Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products

A major focus in global change research is to quantify the amount of gaseous and particulate pollutants emitted from terrestrial vegetation fires. Determination of the emitted radiant energy released during biomass combustion episodes (the so-called fire radiative energy or FRE) has been suggested as a new tool for determining variations in biomass combustion rates and the rate of production of atmospheric pollutants. We review the physical principals behind the remote determination of FRE and present an alternative method for its derivation via analysis of ‘fire pixel’ radiances in the middle infrared spectral region. We compare our method to the existing FRE retrieval approach used in the EOS Moderate Resolution Imaging Spectro-radiometer (MODIS) fire products, and to retrievals of FRE based on derived fire temperature and area made via the so-called Bi-spectral method. We test each FRE retrieval method using both simulated data and imagery from a new experimental space mission, the Bi-spectral InfraRed Detection (BIRD) small satellite, which has sensors specifically designed for the study of active fires. We analyse near simultaneous MODIS and BIRD data of the fires that burned around Sydney, Australia in January 2002. Despite the markedly different pixel size and spectral coverage of these sensors, where the spatial extent of the fire pixel groups detected by MODIS and BIRD are similar, the derived values of FRE for these fires agree to within ±15 %. However, in certain fires, the lower spatial resolution of MODIS appears to prevent many of the less intensely radiating fire pixels being detected as such, meaning MODIS underestimates FRE for these fires by up to 46% in comparison to BIRD. Though the FRE release of each of these low intensity fire pixels is relatively low, their comparatively large number makes their overall FRE significant. Thus, total FRE release of the Sydney fires on 5 January 2002 is estimated to be 6.5×10⁹ J s⁻¹ via BIRD but 4.0×10⁹ J s⁻¹ via MODIS. The ability of BIRD to resolve individual fire fronts further allows the first accurate calculation of ‘radiative’ fireline intensity from spaceborne measurements, providing values of 15-75 kJ s⁻¹ m⁻¹ for fire fronts that are up to 9 km in length. Finally, we analyse the effectiveness of the satellite-based FRE retrieval methods in estimating the FRE from the active flaming and smouldering components only (FRE_Active, believed to be proportional to the rate of biomass combustion), despite the sensor receiving additional radiance from the ‘cooling ground’. The MIR radiance method appears particularly strong in this regard, allowing FRE_Active to be estimated to within ±30% in the range 100-100,000 J s⁻¹ m⁻². These results provide further confidence in the ability of spaceborne missions to derive physically meaningful values of FRE that could be used to support biomass burning emissions inventories. Future comparisons between FRE derived via MODIS and those from higher spatial resolution BIRD or airborne imagery may allow the MODIS-derived FRE values to be ‘calibrated’ for any systematic underestimation. We therefore expect FRE to become an important tool for enhancing global studies of terrestrial vegetation fires with infrared remote sensing, particularly as the majority of large fires are now imaged four times per day via the MODIS instruments on the Terra and Aqua spacecraft.     

Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa

Satellite-based estimates of the fire radiative power (FRP) and energy (FRE) emitted from open biomass burning are affected by the spatiotemporal resolution of polar-orbiting and geostationary sensors. Here the impacts of the MODIS sampling design on estimates of FRE are characterized by superimposing the timing and extents of the Terra and Aqua granules onto the SEVIRI active fire product. Results for different land-cover types across Africa indicate that the FRE measured by SEVIRI during eight days is linearly related to the sum of FRP measured by SEVIRI within the MODIS granules. These relationships are least variable during the height of the fire season when diurnal cycles of FRP measured by SEVIRI are most consistent. Relationships between FRE and the sum of FRP developed using the SEVIRI active fire product are directly applied to the sum of FRP retrieved from the MODIS Terra and Aqua Climate Modeling Grid (CMG) fire products. Estimates of FRE from MODIS herein agree within 5% of those obtained from previously published methods, but remain a factor of 0.72 times those obtained by adjusting SEVIRI measurements of FRE to account for low spatial resolution detection biases. An examination of the MODIS scan geometry suggests that the latter underestimation is attributed to the coupling between a MODIS imaging artefact referred to as the “bow-tie” effect and the typical calculation used to retrieve the sum of FRP from the MODIS CMG fire products. Depending on the availability of MODIS scan angle information, we offer rigorous and simplified calculations to account for the bow-tie effect. Applying the simplified adjustment to the MODIS CMG fire products yields national estimates of monthly FRE that are 1.44 times greater than originally predicted.     

Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)

We present an automated fire detection algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor capable of mapping actively burning fires at 30-m spatial resolution. For daytime scenes, our approach uses near infrared and short-wave infrared reflectance imagery. For nighttime scenes a simple short wave infrared radiance threshold is applied. Based on a statistical analysis of 100 ASTER scenes, we established omission and commission error rates for nine different regions. In most regions the probability of detection was between 0.8 and 0.9. Probabilities of false alarm varied between 9 × 10^− 8 (India) and 2 × 10^− 5 (USA/Canada). In most cases, the majority of false fire pixels were linked to clusters of true fire pixels, suggesting that most false fire pixels occur along ambiguous fire boundaries. We next consider fire characterization, and formulate an empirical method for estimating fire radiative power (FRP), a measure of fire intensity, using three ASTER thermal infrared channels. We performed a preliminary evaluation of our retrieval approach using four prescribed fires which were active at the time of the Terra overpass for which limited ground-truth data were collected. Retrieved FRP was accurate to within 20%, with the exception of one fire partially obscured by heavy soot.     

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

We explore the ability to enhance landscape fire detection and characterization by constructing a ‘virtual’ fire product from a synthesis of geostationary and polar orbiting satellite data. Active fire pixels detected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were spatially and temporally collated across Africa between February 2004 and January 2005. Coincident fire pixels detected by SEVIRI and MODIS were used to populate an empirical database of frequency density (f-D) distributions of fire radiative power (FRP). Frequency density distributions of FRP measured by SEVIRI at 5.0° grid cell resolution and 15-minute temporal resolution were then cross referenced in the database to a set of counterpart f-D distributions of FRP measured by MODIS. This procedure resulted in the first generation of a ‘virtual’ fire product that exhibits the full continental coverage and high temporal resolution of SEVIRI whilst quantifying fire pixel counts and FRP with accuracies approaching those of MODIS. Diurnal cycles extracted from the virtual fire product indicate that SEVIRI measures a greater proportion of the active fire pixels and FRP potentially detectable by MODIS during the day due to the increased prevalence and stronger radiant contribution of highly energetic fire pixels. On a daily basis (sample size n = 365) the peak magnitude in the diurnal cycle of the virtual FRP occurred within the same 15-minute timeslot as in the native SEVIRI fire product. Continental-scale ignition and extinction events, however, were detected on average 44 min earlier (standard deviation s.d. = 40 min) and 137 min later (s.d. = 92 min), respectively. It is anticipated that the methodology developed here can be used to cross-calibrate active fire products between a variety of different satellite platforms.     

Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations

Seven years of data from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) were used to characterize the average diurnal fire cycle in 15 regions of the tropics and sub-tropics. Bias errors in the resulting diurnal cycles were either avoided or removed through a combination of judicious region selection and the application of corrections to compensate for cloud obscuration and time-dependent “blind spots” in the fire-detection capability of the VIRS sensor. Supplementary data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra satellite aided this process. In all regions, the local time of peak burning fell between 13:00 and 18:30, with fire activity peaking distinctly earlier for the heavily forested regions. The time period of the central 50% of total daily fire activity varied from a minimum of 1.3 h in North Central Africa to a maximum of 5.5 h in Eastern Australia. In general, shorter periods of burning were associated with greater tree cover. Using the diurnal cycles obtained for each region, an analysis of the drift in the local overpass times of the NOAA-7 through NOAA-14 afternoon satellites was performed. Results show that very large, spurious trends are likely to occur in a long-term Advanced Very High Resolution Radiometer (AVHRR) fire record due to differences in diurnal sampling over time.     

