scholarly journals Forecasting the regional fire radiative power for regularly ignited vegetation fires

2021 ◽  
Author(s):  
Tero M. Partanen ◽  
Mikhail Sofiev
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Grid Cell ◽  
Radiative Energy ◽  
Fire Intensity ◽  
High Temporal Resolution ◽  
South Central ◽  
Radiative Power ◽  
Cloud Mask ◽  
Fire Radiative Power

Abstract. This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Is burn severity related to fire intensity? Observations from landscape scale remote sensing

2013 ◽  
Vol 22 (7) ◽  
pp. 910 ◽  
Author(s):  
Heather Heward ◽  
Alistair M. S. Smith ◽  
David P. Roy ◽  
Wade T. Tinkham ◽  
Chad M. Hoffman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Burn Severity ◽  
Radiative Energy ◽  
Fire Intensity ◽  
Ecological Change ◽  
Fire Event ◽  
Radiative Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Burn Ratio

Biomass burning by wildland fires has significant ecological, social and economic impacts. Satellite remote sensing provides direct measurements of radiative energy released by the fire (i.e. fire intensity) and surrogate measures of ecological change due to the fire (i.e. fire or burn severity). Despite anecdotal observations causally linking fire intensity with severity, the nature of any relationship has not been examined over extended spatial scales. We compare fire intensities defined by Moderate Resolution Imaging Spectroradiometer Fire Radiative Power (MODIS FRP) products with Landsat-derived spectral burn severity indices for 16 fires across a vegetation structure continuum in the western United States. Per-pixel comparison of MODIS FRP data within individual fires with burn severity indices is not reliable because of known satellite temporal and spatial FRP undersampling. Across the fires, 69% of the variation in relative differenced normalized burn ratio was explained by the 90th percentile of MODIS FRP. Therefore, distributional MODIS FRP measures (median and 90th-percentile FRP) derived from multiple MODIS overpasses of the actively burning fire event may be used to predict potential long-term negative ecological effects for individual fires.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Direct estimation of Byram's fire intensity from infrared remote sensing imagery

2017 ◽  
Vol 26 (8) ◽  
pp. 668 ◽  
Author(s):  
Joshua M. Johnston ◽  
Martin J. Wooster ◽  
Ronan Paugam ◽  
Xianli Wang ◽  
Timothy J. Lynham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Ancillary Data ◽  
Remote Sensing Imagery ◽  
Method Of Calculation ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Thermal Infrared Imagery ◽  
Remote Sensing Methods

Byram’s fire intensity (IB,tot; kWm–1) is one the most important and widely accepted metrics for quantifying wildfire behaviour. Calculation of IB,tot requires measurement of fuel consumption, heat of combustion and rate of spread; existing methods for obtaining these measurements are either inexact or at times impossible to obtain in the field. This paper presents and evaluates a series of remote sensing methods for directly deriving radiative fire intensity (IB,rad; kWm–1) using the Fire Radiative Power (FRP) approach applied to thermal infrared imagery of spreading vegetation fires. Comparisons between the remote sensing data and ground-sampled measurements were used to evaluate the various estimates of IB,tot, and to determine the radiative fraction (radF) of a fire’s emitted energy. Results indicate that the IB,tot along an advancing flame front can be reasonably estimated (and agrees with traditional methods of estimation (R2=0.34–0.73)) from appropriately collected time-series of remote sensing imagery without the need for ground sampling or ancillary data. We further estimate that the radF of the fire’s emitted energy varies between 0.15 and 0.20 depending on the method of calculation, which is similar to previous estimates.

scholarly journals Fire emission rates of NO<sub>x</sub> based on the empirical relationship between satellite-derived tropospheric NO<sub>2</sub> and fire radiative power

