Here, we developed a method to isolate individual fires from daily moderate-resolution burned-area data. The approach used two filters to account for uncertainties in the day of burn, in order to map the location and timing of fire ignitions and the extent and duration of individual fires (Fig. 1). Subsequently, we tracked the growth dynamics of each individual fire to estimate the daily expansion, fire line length, speed, and direction of spread. Based on the Global Fire Atlas algorithm, burned area was broken down into seven fire characteristics in three steps (Fig. 1b). First, burned area was described as the product of ignitions and individual fire sizes. Second, fire size was further separated into fire duration and a daily expansion component. Third, the daily fire expansion was subdivided into fire speed, the length of the fire line and the direction of spread. The Global Fire Atlas algorithm can be applied to any moderate-resolution daily global burned-area product, and the quality of the resulting dataset depends both on the Fire Atlas algorithm as well as the underlying burned-area product. Here, we applied the algorithm to the MCD64A1 Col. 6 burned-area dataset (Giglio et al., 2018), and the minimum detected fire size is therefore one MODIS pixel (approximately 21 ha). Several studies have shown that the MCD64A1 Col. 6 burned-area product is a considerable improvement compared to the previous generation of moderate-resolution global burned-area products (Giglio et al., 2018; Humber et al., 2019; Rodrigues et al., 2019). We also present a preliminary accuracy assessment of the higher-order Global Fire Atlas products using independent fire perimeter data for the continental US and active-fire detections to assess estimated fire duration and the temporal accuracy of individual fire dynamics.

2.1 Individual fires: ignitions, size, perimeter and duration Large burn patches are often made up of multiple individual fires that may burn simultaneously or at different points in time during the fire season, particularly in frequently burning grasslands and savannas with a high density of ignitions from human activity. Separating large clusters of burned area into individual fires is therefore critical to any understanding of the fire regime in human-dominated landscapes. To isolate individual fires, clusters of adjacent burned area for a given fire season (12 months centered on the month of maximum burned area) were subdivided into individual fires based on the spatial structure of estimated burn dates in the MCD64A1 burned-area product. Although we allow individual fires to burn from one fire season into the next, we processed the data on a per-fire-season basis in each 10 ∘ × 10 ∘ MODIS tile. In the rare case that a pixel burned twice during a single fire season (<1 %), we retained only the earliest burn date. This approach results in a small reduction of total burned area in order to create standardized annual data layers in both gridded raster and shapefile formats. To locate candidate ignition points within each burned-area cluster, we mapped the “local minima”, defined as a single grid cell or group of adjacent grid cells with the same burn date surrounded by grid cells with later burn dates. However, because of variability in orbital coverage and cloud cover, burn date estimates are somewhat uncertain (Giglio et al., 2013), which results in many local minima that may not correspond to actual ignition points. We applied a three-step procedure to address burn date uncertainty and distinguish individual fires. First, we developed a filter to adjust the burn date of local minima that do not correspond to ignition points. Second, we set a “fire persistence” threshold that determines how long a fire may take to spread from one 500 m grid cell into the next, to distinguish individual fires that are adjacent but that occurred at different times in the same fire season. Third, we developed a second filter to correct for outliers in the burn date that occurred along the edges of large fires. Each of these steps is described in detail below. The ignition point filter is based on the assumption that the fires progress continuously through time and space. First, all local minima were mapped within the original field of burn dates (Fig. 2a and b). Next, each local minimum was replaced by the next burn date of the surrounding grid cells, and a new map of local minima was created. If the original local minimum remained as a part of a new, larger local minimum with a later burn date, the fire followed a logical progression in time and space, and the original local minimum was retained. If the local minimum disappeared, the original local minimum was likely the product of an inconsistency within the field of burn dates rather than a true ignition point and the burn date was adjusted forward in time to remove the original local minimum. This step can be repeated several times, with each new iteration further reducing the number of local minima and increasing the confidence in ignition points, yet each iteration also results in a greater adjustment of the original burn date information (Fig. A1 in Appendix A). Here, we implemented three iterations of the ignition point filter to remove most local minima that did not spread forward in time while limiting the scope of burn date adjustments (Figs. 2c and d, A1 and A2). For short duration fires, the ignition points were retained associated with the largest possible number of iterations. In all cases, if several local minima were connected through a single cluster of grid cells with the same burn date, only the local minimum with the earliest burn date or largest number of grid cells was retained, unless the required adjustment of the burn date was larger than the specified burn date uncertainty in the MCD64A1 product. If the final ignition location consisted of multiple 500 m grid cells, we used the center coordinates to produce the ignition point shapefile. By design, the ignition point filter cannot adjust the earliest burn date of a fire and thus has no influence on estimated fire duration. Download To establish the location and date of ignition points, as well as to track the daily growth and extent of individual fires, we used a fire persistence threshold that determined how long a fire may take to spread from one 500 m grid cell into the next, taking both fire spread rate and satellite coverage into account (Fig. A3). For example, if ignition points were adjacent to a fire that burned earlier in the season, this threshold allowed the ignition points to be mapped as separate local minima despite the presence of adjacent burned grid cells with earlier burn dates. On the other hand, if an active fire is covered by dense clouds or smoke, multiple days can pass before a new observation can be made, resulting in a break in fire continuity and increasing the risk of artificially splitting single fires into multiple parts. Using such a threshold is particularly