3.1. Sensitivity of Fire Size Distributions to the Time-gap Parameter
A total of 4,477,192 fire observations were included in the analysis. Three different time-gaps used in the algorithm led to different active fire clusters number and size distributions (Table 1
As expected, increasing the time-gap leads to a decrease in the total number of identified active fire clusters and to a smaller proportion of single pixel fires (Table 1
). Increasing the time-gap also increased the proportion of active fire clusters in the larger fire size classes. The same trend was reported by [16
] using four and eight day time-gaps. Figure 4
and Figure 5
respectively map the differences in the number of active fire clusters and Gini coefficient values obtained using 2- and 14-day time-gaps. In Figure 5
, 16,112 out of 31,066 half degree cells (51.86%) show no difference in the number of active fire clusters. There are 122,744 unchanged active fire clusters, with a mean size of 1.7 km2
. Areas with a stable number of active fire clusters occurred mainly in agricultural regions, characterized by small, short duration fires, with similar Gini coefficient values for both time-gaps (Figure 5
). The cell with the largest difference in the number of active fire clusters was located in north-western India (Punjab, Haryana, and Uttar Pradesh districts), where 774 (mean size of 1.5 km2
) and 401 (mean 2.5 km2
) active fire clusters were identified with the 2- and 14-day time-gap parameter values, respectively. This area is responsible for two thirds of grain production in India, and has one of the highest fire densities in the world [20
] due to extensive straw burning [23
]. Large differences in number of active fire clusters as a function of time-gap parameter were also found in Africa, with a high incidence in Miombo savanna woodlands [24
], characterized by a high fire size inequality [25
], and also in Brazil and south-eastern Asia. Figure 5
shows 15,881 cells (51%) with positive values, meaning that the use of the 14-day time-gap led to higher fire size inequality.
The largest positive Gini coefficient difference (0.6752) occurs in a cell located in the eastern Siberian steppe. This cell has a group of five active fire clusters, with mean size of 3.12 km2
for the two-day time-gap, and only two active fire clusters (1 km2
and 16 km2
) with the 14-day time-gap. With sparse population and vast, uninterrupted expanses of grasslands, this region tends to have most of its burned area concentrated in a small number of very large events. Positive differences among Gini coefficients differences were also found in other semi-arid to arid dry lands of Central Asia, namely along the border between Mongolia and northern Kazakhstan. Woodland savannas in both African hemispheres, southeastern USA, the Llanos
savannas of Colombia and Venezuela, the “arc of deforestation” in Brazil, the Chaco
of Paraguay, eastern Australia, and insular south-east Asia also show increasing fire size inequality. A large patch with a high number of active fire clusters is founding the east of Lake Baikal, in the Amur River basin steppe, a sparsely inhabited area characterized by fire regimes dominated by wildfires in semi-arid to arid grasslands and shrublands. In the summer of 2003, this region recorded a large number of wildfires [26
]. Decreasing size inequality in response to a larger time-gap, occurred in 9% of all cells (2836), scattered all over the globe. Most of these negative values correspond to rearrangements of fire size inequality towards a more balanced distribution with the 14-day time-gap, which occurs, for instance, with the largest negative Gini value observed in eastern Siberian steppe. In this case, two active fire clusters exhibit different arrangements according to the time-gap used. With a two-day time-gap, fire sizes of 1 km2
and 15 km2
were individuated, resulting in a value of Gini coefficient of 0.8235. With the 14-day time-gap, a new arrangement led to a more balanced fire size distribution with 7 km2
and 9 km2
and a value to which corresponded a Gini coefficient value of 0.08. Places exhibiting no significant differences between both time-gaps (12,349 cells, 40%) are mainly located in areas of intense land use management and high population density, dominated by small fire sizes. They are found over a very broad area spreading across the five continents: south-eastern/eastern of Mississippi river (USA), south-eastern Brazil, Peru, the Pampas of Uruguay and northern Argentina, large areas over eastern Europe, Kazakhstan, India, and eastern/south-eastern China.
To interpret the impact on fire size distribution, the Gini coefficient was calculated for each time-gap (Figure 6
) and its spatial distribution was analyzed with six Anthromes classes derived from [17
] (Figure 7
) and 13 biomes derived from [18
] (Figure 8
Although all three distributions peak in frequency at a Gini value of 0.2, the distribution for two-day time-gap displays higher frequencies for lower Gini values, indicating a more balanced distribution of active fire clusters by size class compared to those obtained with 8- and 14-day time-gaps, which tend to concentrate fire activity into a smaller number of larger events. Differences between the latter two distributions are smaller.
Gini coefficient values increase from densely settled and managed landscapes to sparsely populated, unmanaged areas, reflecting the shift from balanced distributions with many small fires, to distributions dominated by a small number of very large fires. No major differences between Anthromes in sensitivity to the time-gap parameter are apparent, although the 75th percentile seem to be slightly more sensitive than the 25th percentile in most Anthromes. For each Anthrome class, one 0.5 °cell was selected (Figure 4
) and the total number of active fire clusters and Gini coefficient vales were depicted for the 3 different time-gaps (Table 2
shows that the cells classified as Villages (d) and Croplands (a) have the lowest number of active fire clusters and the smallest difference between time-gaps. The highest number recorded for the three time-gaps belongs to Punjab district in India (f), with the largest difference in the number of active fire clusters occurring between 2- and 14-day time-gaps. As already mentioned, the 14-day time-gap yields considerably higher pixel aggregation as the 2- or 8-day time-gaps.
A biome based analysis [18
], picks up a pattern not discernible with the Anthromes data: Gini coefficient sensitivity to time-gap is relatively higher in tropical biomes, such as Tropical Dry Broadleaf Forest, Tropical Grasslands and Savannas, and Flooded Grasslands and Savannas (most of which are located in the tropics) than in temperate of boreal biomes, with the lowest sensitivities occurring in Temperate Broadleaf Mixed Forests and in Temperate Conifer Forests.
3.2. Performance of the Active Fire Clusters Individuation Algorithm
As an example of algorithm performance, Figure 9
show in detail the consequences of using different time-gaps (2- and 14-day) in a case study located in Sudan (Figure 9
a,b. Figure 9
b, based on a 14-day time-gap, exhibits a clearly more aggregated pattern with fewer active fire clusters than the two-day time-gap case of Figure 9
a. For this cell, the number of active fire clusters for 2- and 14-day time-gaps were respectively 470 and 409 (Table 2
We analyzed in detail the decomposition into active fire clusters for boxes A1 and A2 in the Sudan region (Figure 10
a,b respectively), to investigate if differences in the number of active fire clusters emerge from the two time-gaps (A1: 2-day; A2: 14-day). Figure 10
depicts several active fire pixels aggregated into three FPs
. The common date of burning differences among these three FPs
(six days) is above and below the 2-(Figure 10
a) and 14-day time-gaps (Figure 10
b), respectively. As a consequence, in the former case no temporal contiguity relationships exist, yielding three different active fire clusters. In the latter case, with the time-gap considered FPB
are linked to FPA
by causal relation (dashed arrows) and only one active fire cluster is obtained. No other configuration of causal relation may exist in this case.
shows Box B of Figure 9
a with two distinct decompositions into active fire clusters using a two-day time-gap, induced by different choices of the causal relation configurations (although the number of active fire clusters remains the same in both cases).
Thus, in spite of the fact that adjacent FPs lie within the time-gap considered, the set of FPs is broken up into active fire cluster to preserve consistent fire histories, which helps keeping the number of active fire clusters and their sizes limited, even when larger time-gap thresholds are considered.