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RSS FeedsRemote Sensing, Vol. 10, Pages 823: How Well Does the `Small Fire Boost` Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning? (Remote Sensing)

 
 

4 june 2018 18:02:57

 
Remote Sensing, Vol. 10, Pages 823: How Well Does the `Small Fire Boost` Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning? (Remote Sensing)
 


The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.


 
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Remote Sensing, Vol. 10, Pages 824: Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice (Remote Sensing)
Remote Sensing, Vol. 10, Pages 821: Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm (Remote Sensing)
 
 
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