Fire Pixels Research Articles

Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods

This paper presents a comparative analysis of state-of-the art image processing-based fire color detection rules and methods in the context of geometrical characteristics measurement of wildland fires. Two new rules and two new detection methods using an intelligent combination of the rules are presented, and their performances are compared with their counterparts. The benchmark is performed on approximately two hundred million fire pixels and seven hundred million non-fire pixels extracted from five hundred wildland images under diverse imaging conditions. The fire pixels are categorized according to fire color and existence of smoke; meanwhile, non-fire pixels are categorized according to the average intensity of the corresponding image. This characterization allows to analyze the performance of each rule by category. It is shown that the performances of the existing rules and methods from the literature are category dependent, and none of them is able to perform equally well on all categories. Meanwhile, a new proposed method based on machine learning techniques and using all the rules as features outperforms existing state-of-the-art techniques in the literature by performing almost equally well on different categories. Thus, this method, promises very interesting developments for the future of metrologic tools for fire detection in unstructured environments.

Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power

Crop-residue burning plays an important role in regional air quality and global climate change. The North China Plain, the largest winter wheat production district in China, experiences extensive crop fires during harvest seasons. Traditional methods to estimate emissions from crop fires depend strongly on burned area, biomass density, combustion efficiency, and emission factor. However, the uncertainty of burned area derived from satellites can be very large because the agricultural fires in China have small sizes and short duration. Using fire radiative power (FRP) to estimate emissions depends on a few numbers of parameters and has been applied in recent researches. In our study, emissions from crop burning in fields in the North China Plain were estimated using MODIS FRP derived from the Terra and Aqua satellites. The time-integrated FRP (i.e., fire radiative energy, FRE) in per fire pixel and fire counts are important factors in the estimation. The estimated results showed that high fire emissions were in 2005–2007 and 2012, while low emissions were in the fire years 2003 and 2004. Our estimated emissions were close to a previous study with an agricultural statistical approach, but significantly higher than emissions derived from the MODIS burned area product and Global Fire Emissions Database, respectively. The emission uncertainties in our study mainly originated from MODIS pixel resolution, emission factors, FRP measurements, and FRP parameterization.

Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region

Active fire information provided by TERRA and AQUA instruments on-board sun-synchronous polar MODIS platform is used to describe fire activity in the Western Mediterranean and to identify and characterize the synoptic patterns of several meteorological fields associated with the occurrence of extreme fire activity episodes (EEs). The spatial distribution of the fire pixels during the period of 2003–2012 leads to the identification of two most affected sub-regions, namely the Northern and Western parts of the Iberian Peninsula (NWIP) and Northern Africa (NAFR). The temporal distribution of the fire pixels in these two sub-regions is characterized by: (i) high and non-concurrent inter- and intra-annual variability with maximum values during the summer of 2003 and 2005 in NWIP and 2007 and 2012 in NAFR; and, (ii) high intra-annual variability dominated by a prominent annual cycle with a main peak centred in August in both sub-regions and a less pronounced secondary peak in March only evident in NWIP region. The 34 EEs identified were grouped according to the location, period of occurrence and spatial configuration of the associated synoptic patterns into 3 clusters (NWIP-summer, NWIP-winter and NAFR-summer). Results from the composite analysis reveal similar fire weather conditions (statistically significant positive anomalies of air temperature and negative anomalies of air relative humidity) but associated with different circulation patterns at lower and mid-levels of the atmosphere associated with the occurrence of EEs in each cluster of the Western Mediterranean region.

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field, India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9×9 pixels for ASTER and 27×27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.

Optimized Flame Detection

In this paper, the system focuses on optimizing the flame detection by identifying gray cycle pixels nearby the flame, which is generated because of smoke and of spreading of fire pixel and the area spread of flame. The model uses fuzzy logic or fuzzy inference system (FIS) to detect fire pixels. These techniques can be used to reduce false alarm by giving the accurate result of fire occurrence along with fire detection methods. The system also give the opportunity to adjust the system by applying different combination of fire detecting techniques which will help in implementation of system according to different sensitive area requirement.

