Fire Products Research Articles

Implementasi Strategi Pemasaran Model Segmentassi Terhadap Produk Asuransi Kebakaran

The presence of fire insurance products offered by insurance companies to the public is to provide protection and guarantee for loss or damage to property caused by fire. The segmenting model marketing strategy includes the dominant marketing strategy applied to market fire insurance products. People who have a low or high level of property fire risk are the target market. In the application of this segmenting model marketing strategy, although it has been effective and successful in marketing products, there are still obstacles that result in insurance products not reaching significant sales targets due to the lack of public awareness of their property fire insurance. The marketing strategy in this company uses direct channel and indirect channel marketing strategies, where Direct channel marketing is carried out by agents, staff or other employees and indirect channel marketing is carried out by broker or brokerage services. Because the products marketed are different, the marketing strategies are different. Fire insurance marketing uses three segmenting models, namely the first is general corporate segmentation where the target is in terms of the public such as malls and banks. second, special corporate segmentation where the target is in terms of government such as the DPRD building. And the third is retail segmentation where the target is in terms of individuals such as residential houses. From these three segments, fire insurance products are of interest to the public and also have a good sales rate.

FLAMMABILITY BEHAVIOURS OF KNITTED FABRICS CONTAINING PLA, COTTON, LYOCELL, CHITOSAN FIBERS

In recent years, studies have focused on the development of fire retardant products that are produced without the use of flame retardants with toxic effects and do not have harmful effects on humans and the environment. In this work, it has been compared the flame retardant properties of some biodegradable fibers obtained from different sources. It is important to investigate the performance of textile surfaces obtained from renewable fibers for flame retardant products and to improve their usage possibilities. The fire retardancy properties of knitted fabrics containing PLA, cotton, lyocell, and chitosan fibers were studied by analyzing the LOI values and burning behaviors. Single jersey knitted fabrics composed of 100% PLA, 100% Lyocell, 95% PLA 5% Chitosan, 95% Cotton 5% Chitosan, 95% Lyocell 5%Chitosan, 80%PLA 15%Cotton 5%Chitosan, 80%PLA 15%Lyocell 5%Chitosan were produced. The flame speed of each material was determined and differences in the flammability behaviors of the fabrics were identified. The lowest flammability occurred for the fabric 95% PLA 5% Chitosan. The highest flammability was for 100% Lyocell knitted fabric.

Spatiotemporal Characteristics and Regional Variations of Active Fires in China since 2001

Currently, fires (e.g., biomass burning and/or straw burning) are still prevailing and serious globally. However, the issue of the characteristics, types, and drives of fire occurrence is always a challenge and varies distinctively worldwide. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) active fire products during 2001–2020, here, we analyzed the occurrence frequencies and spatiotemporal characteristics of active fires at the provincial and regional to national scales and at the monthly and annual scales in China. The accumulated occurrence frequencies of MODIS C6 active fires in China were up to 184.91 × 104 in the past two decades, and the average annual level was 9.25 × 104, especially in 2014 (15.20 × 104). The overall trend of active fires was rising and then falling, but with significant spatial and temporal differences in the last 20-years. Temporally, nearly 61% of active fires occurred in spring (36%) and autumn (25%), particularly in August (16%), April (14%), and October (13%). Spatially, about 90% of active fires occurred in the east of the Hu Huanyong Line, particularly in Northeast China (25%), South China (23%), and East China (20%). In China, the most active fires were concentrated in the Northeast Plain, the North China Plain, the southeast hills, and the Yunnan–Kweichow Plateau. In terms of temporal differences across regions, active fires in Northeast China, North China, and Northwest China were concentrated in spring and autumn, especially in March, April, and October; in East China, they were concentrated in summer, especially in June; and in South China and Southwest China, they were concentrated in winter and spring, especially from December to April of the following year. Our study provides a full analysis of spatio–temporal characteristics and changes of active fires in China, and it can also assist in supplying a beneficial reference for higher monitoring and controlling of fires such as straw burning.

