Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra

Snow exerts key controls on many aspects of plant ecology in the Arctic, including community composition. With climate predictions forecasting dramatic changes in winter climate and snow cover in the Arctic in the near future, it is important to improve our understanding of snow effects on plant com...

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Bibliographic Details
Main Author: Jørgensen, Andreas
Format: Master Thesis
Language:English
Published: UiT Norges arktiske universitet 2022
Subjects:
Online Access:https://hdl.handle.net/10037/29223
id ftunivtroemsoe:oai:munin.uit.no:10037/29223
record_format openpolar
spelling ftunivtroemsoe:oai:munin.uit.no:10037/29223 2023-06-11T04:03:02+02:00 Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra Jørgensen, Andreas 2022-05-16 https://hdl.handle.net/10037/29223 eng eng UiT Norges arktiske universitet UiT The Arctic University of Norway https://hdl.handle.net/10037/29223 Copyright 2022 The Author(s) VDP::Mathematics and natural science: 400::Zoology and botany: 480::Ecology: 488 VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488 winter climate change snow depth plant community composition near-remote sensing vegetation indices Svalbard BIO-3950 Master thesis Mastergradsoppgave 2022 ftunivtroemsoe 2023-05-17T23:06:11Z Snow exerts key controls on many aspects of plant ecology in the Arctic, including community composition. With climate predictions forecasting dramatic changes in winter climate and snow cover in the Arctic in the near future, it is important to improve our understanding of snow effects on plant communities in these regions. This study used a snow depth manipulation experiment established in 2006 in Adventdalen, Svalbard, Norway (78°10’N, 16°04’E) to investigate long-term effects of deepened snow on plant community composition. Two common tundra vegetation types were studied (Cassiope heath and mesic meadow) using data from three years (2015, 2020, and 2021). The study further used ‘near-remotely’ sensed vegetation indices (VIs; RGB-based indices, image based, and non-image based NDVI) to describe differences between snow regimes, years, and vegetation types. Green Chromatic Coordinate as well as image and non-image based NDVI were compared with cover of major plant groups in an initial step towards understanding the relationships between VIs and plant cover over several years and in different vegetation types. This study documented general decreases in the cover of live vascular plants, especially shrubs, and simultaneous increases in bryophytes and the forb Bistorta vivipara under deepened snow. Community changes were similar between the Heath and the Meadow vegetation types but changes were more pronounced in Heath. Near-remotely sensed VIs showed differences between snow regimes, possibly reflecting the documented vegetation change. However, relationships between VIs and plant cover were ambiguous when compared between years, vegetation types and snow regimes. The relationships generally differed in magnitude, but sometimes also direction, and were likely confounded by phenology and variations in maximum VI values between years. These findings highlight remaining challenges in the use of near-remote sensing as a tool for vegetation monitoring. Further studies should investigate the relationships between VIs ... Master Thesis Adventdalen Arctic Climate change Svalbard Tundra University of Tromsø: Munin Open Research Archive Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Norway Svalbard
institution Open Polar
collection University of Tromsø: Munin Open Research Archive
op_collection_id ftunivtroemsoe
language English
topic VDP::Mathematics and natural science: 400::Zoology and botany: 480::Ecology: 488
VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488
winter climate change
snow depth
plant community composition
near-remote sensing
vegetation indices
Svalbard
BIO-3950
spellingShingle VDP::Mathematics and natural science: 400::Zoology and botany: 480::Ecology: 488
VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488
winter climate change
snow depth
plant community composition
near-remote sensing
vegetation indices
Svalbard
BIO-3950
Jørgensen, Andreas
Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
topic_facet VDP::Mathematics and natural science: 400::Zoology and botany: 480::Ecology: 488
VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488
winter climate change
snow depth
plant community composition
near-remote sensing
vegetation indices
Svalbard
BIO-3950
description Snow exerts key controls on many aspects of plant ecology in the Arctic, including community composition. With climate predictions forecasting dramatic changes in winter climate and snow cover in the Arctic in the near future, it is important to improve our understanding of snow effects on plant communities in these regions. This study used a snow depth manipulation experiment established in 2006 in Adventdalen, Svalbard, Norway (78°10’N, 16°04’E) to investigate long-term effects of deepened snow on plant community composition. Two common tundra vegetation types were studied (Cassiope heath and mesic meadow) using data from three years (2015, 2020, and 2021). The study further used ‘near-remotely’ sensed vegetation indices (VIs; RGB-based indices, image based, and non-image based NDVI) to describe differences between snow regimes, years, and vegetation types. Green Chromatic Coordinate as well as image and non-image based NDVI were compared with cover of major plant groups in an initial step towards understanding the relationships between VIs and plant cover over several years and in different vegetation types. This study documented general decreases in the cover of live vascular plants, especially shrubs, and simultaneous increases in bryophytes and the forb Bistorta vivipara under deepened snow. Community changes were similar between the Heath and the Meadow vegetation types but changes were more pronounced in Heath. Near-remotely sensed VIs showed differences between snow regimes, possibly reflecting the documented vegetation change. However, relationships between VIs and plant cover were ambiguous when compared between years, vegetation types and snow regimes. The relationships generally differed in magnitude, but sometimes also direction, and were likely confounded by phenology and variations in maximum VI values between years. These findings highlight remaining challenges in the use of near-remote sensing as a tool for vegetation monitoring. Further studies should investigate the relationships between VIs ...
format Master Thesis
author Jørgensen, Andreas
author_facet Jørgensen, Andreas
author_sort Jørgensen, Andreas
title Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
title_short Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
title_full Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
title_fullStr Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
title_full_unstemmed Snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-Arctic tundra
title_sort snow depth effects on vegetation dynamics and development of near-remote sensing techniques in high-arctic tundra
publisher UiT Norges arktiske universitet
publishDate 2022
url https://hdl.handle.net/10037/29223
long_lat ENVELOPE(16.264,16.264,78.181,78.181)
geographic Adventdalen
Arctic
Norway
Svalbard
geographic_facet Adventdalen
Arctic
Norway
Svalbard
genre Adventdalen
Arctic
Climate change
Svalbard
Tundra
genre_facet Adventdalen
Arctic
Climate change
Svalbard
Tundra
op_relation https://hdl.handle.net/10037/29223
op_rights Copyright 2022 The Author(s)
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