Implementation of Computer Vision Based Industrial Fire Safety Automation by Using Neuro-Fuzzy Algorithms

A computer vision-based automated fire detection and suppression system for manufacturing industries is presented in this paper. Automated fire suppression system plays a very significant role in Onsite Emergency System (OES) as it can prevent accidents and losses to the industry. A rule based generic collective model for fire pixel classification is proposed for a single camera with multiple fire suppression chemical control valves. Neuro-Fuzzy algorithm is used to identify the exact location of fire pixels in the image frame. Again the fuzzy logic is proposed to identify the valve to be controlled based on the area of the fire and intensity values of the fire pixels. The fuzzy output is given to supervisory control and data acquisition (SCADA) system to generate suitable analog values for the control valve operation based on fire characteristics. Results with both fire identification and suppression systems have been presented. The proposed method achieves up to 99% of accuracy in fire detection and automated suppression.

Co-variability of smoke and fire in the Amazon basin

The Amazon basin is a hot spot of anthropogenically-driven biomass burning, accounting for approximately 15% of total global fire emissions. It is essential to accurately measure these fires for robust regional and global modeling of key environmental processes. Here we have explored the link between spatio-temporal variability patterns in the Amazon basin's fires and the resulting smoke loading using 11 years (2002–2012) of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Aerosol Robotic Network (AERONET) observations. Focusing on the peak burning season (July–October), our analysis shows strong inter-annual correlation between aerosol optical depth (AOD) and two MODIS fire products: fire radiative power (FRP) and fire pixel counts (FC). Among these two fire products, the FC better indicates the amount of smoke in the basin, as represented in remotely sensed AOD data. This fire product is significantly correlated both with regional AOD retrievals from MODIS and with point AOD measurements from the AERONET stations, pointing to spatial homogenization of the smoke over the basin on a seasonal time scale. However, MODIS AODs are found better than AERONET AODs observation for linking between smoke and fire. Furthermore, MODIS AOD measurements are strongly correlated with number of fires ∼10–20 to the east, most likely due to westward advection of smoke by the wind. These results can be rationalized by the regional topography and the wind regimes. Our analysis can improve data assimilation of satellite and ground-based observations into regional and global model studies, thus improving the assessment of the environmental and climatic impacts of frequency and distribution variability of the Amazon basin's fires. We also provide the optimal spatial and temporal scales for ground-based observations, which could be used for such applications.

