Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems
Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May...
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ftluke:oai:jukuri.luke.fi:10024/553686 2024-02-11T10:07:06+01:00 Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems Pang, Yuwen Räsänen, Aleksi Juselius-Rajamäki, Teemu Aurela, Mika Juutinen, Sari Väliranta, Minna Virtanen, Tarmo orcid:0000-0002-3629-1837 4100311110 Luonnonvarakeskus 4239-4261 true https://jukuri.luke.fi/handle/10024/553686 en eng Taylor and Francis [for] the Remote Sensing Society International journal of remote sensing 10.1080/01431161.2023.2234093 0143-1161 1366-5901 14 44 https://jukuri.luke.fi/handle/10024/553686 URN:NBN:fi-fe2023072490898 CC BY 4.0 publication fi=A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä|sv=A1 Originalartikel i en vetenskaplig tidskrift|en=A1 Journal article (refereed), original research| ftluke 2024-01-25T00:07:20Z Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions. 2023 Article in Journal/Newspaper Northern Finland Natural Resources Institute Finland: Jukuri |
institution |
Open Polar |
collection |
Natural Resources Institute Finland: Jukuri |
op_collection_id |
ftluke |
language |
English |
description |
Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions. 2023 |
author2 |
orcid:0000-0002-3629-1837 4100311110 Luonnonvarakeskus |
format |
Article in Journal/Newspaper |
author |
Pang, Yuwen Räsänen, Aleksi Juselius-Rajamäki, Teemu Aurela, Mika Juutinen, Sari Väliranta, Minna Virtanen, Tarmo |
spellingShingle |
Pang, Yuwen Räsänen, Aleksi Juselius-Rajamäki, Teemu Aurela, Mika Juutinen, Sari Väliranta, Minna Virtanen, Tarmo Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
author_facet |
Pang, Yuwen Räsänen, Aleksi Juselius-Rajamäki, Teemu Aurela, Mika Juutinen, Sari Väliranta, Minna Virtanen, Tarmo |
author_sort |
Pang, Yuwen |
title |
Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
title_short |
Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
title_full |
Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
title_fullStr |
Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
title_full_unstemmed |
Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems |
title_sort |
upscaling field-measured seasonal ground vegetation patterns with sentinel-2 images in boreal ecosystems |
publisher |
Taylor and Francis [for] the Remote Sensing Society |
url |
https://jukuri.luke.fi/handle/10024/553686 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_relation |
International journal of remote sensing 10.1080/01431161.2023.2234093 0143-1161 1366-5901 14 44 https://jukuri.luke.fi/handle/10024/553686 URN:NBN:fi-fe2023072490898 |
op_rights |
CC BY 4.0 |
_version_ |
1790605251721560064 |