2.1.3 Fuel and topographic properties
We used the fractional cover maps of major fuel components across Alaskan tundra (He et al., 2019) to represent fuel type distribution. Here we considered three fuel components, namely woody, herbaceous and nonvascular fuels. Four vegetation indices that are directly related to leaf water content were adopted as estimates of fuel moisture state for large-scale monitoring (Yebra et al., 2008), including two Normalized Difference Infrared Indices using MODIS bands 6 and 7 (NDII6 and NDII7; Hardisky et al., 1983), Normalized Difference Water Index (NDWI; Gao, 1996), and Global Vegetation Moisture Index (GVMI; Ceccato et al., 2002). We computed these indices using the MODIS 8-day surface reflectance data (MOD09A1; Vermote et al., 2015) for our study area (Table S1). The 5m Digital Elevation Model (DEM) data developed with airborne Interferometric Synthetic Aperture Radar (IfSAR) data for Alaska was then used to extract topographical features, including elevation, slope, aspect, and roughness.
2.2 Tundra fire occurrence modeling
Five groups of influencing factors were used as independent variables for modeling tundra fire occurrence: fuel type, fuel moisture state, fire weather, topography, and ignition source (Figure S2). Fire weather, ignition source (CG lightning probability), and fuel moisture state are weather-related conditions and can change rapidly on a daily basis throughout a short period. Although vegetation shifts and fuel type transitions can occur from years to decades under disturbances or climatic variability and change, the vegetation compositions and fuel type distributions are relatively stable without substantial seasonal or diurnal changes.
To fully understand how these dynamic weather-related variables affect the probability of tundra fire occurrence, we developed two types of models, referred to as “Current-day model” and “Previous-day model”. Here we categorized the ignition source, fire weather and fuel moisture state as “dynamic” variables considering their temporal variabilities during fire seasons. While topographic properties and fuel type distributions were considered as “static” variables. The two types of models selected the dynamic variables on different dates as independent variables. The “Current-day model” adopts the dynamic variables simulated on the exact dates of fire occurrence, while the “Previous-day model” uses those extracted from the dates before the detected fire occurrence. Fire occurrence points detected in Section 2.1.1 were used to represent the presence of “Fire” events. We randomly sampled points across the tundra regions on the same fire ignition dates to represent “No Fire” events.
Empirical models were then developed with both the RF classification and logistic regression algorithms to identify the key factors driving tundra fire occurrence and quantify their impacts. Although RF algorithms can provide relative rankings of variable importance in predicting the dependent variable, they are limited in showing the quantitative relationships between each independent variable and fire occurrence probability. We therefore developed logistic regression models as well, to quantify the impacts of environmental factors. Before modeling, we tested the correlations of variables among the five groups of environmental factors using Pearson’s r correlation and removed the highly correlated ones. For both RF classification and logistic regression models, 70% of the records were randomly selected for model training, and the rest 30% were reserved for validation. Welch’s t-test was also conducted to assess the differences of environmental factors between “Fire” and “No fire” events across the study area.
3 Results
3.1 Wildfire occurrences in Arctic tundra of Alaska
Individual fire events were first identified using the MCD14ML data between 2001 and 2019 (Figure 1). The occurrences of wildfire events vary across space in Arctic tundra of Alaska. The majority of the fires occurred in Southwest Alaska (~39.62%), followed by the North Slope (~36.92%) and the Seward Peninsula (~23.46%). A slightly increasing trend of tundra fire occurrences was found during the study period (Figure 1 b). Temporal variability also exists regarding fire season severity, as indicated by the number of annual fire events. During 2001 and 2019, thirteen years have relatively low fire events (< 20 fires per year), and four years have a moderate fire season with 20 ~ 30 fire events per year. An exceptionally severe fire season was detected in 2015, with 49 fire events in total. To cover a variety of fire season severities, we sampled five seasons (2002, 2006, 2008, 2013, 2017) with light severity, two years with moderate severity (2007, 2010), and the year of 2015 as severe with very high fire activity for model development (Table S2).