基于MODIS时序数据的黑龙江流域火烧迹地提取研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MODIS时序数据的黑龙江流域火烧迹地提取 DOI: 10.5846/stxb201312313076 作者: 作者单位: 太原师范学院地理科学学院,中国科学院东北地理与农业生态研究所,太原师范学院城镇与区域发展研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院战略性先导科技专项(XDA05090310) Burned area mapping for Heilongjiang basin based on MODIS time series data Author: Affiliation: TaiYuan normal university,Northeast Institute of Geography and Agroecology, Chinese Academy of Science,TaiYuan Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:火烧迹地信息是研究火灾的重要参数和基础数据,也是研究全球生态系统和碳循环扰动的重要依据之一。以受森林火灾影响较为严重的黑龙江流域为研究区,以MODIS时间序列数据为数据源建立了一个分为两阶段的火烧基地提取算法(即首先设定较为严格的提取条件对最有可能发生火灾的像元--核心像元进行提取,然后设定较为宽松的阈值提取距离核心像元一定范围内的火烧像元),对2000-2011年的火烧迹地信息进行了提取,生成了研究区长时间序列火烧迹地分布图,并对其时空分布特征进行了分析。选择黑龙江省为典型验证区对算法精度进行了验证,结果显示算法的整体精度较之以往的算法有了一定程度的提高。 Abstract:Fire is an important natural disturbance that affects several ecosystems and is also one of the main factors of the terrestrial carbon cycle. As fire modifies the structure and composition of vegetation, it is considered to be an important land management tool. Burned area mapping is an essential step in forest fire research to investigate the relationship between forest fire and climate change and the effect of forest fire on carbon budgets. Traditional data collection of forest fires in field- which are statistically recorded are difficult to manipulate over a large area. The development of the remote sensing technique provides a labor-efficient method for research of land surface processes. At the regional or global scale, in order to obtain a long-time series of burned area maps, a moderate spatial resolution with high temporal resolution remote sensing data is considered as the best alternative. Currently, the most widely used remote sensing data are Advanced Very High Resolution Radiometer (AVHRR) images and Moderate-Resolution Imaging Spectroradiometer (MODIS) images. Although the AVHRR provides continuous observations for burned area analyses, some studies have identified several sources of potential errors in burned area discrimination from this sensor, mainly due to its radiometric instability, cloud obscuration, and transmission problems. Most of these problems have been notably reduced in the MODIS sensor, which offers greater spectral, spatial, and radiometric resolution than the AVHRR. This study proposes an algorithm to map areas burned by forest fire using MODIS time series data in Heilongjiang Valley, China. The algorithm is divided into two steps: First, the "core" pixels were extracted to represent the most possible burned pixels based on a comparison of the temporal change of the Global Environmental Monitoring Index (GEMI), the Burned Area Index (BAI), and the MODIS active fire products between pre- and post-fire spatial patterns. Second, a 15-km distance was set to extract the entire burned area near the "core" pixels. These more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fire spatial patterns, which were represented by the MODIS fire products and the spectral index, respectively. Heilongjiang province in China was selected as the typical study area to validate the accuracy of the algorithm. The results showed that with the use of the MODIS fire products, the accuracy of the algorithm was improved, with an overall accuracy of 71% and a highest accuracy of 84%. Consequently, the algorithm used in this study produced a long-time series of burned area maps of the study area from 2000 to 2011 with a relatively high accuracy. According to the burned area maps, the study area has been seriously affected by fire disasters on average of 0.53 million ha of burned land each year. The most affected years were 2003 and 2008 with burned areas exceeding 1 million ha. The least affected year was 2010 with a burned area of just 0.18 million ha. The relatively large disparity between the maximum and minimum values of the areas burned by forest fire indicates that there is a fluctuation in the severity of disaster during the studied period. 参考文献 相似文献 引证文献

Evaluating Spatial and Temporal Variations of Aerosol Optical Depth and Biomass Burning over Southeast Asia Based on Satellite Data Products

Many severe air quality problems in the major cities of Southeast Asia (SEA) are related to atmospheric aerosols, and these are mainly caused by smoke haze from biomass burning. To better understand the cause and effect relationships for the tempo-spatial distributions of atmospheric aerosols in SEA, a variety of satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) products of aerosol optical depth (AOD), precipitation, burned area (BA) and fire pixel counts (FC, derived from the active fire product) were collected and processed to evaluate the spatial and temporal variations among atmospheric aerosol, climate factors, and biomass burning in SEA during 2002–2011. High AOD zones (HAZs) located in the continental and maritime portion of SEA are identified through hotspot analysis of AOD maps. The peak AOD, BA and FC months are concentrated in the dry seasons of each HAZ. Although BA and FC are mostly identified in Indochina, the HAZ located in maritime SEA has a comparable level of AOD which may be contributed by the fire-related emissions from peatland in Indonesia. Compared to the commonly used fire-effected area dataset (MCD45 product), BA derived from a hybrid approach (MCD64 product) that considers both active fire (AF) and land change information has higher correlation coefficients with AOD in both HAZs. Linear regression models are then developed for the Indochina and the maritime HAZs, to estimate the level of AOD from the MODIS monthly fire datasets. In general the empirical models can better explain the temporal trends of AOD in HAZs by using AF-based products. The links between regional aerosol and local burning in Indochina SEA are relatively complex due to the cross-boundary transport of aerosol from Southern China.

