. Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25°) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2  ×  1015 grams of carbon per year (Pg C yr−1) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1) and minimum in 2013 (1.8 Pg C yr−1). These estimates were 11 % higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr−1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from http://www.globalfiredata.org .

/pdf/global-fire-emissions-estimates-during-1997-2016-lq51nvtc0m.pdf

Global fire emissions estimates during 1997–2016

The collection 6 MODIS active fire detection algorithm and fire products.

The Collection 6 MODIS burned area mapping algorithm and product

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.

/pdf/the-status-and-challenge-of-global-fire-modelling-59kqtguydq.pdf

The status and challenge of global fire modelling

https://pure.mpg.de/pubman/item/item_3033361_3/component/file_3033369/1-s2.0-S0034425719300689-main.pdf

Historical background and current developments for mapping burned area from satellite Earth observation

Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation

Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence

Forest fires are an important environmental factor at a global scale, influencing vegetation dynamics, carbon stocks, land-use change and being an important emission source of CO2 and aerosols. However, large uncertainty exists on the distribution, extend and intensity of fire occurrence over the world. Satellite products are the only source of information on fire occurrence which gives a good spatial resolution at a global scale. Two types of satellite data products have been widely used to study fire occurrence, hotspots and burned area (BA) maps. Hotspots are temperature anomalies registered by the thermal channels of the satellites, while the BA is detected by the contrast between the unburned land and the black carbon, ashes,... and the change between these two states. Till now the existing datasets have been poorly validated, with the hotspot global datasets only been validated by other thermal anomaly detections at higher resolution. Here we studied the relationship between high resolution BA datasets and the global MODIS hotspot dataset (MOD14). The high resolution BA dataset was produced from Landsat-TM/ETM+ scenes covering 10 different areas distributed over the globe. These areas include boreal, temperate, Mediterranean and tropical areas with important fire activity. For each BA dataset a pre and post fire image was analysed and BA, non-burned land and no-data (clouds,...) detected using the ABAMS software. This database, >100 separate BA maps, was produced under the framework of the Fire_cci project (http://www.esa-fire-cci.org/). For each of these BA datasets the MODIS hotspots were extracted for the same spatial and temporal extend. The analysis performed consists in determining the number and size of the omitted fire scares and the commission errors of the hotpots, being those hotspots that could not be related to any fire polygon. Regression analysis was performed to study more in depth the relation between number of hotspots and BA/number of fires. These results were then related to the some characteristics of the fire regime and environmental factors such as fire size, fire number, ecozone and vegetation type.

http://meetingorganizer.copernicus.org/EGU2012/EGU2012-12708.pdf

Dante Corti

Papers

Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation

Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence

A global comparison between MODIS hotspot and high resolution burned area data