Active Fire Research Articles

Spatiotemporal distribution of air pollutants during a heat wave-induced forest fire event in Uttarakhand.

Prevailing dry conditions and rainfall deficit during the spring season in North India led to heat wave conditions which resulted in widespread and intense forest fire events in the Himalayan state of Uttarakhand during April 16-30, 2022. A total of 7589 active fires were detected by VIIRS during the second half of April 2022 compared to 1558 during the first half. The TROPOMI observed total column values of CO and NO2 increased by 4.4% and 11.7%, respectively during April 16-30, 2022 with respect to April 1-15, 2022. A noticeable increase in surface level concentration of trace gases was also observed at Dehradun. In situ measurements of CO, NOx, and O3 during April 16-30, 2022 show an increase of 133, 35, and 6%compared to previous year observations during the same period. Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized to quantitatively estimate the contribution of this event on the distribution of air pollutants over this state. The model results were evaluated against ERA5 reanalysis, upper air soundings, and TROPOMI-retrieved total column density (TCD) of CO, NO2, and O3. Two simulations with (Fire) and without (NoFire) biomassburning emissions input were performed to quantify the contribution of forest fires to the concentration of trace gases and particulates. The CO, NO2, and O3 emitted/produced from forest fire over Uttarakhand during April 2022 contributed approximately 39.95, 35.73, and 9.97% to the surface concentration of respective gas. In the case of aerosols, it was around 71.20, 71.44, and 33.62% for PM2.5, PM10, and BCrespectively. The vertical profile analysis of pollutants revealed that extreme forest fire events can perturb the distribution of air pollutants from the surface up to 450 hPa.

Evaluasi Hasil Inspeksi Alat Pemadam Api Ringan Berdasarkan Peraturan Menteri Tenaga Kerja dan Transmigrasi No. 4 Tahun 1998 di Pabrik Susu

Dairy is an industry in the food manufacturing sector that has priority for development in Indonesia. To maintain its quality, implementing health safety management as a standard operating procedure to create a safe and healthy company work environment. To create a safe and healthy work environment, all areas of the Dairy Company have been equipped with an active factory fire protection system, namely Light Fire Extinguishers (Fire Extinguisher). Even though the potential fire hazard rarely occurs in this company, Fire Extinguisher inspections within this company must still be carried out regularly to minimize the impact of a fire that might occur. The purpose of this study is to identify potential hazards and assess current fire extinguisher inspection procedures using observation methods and secondary data from documents and other archives. The results of this study indicate that the company has implemented safety and health management as evidenced by the company's regular inspections of fire extinguishers. From the results of examining these variables, which are in accordance with the Regulation of the Minister of Manpower and Transmigration No. 4 of 1998 is the maintenance of Fire Extinguisher which covers the condition of the pressure of the contents of the tube, seals, nozzle nozzle, installation, and condition of the Fire Extinguisher tube. The conclusion from this research is that the Dairy Factory has carried out inspections in accordance with the Regulation of the Minister of Manpower and Transmigration No. 4 of 1998.

Biome- and timescale-dependence of Holocene vegetation variability in the Northern Hemisphere.

Global climatic changes expected in the next centuries are likely to cause unparalleled vegetation disturbances, which in turn impact ecosystem services. To assess the significance of disturbances, it is necessary to characterize and understand typical natural vegetation variability on multi-decadal timescales and longer. We investigate this in the Holocene vegetation by examining a taxonomically harmonized and temporally standardized global fossil pollen dataset. Using principal component analysis, we characterize the variability in pollen assemblages, which are a proxy for vegetation composition, and derive timescale-dependent estimates of variability using the first-order Haar structure function. We find, on average, increasing fluctuations in vegetation composition from centennial to millennial timescales, as well as spatially coherent patterns of variability. We further relate these variations to pairwise comparisons between biome classes based on vegetation composition. As such, higher variability is identified for open-land vegetation compared to forests. This is consistent with the more active fire regimes of open-land biomes fostering variability. Needleleaf forests are more variable than broadleaf forests on shorter (centennial) timescales, but the inverse is true on longer (millennial) timescales. This inversion could also be explained by the fire characteristics of the biomes as fire disturbances would increase vegetation variability on shorter timescales, but stabilize vegetation composition on longer timecales by preventing the migration of less fire-adapted species.

