Burnt Forest Areas Research Articles

Catastrophic PM2.5 emissions from Siberian forest fires: Impacting factors analysis

With increased forest fires due to climate change, PM2.5 emissions also intensified. Record PM2.5 emissions according to Copernicus Atmosphere Monitoring Service in Russia amounted to 8 megatons (Mt) in 2021, which is 78% higher than the average level of 2004–2021 (4.5 Mt). Seven federal subjects (the constituent entities) with vast forest areas without fire protection produced 86% of emissions (6.8 Mt) in 2021, the major losses (6.1 Mt) in Yakutia (Sakha Republic). The ambient temperature in Eastern Siberia is increasing, especially in months of winter and spring seasons (up to +3.6 °C) in 1990–2020 compared to 1901–2020 (CEDA Archive); climate change has affected meteorological conditions leading to increased forest fires. The results of the SARIMAX model study for PM2.5 emissions considering meteorological factors using ERA5 and burnt forest area using MODIS (MCD64A1), establishing a significant dependence of PM2.5 emissions on the lack of precipitation and the associated parameters of complete and potential evaporation. This influence long before the fire season (up to 9 months), as it affects the snow cover and the dryness of the fuel by the beginning of forest fires. In turn, high PM2.5 emission values are accompanied by a drop in 2 m air temperature and surface solar radiation downwards due to the aerosol saturation with suspended particles. The average COR for seven federal subjects was 0.79, with the highest forecast result in Yakutia (0.95), indicating the maximum propensity for record emissions due to weather conditions. In combination with forest management without fire protection, meteorological parameters have caused an increase in PM2.5 emissions in recent years in Siberia. The forest needs other ways to manage under the pressures of climate change to reduce environmental pollution associated with PM2.5 emissions from vast Siberian fires.

Carbon stocks in the forests of the Ural Region

This paper examines carbon stocks in the forests of the Ural region forest fund. Carbon deposition in forest biomass, the average, average annual and annual carbon absorption were assessed by age groups and dominant species. The average annual rates of forest destruction were estimated depending on the territory of forest fires and burnt areas. Carbon losses in the biomass pool after continuous logging were estimated according to the methodology "On approving guidance to calculate greenhouse gas emissions” developed by the Ministry of Natural Resources of the Russian Federation. Carbon storage indicators were calculated from the groundwood of coniferous, hard-leaved and soft-leaved forests for all age groups. Changes in carbon stocks are shown for the period 2007-2018. The forests of the study area, the Republic of Bashkortostan, were found to sink carbon in 3,618.9 thousand tons/year. Pinus sylvestris L. forests accumulate the largest amount of carbon, 58,384.3 thousand tons, among coniferous forests. The difference between carbon absorption and its budget was 3,124.4 thousand tons/year. Soft-leaved stands that dominate in the republic accumulate the largest total carbon stock being 195,900.2 thousand tons. However, their average level of carbon stock is lower than that of coniferous and hard-leaved forests. Maintaining the level of carbon dioxide uptake is a strategic task of the republic's forestry sector, with compensatory reforestation being one solution. Keywords: absorption; carbon; coniferous forests; deciduous forests; storage; wood harvesting

Multi-decadal increase of forest burned area in Australia is linked to climate change

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia’s forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change.

Influences of climate fluctuations on northeastern North America’s burned areas largely outweigh those of European settlement since AD 1850

There is a pressing need for a better understanding of changing forest fire regimes worldwide, especially to separate the relative effects of potential drivers that control burned areas. Here we present a meta-analysis of the impacts of climate fluctuation and Euro-Canadian settlement on burned areas from 1850 to 1990 in a large zone (>100 000 km2) in northern temperate and boreal forests of eastern Canada. Using Cox regression models, we tested for potential statistical relationships between historical burned areas in 12 large landscapes (reconstructed with dendrochronological data) with climate reconstructions, changes in the Euro-Canadian population, and active suppression (all reconstructed at the decadal scale). Our results revealed a dominant impact of climate fluctuations on forest burned areas, with the driest decades showing fire hazards between 5 to 15 times higher than the average decades. Comparatively, the Euro-Canadian settlement had a much weaker effect, having increased burned areas significantly only during less fire-prone climate conditions. During periods of fire-prone climate, burned areas were maximum independent of fluctuations in Euro-Canadian populations. Moreover, the development of active fire suppression did not appear to reduce burned areas. These results suggest that a potential increase in climate moisture deficit and drought may trigger unprecedented burned areas and extreme fire events no matter the effects of anthropogenic ignition or suppression.

