in April, May, and June, which plays a secondary role compared with the interannual climate. The maximum daily temperature ranks third in importance for forest fires. At the county level, minimum relative humidity and maximum temperature are the top two factors influencing forest fires, respectively. Meanwhile, the differences in forest fire points between counties correspond to the pathways of the two monsoons. This study applies quantitative expressions to reveal the important environmental factors and mechanisms that cause forest fires. The results provide a reference for monitoring and predicting forest fires.

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).

Long-term effects of forest fires on fungal community and soil properties along a hemiboreal Scots pine forest fire chronosequence

We studied long-term effects of forest fires on the dynamics of soil fungal community along a post-fire chronosequence in hemiboreal Scots pine stands in north-western Estonia. Effects of fire on soil and fungi were studied on six sites that differed in time since fire (10, 21, 36, 67, 78 and 181 years ago), without further management interventions. Soil fungal communities along the chronosequence were dominated by soil saprotrophs and ectomycorrhizal (EcM) fungi. Across the chronosequence, the most dominant phylum was Ascomycota. The most abundant OTUs were identified as Umbelopsis sp., Hyaloscyphaceae sp. and Pezoloma ericae with relative abundances of 9.5, 8.9 and 6.8 %, respectively. Fungal species richness was similar among sample areas except in the area where fire occurred 36 years ago, where it was significantly lower. There were considerable differences in EcM fungal species composition along the chronosequence. The most recently burned site had Piloderma sphaerosporum, Pseudotomentella sp. and Clavulinaceae sp. as most abundant EcM OTUs while in three oldest burned areas Clavulinaceae sp. and Cortinarius sp. were abundant. Soil C and N stocks were lower in the most recently burned area but differences with other areas were not statistically significant. Soil pH had a significant effect on fungal species composition. Older areas had substantially lower pH compared to more recently burned areas.

A REVIEW ON THE CURRENT STATUS OF FOREST FIRE IN UTTARAKHAND STATE OF INDIA

Forest fires have caused a huge loss to the Himalayan biodiversity, which has adversely affected the productive capacity and natural regeneration of forests species. The Uttarakhand state of India is severely affected by forest fire every year, which is one of the main reasons for loss of biodiversity and ecosystem services of the precious forest resources. As per as record, approximate 700 species of bird and animals are at the high risk of extinction as a consequence of adverse effects of forest fire. In the Uttarakhand state, majority of fire occurs in Chir Pine forests, which later on extends to the associated species, such as, Banj Oak. Worldwide, forest fire has been viewed as a serious issue responsible for the climate change. Subjecting forest areas to regular controlled burning can reduce the incidence of forest fire to the great extent. Further, imparting education, training and awareness on forest fire to the local people and state govt. official can considerably control fire incidence as well as fuel load in the forests.

Late Pleistocene montane forest fire return interval estimates from Mount Kenya

AbstractPast forest fire events and fire frequencies are reconstructed with sediment–charcoal records at lake catchment spatial scales. Few quantitative palaeofire analyses exist in tropical montane forests, where fire return intervals are long (decadal and centennial scales) because of the infrequency of fire weather and fuel conditions. Fire return intervals are a key characteristic of fire regimes and changing fire frequencies rapidly alter land cover compositions and vegetation structure. Charcoal records from small lakes with relatively small catchments covered with dense forest provide an opportunity to reconstruct low‐frequency, high‐severity fires through a time series decomposition approach to identify charcoal peaks above a varying background rate as a proxy for palaeofire events. The sediment core from Rumuiku wetland on Mount Kenya, equatorial eastern Africa, accumulated a nearly linear age–depth model and provided a high temporal resolution (10 years cm–1) sieved charcoal count record (>125 µm). Pollen analysis showed a significant change in montane forest assemblage occurred at 21 200 cal abpfrom a montane forest with abundantPodocarpusandJuniperusto a forest with more abundantHagenia. This change in forest altered the vegetation composition and structure with concomitant changes to the fire regime. Forest biomass in theHageniaforests decreased and it is likely that fire activity qualitatively changed toward lower intensity and lower severity fires. The quantitative fire event reconstruction focuses on the interval from 27 000 to 16 500 cal a bp and the older montane forest that experienced higher severity fires from 27 000 to 21 200 cal abp,which reconstructed a temporally heterogeneous fire regime with fire return intervals that ranged from 30–430 years and a mean of 120 years (median 160 years) in the catchment. These are the first estimates of fire return intervals of mountain forests in eastern Africa. We then explore the potential for further comparative research and incremental research contributions to improve quantitative and qualitative palaeofire research in tropical forest ecosystems. We discuss the potential to use these types of data for characterizing variables of fire regimes prior to ostensibly significant modification by anthropogenic activity as well as during the recent past as human land use pressures increased within Afromontane forests.

