Fire Burnt Area Research Articles

Static analysis-based rapid fire-following earthquake risk assessment method using simple building and GIS information

After the occurrences of large-scale earthquakes, secondary damage (e.g., fire following earthquake) can result in tremendous losses of life, properties, and buildings. To reduce these disaster risks, fire following earthquake assessment methods composed of ignition and fire-burned rate estimation models have been utilized. However, previous methods required for large amounts of building and GIS information, and complex modeling and analysis processes, leading to significant time consumption. This paper proposed a static analysis-based rapid fire following earthquake assessment method using simple information and implemented it in Pohang City, South Korea. Based on previous studies, the best-fit model for the ignition rate estimation was selected, and a cluster-based fire-burned rate estimation model was developed using simple building information (e.g., construction year, building occupancy, story, and total floor area) from the public building database (e.g., building registration data). For the fire-burned rate estimation model, fire-resistant structure types were defined using simple building information, and this was utilized to generate clusters of buildings at a regional level by comparing fire-spread distances for each fire-resistant structure type with adjacent distances among the buildings. This proposed method was applied to Pohang City, South Korea, and validated as follows: (1) the selected ignition rate model predicted similar ignition numbers to the actual reported number (actual number of ignitions = 4 vs. predicted number of ignitions = 3), and (2) the fire-burned rate model estimated fire-burned areas with a marginal difference compared to the fire spread simulation (fire-burned area using the proposed model = 13,703.6 m2 vs. results of fire spread simulation = 16,800.0 m2, with an error of approximately 18%).

Research on controlled mining of end slope fire-burned area in open-pit mine

To solve the problem of controlling mining in the open-pit mine end slope fire-burned area, applying multivariate function fitting to the roof and floor modeling of multi-coal seam open pit mines, introducing the factor of coal quality changes in the fire-burned area, determining coal quality information at each location through proximate analysis of coal, to establish the net profit model of the mining area, it is determined the net profit of each mining position by numerical integration, the final mining position was determined without failure by calculating the slope stability based on the numerical simulation of strength reduction. Taking the Dananhu No. 2 open-pit mine in Hami, Xinjiang, China as the engineering background, the fire-burned area III within the southern end slope boundary of the first mining area is 240 m. It was finally determined that the optimal mining position is when the advancement degree is 182 m, the ultimate pit slope angle is 25°, the three-dimensional slope stability is 1.305, the profit is 671.96 million yuan, The deep boundary of the southern end slope fire-burned area of the slope is reduced by 58 m. This paper solves the problem of end slope mining in Dananhu No. 2 mine, and maximizes its net profit under the condition of ensuring safe production.

Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm

Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, this study uses the Grey Wolf Optimizer (GWO) algorithm to optimize the hyperparameters in the eXtreme Gradient Boosting (XGBoost) model, constructing a GWO-XGBoost model. Finally, the optimized ensemble model (GWO-XGBoost) is used to create a fire growth rate warning map for the Liangshan Prefecture in Sichuan Province, China, filling the research gap in forest fire studies in this area. This study comprehensively selects factors such as monthly climate, monthly vegetation, terrain, and socio–economic aspects and incorporates monthly reanalysis data from forest fire assessment systems in Canada, the United States, and Australia as features to construct the forest fire dataset. After collinearity tests to filter redundant features and Pearson correlation analysis to explore features related to the burned area growth rate, the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the positive class samples. The GWO algorithm is used to optimize the hyperparameters in the XGBoost model, constructing the GWO-XGBoost model, which is then compared with XGBoost, Random Forest (RF), and Logistic Regression (LR) models. Model evaluation results showed that the GWO-XGBoost model, with an AUC value of 0.8927, is the best-performing model. Using the SHapley Additive exPlanations (SHAP) value analysis method to quantify the contribution of each influencing factor indicates that the Ignition Component (IC) value from the United States National Fire Danger Rating System contributes the most, followed by the average monthly temperature and the population density. The growth rate warning map results indicate that the southern part of the study area is the key fire prevention area.

Fire behavior simulation of Xintian forest fire in 2022 using WRF-fire model

IntroductionThe behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province.MethodsBased on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data.ResultsThe study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well.DiscussionThe results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management.

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.

