Lidar-derived estimates of boreal shrub biomass in Southcentral Alaska

Abstract

Abstract Despite widespread observations of shrub proliferation and expansion (shrubification), few studies quantify shrub biomass at the regional scale. Here we describe and implement a two-part modeling approach to estimate and map tall shrub (diameter at root collar > 2.5 cm) expected aboveground biomass, or E[SHB], across a 16.6 million ha boreal region where shrubification occurs in wetlands and subalpine ecosystems. Using n = 384 field plots nested within m= 11 study sites across southcentral Alaska, we constructed random forest models of the probability (pSH) that shrub wood volume surpasses tree wood volume, and generalized additive models of aboveground biomass of tall shrubs (SHB) using rasterized aerial lidar variables collected by NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, together with gridded climate data from a Parameter-elevation Regressions on Independent Slopes Model 30-year normal climatology (PRISM). Applying those models to G-LiHT tiles, then averaging across tiles within n = 843 watersheds covering 9.2 million ha, we estimated that below 1000 m asl, the area-weighted mean value of E[SHB] = pSH x SHB = 6.6 Mg ha−1 with sd = 4.5 Mg ha−1. This is the first study to estimate current shrub biomass density at the regional scale in southcentral Alaska and serves as a biomass baseline for measuring and modeling aboveground carbon fluxes where plant communities are undergoing climate-driven change.