Characteristics of humic acids isolated from burned and unburned topsoils in sub-boreal Scotch pine forests by 13C-NMR spectroscopy

Postpyrogenic soil dynamics is an informative tool for studying soil elementary processes in extreme temperature conditions and for predicting short time environmental changes in conditions of catastrophic landscape changes. Soil organic matter (SOM) system evolution is the most rapid process of postpyrogenic soil development. In this relation, the focus on humus structure is important for understanding these important dynamics. Soil restoration after spontaneous forest fires near Togljatty City (Samara Region, Russia) was abandoned in 2010 and further monitoring over the next ten years was organised to evaluate the speed of humus accumulation dynamics. The aim of this study was to apply the 13C-NMR (nuclear magnetic resonance) spectroscopy to analyse the effect of forest fires on SOM degradation in Scotch pine forests on Psamment Entisols of the fragmented steppe in the Samara Region. Three key soil plots were studied for estimating SOM quality changes under the forest fire effect: surface forest fire, crown forest fire and control, i.e. 18 soil samples of top soil horizons were analysed in three replicates. The data obtained indicate that the humus molecular composition was substantially affected by the wildfires. Investigation of the humic acids’ (HAs) molecular structure by 13C-NMR showed a relative increase in aromatic compounds and decrease in aliphatic ones. The aromaticity degree of HA molecules increases in 5.7 and 3.8 times in cases of surface and crown forest fires, correspondingly. In general, crown and surface fires plots are not very different in terms of 13C-NMR spectra of HAs (p = 0.34); however, HAs of control plot have essential differences from pyrogenic ones (p < 0.05). 13C NMR spectra have shown a change in the proportion of oxygen-containing functional groups as well. One of the most important effects observed in wildfire-affected SOM is that the proportion of lignin-like structures increases as a concomitant effect of depletion of C,H-alkyl groups, especially in the case of crown fires.

Effect of Fire Severity on the Species Diversity and Structure of a Temperate Forest in Northern Mexico

Forests experience disturbances, such as fire, that affect their functioning, structure, and species composition. The objective of this work was to compare the species diversity and forest structure at sites affected by different degrees of fire severity, 9 years after a forest fire event. We used the differenced Normalized Burn Ratio (dNBR) index. Vegetation was sampled in three severity levels: high (HS), moderate (MS), and low (LS) and included the unburned (U) level as control. In addition, we calculated the species richness (S), the Shannon index (H), and the Evenness Index (E). The structural diversity of tree diameters and heights was measured applying the indices H, E, and the coefficient of variation (CV). The differences in the indices calculated across the fire severity levels were determined through analyses of variance (ANOVA) and Tukey’s multiple comparison tests. The results showed no significant differences (p ≤ 0.05) in the species diversity indices between fire severity levels. The structural diversity of tree diameters and heights was lower at the HS level. dNBR was negatively related to structural diversity; thus, it is concluded that HS tends to reduce structural variability in terms of diameter, height, and age. These results provide a baseline to understand how fire can modify forest structure and species diversity.

Monthly Burned-Area Mapping using Multi-Sensor Integration of Sentinel-1 and Sentinel-2 and machine learning: Case Study of 2019's fire events in South Sumatra Province, Indonesia

Indonesia has experienced massive historical land and forest fire events, creating transnational environmental and socioeconomic issues. The extent of burned areas (BAs) is one of many indicators that reflect the magnitudes and impacts from fire events, and such information is also used for planning the response and recovery steps after the fire events. This information is usually derived using remote sensing (RS) data. However, the assessment on the performance using available RS data, possible RS data combinations, and existing methods is needed to regularly monitor the BA extent. This study aims to assess the performance from Sentinel-1 synthetic aperture radar polarization (Pol.) and gray-level of co-occurrence matrix (GLCM) textural features and the integration with Sentinel-2 spectral data (Spec.) for the monthly mapping of BA extent using machine learning algorithms, such as random forests (RFs) and extreme gradient boosting (XGB). The study took place in the parts of Ogan Komering Ilir Regency and Banyuasin in South Sumatra Province, Indonesia. This area has complex land-use classes, such as natural vegetation and plantations (pulpwood and oil palm), which were affected by the 2019's fire events. Our study demonstrated that the combination between Pol. from Sentinel-1 and spectral data from Sentinel-2 (Diff.Pol + Spec.) yielded the best classification accuracy with the overall accuracy (OA) values ranging from 91.80% (XGB) to 95.80% (RF) with the producer's accuracy (PA) from 73.33% to 97.66% and user's accuracy (UA) from 76.69% to 89.80% for the BA class. The integration of spectral data using Sentinel-2 reduced the source of misclassification of BAs from false detection from the non-fire-related land-cover conversion, such as logging activities.

