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Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic.

Nill, Leon and Grünberg, Inge and Ullmann, Tobias and Geßner, Matthias and Boike, Julia and Hostert, Patrick (2022) Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 281. Elsevier. doi: 10.1016/j.rse.2022.113228. ISSN 0034-4257.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0034425722003340?via%3Dihub#!

Abstract

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6% and remaining surface components with MAEs between 3.0 and 11.2%. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2% per decade for the entire study area and + 4.2% per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome.

Item URL in elib:https://elib.dlr.de/189593/
Document Type:Article
Title:Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic.
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Nill, LeonUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Grünberg, IngeAlfred-Wegener-Institut, Potsdamhttps://orcid.org/0000-0002-5748-8102UNSPECIFIED
Ullmann, TobiasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Geßner, MatthiasUNSPECIFIEDhttps://orcid.org/0009-0003-3328-0811153592510
Boike, JuliaAlfred-Wegener-Institut, Potsdamhttps://orcid.org/0000-0002-5875-2112UNSPECIFIED
Hostert, PatrickHumboldt Universität zu BerlinUNSPECIFIEDUNSPECIFIED
Date:9 September 2022
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:281
DOI:10.1016/j.rse.2022.113228
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:Remote Sensing, Permafrost, Classification, MACS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Project Polar Monitor
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems > Security Research and Applications
Deposited By: Geßner, Matthias
Deposited On:20 Feb 2024 11:15
Last Modified:26 Feb 2024 11:39

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