Fire Hotspots Research Articles

Assessment of Land Use, Cover Changes, and Fire Hotspots in a Conservation Unit: A 20-Year Analysis

Assessment of Land Use, Cover Changes, and Fire Hotspots in a Conservation Unit: A 20-Year Analysis

The dynamics of burning activity on degraded peatland in two villages in Central Kalimantan, Indonesia

Background Recurring wildfires on degraded peatlands throughout Central Kalimantan have resulted in severe economic and social impacts for local people along with globally significant environmental impacts. The interdependence between the livelihoods of local villagers and wildfire is not well understood in areas of degraded peat in proximity to urban environments. Aims The aim of the study was to consider fire hotspots in two villages close to the regional capital of Palangka Raya. These were Kalampangan, a Javanese transmigrant farming village, and Tumbang Nusa, a Dayaknese fishing village. Methods A system dynamics model was constructed to study the factors contributing to greenhouse gas emissions and wildfire extent resulting from long-term peatland degradation. The model was used to analyse existing policy scenarios where degraded peatlands are cultivated in perpetuity, and then consider hypothetical future policy scenarios where efforts are made to rewet and rehabilitate peatland while alternative livelihoods are enabled. Conclusions Analysis reveals that the assumption within the model of unconstrained alternative livelihoods is insufficient to facilitate full rewetting where the incumbent livelihood is reliant on drained peatland. Implications Only when livelihood alternatives displace drained peatland cultivation is full rewetting and sustained reduction in fire risk achievable in both villages.

Spatial distribution of 2019 forest and land fires in Indonesia

Forest and land fires in 2019 were recorded as major events with extensive impacts on various aspects in Indonesia. An evaluation of forest and land fires in 2019 is required to determine the distribution and pattern of fires to be a significant input in overcoming forest and land fires in the future. This study aimed to evaluate forest fires based on area distribution, land cover, and area functions. This study used hotspot maps, land cover maps and forest area maps in 2019 to analyze the spatial distribution of locations and types of land cover that were indicated to be burning. The 2019 Landsat 8 OLI satellite images were used to identify types of land cover related to fires. We found that forest and land fires in 2019 in Indonesia were mostly spread on non-forest land with the type of land cover of shrubs, swamp shrub, and plantations with an area function for other use. The five provinces with the most fire hotspots detected were Central Kalimantan, West Kalimantan, Jambi, Riau, and South Sumatera. Most of the fire areas in the provinces that are vulnerable to fires are on peatlands. It requires serious and sustainable efforts to increase productivity and land value to reduce the area of unmanaged land and increase supervision of plantation companies that do not have an adequate forest and land fire management system.

Post‐Sovietfire and grazing regimes govern the abundance of a key ecosystem engineer on the Eurasian steppe, the yellow ground squirrelSpermophilus fulvus

AbstractAimGrazing intensity and fire patterns across the Eurasian steppes have changed dramatically over the past decades due to the collapse of the Soviet Union in 1991, and Kazakhstan is now a global fire hotspot. The implications of these changes for ecosystem functioning are largely unclear. We aimed to understand the effects of changed grazing intensity and fire frequency on a key ecosystem engineer, the yellow ground squirrelSpermophilus fulvus, on a very large scale.LocationKazakhstan.MethodsGround squirrels were surveyed in an area of ca. 100,000 ha in the dry steppe of central Kazakhstan, using hierarchical distance sampling at more than 200 random points, stratified by fire frequency and livestock grazing intensity. We modelled abundance as a function of different variables, grouped at the landscape scale (fire and grazing), meso‐scale (soil and vegetation structure) and at burrow scale (plant traits such as palatability, digestibility and nutrient content).ResultsGround squirrels prefer areas of a high wormwood cover (Artemisiaspp.) and high plant species richness, which are moderately grazed, preferably by cattle, with only rare fire occurrence. High squirrel densities were also related to the availability of nitrogen‐rich plants of high nutritional value for herbivores.Main ConclusionsYellow ground squirrels seem to reach their density optima by balancing trade‐offs between optimal foraging in areas of short, nutrient‐rich vegetation and a good visibility of approaching predators. Post‐Soviet changes in grazing pressure, resulting in higher fire recurrence rates due to grass encroachment and litter accumulation (i.e. fuel for wildfire), have likely affected the abundance of burrowing mammals and associated biodiversity across huge parts of the Eurasian steppes and semideserts.