A Neural Network Approach to Forest Fire Burned Area Prediction

Worldwide ecosystems and economies are seriously threatened by forest fires, which can result in extensive damage and financial losses. Strategies for managing and preventing wildfires depend heavily on the accurate prediction of burned areas. To create a predictive model for the burned areas caused by forest fires in Portugal's northeast, this study uses a machine learning technique, namely Neural Network. The model integrates meteorological and spatial-temporal data and makes use of regression algorithms. To maximize the performance of the model, data preprocessing, exploratory data analysis, model building, feature selection, and assessment are done. The results show that burned area is greatly influenced by meteorological factors, including temperature, humidity, and wind speed. The created model shows a respectable level of prediction accuracy of 97.11%, offering insightful information for efficient fire management and early warning systems. The potential for reducing the destructive effects of wildfires is enormous when machine learning is incorporated into the prediction of forest fires.

The Interacting Influence of Fire and Tree Characteristics on Douglas-Fir Beetle Host-Tree Selection Five Years Post-Fire

Fire injury stresses Douglas-fir trees (Pseudotsuga menziesii) that survive a wildfire event, allowing subsequent Douglas-fir beetle (Dendroctonus pseudotsugae) infection to kill trees that may have otherwise survived. This study aimed to determine how fire injury, stand, and tree characteristics drive Douglas-fir beetle host tree selection five years post-fire. We paired 28 adjacent beetle-infected and uninfected stands (infected N = 14) and 140 Douglas-fir trees (infected N = 70) within the 416 Fire burn area in Southwest Colorado. We found no statistically significant differences between infected and uninfected stand characteristics. Individual tree height, DBH, and bark char severity index were significantly higher in infected versus uninfected trees. We created a regression decision tree model to determine the influence of fire injury and tree characteristics on the probability of infection. Trees with a height ≥ 27 m, bark char height < 2.3 m, and DBH < 80 cm had the greatest probability of attack (100%). Trees with a height < 27 m, bark char severity index < 5.5, and DBH < 49 cm had the lowest probability of attack (3.7%). Understanding the influence of fire on Douglas-fir beetle host selection allows land managers to model potential epidemic outbreaks and guide proactive management actions that may reduce beetle outbreak severity or preserve high-value trees not killed by fire.

FOREST FIRE BURNT AREA EXTRACTION USING FUZZY INTEGRATION OF MULTI-SENSOR SATELLITE DATA FOR THE HIMALAYAN STATE

Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.

Wildfire impact on disinfection byproduct precursor loading in mountain streams and rivers

We investigated short (first post-fire precipitation)- and long-term (11-month) impacts of the Caldor and Mosquito Fires (2021 and 2022) on water quality, dissolved organic matter, and disinfection byproduct (DBP) precursors in burned and adjacent unburned watersheds. Both burned watersheds experienced water quality degradation compared to their paired unburned watersheds, including increases in dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and DBP precursors from precipitation events. DBP precursor concentrations during storm events were greater in the Caldor Fire's burned watershed than in the unburned watershed; precursors of trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs), and haloacetamides (HAMs) were 533 µg/L, 1,231 µg/L, 64 and 58 µg/L greater. The burned watershed of the Mosquito Fire also had greater median concentrations of THM (44 µg/L), HAA (37 µg/L), HAN (7 µg/L), and HAM (13 µg/L) precursors compared to the unburned watershed during a storm immediately following the fire. Initial flushes from both burned watersheds formed greater concentrations of more toxic DBPs, such as HANs and HAMs. The Caldor Fire burn area experienced a rain-on-snow event shortly after the fire which produced the greatest degradation of water quality of all seasons/precipitation events/watersheds studied. Over the long term, statistical analysis revealed that DOC and DON values in the burned watershed of the Caldor Fire remained higher than the unburned control (0.98 mg C/L and 0.028 mg N/L, respectively). These short and long-term findings indicate that wildfires present potential treatment challenges for public water systems outside of the two studied here.

Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks

Wildfires have significant impacts on the environment, society, and economy. Consequently, understanding its dynamics is crucial to evaluate such effects. Nonetheless, monitoring and measuring the burned area by traditional, non-automatic methods remains time-consuming and challenging.For several years, automatic semantic segmentation models have been used to describe natural phenomena, but deep learning models have recently achieved very competitive results. However, this new breed of models typically needs annotated datasets of significant dimensions. Nonetheless, datasets for real-time burnt area segmentation are often scarce.In this article, we create tools to support the benchmarking for testing and validating burned area segmentation models in a wildfire context. As such, we propose a new manually annotated dataset for segmentation of forest fire burned area based on a video captured by a UAV to train and evaluate semantic segmentation models. We suggest specific temporal consistency metrics to validate burned area polygons generated by the models in successive frames of non-annotated data. We also explore deep learning-based techniques and establish baselines, including IoU values superior to 95% on the test set.