Evaluating methods to map burned area at 30-meter resolution in forests and agricultural areas of Central India

Biomass burning is a major phenomenon that plays an important role in small-scale ecological processes such as vegetation dynamics and soil erosion, and global processes such as hydrological cycles and climate change. However, global fire databases have low accuracies for burned area detection in areas with small fires, low biomass and in woodlands and open forests that characterize Central India. The present study uses higher resolution (30 meter) Landsat imagery to test accuracies for burned area detection using spectral indices (SI), machine learning (ML) algorithms and supervised classification. We find that detection of burned area by global fire product Fire Information for Resource Management System (FIRMS) is very low (<20%). Accuracies are higher for Landsat-based classification of burned area using supervised classification, random forest (RF) and Support Vector Machines (SVM). Accuracies are higher in April–May than in February–March and vary by azimuth angle on the day of image acquisition. RF produced the most consistently high classification accuracies for April (>80%), but had a tendency to misclassify less frequently available land covers; SVM had similar classification accuracies but had a tendency to overfit the model. Both lead to the potential for increasing commission errors and need to be used carefully when predicting burned area. Inclusion of SI had high relative importance in predicting burned area and reduced commission errors. Given these caveats, we recommend using ML algorithms for mapping burned area in the future, as it requires less time investment than classification and can yield consistent results. Accurate mapping of high-resolution fires is important for more accurate inputs into carbon inventories and ecological understanding of land-use dynamics and drivers.

Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran

Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms of shape and perimeter, which can be further implicated for potential prevention strategies of endangered biodiversity.

A point-surface fusion method for vegetation water content retrievalconsidering optimization of GNSS sites and modeling elements

The normalized microwave reflectance index (NMRI) based on global navigation satellite system (GNSS) interferometric reflectometry has been proven to reflect the changes in vegetation water content (VWC) effectively, but it is limited to point data. A spatially continuous NMRI product can be obtained by fusing NMRI data (point) and moderate-resolution imaging spectroradiometer (MODIS) data (surface). However, the time resolution of the existing results is limited to 16 d, and the research on the selection of modeling elements is not deep enough. In this paper, a point-surface fusion method for VWC retrieval considering the optimization of GNSS sites and elements is proposed. This method is aimed at using MODIS multi-band to synthesize vegetation indices with 8 d spatial-temporal resolution and establishing the initial element set by combining meteorological elements, followed by optimizing the GNSS sites and modeling elements for best modeling scheme, finally constructing the point-surface fusion method for VWC retrieval, and comprehensively evaluating the performance of the method. The results indicate that optimizing GNSS sites and modeling elements are particularly critical to improving modeling accuracy. Compared with other vegetation elements, normalized difference vegetation index (NDVI), gross primary productivity, and leaf area index are essential elements that affect the modeling effect. Among them, NDVI is the critical element. The NMRI products with 8 d/500 m resolution obtained by GA-BPNN can better reflect the change of VWC. Furthermore, the spatial performance of NMRI products is consistent with the fire forecast products and is suitable for drought and fire forecasts.

A Prefatory Approach Towards Monitoring and Detection of Fire Outbreak Using Artificial Neural Network

Fire incidents had always been a primary concern in domestic and industrial properties, buildings, sites and offices. The end results of fire outbreaks can be exceedingly devastating and usually amount to serious losses of lives and properties. They also consist of alarm circuits and some manual call points often referred to as detection zones. Recent researches on fire related systems which are adaptations of the conventional fire systems, have dwelt comprehensively on technologies that can provide possible fire detection services by the integration of sensors that are capable of reacting to certain fire based parameters such as rise in temperature/heat, accumulation of pressure, smoke and other combustible elements etc.. The perceived setback of these sensors is that they need considerable time for responding as they require product of fire (e.g., smoke, temperature etc.) to reach the sensors. However, as computing power increases, and its effect is felt across most spheres of human existence, artificial intelligence based systems have been in the fore front of the discourse due to its ability to adequately minimize or exclude human involvement in most emergency or delicate activities. Therefore, implementing a computer vision-based fire monitoring and detection system using low cost surveillance cameras and artificial neural network is proposed and will sufficiently enhance the ability to monitor, detect and generally manage fire outbreaks in deployed buildings. The proposed system seek to significantly provide a more precise and accurate tool for the detection of fire situations and sufficiently eliminate the tendency for false fire alarms since it will be dependent on convolutional neural network trained with captured frames of video images of fire in order to accurately tell a fire situation. The primary purpose of this study is to develop an intelligent system for monitoring and detection of fire outbreak using computer vision and artificial neural network. Other objectives include: to develop a suitable neural network upon which the model will be trained and to implement the model using python programming language..The technology adopted in this study is expected to perform better unlike the conventional fire detection and alarm systems which are heavily reliant on sensors that are only effective with proximity and location of use.

