Abstract
Detailed spatial-temporal characterization of individual fire dynamics using remote sensing data is important to understand fire-environment relationships, to support landscape-scale fire risk management, and to obtain improved statistics on fire size distributions over broad areas. Previously, individuation of events to quantify fire size distributions has been performed with the flood-fill algorithm. A key parameter of such algorithms is the time-gap used to cluster spatially adjacent fire-affected pixels and declare them as belonging to the same event. Choice of a time-gap to define a fire event entails several assumptions affecting the degree of clustering/fragmentation of the individual events. We evaluate the impact of different time-gaps on the number, size and spatial distribution of active fire clusters, using a new algorithm. The information produced by this algorithm includes number, size, and ignition date of active fire clusters. The algorithm was tested at a global scale using active fire observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Active fire cluster size distributions were characterized with the Gini coefficient, and the impact of changing time-gap values was analyzed on a 0.5° cell grid. As expected, the number of active fire clusters decreased and their mean size increased with the time-gap value. The largest sensitivity of fire size distributions to time-gap was observed in African tropical savannas and, to a lesser extent, in South America, Southeast Asia, and eastern Siberia. Sensitivity of fire individuation, and thus Gini coefficient values, to time-gap demonstrate the difficulty of individuating fire events in tropical savannas, where coalescence of flame fronts with distinct ignition locations and dates is very common, and fire size distributions strongly depend on algorithm parameterization. Thus, caution should be exercised when attempting to individualize fire events, characterizing their size distributions, and addressing their management implications, particularly in the African savannas.
Highlights
Statistical descriptors of fire size distributions (FSD) are often used in the characterization of vegetation fire regimes [1,2,3]
Three different time-gaps used in the algorithm led to different active fire clusters number and size distributions (Table 1)
Three different time-gaps used Table in the1.algorithm led to different active fire clusters number and size distributions (Table 1)
Summary
Statistical descriptors of fire size distributions (FSD) are often used in the characterization of vegetation fire regimes [1,2,3]. Radiometers (ESA ATSR) [10] Availability of such data provides the opportunity to characterize fire. 2016, 8, 663 size distributions in a systematic and consistent way at a global scale This is not a trivial task because it requires the ability to identify individual fire events, by patching together or splitting apart the snapshots of fire spatial/temporal dynamics afforded by satellite imagery. This is problematic in regions with extensive burning and fast spreading fires, such as tropical savannas, where formation of the “seasonal burning mosaic” [11] entails extensive coalescence of flame fronts with distinct ignition sources. Individuating fire events may be difficult too in regions affected by persistent cloud cover or heavy smoke
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