Improved boreal vegetation mapping using imaging spectroscopy to aid wildfire management, Interior Alaska

Abstract

Wildfires are a natural and essential part of Alaska ecosystems, but excessive wildfires pose a risk to the ecosystem's health and diversity, as well as to human life and property. To manage wildfires effectively, vegetation/fuel maps play a critical role in identifying high-risk areas and allocating resources for prevention, suppression, and recovery efforts. Furthermore, vegetation/fuel maps are an important input for fire behavior models, along with weather and topography data. By predicting fire behavior, such as spread rate, intensity, and direction, fuel models allow fire managers to make informed decisions about wildfire suppression, management, and prevention. Traditionally used vegetation/fuel maps in Alaska are inadequate due to a lack of detailed information since they are primarily generated using coarser resolution (30m) multispectral data. Hyperspectral remote sensing offers an efficient approach for better characterization of forest vegetation due to the narrow bandwidth and finer spatial resolution. However, the high cost associated with data acquisition remains a significant challenge to the widespread application of hyperspectral data. The aim of this research is to create accurate and detailed vegetation maps and upscale them for the boreal region of Alaska. The study involves hyperspectral data simulation using Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data and publicly available Sentinel-2 multispectral data, ground spectra convolved to Sentinel-2 and AVIRIS-NG using the spectral response function of each sensor. Simulated data captured the minute details found in the real AVIRIS-NG data and were classified to map vegetation. Using the ground data from Bonanza Creek Long-Term Ecological Research sites, we compared the new maps with the two existing map products (the LANDFIRE's Existing Vegetation Type (EVT) and Alaska Vegetation and Wetland Composite). The maps generated using simulated data showed an improvement of 33% in accuracy and are more detailed than existing map products. In addition to fuel maps, we performed sub-pixel level mapping to generate a needleleaf fraction map, which serves fire management needs since needleleaf species are highly flammable. However, validating the sub-pixel product was challenging. To overcome this, we devised a novel validation method incorporating high-resolution airborne hyperspectral data (1m) and ground data. The study addresses the limitations of traditional fuel/vegetation maps by providing a more detailed and accurate representation of vegetation/fuel in Alaska. The methods and findings advance fuel and vegetation mapping research in Alaska and offer a novel pathway to generate detailed fuel maps for boreal Alaska to aid wildfire management.