Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

Abstract

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires.

Leaf Thermal and Chemical Properties as Natural Drivers of Plant Flammability of Native and Exotic Tree Species of the Valparaíso Region, Chile

“…For example, in Alaska, during the last decade, 2.5-times more surface was burnt compared to the previous decade. In 2019 alone, a total of 378 fires were registered, affecting 2,525,356 acres [ 15 ]. In addition, California registered one of its most destructive and deadly fires during the period of 2018–2019, affecting more than 1.2 million hectares [ 16 , 17 ].…”

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

“…When we performed the fuel type classification, the simulated hyperspectral data achieved an overall classification accuracy of 65%. Smith et al [14] carried out a detailed fuel type mapping from the original AVIRIS-NG data for the same study site and reported an accuracy of 61%. This suggests that simulated hyperspectral data can provide comparable mapping accuracy to real AVIRIS-NG data.…”

“…Remote sensing is a viable approach to map the vegetation of the boreal forests, considering the region's remoteness and vastness [11][12][13][14][15]. The Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) provides geospatial products to state and federal fire suppression agencies for wildfire mitigation [16,17].…”

“…Advancements in airborne hyperspectral remote sensing provide an efficient approach to retrieve essential information for better characterization of forest fuels [14,[19][20][21]. A number of studies have shown that hyperspectral data is much more effective than multispectral data for detailed vegetation mapping at species or stand scales [14,[22][23][24][25][26][27][28][29][30].…”

“…Advancements in airborne hyperspectral remote sensing provide an efficient approach to retrieve essential information for better characterization of forest fuels [14,[19][20][21]. A number of studies have shown that hyperspectral data is much more effective than multispectral data for detailed vegetation mapping at species or stand scales [14,[22][23][24][25][26][27][28][29][30]. The narrower bandwidths and improved spatial resolution of airborne hyperspectral datasets makes them much more effective than multispectral datasets at distinguishing visually similar vegetation classes.…”