Fire Pixel Counts Research Articles

Significant light absorption of brown carbon during the 2020 California wildfires

In this study, the contribution of brown carbon (BrC) to the absorption aerosol optical depth (AAOD) during the August to October 2020 California wildfires in Fresno, Monterey, and the University of California Santa Barbara (UCSB) was investigated using Aerosol Robotic Network (AERONET) column measurements with Moderate Resolution Imaging Spectroradiometer (MODIS) fire pixel counts. There was an approximate three to five times increase in AAOD and fine-mode aerosols during intensive wildfires in August–October 2020 compared to the wildfires in the previous 18 years (2002–2019). Substantial daily variation in the contribution of BrC to AAOD was correlated with the fire pixel counts (correlation coefficients of 0.63, 0.40, and 0.57 at Fresno, Monterey, and UCSB, respectively). This variation was influenced by regional topography, atmospheric conditions, and distance from the fire. Between August and October 2020, the average contribution of BrC to AAOD at 440 nm due to wildfires was 35.3 ± 5.6, 35.1 ± 6.8, and 40.6 ± 9.5% at Fresno, Monterey, and UCSB, respectively. This was approximately twice as high as for those sites without a direct wildfire influence. The BrC contribution with direct wildfire influence over the period of January–December 2020 at Fresno, Monterey, and UCSB (32.8 ± 7.5, 31.6 ± 7.9, and 40.0 ± 3.5%, respectively) and from 2002 to 2019 (30.7 ± 8.3, 28.5 ± 4.8, and 35.7 ± 14.6%, respectively) was approximately 20% greater than other BrC sources including vehicles, fossil fuel combustion, and residential heating.

Large wildfire driven increases in nighttime fire activity observed across CONUS from 2003–2020

Despite the ecological and socioeconomic impacts of wildfires, little attention has been paid to the spatiotemporal patterns of nighttime fire activity across the conterminous United States (CONUS). Daytime fire radiative power (FRP) detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) was nearly evenly split (54% vs. 46%) between inside and outside wildfires from 2003 to 2020. In contrast, 94% of nighttime FRP was detected within wildfires, of which 95% was detected within large wildfires (> 2023 ha). Nighttime proportions (i.e., the proportion of total summed FRP detected by MODIS at night) were lowest (3%) outside wildfires when coincident 1000-hr fuel moistures were highest and vegetation fires were smaller and less intense. As 1000-hr fuel moistures decreased, MODIS active fire pixels shifted out of agricultural and prescribed fires and into wildfires with higher nighttime per-pixel values of FRP such that nighttime proportions peaked at 29% for the largest wildfires. Increases in nighttime proportions within larger wildfires were attributed to increases in nighttime persistence whereby under the driest conditions, daytime fire activity detected by MODIS was more likely to continue burning with sufficient vigour to be detected again at night. From 2003–2020, MODIS detected significant (p<0.01) increasing trends in nighttime wildfire fire activity, with a +54%, +42% and +21% increase in the annual nighttime sum of FRP, annual nighttime active fire pixel counts and annual mean nighttime per-pixel values of FRP, respectively, detected in the latter half of the study period. Nighttime trends were corroborated using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well annual wildfire statistics reported by U.S. federal, state and local agencies. Moreover, MODIS detected a significant positive trend in the nighttime proportion of FRP emitted from wildfires, indicating that in the absence of diurnal differences in detection biases, increases in nighttime fire activity since 2003 have outpaced daytime increases. However an analysis of MODIS omission rates revealed that increasing nighttime proportions were at least partially attributed to a relatively greater improvement in nighttime detection performance compared to the daytime for larger wildfires burning during drier conditions. Nighttime fire activity already poses additional risks to firefighters and communities, and this work suggests that projected increases in the frequency of large wildfires will be accompanied by increases in the extent and intensity of nighttime fire activity.

Carbonaceous aerosol emission reduction over Shandong province and the impact of air pollution control as observed from synthetic satellite data

Aerosol loading and trace gas emissions from agriculture crop residue burning has been one of the main factors leading to the deterioration of air quality especially during the harvest time in China. Here, we evaluate the contribution of atmospheric aerosol loading from biomass burning and its relationship to the air pollution control policy. We focused on Shandong province, China because it has the highest level of agriculture burning pollution in October. Satellite-based data related to biomass burning including aerosol optical depth (AOD) and fire pixel counts from Moderate Resolution Imaging Spectroradiometer (MODIS), carbon monoxide (CO) from Measurements Of Pollution In The Troposphere (MOPITT), aerosol index (AI) and tropospheric NO2 from Ozone Monitoring Instrument (OMI), and aerosol layer depths from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). These metrics were synthetically utilized to evaluate spatial and temporal variation of atmospheric aerosols during the agriculture crop residue burning month from 2006 to 2017. Carbonaceous aerosol emission reduction and the impact of the air pollution control policy can be found in the CALIPSO aerosol types and MODIS fire pixel counts data. The AOD, NO2, CO, and AI over three different land covers (cropland, forest and impervious surfaces) were further obtained according to the land cover data set. The results indicated that there was an obvious decrease in all carbonaceous aerosol-related quantities after year 2012 when the local government began to enact and implement strict air pollution control policies, although a period (2–3 years) of transition after the policy implementation time was identified. We also found that CO is stable and low over the cropland versus forest and impervious surfaces; NO2 exhibits a continuously high value in the northern areas of the oil field. The impact of industry activity rather than straw burning should be also considered for NO2 and CO in certain areas.

