Figure 1 . (a) Arctic tundra region in Alaska as defined by
CAVM. (b) Number of fire events detected with MCD14ML data from 2001 to
2019.
3.2 Empirical modeling performances
Three groups of independent variables show very strong correlations,
including the vegetation indices representing fuel moisture state, the
CFFWIS components representing fire weather conditions, and the
topographic features, with Pearson’s r above 0.8 (Figure S3).
Since all vegetation indices were highly correlated with Pearson’sr above 0.95, we only adopted NDII6 to estimate
fuel moisture state for further modeling efforts. Strong correlations
were also found between the fire behavior indices (ISI and BUI) and fuel
moisture codes (FFMC and DMC) of the CFFWIS. Since we did not focus on
fire propagation, we only selected the three fuel moisture codes to
represent fire weather conditions. Although the near-surface weather
variables show moderate correlations with the CFFWIS components, they
were included to account for meteorological conditions irrespective of
fuels. Additionally, slope and roughness were removed for modeling due
to their strong correlations with elevation.
Both the “Current-day model” and “Previous-day model” developed with
the RF classification algorithm have shown a strong capability in
predicting the fire occurrence probability in the tundra. The overall
out-of-bag (OOB) error rate of the “Current-day model” is 6.03%, with
the overall accuracy reaching 93.97% (Table S3). The “Previous-day
model” shows slightly lower modeling performance, with an overall OOB
error rate of 8.75% and an accuracy of 91.25%. Validation performed
against the reserved dataset shows that both models can reflect (with
the “Current-day model”) and forecast (with the “Previous-day
model”) fire occurrence probability, as indicated by the Receiver
Operating Characteristic (ROC) curves (Figure S4). The Area Under the
Curve (AUC) values reached 0.97 and 0.96 for the “Current-day model”
and the “Previous-day model”, respectively.
3.3 Environmental factors driving tundra fire occurrence
CG lightning probability was identified as the most important variable
in both the “Current-day model” and “Previous-day model” for
predicting tundra fire occurrence, with Mean Decrease in Accuracy (MDA)
of 50.06% and 34.58%, respectively (Figure 2 a-b). A significant
positive relationship was confirmed between CG lightning and fire
occurrence via logistic regression models (p < 0.001; Table
1), suggesting that regions with larger lightning probability are likely
to experience higher fire risks. On fire-occurrence days, the lightning
probability of the “Fire” events were higher than 0.50 on average
across the tundra region and reached over 0.62 in the North Slope and
Southwest Alaska (Figure 2 c). In contrast, the lightning probability
was below 0.15 on average when no fire occurred. Similarly, on the
previous days of fire occurrence, though lower than that on
fire-occurrence days, the lighting probability of the “Fire” events,
was significantly higher (~0.48) than that of the “No
fire” events (< 0.12) on average (Figure 2 d).