2013 ◽  
Vol 13 (11) ◽  
pp. 28453-28510
Author(s):  
S. F. Schreier ◽  
A. Richter ◽  
J. W. Kaiser ◽  
J. P. Burrows
Keyword(s):  
Atmospheric Chemistry ◽  
Empirical Relationship ◽  
Temporal Correlation ◽  
Fire Intensity ◽  
Burned Area ◽  
Emission Rates ◽  
Monthly Means ◽  
Radiative Power ◽  
The Tropics ◽  
Fire Radiative Power

Abstract. Nitrogen oxides (NOx) play key roles in atmospheric chemistry, air pollution, and climate. While the largest fraction of these reactive gases is released by anthropogenic emission sources, a significant amount can be attributed to vegetation fires. In this study, NO2 from GOME-2 on board EUMETSAT's MetOp-A and OMI on board NASA's Aura as well as fire radiative power (FRP) from the measurements of MODIS on board NASA's Terra and Aqua are used to derive fire emission rates (FERs) of NOx for different types of vegetation using a simple statistical approach. Monthly means of tropospheric NO2 vertical columns (TVC NO2) have been analyzed for their temporal correlation with the monthly means of FRP for five consecutive years from 2007 to 2011 on a horizontal 1° × 1° grid. The strongest correlation is found to be largely confined to tropical and subtropical regions, which account for more than 80% of yearly burned area on average globally. In these regions, the seasonal variation of fire intensity, expressed by the FRP data, is similar to the pattern of TVC NO2. As chemical models typically require values for the amount of NOx being released as a function of time, we have converted the retrieved TVC NO2 into production rates of NOx from fire (Pf) by assuming a constant lifetime of NOx. The comparison between Pf and NOx emissions from GFEDv3.1 over 5 characteristic biomass burning regions in the tropics and subtropics indicated good agreement. By separating the monthly means of Pf and FRP according to land cover type, FERs of NOx could be derived for different biomes. The estimated FERs for the dominating types of vegetation burned are lowest for open shrublands and savannas (0.28–1.03 g NOx s−1 MW−1) and highest for croplands and woody savannas (0.82–1.56 g NOx s−1 MW−1). This analysis demonstrates clearly that there are biome-specific, diurnal, and regional differences in FERs for the dominating types of vegetation burned in the tropics and subtropics. Possible factors affecting the magnitude of the obtained values are discussed.

Remote classification of head and backfire types from MODIS fire radiative power and smoke plume observations

2005 ◽  
Vol 14 (3) ◽  
pp. 249 ◽  
Author(s):  
Alistair M. S. Smith ◽  
Martin J. Wooster
Keyword(s):  
Front Propagation ◽  
Pollutant Emissions ◽  
Radiative Energy ◽  
Burned Area ◽  
Remote Measurement ◽  
Radiative Power ◽  
Flaming Combustion ◽  
Order Of Magnitude ◽  
Fire Radiative Power

The classification of savanna fires into headfire and backfire types can in theory help in assessing pollutant emissions to the atmosphere via relative apportionment of the amounts of smouldering and flaming combustion occurring, and is also important when assessing a fire’s ecological effects. This paper provides a preliminary assessment of whether a combination of visible and thermal satellite remote sensing can be used to classify fires into head and backfire categories. Remote determination of the fire radiative power, alongside assessments of the prevailing direction of the wind (through identification of the fire-related smoke plumes) and the fire front propagation (through its relation to the previously burned area) were used to infer the fire type category and to calculate ‘radiative’ fireline intensity (FLI). The ratio of radiative FLI for the head and backfire categories was found similar to that of in situ fireline intensity measurements, but the magnitudes of the radiative FLI values were around an order of magnitude lower. This agrees with other data suggesting that a fire’s radiative energy is around an order of magnitude lower than the fuel’s theoretical heat yield, and suggests that the remote measurement of radiative FLI and classification of headfire and backfire types is a realistic proposition for large wildfire activity.

scholarly journals High Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link Between Fire Behavior and Aerosol and Gas Emissions