MODIS-Derived Fire Characteristics and Greenhouse Gas Emissions from Cropland Residue Burning in Central India

Cropland residue burning is one of the major causes of the emission of greenhouse gases and pollutants into the atmosphere, and is a major global environmental problem. This study analyzes the spatiotemporal changes in greenhouse gas emissions from cropland residue burning in Chhattisgarh, India. The Moderate Resolution Imaging Spectroradiometer (MODIS) active fire data was analyzed over a 21-year (2001–2021) period, and associated greenhouse gas emissions were estimated. A total of 64,370 fire points were recorded for all land cover types. The number of cropland fires increased from 49 to 1368 between 2001 and 2021, with a burning peak observed between December and March. Fires in cropland areas contributed to 32.4% (19,878) of the total fire counts in the last 21 years. The total estimated emissions of greenhouse gases between 2001 and 2021 ranged from 421.5 to 37,233 Gg, with an annual rate of emission of 8972 Gg from wheat residue burning, and from 435.45 to 64,108.1 Gg, with an annual emission of 15,448.16 Gg from rice residue burning. The Chhattisgarh plain region was the cropland fire hotspot of the state. The present study indicates increased cropland residue-burning activity in Chhattisgarh. Therefore, there is an immediate need to develop sustainable alternative methods for agricultural residue management and eco-friendly methods for the disposal of crop residues.

Sixteen years of MOPITT satellite data strongly constrain Amazon CO fire emissions

Abstract. Despite the consensus on the overall downward trend in Amazon forest loss in the previous decade, estimates of yearly carbon emissions from deforestation still vary widely. Estimated carbon emissions are currently often based on data from local logging activity reports, changes in remotely sensed biomass, and remote detection of fire hotspots and burned area. Here, we use 16 years of satellite-derived carbon monoxide (CO) columns to constrain fire CO emissions from the Amazon Basin between 2003 and 2018. Through data assimilation, we produce 3 d average maps of fire CO emissions over the Amazon, which we verified to be consistent with a long-term monitoring programme of aircraft CO profiles over five sites in the Amazon. Our new product independently confirms a long-term decrease of 54 % in deforestation-related CO emissions over the study period. Interannual variability is large, with known anomalously dry years showing a more than 4-fold increase in basin-wide fire emissions relative to wet years. At the level of individual Brazilian states, we find that both soil moisture anomalies and human ignitions determine fire activity, suggesting that future carbon release from fires depends on drought intensity as much as on continued forest protection. Our study shows that the atmospheric composition perspective on deforestation is a valuable additional monitoring instrument that complements existing bottom-up and remote sensing methods for land-use change. Extension of such a perspective to an operational framework is timely considering the observed increased fire intensity in the Amazon Basin between 2019 and 2021.

Extreme Positive Indian Ocean Dipole in 2019 and Its Impact on Indonesia

The evolution of an extreme positive Indian Ocean Dipole (pIOD) that took place in the tropical Indian Ocean during the late boreal summer to early winter of 2019 is examined in terms of coupled ocean–atmosphere dynamics. The patterns of anomalous sea-surface temperature (SST) revealed a typical pIOD characteristic: cooling (warming) in the southeastern (western) tropical Indian Ocean. Based on the Dipole Mode Index (DMI), the evolution of the event started in mid-July and gradually strengthened with an abrupt weakening in early September before coming to its peak in late October/early November. It quickly weakened in November, and then it terminated in mid-December. During the peak phase of the event, the SST anomaly in the southeastern (western) tropical Indian Ocean reached about −2 °C (+1 °C). The pattern of anomalous SST was followed by an anomalous pattern in precipitation, in which deficit precipitation was observed over the eastern Indian Ocean, particularly over the Indonesia region. Earlier study has shown that dry conditions associated with the pIOD event created a favorable condition for a forest-peat fire in southern Sumatra. The number of fire hotspots has increased significantly during the peak phase of the 2019 pIOD event. In addition, anomalously strong upwelling forced by strong southeasterly wind anomalies along the southern coast of Java and Sumatra had induced a surface chlorophyll-a (Chl-a) bloom in this region. High surface Chl-a concentration was collocated with the negative SST anomalies observed off the southwest Sumatra coast and south Java.