Seasonal land use/land cover change and the drivers in Kafta Sheraro national park, Tigray, Ethiopia

Kafta Sheraro National Park (KSNP) has experienced rapid and consecutive destruction of dry woodland vegetation due to the influence of anthropogenic activities in the past three decades. However, to date, the change in woodland cover and its driving factors have not been addressed. This study aims to assess the spatial and temporal trends of land use/land cover change, seasonal vegetation cover change via the normalized difference vegetation index (NDVI), and human-induced drivers of change that occurred in the KSNP between 1988 and 2018 by using satellite imagery sensors (TM, ETM+, OLI), field observations, and local community interview data. The 2018 image results showed kappa coefficients of the dry season and wet season of 0.90 and 0.845, respectively. There was a continuous decline in woodland (29.38%) and riparian vegetation (47.11%) and an increasing trend in shrub bush land (35.28%), grassland (43.47%), bare land (27.52%), and cultivated land (118.36 km2) over the thirty-year period. Moreover, the results showed that bare land expanded from wet to drier months, while cultivated land and grazing land increased from dry to wet months. Based on the NDVI results, high to moderate vegetation was decreased by 21.47%, while sparse and non-vegetation expanded by 19.8% and 1.7%, respectively. Settlement and agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected park resources. Around KSNP, the local communities’ livelihood depends on farming (crop and livestock production). This expansion of farming is the main driver of woodland depletion, which leads to increased resource competition and a challenge for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity by encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

Use of maximum entropy to improve validation and prediction of active fires in a Brazilian savanna region

Monitoring fires using satellite images is a fundamental tool for managers of protected areas (PAs). However, false alerts hinder their ability and make their application unfeasible, since there are few fire brigades available for field checks. Thus, this research proposes the validation, identification, and prediction of active fires for the years 2001 to 2018 for a protected area of the Brazilian savanna. The validation was performed by counting the active fires that were effectively contained in a burning area by cross-referencing MODIS sensor images. Identification was performed by analyzing the spectral behavior of burning and non-burning in the active fire area. The maximum entropy was calculated from the spectral values and compared between an active focus with and without burning. In the prediction, the climatic conditions of the month before burning were analyzed, and the maximum entropy value was evaluated for model viability. The results indicated that only 30% of the active fires were actually in a burned area. The use of maximum entropy favors the improvement of the MODIS active fire product. The confirmed fires were in regions where the probability was above 75%, whereas the non-burning fires were approximately 60%. The variables evapotranspiration, wind speed, water deficit, and road proximity were the most relevant for the active fire prediction model. For the validation years 2019 and 2020, this method predicted the location of fires in 75% of the cases. This methodology, once employed by PA managers, can ensure accurate, efficient, and cost-effective monitoring of forest fire prevention and combat.

Application of MODIS Data-Based Forest Fire Monitoring and Assessment

Forest fires are uncontrollable fires that spread freely within forest land, causing significant harm and damage, and thus its monitoring and assessment are crucial. There is a wide range of applications of MODIS data in forest fires aspect, but they are mainly targeted to solve regional problems. This study addresses MODIS data technology and examples of its application to forest fires in the Heilongjiang, Australia, Fujian Province, and Daxinganling Mountains, confirming its potential for monitoring and assessing forest fires. MODIS images and fire products contribute significantly to the usefulness and accuracy in the dynamic identification monitoring of forest fires and accurate determination of the ignition place due to their high resolution, excellent calibration, and positioning processing. MODIS and its corresponding product datasets can also be used to construct multiple vegetation and associated indicators to acquire vegetation area changes and to analyze the damage caused by forest fires. It is the ideal data source for monitoring and assessing forest fires.

