Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches
In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used...
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Online Access: | https://hdl.handle.net/11250/2651679 https://doi.org/10.3390/s20072102 |
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ftninstnf:oai:brage.nina.no:11250/2651679 2023-05-15T16:06:06+02:00 Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches Ritz, Elmar Bjerke, Jarle W. Tømmervik, Hans 2020 application/pdf https://hdl.handle.net/11250/2651679 https://doi.org/10.3390/s20072102 eng eng urn:issn:1424-8220 https://hdl.handle.net/11250/2651679 https://doi.org/10.3390/s20072102 cristin:1807136 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2020 by the authors. CC-BY 20 Sensors climate change evergreen plants extreme events flavonol and chlorophyll sensor (Dualex) greenness indices mosses near-remote sensing active and passive NDVI sensors Sentinel-2 subarctic vegetation damage VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 Journal article 2020 ftninstnf https://doi.org/10.3390/s20072102 2021-12-23T07:17:22Z In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized di erence vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well With height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = �����0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = �����0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well. publishedVersion Article in Journal/Newspaper Empetrum nigrum Subarctic Norwegian Institute for Nature Research: Brage NINA The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Sensors 20 7 2102 |
institution |
Open Polar |
collection |
Norwegian Institute for Nature Research: Brage NINA |
op_collection_id |
ftninstnf |
language |
English |
topic |
climate change evergreen plants extreme events flavonol and chlorophyll sensor (Dualex) greenness indices mosses near-remote sensing active and passive NDVI sensors Sentinel-2 subarctic vegetation damage VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 |
spellingShingle |
climate change evergreen plants extreme events flavonol and chlorophyll sensor (Dualex) greenness indices mosses near-remote sensing active and passive NDVI sensors Sentinel-2 subarctic vegetation damage VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 Ritz, Elmar Bjerke, Jarle W. Tømmervik, Hans Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
topic_facet |
climate change evergreen plants extreme events flavonol and chlorophyll sensor (Dualex) greenness indices mosses near-remote sensing active and passive NDVI sensors Sentinel-2 subarctic vegetation damage VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 |
description |
In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized di erence vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well With height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = �����0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = �����0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Ritz, Elmar Bjerke, Jarle W. Tømmervik, Hans |
author_facet |
Ritz, Elmar Bjerke, Jarle W. Tømmervik, Hans |
author_sort |
Ritz, Elmar |
title |
Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
title_short |
Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
title_full |
Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
title_fullStr |
Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
title_full_unstemmed |
Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches |
title_sort |
monitoring winter stress vulnerability of high-latitude understory vegetation using intraspecific trait variability and remote sensing approaches |
publishDate |
2020 |
url |
https://hdl.handle.net/11250/2651679 https://doi.org/10.3390/s20072102 |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
The Sentinel |
geographic_facet |
The Sentinel |
genre |
Empetrum nigrum Subarctic |
genre_facet |
Empetrum nigrum Subarctic |
op_source |
20 Sensors |
op_relation |
urn:issn:1424-8220 https://hdl.handle.net/11250/2651679 https://doi.org/10.3390/s20072102 cristin:1807136 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2020 by the authors. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/s20072102 |
container_title |
Sensors |
container_volume |
20 |
container_issue |
7 |
container_start_page |
2102 |
_version_ |
1766402015182716928 |