Forest Fires in India: A Review

A forest fire has become extreme and has increased in frequency worldwide with India being no exception. In view of this, the present-day knowledge about the forest fire condition in India has been reviewed. The forest cover in India is divided into four clusters namely North Himalayan, North-Eastern, Southern, and Central. Of these clusters, it has been found that though the North-Eastern cluster encompasses the maximum area (55%) of all the clusters in India, it accounts for a meager 16% of the burnt forest area. The central cluster has been found to be the most vulnerable to forest fires as ~56% of the total burnt area lies in this region, though it covers only 28% of the forest cover. In North-East fires tend to concentrate in a smaller area that is subject to repeated burns whereas in Central and Southern India they are more expensive. The impacts of forest fires on biodiversity, forest species, climate change, and soil characteristics in India are presented and various strategies adopted by the Indian government in order to detect and control the forest fires are presented. Additionally, the economic impact of forest fires is estimated to be over INR 1101 crore/year.

AN UNSUPERVISED METHOD BASED ON FIRE INDEX ENHANCEMENT AND GRNN FOR AUTOMATED BURNED AREA MAPPING FROM SINGLE-PERIOD REMOTE SENSING IMAGERY

Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

Boreal forest fire CO and CH<sub>4</sub> emission factors derived from tower observations in Alaska during the extreme fire season of 2015

Abstract. Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg−1 dry biomass burned. The CO / CO2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH4 / CO2 emission ratio was 0.010 ± 0.004, and the CH4 emission factor was 5.3 ± 1.8 g kg−1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH4 emission ratios varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

The potential of Göktürk 2 satellite images for mapping burnt forest areas

Using remotely sensed data to identify burnt forest areas produces fast, economical, and highly accurate results. Accordingly, in this study we investigate the capabilities of Göktürk-2, Turkey's national satellite, for mapping burnt forest areas. We compare our results with those obtained from Landsat-8 and Worldview-2 satellite images, which are frequently used for mapping burnt areas. The capabilities of the satellites are compared, in terms of detecting burnt forest areas, using support vector machine (SVM) and rotation forest (RF) classification, which are advanced methods. According to the results of the accuracy analysis, SVM classification gives similar kappa statistics and overall accuracy for Göktürk-2 and Landsat-8 images, while the performance of Worldview-2 shows greater general accuracy. Although there is no significant difference between the two classification methods forLandsat-8 images, SVM gives better results than RF for both Göktürk-2 and Worldview-2. The results of our study show that Göktürk-2 images are an effective source for mapping burnt forest areas.

A novel approach for predicting burned forest area

Forest fire hazard is a major problem in the Mediterranean region of Turkey and has a significant effect on both the climate system and ecosystems. During the last century, many forest fires accounted for the majority of the Mediterranean region in Turkey. Vector singular spectrum analysis (V-SSA) and vector multivariate singular spectrum analysis (V-MSSA) are relatively novel but powerful time series analysis techniques. The present study addresses how to forecast burned forest area (BFA) by V-SSA. One of the most important factors affecting forest fires is weather conditions. The prediction of BFA is therefore also obtained by V-MSSA using meteorological covariates (i.e., relative humidity (RH), temperature (T) and wind speed (WS). In the study, forest fire data records covering the years 2005–2019 were collected and analyzed. To gain forecast accuracy, the years 2017–2019 were used as testing data, and forecast values for 1, 3, 6, 12, 24 and 36 months were obtained. Then, V-SSA and V-MSSA models were compared via the root mean square errors (RMSEs) to reach the best model explaining BFA. Our results indicated that the RMSEs of the eight models were low and close to each other. Further, forecasts for the months of the years 2020–2022 were obtained and compared with actual BFA values by means of the RMSEs. According to RMSEs, the best forecasts are obtained using the V-MSSA model with meteorological covariates BFA, WS and T.

Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems

ABSTRACT An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.

Impacts of wildfire and post‐fire land management on hydrological and sediment processes in a humid Mediterranean headwater catchment

AbstractThe extensive afforestation of the Mediterranean rim of Europe in recent decades has increased the number of wildfire disturbances on hydrological and sediment processes, but the impacts on headwater catchments is still poorly understood, especially when compared with the previous agricultural landscape. This work monitored an agroforestry catchment in the north‐western Iberian Peninsula, with plantation forests mixed with traditional agriculture using soil conservation practices, for one year before the fire and for three years afterwards, during which period the burnt area was ploughed and reforested. During this period, continuous data was collected for meteorology, streamflow and sediment concentration at the outlet, erosion features were mapped and measured after major rainfall events, and channel sediment dynamics were monitored downstream from the agricultural and the burnt forest area. Data from 202 rainfall events with over 10 mm was analysed in detail. Results show that the fire led to a notable impact on sediment processes during the first two post‐fire years, but not on streamflow processes; this despite the small size of the burnt area (10% of the catchment) and the occurrence of a severe drought in the first year after the fire. During this period, soil loss at the burnt forest slopes was much larger than that at most traditionally managed fields, and, ultimately, led to sediment exhaustion. At the catchment scale, storm characteristics were the dominant factor behind streamflow and sediment yield both before and after the fire. However, the data indicated a shift from detachment‐limited sediment yield before the fire, to transport‐limited sediment yield afterwards, with important increases in streamflow sediment concentration. This indicates that even small fires can temporarily change sediment processes in agroforestry catchments, with potential negative consequences for downstream water quality.

Morphology and chemical composition of dispersed particles in the snow cover of burnt forest areas in Western Transbaikalia (Russia)

This article presents the results of long-term observations of the formation of atmospheric pollution by fine particles in burnt forest areas and in the surrounding areas. By studying snow cover, it was established that during the observation period of 4 years, carbon and mineral particles are formed in burnt forest areas, and are transferred by wind to the surrounding territory. Research via an electron microscope has established that microparticles formed in the burnt areas can consist of either individual grains or an association of small particles formed during the condensation-coagulation of organic substances at the time of the fire. The chemical composition of the particles contains carbon, aluminum, silicon, iron, zinc and other chemical elements. The results of the analysis of the chemical composition of the particles are processed by factor analysis using the principal component method. Possible mechanisms for the formation of particles and the formation of their chemical composition are considered.

Study on Global Burned Forest Areas Based on Landsat Data

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

After the Wildfires: The Processes of Social Learning of Forest Owners’ Associations in Central Catalonia, Spain

Over the last few decades, according to the Forest Fire Prevention Services of the Catalan Government, a small number of fires (less than 1%) have been responsible for the destruction of more than three quarters of the burnt forest area in Catalonia. However, while these wildfires have transformed many components of the landscape, including its vegetation and soils, they offer landowners the opportunity to learn from past decisions. This article aims to analyze the responses of forest owners in Central Catalonia after the great forest fires of the 1980s and 1990s, including the way in which their objectives and strategies are defined and their actions implemented. By conducting interviews with the members of forest owners’ associations and by means of participant observation at association meetings, we seek to examine the processes of social learning experienced by this collective and to identify the mechanisms used in their efforts to create socio-ecological structures that are less vulnerable to fire. Associationism is unusual in the world of Catalan forest ownership, despite the great number of private forest areas. In our results, however, associationism emerges as a strategy for cooperation, a recognition of the need to link ecological and social structures in the territory, and one which we define as a form of ‘socio-ecological resistance’. Our study highlights that the goals and actions of forest owners’ associations have both an instrumental and emotional component, so that reason, emotion and action have come to form the three vertices of socio-ecological resistance to fire.