A geographically flexible approach for mapping the Wildland-Urban Interface integrating fire activity data

The Wildland-Urban Interface (WUI) is the area where houses and natural vegetation meet or intermingle. WUI areas are exposed to an increased hazard of wildfires and have significantly expanded worldwide in the past few decades. In this study, we developed a new empirical approach for mapping the WUI by generating a WUI index based on the juxtaposition among buildings, vegetation, and the fire history of the study area. We first calculated the percentage coverage of buildings and three different fuel typologies within circular moving windows with radii of 100, 250, and 500 m, and then acquired the fire history data between 2012 and 2021 for Israel and the West Bank (Palestinian Authority) from the VIIRS active fires remote sensing product. We defined the WUI as cells where the combination of vegetation cover and building cover had more VIIRS fire detections than expected by chance. To assess the effects of using broad vs. local scale parameterizations on resulting WUI maps, we repeated this process twice, first using national-scale data, and then separately in four distinct geographic regions. We assessed the congruence in the amounts and patterns of WUI in regions as mapped by information from these two analysis scales. We found that the WUI in Israel and the West Bank ranged from 0.5% to 1.7%, depending on fuel type and moving window radius. The scale of parameterization (national vs. regional) affected the WUI patterns only in one of the regions, whose characteristics differed markedly than the rest of the country. Our new method differs from existing WUI mapping methods as it is empirical and geographically flexible. These two traits allow it to robustly map the WUI in other countries with different settlement, fuel, climate and wildfire characteristics.

The influence of regional wind patterns on air quality during forest fires near Sydney, Australia

Particulate pollution from forest fire smoke threatens the health of communities by increasing the occurrence of respiratory illnesses. Wind drives both fire behaviour and smoke dispersal. Understanding regional wind patterns would assist in effectively managing smoke risk. Sydney, Australia is prone to smoke pollution because it has a large population close to fire-prone eucalypt forests. Here we use the self-organising maps (SOM) technique to identify sixteen unique wind classes for the Sydney region from days with active fires, including identifying sea breeze occurrence. We explored differences in PM2.5 levels between classes and between hazard reduction burning (HRB) and wildfire days. For HRB days, classes with the highest PM2.5 mostly had a sea breeze, whereas better air quality days usually had winds aligned across the region (e.g. all westerly). The wind class with the most HRB days had low wind speeds and a sea breeze and was among the worst wind classes for air quality. For wildfire days, days with a sea breeze were also generally of poor air quality but many classes had at least some poor air quality days, most of which were during the 2019–2020 east coast wildfires in New South Wales. Some poor air quality days occurred in wind classes that sent strong plumes directly over air quality stations, spread smoke over a wide area or transported smoke from outside the region. The classes identified may be useful in scheduling HRBs, for example, identifying days with low pollution risk to conduct an HRB, or for assisting in understanding pollution risk and sending health warnings during HRBs and wildfires. Further development of the approach may allow the creation of multi-day classifications for fire managers to plan HRB ignitions over several days to ensure better smoke dispersal. Further research could incorporate other weather predictors or focus on other regions.

Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence-based forest fire warning system to prevent major disasters. This work aims to present an overview of vision-based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception-v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision-makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN-RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no-fire photos, the proposed RBFN-RAISR fire detection approach achieves 97.55% accuracy, 93.33% F-Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one-of-a-kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

Hourly emissions of air pollutants and greenhouse gases from open biomass burning in China during 2016–2020

Open biomass burning (OBB) is a significant source of air pollutants and greenhouse gases that have contributed to air pollution episodes in China in recent years. An accurate emission inventory is critical for the precise control of OBB. Existing OBB emission datasets are commonly based on MODIS observations, and most only have a daily-scale temporal resolution. Daily OBB emissions, however, might not accurately represent diurnal variations, peak hours, or any open burning processes. The China Hourly Open Biomass Burning Emissions (CHOBE) dataset for mainland China from 2016 to 2020 was developed in this study using the spatiotemporal fusion of multiple active fires from MODIS, VIIRS S-NPP and Himawari-8 AHI detections. At a spatial resolution of 2 km, CHOBE provided gridded CO, NOx, SO2, NH3, VOCs, PM2.5, CO2, CH4 and N2O emissions from OBB. CHOBE will enhance insight into OBB spatiotemporal variability, improves air quality and climate modelling and forecasting, and aids in the formulation of precise OBB preventive and control measures.

Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions

As the majority of active fire detection algorithms have been developed for worldwide applications using only satellite data without considering observing conditions and environmental factors, their performance varies regionally. This study investigates the viability of an adaptable active fire detection model that is applicable to diverse environmental and observing conditions by fusing numerical model data and satellite images. The model was developed for various land cover and climate types using commonly utilized brightness temperature-related variables (key variables) and supporting variables (sub-variables), including solar zenith angle, satellite zenith angle (SAZ), relative humidity (RH), and skin temperature. A dual-module (DM) convolutional neural network (CNN) structure was adopted to consider the different properties of key variables and sub-variables, and a control without sub-variables was used to assess the impact of observing and environmental variables. The proposed model was further evaluated using existing polar-orbiting and geostationary satellite-based active fire products. The recall and precision of the control model were 0.80 and 0.98, respectively, and the standard deviation of recall for the five focus sites was 0.140. However, the DM CNN model was notable for its higher recall and robustness compared to the control model (recall of 0.84, precision of 0.97, and standard deviation of recall of 0.126). High RH and SAZ, and the day-night transition period contributed to the poor performance of the control model which was mitigated by the DM CNN model. In particular, the use of RH improved the recall of the model, and SAZ contributed to the reduction of performance variation. Our model also outperformed the two geostationary satellite-based active fire products in terms of detection capacity, resulting in a spatial distribution of active fires similar to that of polar-orbiting satellite-based active fire products.

Downwind Fire and Smoke Detection during a Controlled Burn—Analyzing the Feasibility and Robustness of Several Downwind Wildfire Sensing Modalities through Real World Applications

Wildfires have played an increasing role in wreaking havoc on communities, livelihoods, and ecosystems globally, often starting in remote regions and rapidly spreading into inhabited areas where they become difficult to suppress due to their size and unpredictability. In sparsely populated remote regions where freshly ignited fires can propagate unimpeded, the need for distributed fire detection capabilities has become increasingly urgent. In this work, we evaluate the potential of a multitude of different sensing modalities for integration into a distributed downwind fire detection system, something which does not exist today. We deployed custom sensor-rich data logging units over a multi-day-controlled burn event hosted by the Marin County Fire Department in Marin County, CA. Under the experimental conditions, nearly all sensing modalities exhibited signature behaviors of a nearby active fire, but with varying degrees of sensitivity. We present promising preliminary findings from these field tests but also note that future work is needed to assess more prosaic concerns. Larger scale trials will be needed to determine the practicality of specific sensing modalities in outdoor settings, and additional environmental data and testing will be needed to determine the sensor system lifetime, data delivery performance, and other technical considerations. Crucially, this work provides the preliminary justification underscoring that future work is potentially valuable and worth pursuit.

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.

Fire‐Induced Collapse of Automated Rack‐Supported Warehouses

AbstractAutomated self‐supporting rack warehouses are becoming increasingly common in the international logistics sector. Their static and seismic design has been studied in the literature; however, the fire design still needs to be improved. Some Standards, such as the Eurocodes or Italian Fire Code, accept the collapse of rack warehouses during fires but require avoiding a progressive collapse or obtaining an implosive collapse regardless of any other active or passive fire protection systems. A hierarchy that guides the kinematics of collapse needs to be defined appropriately but without any Code going into further details. Moreover, the scientific literature focuses more on optimizing fire protection systems for racks, such as sprinklers, rather than preventing fire‐induced progressive collapse. Here, a case study is presented, which, using finite element modelling, provide a first robustness evaluation and highlights some possible aspects to be considered in the structural design to avoid a progressive collapse in the event of a fire. The analyses are performed using the LOCAFI method for localized fires and nonlinear dynamic finite element simulations.