Climate‐linked increasing vegetation fires in global high mountains

The spatiotemporal variability of vegetation fires is essential for understanding changes in the climate and ecosystem in mountainous regions. MODIS Collection 6 active fire products indicate that the area burned by vegetation fires declined globally from over 4.27 million km2 to less than 3.52 million km2 annually during 2001–2021. In contrast, global high mountains higher than 3000 m have experienced an overall increase in their burned area and suffered record‐breaking wildfires from August to December 2020. Although high mountains accounted for less than 0.03% of the global burned area during 2001–2021, this proportion had more than tripled by 2020. This unprecedented wildfire record in 2020 could be mainly caused by favorable fire weather conditions such as low relative humidity, low soil water and high temperature.

Fire tests with lithium-ion battery electric vehicles in road tunnels

Alternative propulsion technologies, including battery-electric vehicles, are becoming increasingly prevalent. Whilst such vehicles remain a small overall proportion of the vehicle fleet, the combined impact of government policy and technological advances in alternative fuels is expected to accelerate the increase in their numbers in coming years.As a result of these changes, the nature of tunnel safety risk (including the risk of fire) is expected to change over time. The risk implications associated with such vehicles thus requires more detailed consideration. This entails evaluation of incident consequences with particular attention being paid to the impact of fire characteristics and toxic emissions on tunnel users and to the need for specific emergency intervention strategies.In the past, concerns were voiced with respect to the fire safety of the new energy storage device (battery) and the possibility of more difficult firefighting conditions. This resulted in a series of investigations concerning the safety aspects of batteries and battery electric vehicles. However, most of the tests performed merely dealt with battery cells or battery packs. To date, publicly available tests involving actual vehicles remain relatively rare. In 2018, the Austrian Government commissioned a research project concerning the effect of incidents with battery electric vehicles on tunnel safety. This project encompassed fire tests of passenger cars, simulations for heavy duty vehicles, and the impact of incidents with such vehicles on the safety of tunnel users and the tunnel structure.In a series of fire tests, the heat-release and production of (toxic) substances has been evaluated. The burning behaviour of vehicles with different energy storage technologies (i.e. Li-ion batteries, Diesel) was monitored and compared to each other. The heat-release rate and emission of (toxic) substances was measured, and different fire-fighting methods were applied during each test.This paper will focus on the aspect of full-scale fire tests of passenger cars performed in a road tunnel. It should be emphasized that the battery-electric vehicle fire tests presented here were the first of their kind under real road tunnel conditions. This concerns the fire behaviour, but also some new approaches to fighting a BEV fire. All previous tests reported in journals were either carried out in fire halls or only concerned individual battery modules.

Monitoring gas flaring in Texas using time-series sentinel-2 MSI and landsat-8 OLI images

• A high-resolution inventory of gas flaring in Texas is constructed. • Spatial-temporal characteristics of gas flaring over Texas are analyzed. • The greenhouse gas emissions hotspots from the gas flaring regions are explored. Since the shale Oil/Gas revolution, gas flaring and venting in the United States has garnered increasing attention. There is a pressing need to understand the spatial–temporal characteristics of gas flaring and track the associated greenhouse gas emissions. In this context, we use a thermal anomaly index (TAI) incorporating the Google Earth Engine (GEE) cloud computation and local batch processing for monitoring gas flaring and characterizing its spatial–temporal dynamics. We then apply a quantitative analysis of satellite-based carbon dioxide (CO 2 ) and methane (CH 4 ) in the gas flaring region. Here, we generate a gas flaring sites inventory in Texas from 2013 to 2022 based on > 83,500 multi-source moderate-resolution images (including 74,627 Sentinel-2 Multispectral Instrument [MSI] images and 8,969 Landsat-8 Operational Land Imager [ OLI] images). Validations and comparisons demonstrate that our method is reliable for MSI and OLI images, with an overall accuracy of > 95 % and a low commission rate and omission rate. We detected 217,034 gas flares from 9,296 flaring sites in Texas, and the majority (>92 %) were found in the central and western regions of the Permian Basin and the Eagle Ford Shale. The number of detected gas flaring sites vastly outnumbered the existing Visible Infrared Imaging Radiometer Suite (VIIRS) fire products, with an upward trend from 2013 to 2019 and a downward trend from 2020 to 2022. Notably, the gas flaring sites dropped significantly at the beginning of the COVID-19 pandemic (from December 2019 to May 2020), with the lowest average monthly growth rate of −14.38 %, and fell to the level of mid-2017. Application of gas flaring data identifies the localized greenhouse gas (GHG) emission hotspots in Texas and demonstrates that the increased effect of CH4 released from gas flaring regions was significantly stronger than that of CO 2 . These findings can provide references for monitoring similar small industrial sources in the future, can be used as an essential supplement to low-resolution fire products, and improve our understanding of CO 2 and CH 4 emissions from gas flaring at fine spatial scales.