Forest Fire Occurrence Prediction in China Based on Machine Learning Methods

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.

Geospatial characteristics of Colorado wildfire occurrences from 2001 to 2020

Forest fires are a major environmental concern and can pose a direct threat to the environment, communities, and people living in fire-prone areas. Colorado has experienced an increase in wildfire frequency and severity in recent years and with the population increasing and communities expanding, it is imperative to understand the spatial patterns of wildfires and which areas are most at risk. Here we use geostatistical techniques to analyze the spatial distribution and seasonal patterns of fires in Colorado from 2001 to 2020 and assess fire risk for June 2021. Over the past 20 years, forest fires in Colorado have varied seasonally in concentration and dispersion, moving in a circular pattern from the southeast in the winter to the northwest in the summer. On an annual scale, the observed active fires varied latitudinally in recent years, with greater northward expansion. Several active fire hotspots were revealed in Colorado: two hotspots in the northwest towards Grand Junction and a large hotspot in the central north, of great concern for its expansion into the Rocky Mountain National Park and its location near more populated areas. Our fire risk model (which included topography, vegetation, drought intensity, and historical fire occurrences) showed that fire risk was greater along the western border of Colorado, and 45% of active fires occurred within the top two fire risk classes (100% occurred in the top three). Therefore, this fire risk map is a suitable tool to identify and locate areas that are at greater risk for fire occurrence in Colorado.

Climate Change Affecting Forest Fire and Flood Risk—Facts, Predictions, and Perceptions in Central and South Greece

Global Climate change (CC) is featured by long-term changes in the mean values of climatic parameters (predominantly mean temperature) and in the profile of extreme weather events (e.g., increase in frequency, intensity, lengthening, and persistence). These climatic changes are supposed to have a deterioration impact on forest fire and flood disasters. Greece, an east Mediterranean country, is featured by a wide variety of micro-climates due to its unique geographical diversity, including hot and dry summers in the eastern part of the country (where a large amount of precipitation falls in the form of showers and thunderstorms) and wet winters in the western part. The combination of certain climatic zones with unfavorable land use and land cover changing patterns has resulted in several regions being prone to flooding and forest fires. The authors, based on relevant records, consider central and south Greece as flood and forest fire hotspots and attempt to: (a) present scientific estimations of local climate changes; (b) outline recent trends in the number of respective disasters and the amount of losses in these regions; (c) address recent changes in local climatic factors that might have influenced flood and forest fire hazard and risk in these regions; and (d) study the perceptions of the lay public and management authorities regarding the accountability of CC for flood and forest fire risk and hazard changes. The results show the variability of climate changes between neighboring areas, which directly affect the risk of forest fires and floods. Especially since the beginning of the 21st century, central Greece has been experiencing dramatic increases in both risks, while in south Greece the latter remain relatively stable. With regard to the perceptions of citizens and management authorities, the mental connection of local CC with forest fires and floods is still weak if not totally missing. Since knowledge and perceptions of the local “history” of forest fires and floods and the interconnections with CC by region is very important for the local communities to take appropriate mitigation and adaptation measures, this paper outlines a methodological path for similar studies to be conducted also in other regions of the Mediterranean basin and beyond.