important to distinguish individual fires in frequently burning savannas and highly fragmented agricultural landscapes, where many individual small fires may occur within a relatively short time span. Because there are no reference datasets on global fire persistence, we used a spatially varying fire persistence threshold that depends on fire frequency (Andela et al., 2017). We assumed that frequently burning landscapes are generally characterized by faster fires and higher ignition densities, increasing the likelihood of having multiple ignition points within large burn patches, while infrequently burning landscapes will generally be characterized by slower fire spread rates and/or fewer ignitions. In addition, frequently burning landscapes often have a pronounced dry season characterized by low cloud cover, while infrequently burning landscapes may experience a shorter dry season with greater obscuration by clouds. Therefore, we used a 4 d fire persistence threshold for 500 m grid cells that burned more than three times during the study period (2003–2016), and a 6, 8 and 10 d fire persistence period for grid cells that burned three times, twice or once, respectively. These threshold values broadly correspond to biomes, with shorter persistence values for tropical regions and human-dominated landscapes and longer threshold values for temperate and boreal ecosystems with high fuel loads (Fig. A3). Based on the location and date of the established ignition points and the fire persistence thresholds, we tracked the growth of each individual fire through time to determine its size, perimeter and duration (Fig. 2f). For each day of the year, we allowed individual fires to grow into the areas that burned on that specific day, as long as the difference in burn dates between two pixels was equal to or smaller than the fire persistence threshold of the pixel of origin. When two actively burning fires meet, as on day 255 for the example fires shown in Fig. 2, grid cells that burned on the day of the merger were divided based on nearest distance to the fire perimeter on the previous day. Burn date uncertainty may also lead to multiple “extinction points”, outliers in the estimated day of burn along the edges of a fire. Environmental conditions such as cloud cover complicate the precise estimation of the date of fire extinction, as rainfall events extinguish many fires, and pixels at the edge of the fire may be partially burned and therefore harder to detect. In addition, the contextual relabeling phase of the MCD64A1 algorithm increases burn date uncertainty for extinction points based on a longer consistency threshold (Giglio et al., 2009). We used a second filtering step to adjust the burn date for extinction points (if required). Outliers were adjusted to the nearest burn date back in time if (1) they represented a cluster no more than one to four grid cells (0.21–0.9 km2) along the edge of a fire that was as least 10 times larger, and if (2) the difference in burn dates was larger than the fire persistence threshold of the adjacent grid cells and thus mapped as a new fire along the edge of the larger fire. If these criteria were met, the outliers were adjusted to the nearest burn date back in time and incorporated within the larger neighboring fire. However, if these criteria were not met (e.g., for burned areas larger than four grid cells), the original burn dates and ignition points were left unadjusted, resulting in separate fires. For the example fires shown in Fig. 2, the adjustment of these outliers affected four grid cells (Fig. 2e) and effectively reduced the number of ignition points (and resulting individual fires) from five (Fig. 2d) to two (Fig. 2f). After adjusting these outliers (extinction points) and including them within the larger fires, we estimated the size (km2), duration (d) and perimeter (km) of each individual fire based on the adjusted burn dates.

2.2 Daily fire expansion: fire line, speed and direction of spread The revised day-of-burn estimates were used to track the daily expansion (km2 d−1) and length of the fire line (km) for each individual fire. The daily estimates of fire line length were based on the daily perimeter of the fire, where we assumed that once the fire reached the edge of the burn scar this part of the perimeter stops burning after 1 d (Fig. 3a). The expansion of the fire (km2 d−1) is the area burned by a fire each day. The average speed of the fire line (km d−1) can now be calculated as the expansion (km2 d−1), divided by the length of the fire line (km) on the same day. However, this estimate of fire line includes the head, flank and backfire, while it is typically the head fire that moves fastest and may be responsible for most of the burned area. Moreover, fire dynamics tend to be highly variable in space and time. To understand the spatial variability and distribution of fire speeds, we therefore used an alternative method to estimate the speed and direction of fire spread for each individual 500 m grid cell. Download To estimate the speed and direction of spread (Fig. 3), we calculated the most likely path of the fire to reach each individual 500 m grid cell based on shortest distance. More specifically, for each grid cell we estimated the shortest route to connect the grid cell between two points: (1) the nearest point on the fire line with the same day of burn and (2) the nearest point on the previous day's fire line. This route was forced to follow areas burned on the specific day. For each point on this route, or “fire path”, the speed of the fire (km d−1) was estimated as the length of the path (km) divided by 1 d (d−1) and the direction as the direction of the next grid cell on the fire path. Since each grid cell is surrounded by eight other grid cells, this resulted in eight possible spread directions: north, northeast, east, southeast, south, southwest, west and northwest. For ignition points that represented a cluster of 500 m grid cells with the same burn date, we assumed that the fire originated in the center point of the cluster (pixel with largest distance to the final fire perimeter by the end of day 1) and spreads towards the perimeter of the fire by the end of day 1 over the course of 1 d. For single pixel fires, we assumed the fire burned across 463 m (1 pixel) during a single day, and we did not assign a direction of spread. Similarly, fires of all sizes that burned on a single day were not assigned a direction of spread. We corrected estimates of both speed and direction for the orientation between 500 m grid cells on the MODIS sinusoidal projection that vary with location. When a particular grid cell formed part of multiple fire paths, the earliest time of arrival or the highest fire speed and corresponding direction of spread were retained. This assures a logical progression of the fire in time and space and corresponds to fires typically moving fastest in a principal direction and then spreading more slowly along the flank.