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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Published in:Remote Sensing
Main Authors: Helen Anderson, Lennart Nilsen, Hans Tømmervik, Stein Karlsen, Shin Nagai, Elisabeth Cooper
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2016
Subjects:
Online Access:https://doi.org/10.3390/rs8100847
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spelling 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
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container_issue 10
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