New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America

Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source strengths. Geostationary imagers offer important temporal advantages when studying rapidly changing phenomena such as vegetation fires. Here we present a new algorithm for detecting and characterising active fires burning within the imager footprints of the Geostationary Operational Environmental Satellites (GOES), including consideration of cloud-cover and calculation of fire radiative power (FRP), a metric shown to be strongly related to fuel consumption and smoke emission rates. The approach is based on a set of algorithms now delivering near real time (NRT) operational FRP products from the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) imager (available from http://landsaf.meteo.pt/), and the GOES processing chain presented here is designed to deliver a compatible fire product to complete geostationary coverage of the Western hemisphere. Results from the two GOES imagers are intercompared, and are independently verified against the well regarded MODIS cloud mask and active fire products. We find that the detection of cloud and active fires from GOES matches that of MODIS very well for fire pixels having FRP > 30 MW, when the GOES omission error falls to less than 10%. The FRP of fire clusters detected near simultaneously by both GOES and MODIS have a bias of only 22 MW, and a similar bias is found when comparing near-simultaneous GOES East and GOES West FRP observations. However, many fire pixels having FRP < 30 MW remain undetected by GOES, probably unavoidably since it has a much coarser spatial resolution than MODIS. Adjustment using data from the less frequent but more accurate views obtained from high spatial resolution polar orbiting imagers could be used to bias correct regional FRP totals. Temporal integration of the GOES FRP record indicates that during the summer months, biomass burning combusts thousands of millions of tonnes of fuel daily across the Americas. Comparison of these results to those of the Global Fire Emissions Database (GFEDv2) indicate strong linear relationships (r² > 0.9), suggesting that the timely FRP data available from a GOES real-time data feed is likely to be a suitable fire emissions source strength term for inclusion in schemes aiming to forecast the concentrations of atmospheric constituents affected by biomass burning.     

Satellite mapping of CO emission from forest fires in Northwest America using MOPITT measurements

We present a study on MOPITT (Measurements Of Pollution In The Troposphere) detection of CO emission from large forest fires in the year 2000 in the northwest United States. Fire data used are from the space-borne Advanced Very High Resolution Radiometer (AVHRR) at 1-km resolution. The study shows that MOPITT can reliably detect CO plumes from forest fires whenever there are >30 AVHRR hotspots in a 0.25° × 0.25° grid, which is comparable to the pixel area of MOPITT in the region. The spatial CO pattern during the fire events is found to be consistent with the location and density of AVHRR hotspots and wind direction. While the increase of CO abundance inside the study area is closely correlated to the AVHRR-derived hotspot number in general (R > 0.75), the non-linearity of fire emission with fuel consumption is also observed. MOPITT can also capture the temporal variation in CO emission from forest fires through 3-day composites so it may offer an opportunity to enhance our knowledge of temporal fire emission over large areas. The CO emission is quantitatively estimated with a one-box model. The result is compared with a bottom-up approach using surface data including burnt area, biomass density, and fire emission factors. If mean emission factors for the region are used, the bottom-up approach results in total emission estimates which are 10%-50% lower than the MOPITT-based estimate. In spite of the limitations and uncertainties addressed in this study, MOPITT data may provide a useful constraint on uncertain ground-based fire emission estimates.     

Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001-2004)

Wildfires have a range of significant environmental effects with respect to both Earth's surface and atmosphere. Spaceborne remote sensing of active fires has been undertaken for more than two decades, but the bi-spectral Infrared Detection (BIRD) Experimental Small Satellite (2001-04) is the first mission dedicated to this task. This paper summarizes the experience gained during the BIRD mission, which has focused both on active fire detection and active fire characterization, in terms of quantifying effective fire temperature (T_F), effective fire area (A_F) and fire radiative power (FRP). A detailed error analysis for each parameter is undertaken, and the accuracy of FRP retrieval is shown to be significantly better than that of T_F or A_F. For key fire-affected forest, bush and savanna environments (Australia, Benin, Borneo, Brazil, Canada-US, Portugal and Siberia) BIRD data allows FRP estimation to within 30% in 75% of fires examined, and for the first time from space BIRD is able to allow estimation of fireline length, effective fireline depth and radiative fireline intensity for the more pronounced fire fronts. Some indication of the predominant combustion regime (smoldering or flaming), which has implications for the relative concentrations of emitted pollutant products, is possible through use of the T_F parameter. This experience demonstrates the advantages of the new infrared sensor technologies employed in BIRD, and offers suggestions for future fire monitoring sensors based on similar technologies.     

Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FC_A: g·m^− 2) via the ratio of FRE-derived total fuel consumption (FC_T) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FC_T for Africa varies between 0.3 and 20 Tg for the period February 2004-January 2005. We estimate annual continental FC_T to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.     

Testing a MODIS Global Disturbance Index across North America

Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO₂ and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest-agricultural interface. The MGDI indicates that 1.5% (195,580 km²) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km²) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.     

GSM无线数据传输在消防系统中的应用

介绍了一种基于GSM系统短消息机制的无线数据传输方式在消防系统运行监视中的应用,及短消息模块与单片机系统的接口电路设计。无线数据传输方式为消防系统的及时报警和排除故障提供了可靠的技术保证。     

Determination of spring onset and growing season leaf development using satellite measurements

An integrated approach to retrieve microwave emissivity difference vegetation index (EDVI) over land regions has been developed from combined multi-platform/multi-sensor satellite measurements, including SSM/I measurements. A possible relationship of the remotely sensed EDVI and the leaf physiology of canopy is explored at the Harvard Forest site for two growing seasons. This study finds that the EDVI is sensitive to leaf development through vegetation water content of the crown layer of the forest canopy, and has demonstrated that the spring onset and growing season duration can be determined accurately from the time series of satellite estimated EDVI within uncertainties of approximately 3 and 7 days for spring onset and growing season duration, respectively, compared to in situ observations. The leaf growing stage can also be monitored by a normalized EDVI. EDVI retrievals from satellite generally are possible during both daytime and nighttime when it is not raining. The EDVI technique studied here may provide higher temporal resolution observations for monitoring the onset of spring, the duration of growing season, and leaf development stage compared to current operational satellite methods.     

新能源汽车节能减排技术研究进展（1）——发动机系统

综述了电动力汽车（EV）、混合动力汽车（HEV）和燃料电池汽车（FCV）节能减排技术研究进展,主要从以下三方面论述：发动机系统、能量管理系统、传感器系统,并指出了存在问题和发展方向。本文主要介绍了发动机系统,包括：EV、HEV和FCV的关键技术、电机设计和驱动技术、控制技术。     

Ku波段星载行波管混合型预失真线性化器的研究

提出了一种改善行波管放大器非线性特性的预失真新方法,采用载波复幂级数法分析了星载行波管的非线性传输特性,提出一种反向复幂级数的预失真线性技术。所提出的预失真器非线性特性由混合型结构实现,包括肖特基二级管、砷化镓场效应管,改善了行波管在Ku波段的幅度-幅度失真特性,线性增益最大增加30dB,对行波管线性化器的后续研究有一定参考和实用价值。     

新能源汽车节能减排技术研究进展（2）——能量管理系统（下）

描述了电动力汽车（EV）、混合动力汽车（HEV）和燃料电池汽车（FCV）节能减排技术的研究进展,主要从以下三方面论述：发动机系统、能量管理系统、传感器系统,并指出了存在问题和发展方向。本文介绍了能量管理系统,包括功率转换器、电力半导体器件、电池和超大电容器、节能技术。     