Evaluating Satellite Fire Detection Products and an Ensemble Approach for Estimating Burned Area in the United States

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as the GOES-16 Advanced Baseline Imager or MODIS, on the other hand, are available in near real-time. Using a ground fire dataset of 2699 fires during 2017–2019, we fit a series of linear models that use multiple satellite fire detection products (HMS aggregate fire product, GOES-16, MODIS, and VIIRS) to assess the ability of satellite data to detect and estimate total burned area. It was found that on average models fit with fire detections from GOES-16 products performed better than those developed from other satellites in the study (modelled R2 = 0.84 and predictive R2 = 0.88). However, no single satellite product was found to best estimate incident burned area, highlighting the need for an ensemble approach. With our proposed modelling ensemble, we demonstrate its ability to estimate burned area and suggest its further use in daily fire tracking and emissions-modeling frameworks.

Does fire and herbicide benefit cattle production in invaded grassland landscapes?

Invasive species management is promoted as a general practice to maintain rangeland biodiversity and mitigate livestock performance losses (e.g., weight gain) stemming from invasive-species effects (e.g., competitive exclusion of palatable forages). It is hypothesized that altering fire-timing (e.g., moving from early to late-growing season burning) or integrating herbicide application into fire management regimes may lead to more successful reduction of certain invasive species without negatively affecting livestock weight gains. However, large-scale, replicated experimental evidence linking invasive species abundance or management practices to cattle weight gain is mostly lacking across grasslands. We manipulated eight large (333 – 766 ha) pastures managed with fire and grazing (i.e., pyric herbivory) to test how growing (i.e., August-September) and dormant (i.e., March-April) season fires, herbicide application, or invasive species abundance (namely Lespedeza cuneata) affected yearling stocker cattle weight gain – i.e., per head weight gain – from 2018 to 2020. We found that yearling stocker cattle gained significantly less weight in pastures burned during the growing season, and herbicide application did not increase weight gains overall. Moreover, we did not find a significant relationship between L. cuneata abundance and per head yearling stocker cattle weight gain. Our results add to a growing line of evidence that invasive species management does not always benefit livestock producers. The hypothesized negative relationship between invasive species abundance and cattle weight gain may be minimal (or neutral) across large grassland landscapes, particularly those managed with pyric herbivory. To date, pyric herbivory is the only management regime known to simultaneously promote biodiversity, and restrict some invasive plant species like L. cuneata.

Intelligent planning of fire evacuation routes using an improved ant colony optimization algorithm

The evacuation routes on a conventional evacuation diagram are fixed as they do not consider the real-time impact of fire products on the routes in the event of a fire, thus probably resulting in an ineffective evacuation. To resolve this problem, this study proposes an improved ant colony optimization (IACO) algorithm to determine the optimal evacuation route in a supermarket building with unfavourable fire conditions under the combined effects of temperature and fire products. First, a simulation software (PyroSim) is used to simulate the real fire scenario with the real-time temperature, CO concentration, and smoke concentration at each location, obtained from monitors. The heuristic function and pheromone update strategy of the basic ACO are improved based on the monitoring data. Next, the optimal path planning is achieved by using the IACO while considering fire products. Compared with the paths planned by the basic ACO and a commercial software (Pathfinder), the path planned by the IACO can effectively avoid areas with harsh fire environments and reducing casualties due to failed evacuation routes caused by changes in the fire environment. Finally, based on the IACO, a visualized evacuation guidance system is developed to demonstrate the optimal routes for users.