Neural network predictions of pollutant emissions from open burning of crop residues: Application to air quality forecasts in southern China

Open burning of crop residues is a strong seasonal source of air pollutants in many parts of China, but the large day-to-day variability of the associated emissions pose a great challenge for air quality forecasts. Here we developed back-propagation neural network (BPNN) ensembles to forecast the daily fire pixel counts in Southern China during the month of January. The BPNN ensembles were trained using daily assimilated surface meteorological data (including air temperature, relative humidity, pressure, and winds) and daily fire pixel observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the month of January during the years 2003–2012. We showed that the BPNN ensembles successfully forecasted the day-to-day variability and the interannual variability of fire pixel counts over Southern China in January of the years 2013–2015, with correlation coefficients of 0.6–0.8 against the MODIS observations. We used the forecasted daily fire pixel counts in January 2014 and January 2015 to scale the climatological January biomass burning emissions from the Fire Inventory from NCAR (FINN) and applied the resulting forecasted daily biomass burning emissions to drive the WRF-Chem regional air quality model. The use of BPNN-ensemble-forecasted daily biomass burning pollutant emissions led to significant improvements in the daily forecasts of PM2.5 concentrations in Southern China for January 2014, with the mean bias of the simulated surface PM2.5 concentrations reduced from −9.1% to −1.2%. We repeated the sensitivity simulations for January 2015 and also found a modest improvement when using the forecasted daily biomass burning pollutant emissions (mean bias of the simulated surface PM2.5 concentrations reduced from −5.8% to −2%). Our approach can be applied to other source regions of biomass burning emissions to improve the accuracy of daily air quality forecasts.

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.

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.

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.

An integrated approach to identify the biomass burning sources contributing to black carbon episodes in Hong Kong

Biomass burning is the largest source of primary fine carbonaceous particles and globally the second largest source of trace gases, contributing to climate change and regional air pollution. This study investigates the most serious black carbon (BC) episodes in Hong Kong in 2010, which occurred on 22 February, 18 March, 6, 20 and 21 December. The contributing sources were identified using an integrated approach of ground-based measurement, satellite data analyses and model simulations. Hourly maximum BC concentrations from continuous monitoring ranged from 15.6 to 18.9 μg m−3. The correlation coefficients between hourly BC and carbon monoxide (CO) concentrations (CO as an indicator of biomass burning) varied from 0.88 to 0.97 during episodic/high BC days whereas daily ΔBC/ΔCO ratios for the episodes were between 9.05 and 13.1 ng m−3 ppbv−1, significantly higher than the seasonal averages. Non-sea-salt (nss)-K+ (daily), another indicator of biomass burning, correlated moderately with BC (r = 0.52) for concentrations above the 80th percentile. The area-averaged statistics for fire pixel counts from satellite measurement showed the intensity of biomass burning in 2010 was strongest in Africa, Southwest China and Indochina, followed by North/Central, South China and India. Except for North/Central China, all sources are upwind of Hong Kong when the northeast monsoon and the mid/upper-tropospheric westerlies (subtropical jet) prevail. GEOS-Chem simulations indicate that biomass burning contributed most significantly from Indochina (southwest China included) in the spring of 2010. This model sensitivity analysis complements the MODIS-based fire map(s), the high-level vector wind plots, the AIRS CO and backward trajectory analyses. Results suggest that other contributors of BC include not only South China, but also the Indian subcontinent (in spring) and Africa in winter. The latter's influence is evident in the February and December episodes.

Analysis of the Relationship between Wildfire Occurrences and Population Trend within the Shores of Lake Chad Basin Using Geoinformation

The study focuses and examines the relationship that exists between wildfire occurrence and the population trend within the shore of the Lake Chad. Geospatial analysis of fire pixel counts from MODIS images of the period 2001 – 2009 was performed to determine the relationship between the wildfire occurrences and the population trend. The interpretation of SPOT panchromatic imagery in combination with field survey provided perimeters of settlements and estimated population in the area with the highest fire density in the north basin of Lake Chad. Major findings shows that the distribution of wildfires on the floor of Lake Chad for the period 2001-2009 reflected a spatial relationship with the distribution of vegetation and a temporal relationship with the seasonality of human-ecosystem interactions. The total number of fires recorded on the Lake floor is 25,690 leading to a burnt area of 19,274 km2. The population trend however, is increasing while that of wildfire occurrences is decreasing. The increasing trend in population against a decreasing trend in wildfire occurrences is an indication of the gradual conversion of the wild habitat between the old and the new shorelines of Lake Chad through human-ecosystem interactions from wild land to human occupation and exploitation.