The air quality of Palangka Raya, Central Kalimantan, Indonesia: The impacts of forest fires on visibility

ABSTRACT Airborne particles in urban Palangka Raya, Kalimantan from Oct 2011 until Oct 2020 have been collected and analyzed for PM2.5, PM10, and Black Carbon (BC) concentrations. Palangka Raya is a city that serves the capital of the Central Kalimantan province on the island of Borneo. Kalimantan is affected by peat fires that occur periodically. There were identified increases in PM2.5 and PM10 concentrations during El Niño periods. During the forest fire episode in September – October 2015, PM2.5 and PM10 concentrations increased significantly, to nearly 400 µg/m3 and 800 µg/m3, respectively, and visibility in the city was reduced to < 0.2 miles. The highest BC concentrations were observed during this massive forest fires episode. The regression analyses for PM2.5, PM10 and visibility in Palangka Raya during the period of 2011–2020, showed a non-linear correlation with reduction in visibility due to increased PM2.5 and PM10. There was no correlation for BC with visibility. Air quality in Palangka Raya was at a relatively good level with concentrations below the national ambient air quality standard when there were no forest fires event. Emissions from forest fires caused a substantial reduction in air quality reaching concentrations well above ambient air quality standards and are likely to have caused adverse health effects on the people living in the area. Implications: Indonesia has repeatedly experienced forest fires, especially on Kalimantan and Sumatera Islands, which burned large areas of peatland. The forest fires leading to increasing PM concentrations especially in the PM2.5 size range which influence visibility. The seasonal variations of BC in Palangka Raya and the relationships of fine particulates with visibility were assessed. The results of regression analyses for PM2.5 and PM10 to visibility during the period of 2011-2020 showed non-linear relationships. An increasing of PM2.5 and PM10 concentrations during El Nino periods were detected well above the ambient air quality standard. To ensure effective and continued handling and prevention of forest and peatland fires, the government set up a special task force and review on several rules, including laws and government regulations as well as governor regulations that permit the burning of forest and peatland areas. These results are expected to be used to formulate more effective mitigations in reducing forest fires events in Indonesia.

IMPACT OF FOREST FIRE EMISSIONS ON AIR QUALITY OVER WESTERN HIMALAYA REGION

Abstract. Emissions from forest fires give out huge amounts of greenhouse gases into the atmosphere degrading the surrounding air quality. Climate change results in prolonged summer days and less precipitation thus increasing fuel accumulation facilitating recurring forest fires in the Western Himalayan regions. Most of the forest fire cases are human ignited that damaging the forest irreparably. This study attempts to analyse the impact of the forest fire events on the tropospheric concentrations of five major gaseous pollutants aerosols, Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Formaldehyde (HCHO). A clear increase in daily average of the air pollutants considered in the study was observed as the number of forest fires were also increasing. The gases responded inversely with precipitation. Precipitation washed away majority of the air pollutants. The high-resolution air quality data were retrieved from the Google Earth Engine platform using Sentinel 5 Precursor datasets, daily active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument of Suomi National Polar-orbiting Partnership (SNPP) and daily precipitation data from CHIRPS Daily Version 2.0 Final. Four out of five gases i.e., CO, aerosols, NO2, HCHO pollutants gave moderate to high correlation values with the forest fire incidences. Air pollutants responded inversely with the precipitation pattern.

Effect of Forest Fire on Tree Species Diversity in the Tropical Dry Deciduous Forest of Nauradehi Wildlife Sanctuary, Madhya Pradesh, Central India

A study was conducted to analyze the tree species composition and diversity in the Central Indian dry deciduous forest of Nauradehi Wildlife Sanctuary, Madhya Pradesh. The objective of the current study was to understand the impact of forest fire on the tree species diversity in the Central Indian tropical dry deciduous forest. Identification of fire-affected areas was done based on satellite remote sensing data of Landsat 5,7 and 8 satellite images, and different fire zones were classified in the study area into five fire frequency classes i.e., F1 to F4 and one unburned class i.e., F0. Five plots were laid within each fire class including the unburned class. A total of 3128 trees belonging to 49 plant species, 39 genera, and 14 families were recorded in all the fire frequency classes. Tectona grandis is the dominant tree species in the unburned, low fire, moderate fire, and in high fire zones where Terminalia tomentosa dominated in the severe burn areas. The results show that with the increased in fire frequency the tree species diversity, basal area and IVI increased from low, medium and high fire zones. However, these parameters decreased and again increased in the severe fire zones. The results of different indices indicate that tree diversity fluctuates with the increase in fire frequency.

