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 unma...

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Bibliographic Details
Published in:Arctic Science
Main Authors: Orndahl, Kathleen M., Ehlers, Libby P. W., Herriges, Jim D., Pernick, Rachel E., Hebblewhite, Mark, Goetz, Scott J.
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2022
Subjects:
General Earth and Planetary Sciences
General Agricultural and Biological Sciences
General Environmental Science
Arctic
Canada
Tundra
Alaska
Online Access:http://dx.doi.org/10.1139/as-2021-0044
https://cdnsciencepub.com/doi/pdf/10.1139/AS-2021-0044
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 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<sup>-2</sup>, and per-PFT RMSE ranged from 17.14-164.3 g m<sup>-2</sup>. 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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