Analysis of aerosol characteristics and their relationships with meteorological parameters over Anhui province in China

The characteristics of Aerosol Optical Depth (AOD) and Ångström exponent are analyzed and AODs are compared with CE318 measurements from 2001 to 2009 over Anhui province in China, with an evaluation of potential contributing factors, including regional burning emissions and meteorology. The result indicates that MODIS AODs (Collection 5, C005) are in good agreement with those from CE318 with correlation coefficient of above 0.80 and RMS of below 0.21 in Anhui. Most of MODIS cases fall in the range of ∆τ=±0.05±0.20τ at four AERONET sites. Aerosol optical properties show a notable spatial–temporal distribution feature in Anhui. The values of lower AOD and higher Ångström exponent both appear at southern mountain area. We document seasonal variations in patterns of AOD and Ångström exponent in Anhui, for which peak values respectively occur in June and August. Time series plots for AOD and fire pixel counts are produced to provide a better understanding of influence of burning emission for aerosol. Agricultural residue burning occurs seriously in June, which leads to appearance of AOD peak values. The results between AODs and meteorological parameters show good relationships at Fuyang. Wind speed and wind direction can strongly influence aerosol optical depth and its size, meanwhile, relative humidity and visibility respectively have positive and negative correlation with AODs. Mixed layer depths and AODs have similar monthly variation trend. We use HYSPLIT model for trajectory analysis to reveal the origins of air masses for different levels AODs and Ångström exponent, with the aim of discriminating contributions of different sources.

Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shortwave infrared subsystem can acquire images of active fires during daytime and night-time from a polar orbit, providing useful data on fire properties at a nominal spatial resolution of 30 m. Binary fire/no-fire counts of ASTER pixels have also been useful in evaluating the performance of widely-used fire products from the Moderate-Resolution Imaging Spectroradiometer (MODIS), which have a nominal spatial resolution of 1 km. However, the ASTER fire pixels are actually mixed pixels that can contain flaming, smouldering and non-burning components, and ASTER fire pixel counts provide no information about the sizes or temperatures of these subpixel components. This paper uses multiple endmember spectral mixture analysis (MESMA) to estimate subpixel fire sizes and temperatures from a night-time ASTER image of a fire in California, USA, demonstrating new methods that can provide information on fires not available from other sources. As a fire's size and its temperature exert strong influences on its gas and aerosol emissions, ecological impact and spreading rates, these MESMA estimates from ASTER imagery could contribute valuable new information towards monitoring, forecasting and understanding the behaviour and impacts of many fires worldwide.

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.

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.

Analyzing wildfire threat counts using a negative binomial regression model

AbstractThe fire‐monitoring program managed by the Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis collected fire pixel counts from 1998 to 2002 and used them as a measure of wildfire threats for the Amazon region. The objective of the study was to identify the most relevant explanatory variables related to the frequency of fire pixel occurrence. The sample unit was the municipality, the dependent variable was a function of fire pixel counts, and the explanatory variables were related to land management, census, and agricultural data. A generalized longitudinal linear model was used. The most relevant explanatory variables were administrative limits, year, type of region, season, percentages of deforested area and male population, extent of unpaved road, and density of cattle. Approximately 95% of the standardized residuals resulting from fitting the model were in the interval [−2, +2]. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Dust aerosols over India and adjacent continents retrieved using METEOSAT infrared radiance Part I: sources and regional distribution

