Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different...
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ftmdpi:oai:mdpi.com:/2072-4292/8/10/847/ 2023-08-20T04:04:07+02:00 Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation Helen Anderson Lennart Nilsen Hans Tømmervik Stein Karlsen Shin Nagai Elisabeth Cooper agris 2016-10-17 application/pdf https://doi.org/10.3390/rs8100847 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs8100847 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 8; Issue 10; Pages: 847 NDVI greenness index RGB camera vegetation phenology active sensor passive sensor Svalbard Text 2016 ftmdpi https://doi.org/10.3390/rs8100847 2023-07-31T20:58:22Z To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic. Text Arctic Cassiope tetragona Dryas octopetala Salix polaris Svalbard MDPI Open Access Publishing Arctic Sharp Peak ENVELOPE(-37.900,-37.900,-54.050,-54.050) Svalbard Remote Sensing 8 10 847 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
NDVI greenness index RGB camera vegetation phenology active sensor passive sensor Svalbard |
spellingShingle |
NDVI greenness index RGB camera vegetation phenology active sensor passive sensor Svalbard Helen Anderson Lennart Nilsen Hans Tømmervik Stein Karlsen Shin Nagai Elisabeth Cooper Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
topic_facet |
NDVI greenness index RGB camera vegetation phenology active sensor passive sensor Svalbard |
description |
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic. |
format |
Text |
author |
Helen Anderson Lennart Nilsen Hans Tømmervik Stein Karlsen Shin Nagai Elisabeth Cooper |
author_facet |
Helen Anderson Lennart Nilsen Hans Tømmervik Stein Karlsen Shin Nagai Elisabeth Cooper |
author_sort |
Helen Anderson |
title |
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
title_short |
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
title_full |
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
title_fullStr |
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
title_full_unstemmed |
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation |
title_sort |
using ordinary digital cameras in place of near-infrared sensors to derive vegetation indices for phenology studies of high arctic vegetation |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2016 |
url |
https://doi.org/10.3390/rs8100847 |
op_coverage |
agris |
long_lat |
ENVELOPE(-37.900,-37.900,-54.050,-54.050) |
geographic |
Arctic Sharp Peak Svalbard |
geographic_facet |
Arctic Sharp Peak Svalbard |
genre |
Arctic Cassiope tetragona Dryas octopetala Salix polaris Svalbard |
genre_facet |
Arctic Cassiope tetragona Dryas octopetala Salix polaris Svalbard |
op_source |
Remote Sensing; Volume 8; Issue 10; Pages: 847 |
op_relation |
Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs8100847 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs8100847 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
10 |
container_start_page |
847 |
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1774714530713042944 |