Ecological impacts of forest fire on composition and structure of tropical deciduous forests of central India

The present study highlights the forest fire hotspots based on daily forest fire occurrence during 2000–2020 and its subsequent long-term impacts on different vegetation types in Central India. The MODIS (MCD14DL) based study exhibited the recurrence of fire incidences in central and southern parts of forests and affected substantial parts of the deciduous broadleaf forest (31.56% of select forest type) and deciduous needleleaf forest (30.23%), shrubland (7.52%), mixed forest (9.80%), with the major forest fire peaks during March and April, while negligible fire incidences were observed in plantation and grassland. 72% of field transects (157 out of the 218 field transects) were observed under high anthropogenic influences as evidenced by a large number of cut stumps. Six species (out of 106 studies tree species) including Tectona grandis L.f. (occurrence in 98% of fire hotspots), Butea monosperma (Lam.) Taubert (66%), were observed to be highly fire-resistant, recorded in >50% of the forest fire hotspots regions. The major forest fires hotspots (p < 0.01) were observed in the Malwa plateau, Vindhyan ranges, and Satpura range during 2000–2020, as observed with high fire intensity. While the forest in the northern parts attributed a cold spot with reference to forest fires (p < 0.01). The study elucidated the high susceptibility of forest fire in deciduous forests in Central India and necessitated proper management of forest fires by encouraging fire-resistant species, and prioritization of regions under fire hazards through the adoption of preparedness and response strategies to minimize the forest fire impacts.

Spatio-Temporal Characterization of Fire Using MODIS Data (2000–2020) in Colombia

Fire is a process of disturbance of natural ecosystems that can be used for land management and soil preparation for agricultural purposes, but can also drastically affect biodiversity and the distribution and abundance of species by changing land use and altering the microclimate. The analysis of data on thermal anomalies has become a valuable tool for the study of places with low monitoring of the occurrence of fires. In this study, information from the MODIS sensor was used to analyze the spatio-temporal distribution of fires in the five natural regions of Colombia (Caribbean, Andean, Pacific, Orinoquia, and Amazon) in the period of 2000–2020. Nevertheless, MODIS fire hotspots present some difficulties in estimating the magnitude of fire activity, due the relations between active fires and burned areas, which are not constant in space and time. The method used in this work consisted to performance an inter-annual and intra-annual analysis of thermal anomalies data and identifying the incidence of temperature in the occurrence of fires. The fire density (defined as the number of fires per unit area) and the fire trends over the study period were also analyzed. Inter-annual fire peaks were recorded in 2004 (8.21%) and 2007 (8.04%), and three main fire hotspots were identified in the Orinoquia, Andean, and Caribbean regions. Moreover, 87% of fire peaks were observed in the dry season (December–March). On the other hand, the highest incidence of thermal anomalies occurred in the Orinoquia region (83409 ± 185 fires), and the highest incidence of fires per unit area was recorded in the Andean region (0.162 ± 0.086 fires-km2-year). Fire activities varied strongly according to region and year over the study period. Significant correlations were observed between temperature and fire density in the Andean (Rho = 0.7506), Pacific (Rho = 0.7364), and Caribbean (Rho = 0.5571) regions. Thus, temperature seem to be a driver of fire density in these regions.

Prediction of air quality in Sydney, Australia as a function of forest fire load and weather using Bayesian statistics.

Smoke from Hazard Reduction Burns (HRBs) and wildfires contains pollutants that are harmful to human health. This includes particulate matter less than 2.5 μm in diameter (PM2.5), which affects human cardiovascular and respiratory systems and can lead to increased hospitalisations and premature deaths. Better models are needed to predict PM2.5 levels associated with HRBs so that agencies can properly assess smoke pollution risk and balance smoke risk with the wildfire mitigation benefits of HRBs. Given this need, our aim was to develop a probabilistic model of daily PM2.5 using Bayesian regression. We focused on the region around Sydney, Australia, which regularly has hazard reduction burning, wildfires and associated smoke. We developed two regional models (mean daily and maximum daily) from observed PM2.5, weather reanalysis and satellite fire hotspot data. The models predict that the worst PM2.5 in Sydney occurs when PM2.5 was high the previous day, there is low ventilation index (i.e. the product of wind speed and planetary boundary layer height), low temperature, west to northwest winds in the Blue Mountains, an afternoon sea breeze and large areas of HRBs are being conducted, particularly to the west and north of Sydney. A major benefit of our approach is that models are fast to run, require simple inputs and Bayesian predictions convey both predicted PM2.5 and associated prediction uncertainty. Future research could include the application of similar methods to other regions, collecting more data to improve model precision and developing Bayesian PM2.5 models for wildfires.