Improved estimation of fire particulate emissions using a combination of VIIRS and AHI data for Indonesia during 2015–2020

Smoke aerosol emissions from Indonesian fires frequently cause adverse environmental consequences across southeast Asia. Satellite observations provide us with a great opportunity to monitor such emissions at large scales. However, existing satellite-based estimates of Indonesian fire emissions vary considerably in magnitude, differing by a factor of four. Here, we aim to improve Indonesian fire emissions estimates through improved calculations of fire radiative energy (FRE: time-integrated fire radiative power (FRP)) and smoke aerosol emission coefficients (Ce) using multiple new-generation satellite observations. Specifically, peatland and non-peatland Ce values were derived from FRP and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution VIIRS. Then, fuel type-specific Ce values and fused AHI-VIIRS FRE were used to produce hourly and daily fire emission data from 2015 to 2020 across Indonesia. To evaluate AHI-VIIRS estimates, we generated a reference dataset of total particulate matter (TPM) by computing Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol observation differences from successive Terra and Aqua satellites overpasses over a set of manually-selected smoke plumes. AHI-VIIRS-based fire emissions correlated significantly with the MODIS reference data (r = 0.84; p < 0.001). The interannual time series analysis of AHI-VIIRS emissions showed extreme variability (~26 fold), with the greatest amount in 2015 (6.09 Tg) and the least in 2020 (0.23 Tg), during the study period. Mean annual fire emissions distributed across Indonesia's islands were primarily from Kalimantan (43.8%) and Sumatra (41.7%), followed by Sulawesi (5.6%), Java (4.5%), and Papua (4.4%). Additionally, contributions of Indonesian fire emissions from peatland, forest, cropland, and savanna/grassland fuel types were 51.4%, 37.8%, 7.0%, and 3.8%, respectively, during the fire season of the strong 2015 El Niño event. The analysis suggests that the majority of Indonesian fire emissions are very likely associated with large-scale land use conversion from peatland to agriculture, as well as prolonged droughts induced by El Niño events.

Dynamics of pollutant emissions from wildfires in Mainland China

We analyzed the dynamics of pollutant emissions from wildfires in mainland China from 2001 to 2019 using MODIS fire products combined with the measurements of emission factors of different vegetation types. The biomass distribution in Mainland China has heterogeneous temporal and spatial pattern, with inter-year variations and a decreasing trend from east to west. Overall, from 2001 to 2019, biomass combustion in Mainland China reached 479.59Tg (25.24Tg·a-1), in which northeast, north, east, south, central, northwest, and southwest regions accounted for 20.95%, 31.14%, 8.89%, 9.06%, 3.98%, 0.33% and 25.64% of total biomass combustion, respectively. The emissions of CO, CO2, CxHy, NOx, PM2.5, TC, OC and EC were 47.30, 288.05, 12.90, 0.40, 1.43, 0.83, 0.70, and 0.12Tg (1Tg=1012g), respectively. PM2.5, TC and OC emissions increased in the southwest, while all pollutant emissions declined significantly in the southern region. For particulate matter from wildfires, both the ratio of its emissions to total dust and the ratio of its concentration to atmospheric PM2.5 showed an increasing trend, implying that the relative environmental impacts of particulate emissions from wildfires may be rising. In addition, our results show that the current Chinese wildfire management has successfully reduced on average more than 80% of pollutant emissions from wildfire from 2001 to 2019 compared to the natural wildfire regime (no strict wildfire management). This research on the temporal-spatial changes of pollutant emissions from wildfires in Mainland China provides support for further exploration of wildfire impacts on regional environments, and indicates the effectiveness of Chinese current wildfire policy on the pollutant emission mitigation.

GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network

Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R Advanced Baseline Imager (ABI) can acquire images every 15 min at 2 km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375 m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

Bio-Inspirations - Melanophila Acuminata can Sense and Detect Forest Fires

Abstract: Forest-fires are caused due to various factors including natural ones, such as hot lightning striking the ignitable trees, and human ones such as negligent use of fire or fire-causing products like cigarettes and explosives, disruptive machinery deployed within the forest, etc. The spread of these fires, however, is largely determined by parameters such as temperature, wind, relative humidity, and rain; unsurprisingly, the most prominent of these factors is the temperature. Detection of anomalies in the temperature can be a crucial indication towards the possibility of a fire, therefore enabling a potentially quicker response and minimising the loss of land and other resources, besides human life. A specific beetle called the Fire Beetle, or the melanophila acuminata, is known to have evolved with a collection of sensors which can detect IR radiation with remarkable sensitivity, even from long distances. These beetles can potentially sense the emergence of a fire from as long as a few miles away. Since early detection of forest-fires is a decisive step towards dealing with it, taking inspiration from melanophila acuminata can be a way of doing so.