Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles

Abstract

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 assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) 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 root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m -2 , and per-PFT RMSE ranged from 17.14-164.3 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.

Plant phenotyping relevance

UAV画像とSfMから植物機能タイプの被覆、草丈、地上部バイオマスを推定し、現地推定値との比較で精度評価を行うことが研究の中心であるため。

Code and data availability

The paper's plant-phenotyping data (UAV-derived PFT cover, canopy height, biomass, and field validation measurements) are explicitly stated to be publicly archived at the NSF Arctic Data Center under DOI 10.18739/A2R785Q5B. No author analysis code or trained model checkpoints are described with a public deposit.