Hotspot and trend analysis of forest fires and its relation to climatic factors in thewestern Himalayas.

Forest fire is one of the main issues of forest ecosystems around the world which has resulted in loss of biodiversity, forest degradation, soil erosion, and greenhouse gas emission. Ironically, the information on the forest fire regime and its pattern are still lacking in the Himalayan region. In this study, Moderate-Resolution Imaging Spectroradiometer active fire data products from 2001 to 2020 have been analysed for understanding the forest fire trends and its hotspots patterns during the active fire season (February to June). About 1347 average fire counts/year were recorded in six natural vegetations with the highest number of fires observed during the year 2012 (n = 3096) and minimum in 2011 (n = 210). Mann–Kendall trends analysis for the spatial and temporal pattern of fires indicated that there is a significant increase of forest fires towards higher elevation. Forest fire hotspot analysis using fire radiative power, fire frequency, and fire density showed that Uttarakhand is the most forest fire-prone state as compared to other north-western Himalayan states. It is also revealed that the May month has a higher number of fire counts and the evergreen needle forests have higher fire frequencies amongst the forest types. The forest fires were found to be more influenced by land surface temperature as compared to rainfall. The outcomes in this study on the temporal and spatial patterns for forest fire can be used for forest fire modelling.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11069-022-05530-5.

Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2

This study presents our efforts to automate the detection of unofficial roads (herein, roads) in the Brazilian Amazon using artificial intelligence (AI). In this region, roads are built by loggers, goldminers, and unauthorized land settlements from existing official roads, expanding over pristine forests and leading to new deforestation and fire hotspots. Previous research used visual interpretation, hand digitization, and vector editing techniques to create a thorough Amazon Road Dataset (ARD) from Landsat imagery. The ARD allowed assessment of the road dynamics and impacts on deforestation, landscape fragmentation, and fires and supported several scientific and societal applications. This research used the existing ARD to train and model a modified U-Net algorithm to detect rural roads in the Brazilian Amazon using Sentinel-2 imagery from 2020 in the Azure Planetary Computer platform. Moreover, we implemented a post-AI detection protocol to connect and vectorize the U-Net road detected to create a new ARD. We estimated the recall and precision accuracy using an independent ARD dataset, obtaining 65% and 71%, respectively. Visual interpretation of the road detected with the AI algorithm suggests that the accuracy is underestimated. The reference dataset does not include all roads that the AI algorithm can detect in the Sentinel-2 imagery. We found an astonishing footprint of roads in the Brazilian Legal Amazon, with 3.46 million km of roads mapped in 2020. Most roads are in private lands (~55%) and 25% are in open public lands under land grabbing pressure. The roads are also expanding over forested areas with 41% cut or within 10 km from the roads, leaving 59% of the 3.1 million km2 of the remaining original forest roadless. Our AI and post-AI models fully automated road detection in rural areas of the Brazilian Amazon, making it possible to operationalize road monitoring. We are using the AI road map to understand better rural roads’ impact on new deforestation, fires, and landscape fragmentation and to support societal and policy applications for forest conservation and regional planning.

Environmental variable importance for under-five mortality in Malaysia: A random forest approach

BackgroundEnvironmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach. MethodWe applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation. ResultSome heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality. ConclusionHeat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.