Abstract. Mineral dust constitutes the single largest contributor to continental aerosols. To accurately assess the impact of dust aerosols on climate, the spatial and temporal distribution of dust radiative properties is essential. Regional characteristics of dust radiative properties, however, are poorly understood. The magnitude and even sign of dust radiative forcing is uncertain, as it depends on a number of parameters, such as vertical distribution of dust, cloud cover and albedo of the underlying surface. In this paper, infrared radiance (10.5-12.5 µm), acquired from the METEOSAT-5 satellite ( resolution), was used to retrieve regional characteristics of dust aerosols for all of 1999. The infrared radiance depression, due to the presence of dust in the atmosphere, has been used as an index of dust load, known as the Infrared Difference Dust Index (IDDI). There have been several studies in the past carried out over the Sahara using IDDI as a measure of dust load. Over the Indian region, however, studies on dust aerosols are sparse. Spatial and temporal variability in dust loading and its regional distribution over various arid and semiarid regions of India and adjacent continents (0-35° N; 30° E-100° E) (excluding Sahara) have been studied and the results are examined along with surface soil conditions (such as vegetation cover and soil moisture). The advantage of the IDDI method is that information on aerosol properties, such as chemical composition or microphysical properties, is not needed. A large day-to-day variation in IDDI was observed over the entire study region, with values ranging from 4 to 22 K. It was observed that dust activity starts by March over the Indian deserts, as well as over deserts of the Africa and Arabian regions. The IDDI reaches maximum during the period of May to August. Regional maps of IDDI, in conjunction with biomass burning episodes (using TERRA satellite fire pixel counts), suggest that large IDDI values observed during the winter months over Northern India could be due to a possible deposition of black carbon on larger dust aerosols. The IDDI values have been compared with another year (i.e. 2003), with a large number of dust storms reported by meteorological departments based on visibility data. During the dry season, the magnitude of the monthly average IDDI during 2003 was slightly higher than that of 1999. The monthly mean IDDI was in the range from 4 to 9 K over the Indian deserts, as well as over the deserts of Africa and Arabia. The maximum IDDI during a month was in the range from 6 to 18 K. Large IDDI values were observed even over vegetated regions (such as the vegetated part of Africa and central India), attributed to the presence of transported dust from nearby deserts.

A method to derive smoke emission rates from MODIS fire radiative energy measurements

Present methods of emissions estimation from satellite data often use fire pixel counts, even though fire strengths and smoke emission rates can differ by some orders of magnitude between pixels. Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of fire radiative energy (FRE) release rates R/sub fre/ range from less than 10 to more than 1700 MW per pixel at 1-km resolution. To account for the effect of such a wide range of fire strengths/sizes on smoke emission rates, we have developed direct linear relationships between the MODIS-measured R/sub fre/ and smoke aerosol emission rates R/sub sa/ (in kilograms per second), derived by analyzing MODIS measurements of aerosol spatial distribution around the fires with National Center for Environmental Prediction/National Center for Atmospheric Research wind fields. We applied the technique to several regions around the world and derived a FRE-based smoke emission coefficient, C/sub e/ (in kilograms per megajoule), which can be simply multiplied by R/sub fre/ to calculate R/sub sa/. This new coefficient C/sub e/ is an excellent remote sensing parameter expressing the emission strength of different ecosystems and regions. Analysis of all 2002 MODIS data from Terra and Aqua satellites yielded C/sub e/ values of 0.02-0.06 kg/MJ for boreal regions, 0.04-0.08 kg/MJ for both tropical forests and savanna regions, and 0.08-0.1 kg/MJ for Western Russian regions. These results are probably overestimated by about 50% because of uncertainties in some of the data, parameters, and assumptions involved in the computations. This 50% overestimation is comparable to uncertainties in traditional emission factors. However, our satellite method shows great promise for accuracy improvement, as better knowledge is gained about the sources of the uncertainties.

Biomass burning losses of carbon estimated from ecosystem modeling and satellite data analysis for the Brazilian Amazon region

To produce a new daily record of gross carbon emissions from biomass burning events and post-burning decomposition fluxes in the states of the Brazilian Legal Amazon (Instituto Brasileiro de Geografia e Estatistica (IBGE), 1991. Anuario Estatistico do Brasil, Vol. 51. Rio de Janeiro, Brazil pp. 1–1024). We have used vegetation greenness estimates from satellite images as inputs to a terrestrial ecosystem production model. This carbon allocation model generates new estimates of regional aboveground vegetation biomass at 8-km resolution. The modeled biomass product is then combined for the first time with fire pixel counts from the advanced very high-resolution radiometer (AVHRR) to overlay regional burning activities in the Amazon. Results from our analysis indicate that carbon emission estimates from annual region-wide sources of deforestation and biomass burning in the early 1990s are apparently three to five times higher than reported in previous studies for the Brazilian Legal Amazon (Houghton et al., 2000. Nature 403, 301–304; Fearnside, 1997. Climatic Change 35, 321–360), i.e., studies which implied that the Legal Amazon region tends toward a net-zero annual source of terrestrial carbon. In contrast, our analysis implies that the total source fluxes over the entire Legal Amazon region range from 0.2 to 1.2 Pg C yr −1, depending strongly on annual rainfall patterns. The reasons for our higher burning emission estimates are (1) use of combustion fractions typically measured during Amazon forest burning events for computing carbon losses, (2) more detailed geographic distribution of vegetation biomass and daily fire activity for the region, and (3) inclusion of fire effects in extensive areas of the Legal Amazon covered by open woodland, secondary forests, savanna, and pasture vegetation. The total area of rainforest estimated annually to be deforested did not differ substantially among the previous analyses cited and our own.