Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling
Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (...
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ftdatacite:10.6084/m9.figshare.7314572 2023-05-15T14:52:04+02:00 Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling Räsänen, Aleksi Juutinen, Sari Aurela, Mika Virtanen, Tarmo 2018 https://dx.doi.org/10.6084/m9.figshare.7314572 https://tandf.figshare.com/articles/Predicting_aboveground_biomass_in_Arctic_landscapes_using_very_high_spatial_resolution_satellite_imagery_and_field_sampling/7314572 unknown Taylor & Francis https://dx.doi.org/10.1080/01431161.2018.1524176 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Plant Biology dataset Dataset 2018 ftdatacite https://doi.org/10.6084/m9.figshare.7314572 https://doi.org/10.1080/01431161.2018.1524176 2021-11-05T12:55:41Z Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R 2 0.21–0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R 2 0.33–0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling. Dataset Arctic Tundra DataCite Metadata Store (German National Library of Science and Technology) Arctic Canada |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Plant Biology |
spellingShingle |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Plant Biology Räsänen, Aleksi Juutinen, Sari Aurela, Mika Virtanen, Tarmo Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
topic_facet |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Plant Biology |
description |
Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R 2 0.21–0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R 2 0.33–0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling. |
format |
Dataset |
author |
Räsänen, Aleksi Juutinen, Sari Aurela, Mika Virtanen, Tarmo |
author_facet |
Räsänen, Aleksi Juutinen, Sari Aurela, Mika Virtanen, Tarmo |
author_sort |
Räsänen, Aleksi |
title |
Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
title_short |
Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
title_full |
Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
title_fullStr |
Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
title_full_unstemmed |
Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling |
title_sort |
predicting aboveground biomass in arctic landscapes using very high spatial resolution satellite imagery and field sampling |
publisher |
Taylor & Francis |
publishDate |
2018 |
url |
https://dx.doi.org/10.6084/m9.figshare.7314572 https://tandf.figshare.com/articles/Predicting_aboveground_biomass_in_Arctic_landscapes_using_very_high_spatial_resolution_satellite_imagery_and_field_sampling/7314572 |
geographic |
Arctic Canada |
geographic_facet |
Arctic Canada |
genre |
Arctic Tundra |
genre_facet |
Arctic Tundra |
op_relation |
https://dx.doi.org/10.1080/01431161.2018.1524176 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.7314572 https://doi.org/10.1080/01431161.2018.1524176 |
_version_ |
1766323184281321472 |