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Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidarWarming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
Document ID
20210013896
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Michael Alonzo ORCID
(American University Washington D.C., District of Columbia, United States)
Roman J.Dial
(Alaska Pacific University Anchorage, Alaska, United States)
Bethany K. Schulz
(US Forest Service Washington D.C., District of Columbia, United States)
Hans Erik Andersen
(US Forest Service Washington D.C., District of Columbia, United States)
Eric Lewis-Clark
(Alaska Pacific University Anchorage, Alaska, United States)
Bruce D. Cook
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Douglas C. Morton
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
April 16, 2021
Publication Date
April 29, 2020
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 245
Issue Publication Date: August 1, 2020
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
WBS: 217140.04.01.01.13
CONTRACT_GRANT: NNX17AJ66G
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
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External Peer Committee
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