Estimation and Validation of Sub-Pixel Needleleaf Cover Fraction in the Boreal Forest of Alaska to Aid Fire Management

Abstract: Wildfires are a natural part of the boreal ecosystem in Alaska. However, in recent years, Alaska is experiencing frequent and large fires. Environmental conditions (high tempera-ture, low precipitation, and high lightning events) are becoming favorable for severe fire events. Fire releases greenhouse gasses like carbon dioxide into the environment, creating a positive feedback loop for the environment. Needleleaf species are the dominant vegetation in boreal Alaska and are highly flammable. They burn much faster due to the presence of resin, and their low-lying canopy structure promotes fire to spread from the ground to the canopy. Knowing the needleleaf vegetation distribution is crucial for better forest and wildfire management practices. Our study focuses on needleleaf fraction mapping using a well-documented spectral unmixing approach: Multiple Endmember Spectral Mixture Analysis (MESMA). We used an AVIRIS-NG image (5m), upscaled it to 10m and 30m spatial resolutions, and applied MESMA on all three images to assess the impact of spatial resolution on sub-pixel needleleaf fraction estimates. We tested a novel method to validate fraction maps using field data and high-resolution classified hyperspectral image. Our validation method produced needleleaf cover fraction estimates with accuracies of 73%, 79%, and 78% for 5 m, 10m, and 30 m spatial resolution image data, respec-tively. To determine whether these accuracies varied significantly across different spatial scales, we used McNemer statististcal test and found no significant differences between the accuracies. The findings of this study are useful in wildfire management and for understanding changes in forest demography in the boreal region of Alaska.