Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling, and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assessed the viability of using high-resolution UAV imagery and UAV-derived Structure from Motion to predict cover, height, and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with a root-mean-square error (RMSE) of 6.29%–14.2%, and height was estimated with an RMSE of 3.29–10.5 cm depending on the PFT. Total aboveground biomass was predicted with an RMSE of 220.5 g m−2, and per-PFT RMSE ranged from 17.14 to 164.3 g m−2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.
- Arctic tundra
- structure from motion
- vegetation mapping
ASJC Scopus subject areas
- Environmental Science(all)
- Agricultural and Biological Sciences(all)
- Earth and Planetary Sciences(all)