Estimation of Forest Fire Burned Area by Distinguishing Non-Photosynthetic and Photosynthetic Vegetation Using Triangular Space Method

The forest fire burned area is one of the most basic factors used to describe forest fires and plays a vital role in damage assessment. The development of the NSSI-NDVI vegetation index triangular space method enables simultaneous calculation of the flammable non-photosynthetic vegetation (NPV), combustible photosynthetic vegetation (PV), and incombustible bare soil (BS) fractional cover in forest areas. This can be used to compensate for the calculation method that was based on NDVI vegetation index only by comparing vegetation cover before and after forest fires, with the omission of the NPV burned area. To this end, the NSSI-NDVI triangular space shape consistency before and after forest fires was elucidated through combustion and ash wetting experiments. In addition, the feasibility of the NSSI-NDVI triangular space method for the accurate calculation of the post-fire vegetation damage area was verified. Finally, the applicability and accuracy of this research method were verified based on 10 m spatial resolution satellite hyperspectral images from before and after the forest fire in Lushan, Sichuan Province, China. The NSSI-NDVI triangular space method was used to calculate the PV, NPV, and BS coverage simultaneously, and component transformation was used to calculate the burned area and burned site separately.

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat

Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology.

The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China

Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area.

Forest fire pattern and vulnerability mapping using deep learning in Nepal

BackgroundIn the last two decades, Nepal has experienced an increase in both forest fire frequency and area, but very little is known about its spatiotemporal dimension. A limited number of studies have researched the extent, timing, causative parameters, and vulnerability factors regarding forest fire in Nepal. Our study analyzed forest fire trends and patterns in Nepal for the last two decades and analyzed forest fire-vulnerability risk based on historical incidents across the country.ResultsWe analyzed the spatial and temporal patterns of forest fires and the extent of burned area using the Mann-Kendall trend test and two machine-learning approaches maximum entropy (MaxEnt), and deep neural network (DNN). More than 78% of the forest fire burned area was recorded between March and May. The total burned area has increased over the years since 2001 by 0.6% annually. The forest fire-vulnerability risk obtained from both approaches was categorized into four classes—very high, high, low, and very low.ConclusionsAlthough burned area obtained from both models was comparable, the DNN slightly outperformed the MaxEnt model. DNN uses a complex structure of algorithms modeled on the human brain that enables the processing of the complex relationship between input and output dataset, making DNN-based models recommended over MaxEnt. These findings can be very useful for initiating and implementing the most suitable forest management intervention.