2020 ◽  
Vol 47 (23) ◽  
Author(s):  
Elizabeth B. Wiggins ◽  
Amber J. Soja ◽  
Emily Gargulinski ◽  
Hannah S. Halliday ◽  
R. Bradley Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals The empirical relationship between satellite-derived tropospheric NO<sub>2</sub> and fire radiative power and possible implications for fire emission rates of NO<sub>x</sub>

2014 ◽  
Vol 14 (5) ◽  
pp. 2447-2466 ◽  
Author(s):  
S. F. Schreier ◽  
A. Richter ◽  
J. W. Kaiser ◽  
J. P. Burrows
Keyword(s):  
Empirical Relationship ◽  
Fire Intensity ◽  
Burned Area ◽  
Emission Rates ◽  
Monthly Means ◽  
Fire Emissions ◽  
Vegetation Fires ◽  
Radiative Power ◽  
The Tropics ◽  
Fire Radiative Power

Abstract. Nitrogen oxides (NOx) play key roles in atmospheric chemistry, air pollution, and climate. While the largest fraction of these reactive gases is released by anthropogenic emission sources, a significant amount can be attributed to vegetation fires. In this study, NO2 from GOME-2 on board EUMETSAT's MetOp-A and OMI on board NASA's Aura as well as fire radiative power (FRP) from the measurements of MODIS on board NASA's Terra and Aqua satellites are used to derive fire emission rates (FERs) of NOx for different types of vegetation using a simple statistical approach. Monthly means of tropospheric NO2 vertical columns (TVC NO2) have been analyzed for their temporal correlation with the monthly means of FRP for five consecutive years from 2007 to 2011 on a horizontal 1° × 1° grid. The strongest correlation is found to be largely confined to tropical and subtropical regions, which account for more than 80% of yearly burned area, on average, globally. In these regions, the seasonal variation of fire intensity, expressed by the FRP data, is similar to the pattern of TVC NO2. As chemical models typically require values for the amount of NOx being released as a function of time, we have converted the retrieved TVC NO2 into production rates of NOx from fire (Pf) by assuming a constant lifetime of NOx. The comparison between Pf and NOx emissions from the Global Fire Emissions Database (GFEDv3.1) over 5 characteristic biomass burning regions in the tropics and subtropics shows good agreement. By separating the monthly means of Pf and FRP according to land cover type, FERs of NOx could be derived for different biomes. The estimated FERs for the dominating types of vegetation burned are lowest for open shrublands and savannas (0.28–1.03 g NOx s−1 MW−1) and highest for croplands and woody savannas (0.82–1.56 g NOx s−1 MW−1). This analysis demonstrates that the strong empirical relationship between TVC NO2 and FRP and the following simplified assumptions are a useful tool for the characterization of NOx emission rates from vegetation fires in the tropics and subtropics. Possible factors affecting the magnitude of the obtained values are discussed.

scholarly journals New eastern China agricultural burning fire emission inventory and trends analysis from combined geostationary (Himawari-8) and polar-orbiting (VIIRS-IM) fire radiative power products

2020 ◽  
Author(s):  
Tianran Zhang ◽  
Mark C. de Jong ◽  
Martin J. Wooster ◽  
Weidong Xu ◽  
Lili Wang
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Crop Residue ◽  
Eastern China ◽  
Post Harvest ◽  
Crop Residue Burning ◽  
Radiative Power ◽  
Agricultural Burning ◽  
Emissions Inventories ◽  
Fire Radiative Power

Abstract. Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning MODIS-based fire emissions inventories such as GFAS and GFED are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser spatial resolution remote sensing observations. To address this issue, we here use twice daily fire radiative power (FRP) observations from the ‘small fire optimised’ VIIRS-IM FRP product, and combine it with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high spatio-temporal resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5 and black carbon. We calculate DMB totals 100 to 400 % higher than reported by GFAS and GFED4.1s, and quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (Sept–Oct) is associated with rice and corn residue burning. We further identify a new winter (Nov–Dec) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional ‘crop yield-based approaches’ (CYBA) by a factor of between 2 and 5 x. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA approach, leading to the overestimation of DMB. Therefore we conclude that that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.

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