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

Nill, Leon und Grünberg, Inge und Ullmann, Tobias und Geßner, Matthias und Boike, Julia und 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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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0034425722003340?via%3Dihub#!

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/189593/
Dokumentart:Zeitschriftenbeitrag
Titel:Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic.
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Nill, Leonleon.nill (at) geo.hu-berlin.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Grünberg, IngeAlfred-Wegener-Institut, Potsdamhttps://orcid.org/0000-0002-5748-8102NICHT SPEZIFIZIERT
Ullmann, Tobiastobias.ullmann (at) uni-wuerzburg.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Geßner, MatthiasMatthias.Gessner (at) dlr.dehttps://orcid.org/0009-0003-3328-0811153592510
Boike, JuliaAlfred-Wegener-Institut, Potsdamhttps://orcid.org/0000-0002-5875-2112NICHT SPEZIFIZIERT
Hostert, PatrickHumboldt Universität zu BerlinNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:9 September 2022
Erschienen in:Remote Sensing of Environment
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:281
DOI:10.1016/j.rse.2022.113228
Verlag:Elsevier
ISSN:0034-4257
Status:veröffentlicht
Stichwörter:Remote Sensing, Permafrost, Classification, MACS
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Projekt Polar Monitor
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Optische Sensorsysteme > Sicherheitsforschung und Anwendungen
Hinterlegt von: Geßner, Matthias
Hinterlegt am:20 Feb 2024 11:15
Letzte Änderung:26 Feb 2024 11:39

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