FIRE VEHICLE ROUTE, RESPONSE TIME, AND SERVICE COVERAGE OPTIMIZATIONS IN PEKOJAN URBAN VILLAGE,TAMBORA SUBDISTRICT FIRE HOTSPOT OF JAKARTA CITY INDONESIA

One challenge in managing fire hazards in an urban setting is how to optimize the fire service route, increase the response time, and increase service coverage. Recently, this challenge is becoming imminent due to road traffic congestion and insufficient road widths that are common in populated cities in the Southeast Asia regions. One of the urban fire hotspots in populated Jakarta City is Pekojan Urban Village, Tambora Subdistrict. This subdistrict is served by Angke Fire Station located in Pekojan’s southwestern parts. Then this research aims to evaluate and compare optimized routes for fire vehicle dispatched from Angke Fire Station to serve 12 neighborhood units (in Bahasa is RW) in Pekojan. The method used the route optimization and network analysis tools in Geographic Information System (GIS) and its related geospatial data including neighborhood units, road networks, traffic congestion, and fire station locations. Geospatial network analysis of data by GIS has an advantage as a method to design and analyze the routing strategy and determine the most optimized route for fire vehicles. Based on the results and with the fire vehicle speed of 40 km/h, the average optimized route distances to travel from the fire station to RWs were 1.092 km (95%CI: 0.888-1.3 km) with an average response time of 1.638 minutes (95%CI: 0.869-2.41 min.). According to the GIS, model, response time of 1 minute only covers 22.77% of Pekojan areas. By increasing response time to 2 minutes, then fire vehicle can cover 98.9% of Pekojan area (AIC= 0.06). Despite the fact that the fire vehicle routes and response times can be optimized, those routes are challenged by the road traffic congestion. This congestion limits the speeds of fire vehicles to less than 20 km/h, as observed in 11.59% of the optimized routes. The service coverages of fire vehicles was also limited due to the narrow street.

Hotspots Forest Fire Susceptibility Mapping for Land Use or Land Cover using Remote Sensing and Geographical Information Systems (GIS)

Forest fire is a very common natural disaster or caused by humans that happens throughout the year in a humid country like Malaysia. Forest fire had caused many problems like haze and disruption of the environment. The peat-swamp areas are more susceptible to forest fire due to having a relatively spongy forest floor and covered with a thick and dense layer of humus. The objective of this research is to determine active fire hotspots at Raja Musa Forest Reserve, Kuala Selangor by using Landsat 8 infrared based images for forest fire mapping. The method used to develop a hotspot susceptibility forest map is by using the Analytical Hierarchy Process (AHP). Land cover or land use (LULC), Land Surface Temperature (LST), Fuel type, road to map and Normalized Difference Vegetation Index (NDVI) were used to determine the vulnerability of study by using satellite images from LANDSAT 8. The findings from the MODIS real-time forest fire hotspot data and kappa coefficient to check the consistency of the LULC classification. After obtaining the weightage value of each factors map, the weightage overlay tool from GIS were used to find the significant weight of the hotspot susceptibility forest fire map.

Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China

Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity.

Human Activity Behind the Unprecedented 2020 Wildfire in Brazilian Wetlands (Pantanal)

The Pantanal natural region in South America is the largest wetland worldwide. Due to floods and a diverse lithology, this wetland area exhibits high plant and animal biodiversity levels and encompasses more than 185,000 km2. In 2020, the Pantanal experienced the worst recorded fire episode in history. Hundreds of thousands of hectares burned, threatening habitats and corresponding ecosystem functions. The fire reached regions never burned before, including national conservation units. Although federal authorities have blamed climate change and cultural indigenous practices, environmentalists, scientists, and non-governmental organizations have questioned this foregone conclusion. To better understand this historical event, we examined the burn severity in conservation units most affected by fire and the association with human and climate factors (represented by the fire occurrence distance to roads/waterways/railways and drought severity, respectively) and analyzed fire spatial patterns and durations. Via a comparison of these two factors, we demonstrated that 60% of the fire outbreaks was concentrated at distances less than 5 km from roads, waterways, and railways and that 80% was concentrated at distances less than 10 km from areas with human activities. Since values of the SPEI (drought index) &amp;lt; −2.6 were critical to the spread of fire, a combination of more favorable climate conditions enabled the rapid and irreversible spread of fire. The observed association between fire occurrence and distance to roads makes the ongoing plans for road expansion of great concern, considering the 2020 fire episode, which greatly affected natural vegetation and conservation units across the Pantanal. The determination of trends in fire hot spot regions in the Pantanal can help environmental surveillance and fire control.