The Impact of a Forest Fire Event on Moth Assemblages in Western Bohemia, Czech Republic

Fire has major impact on species diversity in natural ecosystems around the world. The consequences of fires for invertebrates can be immediate, and recovery of species assemblages may be a long-term process. We recorded moth communities from May to November 2019 in a pine forest in Western Bohemia, Czech Republic, where a local fire occurred in April of that year. We used UV light traps at one burned site and two neighboring unburned sites with a similar character as the burned site before the fire event. We analyzed moth assemblages using several species diversity indices – index of dominance, Shannon-Wiener index and Shannon-Wiener evenness, Simpson index, and Chao1 nonparametric estimation. We detected 67 species at the burned site, and 97 and 106 at the unburned sites. The three most frequently trapped species at all sites were Macaria liturata, Buphalus piniaria and Thera obeliscata, representing common forest geometrid moths. We observed a decline in species number and abundance at the burned site compared to the unburned sites, probably due to the direct mortality of larvae. On the contrary, the burned site was more diverse according to the diversity indices. The fire disrupted the original character of the coniferous forest, which was proved by a decline of species feeding on conifers and Vaccinium spp. The burned site attracted species associated with dry habitats such as Eublemma purpurina, Charissa obscurata and Scopula rubiginata. Even though wildfires are viewed negatively in general, they might temporarily enhance diversity in a homogeneous landscape.

FIRE SEVERITY ASSESSMENT OF AN ALPINE FOREST FIRE WITH SENTINEL-2 IMAGERY

Abstract. Fire is a common phenomenon in many forests and is considered an important ecological tool. Fire severity mapping presents an effective way to assess post-fire management intervention and is helpful in environmental and climate change research. The objective of this study was to determine the severity of a forest fire event that occurred from 24th to 27th October 2019 at Taibon Agordino using Sentinel-2A satellite images and creating a severity map suitable as a decision-making tool for post-fire management intervention. The Sentinel-2A satellite data was classified into the following five classes: Unburned, Low Severity, Moderate Severity, High Severity, and Shadow with the non-parametric Random Forest (RF) classifier, and the resulting classified image was validated using validation sites. The RF classifier was applied first to the ten original band reflectance of Sentinel-2. In a second step, additional variables were added to the classification, namely the digital elevation model (DEM), the slope, and five vegetation indices (i.e., Differenced Normalized Burn Ratio (dNBR), Relative Differenced Normalized Burn Ratio (RdNBR), Differenced Bare Soil Index (dBSI), Global Environmental Monitoring Index (GEMI) and Burn Area Index (BAI)) The inclusion of vegetation indices and DEM-related variables increased the classification accuracy from 99.26% to 99.61% and the overall accuracy from 70.51% to 83.33%. In the classification with the ten original band reflectance, the variable of importance plot ranked the Red-Edge-3, Red, and SWIR 1 band reflectance as the top three most important input features, while for the classification with 17 variables, RdNBR, DEM and dNBR were the top three most important input features.

Effects of forest fires on headwater streamflow and the habitat suitability for benthic macroinvertebrates

ABSTRACT We modelled hydrological, hydraulic and habitat suitability changes in a Mediterranean catchment after a wildfire. Gauged pre/post-fire hourly discharges were used to assess actual pre/post-fire hydrological conditions. To remove the weather variability effect, daily pre/post-fire discharges were simulated under the same weather conditions. An eco-hydraulic model was employed to calculate flow velocities, water depths and macroinvertebrate habitat suitability at various discharges across a 165 m long reach. The fire increased the magnitude and frequency of peak velocities and depths by 6.5–475%. Increased post-fire rainfall exacerbated hydrological-hydraulic extremes. However, the short duration of post-fire extremes led to small impacts on the habitat suitability for macroinvertebrates, which were stressed for a maximum 18.4% of the time after the fire (350 days in five years). In small Mediterranean-climate headwaters, after wildfires, less frequent streamflow becomes more frequent, high and of short duration. Macroinvertebrates largely resist such short-term, low-magnitude hydrological-hydraulic changes.