变化环境下火干扰研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 变化环境下火干扰研究进展 DOI: 10.5846/stxb202210142923 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41991230） Research progress on fire disturbance in a changing environment Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:火是地球系统的重要过程，也是一种剧烈的环境干扰因素。火是生态系统变化的驱动力和催化剂，调节着生态系统的结构和功能，同时反馈给气候系统。近年来，世界多个国家相继爆发了历史上罕见的极端火事件，使得火干扰、气候变化和人类活动之间的相互作用关系得到了空前的关注。主要从3个方面回顾了变化环境下火干扰研究的进展，包括（1）火干扰的时空格局；（2）火干扰的驱动机制；（3）火干扰的生态效应。概括起来，遥感技术的发展使得火监测精度不断提高，对火时空格局的刻画由过去侧重火燃烧面积单一因素转向具有多重属性的火干扰体系。气候变化和人类活动共同决定着火干扰的分布格局、频率和强度，考虑气候的季节性能够提高火干扰的预测能力。火干扰调节着生态系统的草木平衡，对于生物多样性和生境的维持非常重要。此外，火干扰通过生物质燃烧释放的大量温室气体影响大气组成和空气质量，同时通过改变地表状况和陆-气相互作用来影响气候系统。正确理解气候-植被-火之间的相互作用和反馈机制有助于未来火干扰体系的预测。随着高温、大风、干旱等极端气候事件增多，未来全球大部分区域火发生的风险增加，但是人类活动可能会使火和气候之间的关系发生解耦。可持续的火管理应充分结合生态学知识和土著居民火文化，以保护生物多样性、维持重要生态系统服务和减缓气候变化为目标，减少极端火灾的风险。 Abstract:Fire is an essential component of the Earth system and a severe environmental disturbance factor. Fire is an important driver and catalyst for ecosystem changes, which regulates the structure and function of the ecosystem and provides feedback to regional and global climate systems. In recent years, extreme fire events that are rare in history have erupted in many countries around the world, drawing unprecedented attention to the interaction between fire disturbance, climate change, and human activities. This paper mainly reviews the progress of fire disturbance research in a changing environment from three aspects, including (1) spatiotemporal patterns of fire disturbance; (2) driving mechanisms of fire disturbance; (3) ecological effects of fire disturbance. In summary, state-of-the-art remote sensing technology has greatly improved the accuracy of fire monitoring, and the portrayal of the spatiotemporal pattern of fire has shifted from focusing on a single factor of fire burned area to the fire regime with multiple attributes. Climate change and human activities jointly determine the pattern, frequency, and intensity of fire disturbance. Taking into account the seasonality of climate can improve the prediction ability of the global fire system. Fires play an important role in regulating the competition between herbaceous and woody vegetation and thus are very important for biodiversity and habitat maintenance. In addition, fire disturbance affects the atmospheric composition and air quality through the release of large amounts of greenhouse gases from biomass burning, while impacting the climate system by altering land surface conditions and affecting land-atmosphere interactions. A better understanding of the interactions and feedback of climate-vegetation-fire can facilitate the prediction of fire regimes. In the future climate scenario, increases in extreme climate events, such as high temperatures, strong winds, and droughts, create a fire-prone environment. Although an increased risk of fire disturbances in most parts of the world, human activity may decouple the relationship between fire and climate. Sustainable fire management should fully integrate ecological knowledge and indigenous fire culture to reduce the risk of extreme fires, with the goal of protecting biodiversity, maintaining vital ecosystem services, and mitigating climate change. 参考文献 相似文献 引证文献

Quantifying the direct fire threat to a critically endangered arboreal marsupial using biophysical, mechanistic modelling

AbstractHistorically unprecedented areas of forest habitat have been impacted by fire, as climate change and other anthropogenic disturbances drive increases in fire burned area and severity. Although 88% of Australia's [threatened] land mammals are threatened by inappropriate fire regimes, calculations of animal mortality resulting from specific events have been impeded by knowledge gaps relating to both the direct (first‐order) and long‐term (second‐order) effects of fire on different species. This study addresses the need for a quantified, mechanistic understanding of first‐order effects, presenting an extension of the Fire Research and Modelling Environment (FRaME) to allow prediction of species‐specific mortality. FRaME is demonstrated and tested here by replicating an incident in which a prescribed burn caused 77% mortality of a population of the critically endangered ngwayir (Pseudocheirus occidentalis, Pseudocheiridae). FRaME correctly predicted heavy mortality (62–79%) arising from partial and full‐thickness burns and asphyxiation due to burns in the respiratory tract. Mortality varied with animal fire‐avoidance strategies (p < 0.001) and the thickness of tree hollow walls (r = −0.95, p < 0.001). Although management guidelines specified low intensity fire, mortality had no significant relationship with Byram intensity and larger flames due to ‘torching’ were most frequent when fire spread was slowest. FRaME modelling predicted that individuals would be impacted by temperatures exceeding 500°C for several minutes. Fire management that is premised on discredited notions of fire behaviour and overly simple models can lead to catastrophic management outcomes such as those documented here. FRaME addresses this need by providing a platform to account for heterogeneous fire behaviour as well as animal behaviour and habitat quality, calculating fire risk to fauna and guiding management that maximizes safe habitat.

Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS

Forest fires cause damage to property and the environment around the world every year. North Korea has suffered from fires every year. Fires may lead to temporary or permanent damage to forest ecosystems, long-term site degradation, and alteration of hydrological regimes, producing detrimental impacts on economies, human health, and safety. In North Korea, fires cause serious damage to the affected mountainous environment. However, it is very difficult to obtain ground information or perform field checks because of the political isolation of North Korea. Thus, there are few studies that have investigated North Korean fires. In this situation, remote sensing techniques and digital topographic data can be used to investigate fire characteristics in North Korea. In this study, fire trends were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Land Processes Distributed Active Archive Center (LPDAAC) from 2004 to 2015, and Landsat data were processed to estimate burned areas in South Hamgyong Province (SHP) and Gangwon Province (GWP) in North Korea. The burn severity of large fires in elevation, slope, and landform features was also analyzed to investigate large fire-burned areas using 30-m-resolution Global Digital Elevation Model (DEM) data from the United States National Aeronautics and Space Administration (NASA). After the results were compared and discussed, the following conclusions were derived. (1) In terms of location, fires in SHP were relatively concentrated along BaekDu-DaeGan (BDDG), while fires in GWP were scattered throughout the province. (2) In terms of size, the large fire-burned areas with an area greater than 1000 ha are significantly more frequent in SHP than in GWP. In brief, large fires occurred more frequently and were more serious in SHP than in GWP. (3) In terms of forest type, coniferous areas were more susceptible to damage from fires and large fires than deciduous areas in both GWP and SHP. This is attributed to the combustible resin within the coniferous trees. Particularly, when a crown fire occurs, it tends to spread rapidly throughout the coniferous forest. (4) Regarding landforms, most large fires occurred along windward-side open slopes, while there were very few fires in shallow valleys, high ridges, or U-shaped valleys. It is believed that cultivation in high-elevation terrain and a lack of fire-extinguishing equipment and systems allow large fires to spread quickly. North Korea is very susceptible to large fire damage and must develop preparation measures against such situations.

Fire regime of peatlands in the Angolan Highlands.

The Angolan Highlands region includes the Angolan miombo woodland ecoregion which supports miombo woodland, grasslands, subsistence agricultural land, and peatland deposits. Extensive fires, slash and burn agriculture, peat fuel extraction, and peatland drainage are among the anthropogenic practices that threaten these peatland deposits. Peat fires cause peatland degradation, release significant amounts of greenhouse gases, deteriorate air quality, and contribute towards climate change and biodiversity loss. This study presents an analysis of the fire regimes over the period 2001 to 2020 in an under-studied area of the Angolan Highlands. Moderate Resolution Imaging Spectroradiometer (MODIS) fire and vegetation data were used in combination with a land use/land cover (LULC) classification map to calculate fire frequency, burn area, and fire regimes. The fire patterns within the study site are comparable to those found in African woodland savannas. Across the study site, 6976 km2 (11.31%) of the land surface area burned at least nine times from 2001 to 2020, occurring largely within in the river valley environment. Considering the different LULC classes, peatlands were calculated to (a) burn more frequently (average fire frequency from 2001 to 2020 = 9.12), (b) have the smallest proportion (4.11%) of area which remained unburnt over the fire archive, and (c) have the largest average proportion (45.65% or 746 km2) of burnt area per year. Peatland burning occurred predominantly during drier months from May to September. The results of this study highlight the strong influence of LULC on the fire frequency and distribution in the study area, requiring unique fire management strategies. As has been documented for boreal and tropical peatlands across the globe, we stress the importance of peatland conservation and protection; continued unsustainable management practices may lead to the loss of these important peatland deposits.

A study on wildfire impacts on greenhouse gas emissions and regional air quality in South of Orléans, France

Wildfire events are increasing globally which may be partly associated with climate change, resulting in significant adverse impacts on local, regional air quality and global climate. In September 2020, a small wildfire (burned area: 36.3 ha) event occurred in Souesmes (Loir-et-Cher, Sologne, France), and its plume spread out over 200 km on the following day as observed by the MODIS satellite. Based on measurements at a suburban site (∼ 50 km northwest of the fire location) in Orléans and backward trajectory analysis, young wildfire plumes were characterized. Significant increases in gaseous pollutants (CO, CH4, N2O, VOCs, etc.) and particles (including black carbon) were found within the wildfire plumes, leading to a reduced air quality. Emission factors, defined as EF (X) = ∆X/∆CO (where, X represents the target species), of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies. Changes in the ambient ions (such as ammonium, sulfate, nitrate, chloride, and nitrite in the particle- and gas- phase) and aerosol properties (e.g., aerosol water content, aerosol pH) were also quantified and discussed. Moreover, we estimated the total carbon and climate-related species (e.g., CO2, CH4, N2O, and BC) emissions and compared them with fire emission inventories. Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions. For instance, we found that the Global Fire Assimilation System (GFAS) may underestimate emissions (e.g., CO) of this small wildfire while other inventories (GFED and FINN) showed significant overestimation. Considering that it is the first time to record wildfire plumes in this region, related atmospheric implications are presented and discussed.