Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product

Zhang, Xiaoyang; Kondragunta, Shobha

doi:10.1016/j.rse.2008.02.006

Cited by 67 publications

(59 citation statements)

References 52 publications

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“…With geostationary satellites, it is possible to characterize the diurnal cycle of the fires (Kaiser et al, 2009b;Andela et al, 2015). Zhang and Kondragunta (2008) analyzed the daily variability by considering variations of the fire pixel size. The diurnal cycle is not only a function of meteorological conditions but also dependent on the vegetation type of the burning biomass (Giglio, 2007).…”

Section: Diurnal Cyclementioning

confidence: 99%

“…The diurnal cycle is not only a function of meteorological conditions but also dependent on the vegetation type of the burning biomass (Giglio, 2007). According to Zhang and Kondragunta (2008), the daily maximum of the fire pixel size is reached between 10:00 and 15:00 local time (LT). During this period, 52.1 % of the daily amount of emissions are released in a forest.…”

Section: Diurnal Cyclementioning

confidence: 99%

“…We designate the values as w = 0.3 , t 0 = 12.5 h, and σ = 2.5 h. Subsequently, the equation expresses the diurnal cycle for forest fires as described by Zhang and Kondragunta (2008), and it is shown in Fig. 2.…”

Section: Diurnal Cyclementioning

confidence: 99%

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The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol

et al. 2016

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Abstract. We quantified the effects of the plume rise of biomass burning aerosol and gases for the forest fires that occurred in Saskatchewan, Canada, in July 2010. For this purpose, simulations with different assumptions regarding the plume rise and the vertical distribution of the emissions were conducted. Based on comparisons with observations, applying a one-dimensional plume rise model to predict the injection layer in combination with a parametrization of the vertical distribution of the emissions outperforms approaches in which the plume heights are initially predefined. Approximately 30 % of the fires exceed the height of 2 km with a maximum height of 8.6 km. Using this plume rise model, comparisons with satellite images in the visible spectral range show a very good agreement between the simulated and observed spatial distributions of the biomass burning plume. The simulated aerosol optical depth (AOD) with data of an AERONET station is in good agreement with respect to the absolute values and the timing of the maximum. Comparison of the vertical distribution of the biomass burning aerosol with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) retrievals also showed the best agreement when the plume rise model was applied. We found that downwelling surface short-wave radiation below the forest fire plume is reduced by up to 50 % and that the 2 m temperature is decreased by up to 6 K. In addition, we simulated a strong change in atmospheric stability within the biomass burning plume.

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Section: Diurnal Cyclementioning

confidence: 99%

Section: Diurnal Cyclementioning

confidence: 99%

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The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol

et al. 2016

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“…Correct transport modeling of smoke aerosol is almost entirely dependent on satellite-derived products that either identify and count fire hotspots or more quantitatively measure fire radiative power and relate that to aerosol emissions Pereira et al, 2009;Reid et al, 2009;Giglio et al, 2010;van der Werf et al, 2010;Ichoku et al, 2012 and references therein;Petrenko et al, 2012). The diurnal pattern of smoke emissions has been determined by applying overpasses at multiple times per day by the twin MODIS sensors on the polar orbiting Terra and Aqua satellites (Vermote et al, 2009;Ichoku et al, 2008), or using geostationary satellite observations (Reid et al, 2004;Zhang and Kondragunta, 2008). For mineral dust, most models rely on satellite data of land surface classification to identify the location of deserts, and a few models use satellite vegetation index data to impose the seasonal variation of the surface bareness for better temporal variation of dust emission (e.g., Zender et al, 2003;Kim et al, 2013).…”

Section: Source Characterizationmentioning

confidence: 99%

Satellite perspective of aerosol intercontinental transport: From qualitative tracking to quantitative characterization

Remer

Kahn

et al. 2013

Atmospheric Research

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Evidence of aerosol intercontinental transport (ICT) is both widespread and compelling. Model simulations suggest that ICT could significantly affect regional air quality and climate, but the broad inter-model spread of results underscores a need of constraining model simulations with measurements. Satellites have inherent advantages over in situ measurements to characterize aerosol ICT, because of their spatial and temporal coverage. Significant progress in satellite remote sensing of aerosol properties during the Earth Observing System (EOS) era offers the opportunity to increase quantitative characterization and estimates of aerosol ICT beyond the capability of pre-EOS era satellites that could only qualitatively track aerosol plumes. EOS satellites also observe emission strengths and injection heights of some aerosols, aerosol precursors, and aerosol-related gases, which can help characterize aerosol ICT. We review how the current generation of satellite measurements have been used to (1) characterize the evolution of aerosol plumes (e.g., both horizontal and vertical transport, and properties) on an episodic basis, (2) understand the seasonal and inter-annual variations of aerosol ICT and their control factors, (3) estimate the export and import fluxes of aerosols, and (4) evaluate and constrain model simulations. Substantial effort is needed to further explore an integrated approach using measurements from on-orbit satellites (e.g., A-Train synergy) for observational characterization and model constraint of aerosol intercontinental transport and to develop advanced sensors for future missions.

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“…Despite the aforementioned caveats of characterizing diurnal cycles of fire activity from geostationary sensors, modern applications are using high-frequency observations of active fire pixel counts [19], sub-pixel active fire area (e.g., [20]), and fire radiative power, FRP (e.g., [15,21]), to generate diurnal cycles of trace gas and aerosol emission fluxes (e.g., [22]) and smoke injection heights (e.g., [23,24]), which when input into atmospheric transport models can be used to forecast plume dispersion and air quality (e.g., [25,26]). As cautioned by Eva and Lambin [8], however, a failure to understand the accuracy and limitations of satellite-based fire products could lead to improper interpretations of the spatiotemporal pattern of biomass burning (e.g., [27,28]) and flawed estimates of fuel consumption and smoke production (e.g., [29,30]).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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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 OPEN ACCESSRemote Sens. 2014, 6 1891 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.

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Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product

Cited by 67 publications

References 52 publications

The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol

The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol

Satellite perspective of aerosol intercontinental transport: From qualitative tracking to quantitative characterization

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

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