Efficient Colour Based Fire Pixel Classification Using Image Processing

In this paper an efficient colour based fire pixel segmentation using image processing is proposed. The proposed method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB and rgb. The proposed method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameter of fire image in YCbCr colour space like standard deviation. The results obtained are compared with other methods in the literature and shows higher fire detection rate and less false detection rate. The proposed method can be used for real time forest fire detection with moving camera.

Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation

Most active fire detection algorithms applied to data from geostationary Earth Observation (EO) satellites are adjustments of those originally developed for polar-orbiting systems, and thus the high temporal imaging frequencies offered from geostationary systems are often not fully utilized within such detection approaches. Here we present a new active fire detection algorithm that fully exploits geostationary data's temporal dimension, including both for detecting actively burning fires and quantifying their fire radiative power (FRP). The approach uses a robust matching algorithm to model each pixels diurnal temperature cycle (DTC) in the middle infra-red (MIR) spectral band, the most important band for active fire detection. For each pixel, a Kalman filter (KF) is used to blend the set of basis DTCs with the actual geostationary observations during times of confirmed cloud- and fire-free measurements, allowing for estimates of the pixels true non-fire ‘background’ signal to be provided throughout the day, even when a fire maybe present. This is different to the standard ‘spatial contextual’ approach, where the non-fire background signal is estimated from nearby non-fire pixels. A series of spectral thresholds are then applied to the analyzed pixel in order to identify whether the actual observation departs sufficiently from the estimated non-fire signal to confidently suggest that the pixel contains an active fire. If it does, the difference between the fire pixel and non-fire background signal estimate in the MIR is used to estimate the fire radiative power (FRP) output. We apply this new ‘Kalman Filter Algorithm’ (KFA) to one month of African imagery acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI), which is carried onboard the geostationary Meteosat satellite. We compare the resulting active fire detections and FRPs to those produced using the prototype (offline) version of the ‘spatial contextual’ based ‘Fire Thermal Anomalies’ (FTA) algorithm, here termed the pFTA, now used to generate the operational SEVIRI FRP-PIXEL product in the EUMETSAT Land Surface Analysis Satellite Applications Facility (http://landsaf.meteo.pt/), and also compare results to simultaneous detections from the MODIS MOD14/MYD14 active fire products. The KFA shows some advantages over the pFTA algorithm, detecting a greater number of fire pixels, up to ~80% more at the peak of the diurnal fire cycle. These additional fire pixels are primarily low FRP fires (<30MW), which results in the overall fire radiative energy (FRE) increasing by only 20% compared to the pFTA algorithm, though this is still a substantial difference. Comparison against simultaneous MODIS active fire observations confirms that the KFA detects more of the MODIS-detected active fires than does the pFTA (60% more), but at the expense of doubling the false alarm rate. Analysis of the ability of the KFA to aid in the estimation of FRP by providing a more certain estimate of the fire pixels background signal indicates a small 0.2K reduction (rmsd) between the ‘estimate’ of the background temperature and the ‘truth’. One limitation of the KFA approach currently is that it is computationally costly, and also requires the full day diurnal variation to be measured prior to the multi-temporal fire detection process. In its present form it is therefore unsuited to the generation of real-time products. We recommend future work concentrate on extracting maximum performance and utility of geostationary active fire observations by blending both ‘spatial contextual’ and ‘multi-temporal’ approaches.

A global feasibility assessment of the bi-spectral fire temperature and area retrieval using MODIS data

The use of the sub-pixel bi-spectral fire temperature and area retrieval with moderate and coarse spatial resolution satellite data has grown in recent years despite the numerous significant limitations of the method. Many of these limitations arise from a well-known sensitivity to errors in the 11-μm background radiance estimate used in the retrieval. Since this estimate is almost always obtained by averaging neighboring pixels, the accuracy of the bi-spectral retrieval is intrinsically coupled to the local 11-μm surface variability at the scale of the sensor footprint. In this paper, we explore the impact of this variability on the accuracy of the retrieval using ten years of 1-km MODIS fire data. In addition, we propose a simple a priori rejection test to identify and eliminate cases in which the retrieval is destined for failure (i.e., has no physically valid solution), or has a greater likelihood of yielding highly inaccurate fire area and average temperature estimates as a result of this variability. Finally, we examine the implications of our rejection scheme on the feasibility of performing the bi-spectral retrieval globally using MODIS and other comparable 1-km sensors. Under our proposed rejection criterion based on 11-μm background variability, the bi-spectral retrieval could be performed with sufficient accuracy (here defined as limiting the average bias in retrieved fire fraction to approximately a factor of two) for only 7% of all MODIS fire pixels detected globally during the ten-year study period. Consideration of additional error sources is likely to further reduce this fraction.