Discrete variable optimization of frames using a strain energy criterion

This paper reports on recent advances concerning a new method for the weight (discrete variable) optimization of frames under both stress and multi-displacement constraints using a criterion based on the distribution of the virtual strain energy density. The proposed methodology is based on an idea that has been previously demonstrated and successfully applied on truss structures. The influence of each participating beam is defined through properly normalized sensitivity factors. An iterative formula, similar to that used for trusses, is proposed for updating the second moment of inertia followed by an improvement of shape variables (e.g. height, width, thickness) on the cross-section. The proposed method is successfully compared with the sequential quadratic programming method and other techniques published in the most recent literatures.     

新能源汽车节能减排技术研究进展（2）——能量管理系统（上）

描述了电动力汽车（EV）、混合动力汽车（HEV）和燃料电池汽车（FCV）节能减排技术研究进展,主要从以下三方面论述：发动机系统、能量管理系统、传感器系统,并指出了存在问题和发展方向。本文介绍了能量管理系统,包括功率转换器、电力半导体器件、电池和超大电容器、节能技术。     

贝叶斯正则化神经网络预测金属晶体结合能的研究

采用贝叶斯正则化神经网络(BRNN)对61种金属晶体结合能进行了预测。对网络结构、训练集、预测集以及学习次数进行了优化,并用独立预测样本对贝叶斯正则化神经网络作了检验。预测结果表明,在推广能力方面,贝叶斯正则化神经网络优于熟知的反向传播(BP)神经网络和多元线性回归方法(MLR)。它可望成为元素和化合物构效关系研究的辅助手段。     

Detection and mapping of long-term land degradation using local net production scaling: Application to Zimbabwe

Degradation of vegetation and soils in drylands, sometimes called desertification, is thought to be a serious threat to the sustainability of human habitation, but maps of the extent and severity of degradation at country and global scales do not exist. Degraded land, by definition, has suffered a change relative to its previous condition set by its climate, soil properties, topography and expectations of land managers. The local net production scaling (LNS) method, tested here in Zimbabwe, estimates potential production in homogeneous land capability classes and models the actual productivity using remotely-sensed observations. The difference between the potential and actual productivities provides a map of the location and severity of degradation. Six years of 250 m resolution MODIS data were used to estimate actual net production in Zimbabwe and calculate the LNS using three land capability classifications. The LNS maps agreed with known areas of degradation and with an independent degradation map. The principal source of error arose because of inhomogeneity of some land capability classes caused by, for example, the inclusion of local hot-spots of high production and differences in precipitation caused by local topography. Agriculture and other management can affect the degradation estimates and careful inspection of the LNS maps is essential to verify and identify the local causes of degradation. The Zimbabwe study found that approximately 16% of the country was at its potential production and the total loss in productivity due to degradation was estimated to be 17.6 Tg Cyr^− 1, that is 13% of the entire national potential. Since the locations of degraded land were unrelated to natural environmental factors such as rainfall and soils, it is clear that the degradation has been caused by human land use, concentrated in the heavily-utilized, communal areas.     

全叉指电极d33模式压电悬臂梁俘能器研究

为了克服表面叉指电极d33模式微机电系统(MEMS)悬臂梁振动俘能器中存在的压电材料极化不完全、存在弯曲电场等问题,提出了一种电极贯穿于整个压电层的全d33模式MEMS悬臂梁振动俘能器.根据机电耦合模型,分析了电极尺寸与材料厚度对压电俘能器输出功率的影响.优化结果表明:当硅基底厚度为20μm、电极宽度1μm时,电极间距最优范围为25～75 μm,PZT材料最优厚度为7μm,归一化后得到功率密度为34.5mWcm-3gn-2.通过在表面叉指电极d33模式俘能器的基础上增加电镀电极工艺,设计了不锈钢基底的全d33模式MEMS俘能器的工艺流程,完成了部分单元工艺.