Assessment of forest fire effects based on automated processing of Earth remote sensing imager

The article presents a study of the automated detection specifics within forest-covered areas traversed by fires based on the different time satellite imagery from the Sentinel-2A and Sentinel-2B using the differential normalised burn ratio index (dNBR) for the pre-fire and post-fire periods the calculation. The studies carried out on the research topic are given and a review of the currently functioning forest fire monitoring systems has been implemented. The urgency of the development and testing of an automated system for assessing the forest fire consequences using open source software and Earth remote sensing data has been substantiated. It has been established that the differential index dNBR, calculated from the Sentinel-2A and Sentinel-2B satellite images captured on different dates makes it possible to effectively detect burned-out areas. It is shown that the Python ecosystem makes it possible to successfully create systems for automated processing of Earth remote sensing data. A prototype of a system for the automated detection of forest-covered areas traversed by fires has been developed, based on the materials of different dates satellite imagery from Sentinel-2A and Sentinel-2B spacecraft. The flowchart of the algorithm of processing Earth remote sensing data using the proposed system was presented. For the Sentinel-2 satellite images for the dates before and after the fire, the differential index dNBR was calculated, the analysis of the results of which showed a close correlation of the dNBR index with the degree of burnout of the territory. A schematic map of the areas affected by the fire has been drawn up and the accuracy of identifying burnedout areas has been assessed by calculating the confusion matrix. An assessment of the effectiveness of the automated system for identifying areas affected by forest fires, ways of its modernisation and improvement, as well as the prospects for implementation in production has been carried out. It is noted that the results of the created system have high reliability indicators. At the same time, the need was revealed to increase the sensitivity of the system when identifying areas that have undergone partial burnout. A variant of improving the algorithms used in the work by introducing the multilevel Otsu’s method, intended to significantly increase the sensitivity of the system, has been proposed.

Forest Fire Monitoring and Positioning Improvement at Subpixel Level: Application to Himawari-8 Fire Products

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.

Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection

The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.

Forest fire effects on stream water quality at continental scales: a meta-analysis

Forested watersheds supply over two thirds of the world’s drinking water. The last decade has seen an increase in the frequency and intensity of wildfires that is threatening these source watersheds, and necessitating more expensive water treatment to address degrading water quality. Given increasing wildfire frequency in a changing climate, it is important to understand the magnitude of water quality impacts following fire. Here, we conducted a meta-analysis to explore post-fire changes in the concentrations of nitrogen (N) and phosphorus (P) species, dissolved organic carbon, and total suspended sediments in 121 sites around the world. Changes were documented over each study’s respective duration, which for 90% of sites was five years or fewer. We find concurrent increases in C, N and P species, highlighting a tight coupling between biogeochemical cycles in post-fire landscapes. We find that fire alters N and P speciation, with median increases of 40%–60% in the proportion of soluble inorganic N and P relative to total N and P. We also found that fire decreases C:N and C:P ratios, with median decreases ranging from 60% to 70%. Finally we observe a ‘hockey stick’-like response in changes to the concentration distribution, where increases in the highest concentration ranges are much greater than increases at lower concentrations. Our study documents strong heterogeneity in responses of water quality to wildfire that have been unreported so far in the literature.

Long-term health effects of wildfire exposure: A scoping review

Wildfires pose a number of acute and chronic health threats, including increased morbidity and mortality. While much of the current literature has focused on the short-term health effects of forest fires and wildfire smoke, few reviews have sought to understand their long-term impact on human health. This scoping review aims to map the state of evidence as it pertains to the long-term health effects of wildfires, including physical health, mental health, and healthcare costs. A literature search identified 17 research studies meeting inclusion and review criteria. Descriptive and thematic analyses were conducted and synthesized in a narrative form for the following health topics: premature mortality; increased morbidity including respiratory disease, cardiovascular disease, cancer, mental health, and other health outcomes; and health-related economic outcomes or healthcare costs. The resulting evidence revealed limited papers, many of which were of low or mixed quality, that pointed to increased population-level mortality due to wildfire exposure and increased respiratory morbidity. While the results of lung cancer research were mixed, exposure to PM2.5 and chemicals in wildfire smoke were correlated with an increased risk of cancer of all types. Future research should include prospective longitudinal studies and collection of demographic information to assess the impacts of wildfires on the most vulnerable, expand the evidence-base for the mental health consequences of wildfire events, and include more research in low- and middle-income countries.