Global distribution, trends and types of active fire occurrences

Fire occurrence is synonymous to terrestrial ecosystems and an important component of the Earth system. Climate change, vegetation characteristics, and human activity regulate fire occurrence and spread, however, fires also interact with them in multiple ways. Due to the complicated mechanisms of interactions between fire and land use or cover, the spatial distribution, change trends and land use or cover types of fire occurrences exist wide discrepancies in different regions or countries around the world. Therefore, the quantitative and spatial relationship and differences between fire and land use or cover at the global scale remain poorly understood systematically. Here, we combine active fire and land cover products during 2001–2020 to explore the spatio-temporal features, trends, and types of active fires from global to continental scales. Globally, the annual changes of monthly active fire occurrences kept a dramatic increase in first two or three years but a circuitous decrease since then. Most areas prevailingly experienced active fires for once to five times, a small part of areas clustered in Africa, Southeast Asia, and South America experienced active fires for over five times in the last 20-years. In particular, above 60 % of active fires (re-)occurred in forest and 20–25 % in cropland, whereas grassland and construction land only accounted for about 5 % and less than 2 % respectively. Driven by active fires, the conversion of forest to cropland accounted for nearly 60 % and the transition of cropland to forest (about 10 %) followed and formed an interactive circle. Our findings improve the understanding of fire-land cover change interactions, particularly agricultural expansion and forest loss driven by active fires. Future efforts on agricultural expansion, urban safety, carbon sequestration and biodiversity conservation should take the results of this research into account.

Wildfires Risk Assessment Using Hotspot Analysis and Results Application to Wildfires Strategic Response in the Region of Tangier-Tetouan-Al Hoceima, Morocco

In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic responses to wildfires in the Tangier-Tetouan-Al Hoceima (TTA) region, widely reputed to exhibit the most significant incidences of wildfires in Morocco. We adopted a combined approach, using burned area products (Fire_CCI51: 2002–2020) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and active fires from the Fire Information for Resource Management System (FIRMS: 2001–2022) and processing them with spatiotemporal statistical methods: optimized hotspot analysis (OHA) and emerging hotspot analysis (EHA). The main findings indicate that the TTA region recorded an average of 39.78 km2/year of burned areas, mostly located in forests (74%), mainly cork oak and matorral stands (50%). The OHA detected hotspots covering 2081 km2, with 63% concentrated in the provinces of Chefchaouen and Larache. Meanwhile, clusters of EHA extended over 740 km2 and were composed of the oscillating coldspot (OCS) and oscillating hotspot (OHS) patterns at 50% and 30%, respectively. Additionally, an average of 149 fires/year occurred, located mostly in forests (75%), mainly cork oak and matorral stands (61%). The OHA detected active fire hotspots covering 3904 km2, with 60% located in the provinces of Chefchaouen and Larache. Clusters of EHA over 941 km2 were composed of the oscillating hotspot (OHS) and new hotspot (NHS) patterns at 57% and 25%, respectively. The prevalence of the oscillating and new models mirrors, respectively, the substantial fluctuations in wildfires within the region alternating between periods of high and low wildfire activities and the marked increase in fires in recent times, which has occasioned the emergence of novel hotspots. Additionally, we identified six homogeneous wildfire zones to which we assigned three strategic responses: “maintain” (73% of the territory), “monitor and raise awareness” (14% of the territory), and “reinforce” (13% of the territory). These strategies address current wildfire management measures, which include prevention, risk analysis, preparation, intervention, and rehabilitation. To better allocate firefighting resources, strategic responses were classified into four priorities (very high, high, medium, and low). Last, the wildfire zoning and strategic responses were validated using burned areas from 2021 to 2023, and a global scheme was suggested to assess the effectiveness of future wildfire measures.

Burned area detection and mapping using time series Sentinel-2 multispectral images

Accurate mapping of burned areas (BAs) is essential for post-disaster reconstruction, air quality assessment, and estimation of fire emissions. Remote sensing has become the most practical means to map global/regional BAs. Space-borne coarse-resolution (250–1000 m) BA products play an essential role in fire-related science and applications, but small BAs are typically poorly represented due to resolution limitations. Moderate-resolution (10–30 m) imagery provides a more detailed spatial extent of BAs, but the various disturbances (e.g., plant phenology, cloud shading, flooding, harvesting, grazing, pests, and diseases) make application on a global scale challenging. Here, we propose an automatic BA detection algorithm based on time series images acquired by the Sentinel-2 MultiSpectral Instrument (MSI) and the Google Earth Engine. Specifically: (i) We determined an optimal set of spectral indices for MSI images based on global sampling to mitigate the background heterogeneity and BA spectral diversity. (ii) We jointly used three temporal scales, i.e., the single-temporal, dual-temporal, and short-time series MSI images, to identify potential BA regions (i.e., BA candidates). (iii) We integrated multi-source active fire (AF) products derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Operational Land Imager (OLI), and MSI instead of single-source products to refine BA candidates. (iv) We constructed an interannual time series to improve the BA candidates not confirmed by AF products. The proposed BA detection method was applied to different land cover and regional scales, including forests, grasslands, savannas, shrublands, and croplands at the regional (scene) and national levels (Portugal). Validation and inter-comparison with similar products demonstrated that the proposed method is reliable: the Dice coefficient of our detection results was improved by ∼7% and ∼ 9%, respectively, compared to the moderate-resolution BA products (e.g., LBA_CU for the conterminous United States and FireCCISFD20 for sub-Saharan Africa in 2019). These results suggest that the proposed method can accurately and automatically map BAs, providing a foundation for fire monitoring programs and climate change research.