A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic

Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through space and time to determine their cumulative impact on the characterization of individual fire regime parameters. Here we perform the first ever decade long, multi-scale map comparison of fire chronologies and fire seasonality derived from three publicly available satellite-based fire products: the MODIS active fire product (MCD14ML), the ATSR nighttime World Fire Atlas (WFA), and the MODIS burned area product (MCD45A1). Results indicate that: (i) the agreement between fire chronologies derived from two dissimilar satellite products improves as fire pixels are aggregated into coarser grid cells, but diminishes as the number of years included in the time series increases; and (ii) all three datasets provide distinctly different portraits of the onset, peak, and duration of the fire season regardless of the map resolution. Differences in regional, long-term fire regime parameters derived from the three datasets are attributed to the unique capability of each sensor and detection algorithm to recognize geographical gradients, seasonal oscillations, decadal trends, and interannual variability in active fire characteristics and burned area patterns. Since different satellite sensors and detection algorithm strategies are sensitive to different types of fires, we demonstrate that disagreements in fire regime maps derived from dissimilar satellite-based fire products can be used as an advantage to highlight spatial and temporal transitions in landscape fire activity. Given access to multiple, publically available datasets, we caution against describing fire regimes using a single satellite-based active fire or burned area product.

Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. This work addresses this issue and quantifies the precision of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of FRP by collecting duplicate, off-nadir, overlapping observations of the same fires. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

Quantifying the potential for high-altitude smoke injection in the North American boreal forest using the standard MODIS fire products and subpixel-based methods

All chemical transport models require an estimation of the vertical distribution of smoke particles near the source. This study quantitatively examines the strengths and weaknesses of several fire products for characterizing plume buoyancy and injection heights in the North American boreal forest during 2004–2005. Observations from the Multiangle Imaging Spectroradiometer show that 21% of smoke plumes are injected more than 500 m above the boundary layer (BL500) and 8% exceed 2.5 km above ground level. Corresponding observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) show that probability of injection above the BL500 exceeds 60% for pixel-based fire radiative power (FRPp) values above ~2500 MW. Increasing values of subpixel-retrieved fire area and temperature also correspond to higher injections but only after removing fire pixels with a weak 11 µm fire signal and clustering. The probability of injection above the BL500 reaches 50% when the subpixel radiant flux (FRPf flux) exceeds 20 kW/m2, highlighting its potential for estimating plume buoyancy. However, these data have limitations similar to FRPp, where the highest probability of injection corresponds to a small percentage of the data set (5–18%), and many high-altitude injections occur with lower values. Examinations of individual smoke plumes highlight the importance of combining pixel-level and subpixel outputs and show that plume injection is also sensitive to the fire pixel spatial distribution and meteorology. Therefore, an optimal method for predicting high-altitude injections will require some combination of injection climatology, FRPp, FRPf flux, and meteorology, but each variable's importance will depend on fire event characteristics.

