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

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 unm...

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Bibliographic Details
Published in:Arctic Science
Main Authors: Kathleen M. Orndahl, Libby P.W. Ehlers, Jim D. Herriges, Rachel E. Pernick, Mark Hebblewhite, Scott J. Goetz
Format: Article in Journal/Newspaper
Language:English
French
Published: Canadian Science Publishing 2022
Subjects:
Arctic tundra
vegetation mapping
drones
UAV
structure from motion
toundra arctique
Environmental sciences
GE1-350
Environmental engineering
TA170-171
Arctic
Canada
Arctique*
toundra
Tundra
Alaska
Online Access:https://doi.org/10.1139/as-2021-0044
https://doaj.org/article/ae0b96c09c274603adf4f9110b86c1ae
Description
Summary: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.

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