Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data
Abstract The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome samplin...
Published in: | Environmental Research Letters |
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2021
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Online Access: | http://dx.doi.org/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464/pdf |
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crioppubl:10.1088/1748-9326/abf464 2024-09-30T14:30:03+00:00 Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data Thomson, Eleanor R Spiegel, Marcus P Althuizen, Inge H J Bass, Polly Chen, Shuli Chmurzynski, Adam Halbritter, Aud H Henn, Jonathan J Jónsdóttir, Ingibjörg S Klanderud, Kari Li, Yaoqi Maitner, Brian S Michaletz, Sean T Niittynen, Pekka Roos, Ruben E Telford, Richard J Enquist, Brian J Vandvik, Vigdis Macias-Fauria, Marc Malhi, Yadvinder Norges Forskningsråd SIU-foundation project Jackson Foundation Natural Environment Research Council 2021 http://dx.doi.org/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 16, issue 5, page 055006 ISSN 1748-9326 journal-article 2021 crioppubl https://doi.org/10.1088/1748-9326/abf464 2024-09-17T04:18:07Z Abstract The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values ( r 2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic. Article in Journal/Newspaper Arctic Longyearbyen Svalbard Tundra IOP Publishing Arctic Longyearbyen Svalbard Environmental Research Letters 16 5 055006 |
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IOP Publishing |
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description |
Abstract The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values ( r 2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic. |
author2 |
Norges Forskningsråd SIU-foundation project Jackson Foundation Natural Environment Research Council |
format |
Article in Journal/Newspaper |
author |
Thomson, Eleanor R Spiegel, Marcus P Althuizen, Inge H J Bass, Polly Chen, Shuli Chmurzynski, Adam Halbritter, Aud H Henn, Jonathan J Jónsdóttir, Ingibjörg S Klanderud, Kari Li, Yaoqi Maitner, Brian S Michaletz, Sean T Niittynen, Pekka Roos, Ruben E Telford, Richard J Enquist, Brian J Vandvik, Vigdis Macias-Fauria, Marc Malhi, Yadvinder |
spellingShingle |
Thomson, Eleanor R Spiegel, Marcus P Althuizen, Inge H J Bass, Polly Chen, Shuli Chmurzynski, Adam Halbritter, Aud H Henn, Jonathan J Jónsdóttir, Ingibjörg S Klanderud, Kari Li, Yaoqi Maitner, Brian S Michaletz, Sean T Niittynen, Pekka Roos, Ruben E Telford, Richard J Enquist, Brian J Vandvik, Vigdis Macias-Fauria, Marc Malhi, Yadvinder Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
author_facet |
Thomson, Eleanor R Spiegel, Marcus P Althuizen, Inge H J Bass, Polly Chen, Shuli Chmurzynski, Adam Halbritter, Aud H Henn, Jonathan J Jónsdóttir, Ingibjörg S Klanderud, Kari Li, Yaoqi Maitner, Brian S Michaletz, Sean T Niittynen, Pekka Roos, Ruben E Telford, Richard J Enquist, Brian J Vandvik, Vigdis Macias-Fauria, Marc Malhi, Yadvinder |
author_sort |
Thomson, Eleanor R |
title |
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
title_short |
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
title_full |
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
title_fullStr |
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
title_full_unstemmed |
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data |
title_sort |
multiscale mapping of plant functional groups and plant traits in the high arctic using field spectroscopy, uav imagery and sentinel-2a data |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464 https://iopscience.iop.org/article/10.1088/1748-9326/abf464/pdf |
geographic |
Arctic Longyearbyen Svalbard |
geographic_facet |
Arctic Longyearbyen Svalbard |
genre |
Arctic Longyearbyen Svalbard Tundra |
genre_facet |
Arctic Longyearbyen Svalbard Tundra |
op_source |
Environmental Research Letters volume 16, issue 5, page 055006 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/abf464 |
container_title |
Environmental Research Letters |
container_volume |
16 |
container_issue |
5 |
container_start_page |
055006 |
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1811635141017075712 |