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

2020 ◽  
Author(s):  
Elizabeth Brooke Wiggins ◽  
Amber Jeanine Soja ◽  
Emily M. Gargulinski ◽  
Hannah Selene Halliday ◽  
Brad Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals Meteosat SEVIRI Fire Radiative Power (FRP) products from the Land Surface Analysis Satellite Applications Facility (LSA SAF) – Part 1: Algorithms, product contents and analysis

2015 ◽  
Vol 15 (11) ◽  
pp. 15831-15907 ◽  
Author(s):  
M. J. Wooster ◽  
G. Roberts ◽  
P. H. Freeborn ◽  
W. Xu ◽  
Y. Govaerts ◽  
...  
Keyword(s):  
Surface Analysis ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface ◽  
Active Fire ◽  
Fire Activity ◽  
Radiative Power ◽  
Satellite Applications ◽  
The Impact ◽  
Fire Radiative Power

Abstract. Characterising changes in landscape scale fire activity at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from these types of geostationary observations, often with the aim of supporting the generation of data related to biomass burning fuel consumption and trace gas and aerosol emission fields. The Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) are one such set of products, and are freely available in both near real-time and archived form. Every 15 min, the algorithms used to generate these products identify and map the location of new SEVIRI observations containing actively burning fires, and characterise their individual rates of radiative energy release (fire radiative power; FRP) that is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the highest spatial resolution FRP dataset, delivered for all of Europe, northern and southern Africa, and part of South America at a spatial resolution of 3 km (decreasing away from the west African sub-satellite point) at the full 15 min temporal resolution. The FRP-GRID product is an hourly summary of the FRP-PIXEL data, produced at a 5° grid cell size and including simple bias adjustments for meteorological cloud cover and for the regional underestimation of FRP caused, primarily, by the non-detection of low FRP fire pixels at SEVIRI's relatively coarse pixel size. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) algorithm used to detect the SEVIRI active fire pixels, and detail methods used to deliver atmospherically corrected FRP information together with the per-pixel uncertainty metrics. Using scene simulations and analysis of real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe some of the sensor and data pre-processing characteristics influencing fire detection and FRP uncertainty. We show that the FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel, and is more sensitive to fire than algorithms used within many other widely exploited active fire products. We also find that artefacts arising from the digital filtering and geometric resampling strategies used to generate level 1.5 SEVIRI data can significantly increase FRP uncertainties in the SEVIRI active fire products, and recommend that the processing chains used for the forthcoming Meteosat Third Generation attempt to minimise the impact of these types of operations. Finally, we illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity. A companion paper (Roberts et al., 2015) provides a full product performance evaluation for both products, along with examples of their use for prescribing fire smoke emissions within atmospheric modelling components of the Copernicus Atmosphere Monitoring Service (CAMS).

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.

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.

scholarly journals Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia

2012 ◽  
Vol 12 (9) ◽  
pp. 4341-4364 ◽  
Author(s):  
V. Huijnen ◽  
J. Flemming ◽  
J. W. Kaiser ◽  
A. Inness ◽  
J. Leitão ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Forecast Accuracy ◽  
Satellite Observations ◽  
Atmospheric Composition ◽  
Fire Emissions ◽  
Radiative Power ◽  
The Impact ◽  
Assimilation System ◽  
Fire Radiative Power

Abstract. The severe wildfires in western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 60% (O3) and 75% (NO2) during the first 24 h with respect to independent satellite observations, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 67 and 90%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly dependent on the assumptions made for forecasted fire emissions. This was well visible from a relatively poor forecast accuracy quantified by the root mean square error, as well as the temporal correlation with respect to ground-based CO total column data and AOD. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

scholarly journals Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations

2020 ◽  
Vol 12 (3) ◽  
pp. 498 ◽  
Author(s):  
Tri Wandi Januar ◽  
Tang-Huang Lin ◽  
Chih-Yuan Huang ◽  
Kuo-En Chang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Spatiotemporal Resolution

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

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