Soil fauna of cut and burnt forest areas in the Khibiny Mountains

In the framework of monitoring the restoration of anthropogenic disturbed soil cover and its invertebrates fauna in the polar mountains, a study of pine forest cut and burnt forest areas have been conducted in the inter-mountain valley of the Khibiny Mts. Changes of edaphic factors that determine the diversity and abundance of soil fauna were revealed five years after exposure the fire: decrease of soil pH (from 3.8 to 4.2), and content of total carbon and nitrogen, and a steady increase of humidity in the upper soil horizon in the felling and in the burned pine forest. In soil samples at four sites, representatives of 20 taxonomic groups of invertebrates have been taken into account, with greater diversity and minimal numbers in the burnt pine forest. A significant reduction of soil fauna number has been detected as a result of all types of anthropogenic impact, with insignificant differences in this parameter between disturbed sites (burnt pine forest, forest cutting, and burnt forest cutting). In a pair of control pine forest and a burnt pine forest an increasing differences in the structure of the soil fauna has been revealed in comparison with the study of these territories in 2015 or two years after a ground fire. The importance of a vegetation layer confirmed for preserving the soil fauna and maintaining the hydrothermal regime favorable for its recovery. The attraction of light- and thermophilic species of ground beetles and staphylinids, which are not typical for Khibiny Mts has been revealed on the burning felling and burning pine forest. Thus, burnt territories enrich the local fauna of this Polar Mountain Massif and the fauna of the Murmansk region as a whole.

Sore Spots in the Forest Restoration Management System (the Case of Irkutsk Region)

Forest conservation and restoration are the basis for stable ecological wellbeing and sustainable forestry. The article is dedicated to the issues of enhancement of efficiency of forest restoration in Russia in the case of Irkutsk Region. Sore spots in this area are revealed in terms of a forest restoration analysis. Forest conservation in the region is provided mainly due to the natural processes of forest stand regeneration. The main part of the regional fund for the restoration of forests is comprised of deforestation and burnt forest areas. Efficiency of forest restoration activities is determined not only by the area of artificial or natural (mixed) reforestation but also by the area of forest tending. The global experience shows the need for a switch to a different understanding of allocation of funds and forces when taking an optimal decision in the field of improving the quality of forest restoration. Sustainable forestry is possible only when combining activities of forest restoration and tending. An increase in tending young-growth stand will significantly increase not only its survival capacity but also its quality. Furthermore, the author considers the mechanisms of government control of forest restoration including the national project «Forest Conservation» and the federal law of July 19, 2018 № 212-FL.

Comparative study of collembola community on burnt forest areas and unburn forest areas in ijen crater nature park Banyuwangi

Forest fire is one of the most crucial environmental and forestry issues in Indonesia. Forest fires are also frequent in the Ijen Crater Nature Park. Forest fires cause loss of filter and humus which is source of food for soil animals. One of the soil animals that inhabit litter and soil is Collembola. This study aims to compare Collembola community in bunt and unburnt areas of Ijen Crater Nature Park. As well as to know the relation of abiotic factors with diversity, evennes, and richness of Collembola in burnt and unburnt areas. This research is a descriptive explorative research. Data retrieval is done with line transect method. The result showed that Collembola index value on burnt and unburnt areas are significantly different. The dominant species in the burnt area is Onychiurus sp, whereas in the unburnt area is Isotomurus sp. The result of abiotic factor regression analysis showed that temperature, humidity and pH level have relation to index of Collembolas diversity, evenness and richness.

Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices

Fire scar detection through orbital data can be done using specific techniques, such as the use of spectral indices like the normalized burn ratio (NBR), which are designed to help identify burnt areas as they have typical spectral responses. This paper aims to characterize burn severity and regrowth in areas hit by three fires in the Chapada Diamantina National Park (Bahia, Brazil) and its surrounding area through the differenced normalized burn ratio (dNBR) and relative differenced normalized burn ratio (RdNBR) spectral indices. The data acquired were pretreated and prepared adequately to calculate the indices. We conclude that for the study area, considering the limitations of fieldwork, the multitemporal index dNBR and the relative index RdNBR are important tools for classifying burnt areas and can be used to assess the regrowth of vegetation.

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 × 104 km2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 104 km2, accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.