An automated two-step NDVI-based method for the production of low-cost historical burned area map records over large areas

The recognition and understanding of long-term fire-related processes and patterns, such as the possible connection between the increased frequency of wildfires and global warming, requires the study of historical data records. In this study, a methodology was proposed for the automated production of long historical burned area map records over large-scale regions. The methodology was based on remotely sensed, high temporal resolution, normalized difference vegetation index (NDVI) data that could be easily acquired at medium or low spatial resolution. The proposed methodology was used to map the burned areas of the wildfires that occurred over the Peloponnese peninsula, Greece, during the summer of 2007. The method was built upon the NDVI data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT)-VEGETATION. The higher spatial resolution data of MODIS resulted in higher burned area user accuracy (91.10%) and kappa (0.85) values than the respective ones for VEGETATION (79.29% and 0.77). The majority of classification errors were located along the perimeter of the burned areas and were mainly attributed to spatial resolution limitations of the remotely sensed data. The commission errors located away from the fire perimeter were primarily attributed to topographically shaded areas and land-cover types spectrally similar to burned areas. The omission errors resulted primarily from the small size and elongated shape of remote burned areas. Owing to their geometry, they have a high proportion of mixed pixels, whose spectral properties failed to meet the strict set of criteria for core fire pixels. The benefits of the proposed methodology are maximized when applied to data of the highest available spatial resolution, such as those collected by MODIS and the Project for On-Board Autonomy – Vegetation (PROBA-V) and when land-cover types spectrally similar to burned areas are masked prior to its application.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched in October 2011 aboard the Suomi-National Polar-orbiting Partnership (S-NPP) satellite. The VIIRS instrument carries two separate sets of multi-spectral channels providing full global coverage at both 375m and 750m nominal resolutions every 12h or less depending on the latitude. In this study, we introduce a new VIIRS active fire detection algorithm, which is driven primarily by the 375m middle and thermal infrared imagery data. The algorithm builds on the well-established MODIS Fire and Thermal Anomalies product using a contextual approach to detect both day and nighttime biomass burning and other thermal anomalies. Here we present the fire algorithm's design and implementation, including important information describing the input data characteristics and potential artifacts associated with pixel saturation and the South Atlantic Magnetic Anomaly, both found to affect the middle infrared channel data. Initial assessment using results derived from the global processing of the algorithm indicated small, although variable, commission errors (<1.2%) for nominal confidence fire pixels. We achieved improved performance using the 375m active fire data compared to the VIIRS 750m baseline fire product, resulting in a 3× and 25× factor increase in the absolute number of fire pixels detected using day and nighttime data, respectively. Similarly, VIIRS 375m fire data showed significantly superior mapping capabilities compared to current MODIS fire detection data with improved consistency of fire perimeter delineation for biomass burning lasting multiple days.

Flame detection in grey-scale images of a B/W camera

Purpose – Fire is a common disaster. Even though simple sensors such as those detecting smoke or heat are popularly employed, they require close proximity to fire. In order to obtain more reliable and more complete information, fire detection by vision sensing has recently acquired increasing attention. In the vision-based fire sensing, colour is usually used as an important cue for flame detection. However, considering there are still a large number of black-and-white (B/W) CCTV cameras installed for security purposes, a technique that can detect flame reliably in grey-scale images will be useful to protect human lives and property from the fire disaster. The paper aims to discuss these issues. Design/methodology/approach – This article describes the automatic detection of fire flames in the grey-scale image sequences by a two-level image processing scheme: pixel-level and frame-level. In pixel-level processing, an evaluation function is devised to extract pixels that possibly belong to the flame region, particularly to its boundaries. Extracted fire pixel candidates are verified in frame-level processing by monitoring their distribution variations in sequential images. A circle is fitted to the candidate pixels in each image for efficient monitoring, and the presence of flame is reasoned when the position and size of the circle increase with high fluctuations. Findings – Experimental results show that the proposed method can detect flame quite reliably using the intensity information and its temporal variations in grey-scale image sequences. Originality/value – This paper presents a novel technique of vision-based flame detection. Unlike most existing techniques, the proposed technique is based on the grey-scale images of a B/W camera. To the best of the author's knowledge, it may be the first of its kind developed for general application to indoor and outdoor scenes.

Integrated active fire retrievals and biomass burning emissions using complementary near-coincident ground, airborne and spaceborne sensor data

Ground, airborne and spaceborne data were collected for a 450ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near-coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high-quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (<1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5% difference between Aqua/MODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261kgMJ−1, total energy release of 14.5e+06MJ, and total fuel consumption of 3.8e+06kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15% difference for various species.