Comparative Analysis of the Impact of Two Common Residue Burning Parameters on Urban Air Quality Indicators

Crop residue burning produces a lot of polluting gases and fine particles, endangering human health, damaging soil structure, and causing fire accidents. In addition to the impact of residue burning on the local environment, pollutants can spread with the wind to more distant areas and impact their air quality. Nevertheless, a comparative analysis of the impact of two common residue burning parameters, the number of residue fire points, and residue burned area on urban air quality indicators has not been reported. In this study, the correlation between these two different residue burning parameters on air quality in Daqing City (Western Heilongjiang Province, China) was investigated comparatively using the Visible Infrared Imaging Radiometer Suite (VIIRS) fire point product, the Moderate-resolution Imaging Spectroradiometer (MODIS) burned area product, and buffer zone analysis. The association between MODIS burned area products and air quality index (AQI) was found to be around 0.8. Meanwhile, it was found that the correlation between the number of residue fire points extracted from the VIIRS active fire products and air quality was above 0.6, again with a maximum of 0.75 at a buffer radius of 50 km. Within other levels of buffer zones, the correlation between residue burned area and AQI was consistently higher than that between residue fire points and AQI. By comparing the correlation between VIIRS fire points, MODIS burned area, and the concentration of each AQI pollutant, it can be found that the correlation between the concentration of each AQI pollutant and the residue burned area was higher than that and the fire points number. MODIS burned area monitoring, on the other hand, detects changes in the time series of images taken by satellite at two transit moments to obtain a new burned area and cumulative burned area during this period, allowing the monitoring of fire traces caused by fire points at non-transit moments. From analyzing the correlation between residue fire points, residue burned area, and the concentration of each pollutant (PM2.5, PM10, CO, NO2, SO2, and O3), we found significant correlations between residue burning and PM2.5, PM10, CO, and NO2 concentrations, with the highest correlation (R2) of 0.81 for PM2.5. Moreover, the correlation between residue burned area and PM2.5, PM10, CO, and NO2 concentrations was significantly higher than that between the number of residue fire points and their concentrations.

System-level feedbacks of active fire regimes in large landscapes

BackgroundClimate is a main driver of fire regimes, but recurrent fires provide stabilizing feedbacks at several spatial scales that can limit fire spread and severity—potentially contributing to a form of self-regulation. Evaluating the strength of these feedbacks in wildland systems is difficult given the spatial and temporal scales of observation required. Here, we used the REBURN model to directly examine the relative strengths of top-down and bottom-up drivers of fire over a 3000-year simulation period, within a 275,000-ha conifer-dominated landscape in north central Washington State, USA.ResultsWe found strong support for top-down and bottom-up spatial and temporal controls on fire patterns. Fire weather was a main driver of large fire occurrence, but area burned was moderated by ignition frequencies and by areas of limited fuels and fuel contagion (i.e., fire fences). Landscapes comprised of >40% area in fire fences rarely experienced large fire years. When large fires did occur during the simulation period, a recovery time of 100–300 years or more was generally required to recover pre-fire vegetation patterns.ConclusionsSimulations showed that interactions between fire weather, fuel contagion, topography, and ignitions manifest variability in fire size and severity patch size distributions. Burned and recovering vegetation mosaics provided functional stabilizing feedbacks, a kind of metastability, which limited future fire size and severity, even under extreme weather conditions. REBURN can be applied to new geographic and physiographic landscapes to simulate these interactions and to represent natural and culturally influenced fire regimes in historical, current, or future climatic settings.

Attention-Based Wildland Fire Spread Modeling Using Fire-Tracking Satellite Observations

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

Contribution of remote sensing to wildfire trend and dynamic analysis in two of Ghana’s ecological zones: Guinea-savanna and Forest-savanna mosaic

BackgroundTwo of Ghana’s ecological zones—Guinea-savanna zone (GSZ) and Forest-savanna mosaic zone (FSZ)—are practically homologous in terms of structure and floristic composition, with some differences. The various sub-ecosystems that make up these areas are being depleted and losing their natural values due to various threats. There is little understanding about the fire trends in these areas due to a lack of data and poor accessibility to existing fire statistics. This study aimed to contribute to the understanding of the trends of area burned and active fire in the Guinea-savanna and Forest-savanna mosaic zones in order to inform policy-makers about sustainable management options. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire (MDC14ML) and burned-area (MCD64A1) products to characterize the fire regime in terms of seasonality, intensity, density, burned area, frequency, and trends during the study period of 2001 to 2021.ResultsThis study indicated that fire activity started in October and peaked in December (GSZ) and January (FSZ). The mean proportion burned was approximately 39.95% (burned area of 2659.31 km2; FSZ) and 60.05% (burned area of 3996.63 km2: GSZ), while the frequency was approximately 42.87% (1759.95 of active fires; FSZ) and 57.13% (2345.26 of active fires: GSZ). In 2018, GSZ recorded the largest burned area (19 811.2 km2, which represents an average of 825.5 km2 of the total area burned from 2001 to 2021) with 4719 active points detected. FSZ recorded its greatest burned area in 2015 (8727.4 km2; which represents an average of 363.6 km2 of the total area burned from 2001 to 2021) with 5587 active points recorded. In addition, it was found that specific times of the day (1000 h to 1420 h) recorded the majority of burned areas. In overview, between 2001 and 2021, burned areas increased by an average of 1.4 km2 (FSZ) and 4.6 km2 (GSZ), and the number of active fires increased by an average of 4.7 (FSZ) and 4.4 (GSZ) active fires per km2.ConclusionsIn conclusion, burned areas and active fires are increasing in both ecological zones. This study demonstrated the relevance of remote sensing to describe spatial and temporal patterns of fire occurrence in Ghana and highlighted the need for fire control and fuel management by the policies and institutions (e.g., Ghana National Fire and Rescue Service) in these important and vulnerable zones (GSZ and FSZ). This is especially true in the Forest-savanna mosaic zone, which is increasingly affected by the disasters of wildfires and records more active fires than GSZ, indicating that this zone is becoming more and more vulnerable. Therefore, rigorous continuous monitoring is essential, and collaboration between organizations fighting for the conservation of natural resources in the field is strongly recommended.

ANALYSIS OF FIRE PROTECTION SYSTEM ON THE MULTIFUNCTIONAL LABORATORY BUILDING OF AR-RANIRY STATE ISLAMIC UNIVERSITY BANDA ACEH INDONESIA

Fire can occur if three main elements are met, namely heat, oxygen, and fuel or it is called the fire triangle. Risks arising from fire incidents can be minimized by a fire protection system and the reliability of building safety systems. To ensure that the two systems run optimally, an evaluation and examination are needed on the existing conditions based on the applicable safety technical standards. This study aims to determine the existing condition of the applied fire protection system and to determine the reliability of the building safety system in the Ar-Raniry Multifunctional Laboratory Building. This research was conducted using a quantitative descriptive method. Research data collection was carried out using observation and interview methods. The observation method for existing conditions was carried out using a checklist compiled based on the Indonesian Minister Public Works’ Regulation Number 26/PRT/M/2008 of 2008 for fire protection systems and fire protection code from the National Standardization Agency of Indonesia Number Pd-T-11-2005-C on the reliability of building safety systems. The results show that the fire protection system in the UIN Ar-Raniry Multifunction Laboratory building was in the Enough category, with a value of 3.25. This is due to the absence of several active fire protection instruments such as fire extinguishers, sprinklers, and hydrants. Addionally, from the results of the reliability of the building safety system for the UIN Ar-Raniry Multifunction Laboratory building, it is in the Enough category with a value of 63.02. This is caused by the absence of emergency lighting and water sources as a source of hydrant water.